Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization.

PubMed

Ibrahim, Tamer M; Bauer, Matthias R; Boeckler, Frank M

2015-01-01

Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details. Using a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking. In conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule. Graphical AbstractUsing DEKOIS 2.0 benchmark sets in structure-based virtual screening to probe the impact of molecular preparation and score normalization.

Rapid identification of Keap1-Nrf2 small-molecule inhibitors through structure-based virtual screening and hit-based substructure search.

PubMed

Zhuang, Chunlin; Narayanapillai, Sreekanth; Zhang, Wannian; Sham, Yuk Yin; Xing, Chengguo

2014-02-13

In this study, rapid structure-based virtual screening and hit-based substructure search were utilized to identify small molecules that disrupt the interaction of Keap1-Nrf2. Special emphasis was placed toward maximizing the exploration of chemical diversity of the initial hits while economically establishing informative structure-activity relationship (SAR) of novel scaffolds. Our most potent noncovalent inhibitor exhibits three times improved cellular activation in Nrf2 activation than the most active noncovalent Keap1 inhibitor known to date.

Synergism of virtual screening and medicinal chemistry: identification and optimization of allosteric antagonists of metabotropic glutamate receptor 1.

PubMed

Noeske, Tobias; Trifanova, Dina; Kauss, Valerjans; Renner, Steffen; Parsons, Christopher G; Schneider, Gisbert; Weil, Tanja

2009-08-01

We report the identification of novel potent and selective metabotropic glutamate receptor 1 (mGluR1) antagonists by virtual screening and subsequent hit optimization. For ligand-based virtual screening, molecules were represented by a topological pharmacophore descriptor (CATS-2D) and clustered by a self-organizing map (SOM). The most promising compounds were tested in mGluR1 functional and binding assays. We identified a potent chemotype exhibiting selective antagonistic activity at mGluR1 (functional IC(50)=0.74+/-0.29 microM). Hit optimization yielded lead structure 16 with an affinity of K(i)=0.024+/-0.001 microM and greater than 1000-fold selectivity for mGluR1 versus mGluR5. Homology-based receptor modelling suggests a binding site compatible with previously reported mutation studies. Our study demonstrates the usefulness of ligand-based virtual screening for scaffold-hopping and rapid lead structure identification in early drug discovery projects.

Structure Based Virtual Screening Studies to Identify Novel Potential Compounds for GPR142 and Their Relative Dynamic Analysis for Study of Type 2 Diabetes

NASA Astrophysics Data System (ADS)

Kaushik, Aman C.; Kumar, Sanjay; Wei, Dong Q.; Sahi, Shakti

2018-02-01

GPR142 (G protein receptor 142) is a novel orphan GPCR (G protein coupled receptor) belonging to ‘Class A’ of GPCR family and expressed in beta cells of pancreas. In this study, we reported the structure based virtual screening to identify the hit compounds which can be developed as leads for potential agonists. The results were validated through induced fit docking, pharmacophore modeling and system biology approaches. Since, there is no solved crystal structure of GPR142, we attempted to predict the 3D structure followed by validation and then identification of active site using threading and ab initio methods. Also, structure based virtual screening was performed against a total of 1171519 compounds from different libraries and only top 20 best hit compounds were screened and analyzed. Moreover, the biochemical pathway of GPR142 complex with screened compound2 was also designed and compared with experimental data. Interestingly, compound2 showed an increase in insulin production via Gq mediated signaling pathway suggesting the possible role of novel GPR142 agonists in therapy against type 2 diabetes.

Structure-Based Virtual Screening of Commercially Available Compound Libraries.

PubMed

Kireev, Dmitri

2016-01-01

Virtual screening (VS) is an efficient hit-finding tool. Its distinctive strength is that it allows one to screen compound libraries that are not available in the lab. Moreover, structure-based (SB) VS also enables an understanding of how the hit compounds bind the protein target, thus laying ground work for the rational hit-to-lead progression. SBVS requires a very limited experimental effort and is particularly well suited for academic labs and small biotech companies that, unlike pharmaceutical companies, do not have physical access to quality small-molecule libraries. Here, we describe SBVS of commercial compound libraries for Mer kinase inhibitors. The screening protocol relies on the docking algorithm Glide complemented by a post-docking filter based on structural protein-ligand interaction fingerprints (SPLIF).

Discovery of new GSK-3β inhibitors through structure-based virtual screening.

PubMed

Dou, Xiaodong; Jiang, Lan; Wang, Yanxing; Jin, Hongwei; Liu, Zhenming; Zhang, Liangren

2018-01-15

Glycogen synthase kinase-3β (GSK-3β) is an attractive therapeutic target for human diseases, such as diabetes, cancer, neurodegenerative diseases, and inflammation. Thus, structure-based virtual screening was performed to identify novel scaffolds of GSK-3β inhibitors, and we observed that conserved water molecules of GSK-3β were suitable for virtual screening. We found 14 hits and D1 (IC 50 of 0.71 μM) were identified. Furthermore, the neuroprotection activity of D1-D3 was validated on a cellular level. 2D similarity searches were used to find derivatives of high inhibitory compounds and an enriched structure-activity relationship suggested that these skeletons were worthy of study as potent GSK-3β inhibitors. Copyright © 2017. Published by Elsevier Ltd.

Automated recycling of chemistry for virtual screening and library design.

PubMed

Vainio, Mikko J; Kogej, Thierry; Raubacher, Florian

2012-07-23

An early stage drug discovery project needs to identify a number of chemically diverse and attractive compounds. These hit compounds are typically found through high-throughput screening campaigns. The diversity of the chemical libraries used in screening is therefore important. In this study, we describe a virtual high-throughput screening system called Virtual Library. The system automatically "recycles" validated synthetic protocols and available starting materials to generate a large number of virtual compound libraries, and allows for fast searches in the generated libraries using a 2D fingerprint based screening method. Virtual Library links the returned virtual hit compounds back to experimental protocols to quickly assess the synthetic accessibility of the hits. The system can be used as an idea generator for library design to enrich the screening collection and to explore the structure-activity landscape around a specific active compound.

Discovery of potent inhibitors of soluble epoxide hydrolase by combinatorial library design and structure-based virtual screening.

PubMed

Xing, Li; McDonald, Joseph J; Kolodziej, Steve A; Kurumbail, Ravi G; Williams, Jennifer M; Warren, Chad J; O'Neal, Janet M; Skepner, Jill E; Roberds, Steven L

2011-03-10

Structure-based virtual screening was applied to design combinatorial libraries to discover novel and potent soluble epoxide hydrolase (sEH) inhibitors. X-ray crystal structures revealed unique interactions for a benzoxazole template in addition to the conserved hydrogen bonds with the catalytic machinery of sEH. By exploitation of the favorable binding elements, two iterations of library design based on amide coupling were employed, guided principally by the docking results of the enumerated virtual products. Biological screening of the libraries demonstrated as high as 90% hit rate, of which over two dozen compounds were single digit nanomolar sEH inhibitors by IC(50) determination. In total the library design and synthesis produced more than 300 submicromolar sEH inhibitors. In cellular systems consistent activities were demonstrated with biochemical measurements. The SAR understanding of the benzoxazole template provides valuable insights into discovery of novel sEH inhibitors as therapeutic agents.

Three-dimensional compound comparison methods and their application in drug discovery.

PubMed

Shin, Woong-Hee; Zhu, Xiaolei; Bures, Mark Gregory; Kihara, Daisuke

2015-07-16

Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS) methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D). Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed.

Discovery of novel inhibitors for DHODH via virtual screening and X-ray crystallographic structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

McLean, Larry R.; Zhang, Ying; Degnen, William

2010-10-28

Amino-benzoic acid derivatives 1-4 were found to be inhibitors for DHODH by virtual screening, biochemical, and X-ray crystallographic studies. X-ray structures showed that 1 and 2 bind to DHODH as predicted by virtual screening, but 3 and 4 were found to be structurally different from the corresponding compounds initially identified by virtual screening.

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors.

PubMed

Wang, Quan; Birod, Kerstin; Angioni, Carlo; Grösch, Sabine; Geppert, Tim; Schneider, Petra; Rupp, Matthias; Schneider, Gisbert

2011-01-01

Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

gWEGA: GPU-accelerated WEGA for molecular superposition and shape comparison.

PubMed

Yan, Xin; Li, Jiabo; Gu, Qiong; Xu, Jun

2014-06-05

Virtual screening of a large chemical library for drug lead identification requires searching/superimposing a large number of three-dimensional (3D) chemical structures. This article reports a graphic processing unit (GPU)-accelerated weighted Gaussian algorithm (gWEGA) that expedites shape or shape-feature similarity score-based virtual screening. With 86 GPU nodes (each node has one GPU card), gWEGA can screen 110 million conformations derived from an entire ZINC drug-like database with diverse antidiabetic agents as query structures within 2 s (i.e., screening more than 55 million conformations per second). The rapid screening speed was accomplished through the massive parallelization on multiple GPU nodes and rapid prescreening of 3D structures (based on their shape descriptors and pharmacophore feature compositions). Copyright © 2014 Wiley Periodicals, Inc.

Adapting Document Similarity Measures for Ligand-Based Virtual Screening.

PubMed

Himmat, Mubarak; Salim, Naomie; Al-Dabbagh, Mohammed Mumtaz; Saeed, Faisal; Ahmed, Ali

2016-04-13

Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.

Large-scale virtual screening on public cloud resources with Apache Spark.

PubMed

Capuccini, Marco; Ahmed, Laeeq; Schaal, Wesley; Laure, Erwin; Spjuth, Ola

2017-01-01

Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the available parallel implementations are based on message passing interface, relying on low failure rate hardware and fast network connection. Google's MapReduce revolutionized large-scale analysis, enabling the processing of massive datasets on commodity hardware and cloud resources, providing transparent scalability and fault tolerance at the software level. Open source implementations of MapReduce include Apache Hadoop and the more recent Apache Spark. We developed a method to run existing docking-based screening software on distributed cloud resources, utilizing the MapReduce approach. We benchmarked our method, which is implemented in Apache Spark, docking a publicly available target receptor against [Formula: see text]2.2 M compounds. The performance experiments show a good parallel efficiency (87%) when running in a public cloud environment. Our method enables parallel Structure-based virtual screening on public cloud resources or commodity computer clusters. The degree of scalability that we achieve allows for trying out our method on relatively small libraries first and then to scale to larger libraries. Our implementation is named Spark-VS and it is freely available as open source from GitHub (https://github.com/mcapuccini/spark-vs).Graphical abstract.

Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

PubMed Central

Schneider, Petra; Hoy, Benjamin; Wessler, Silja; Schneider, Gisbert

2011-01-01

Background The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention. Methodology/Principal Findings We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector (‘virtual ligand’) for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium. Conclusions/Significance This study demonstrates that receptor-based virtual screening with a permissive (‘fuzzy’) pharmacophore model can help identify small bioactive agents for combating bacterial infection. PMID:21483848

SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition.

PubMed

Wei, Ning-Ning; Hamza, Adel

2014-01-27

We present an efficient and rational ligand/structure shape-based virtual screening approach combining our previous ligand shape-based similarity SABRE (shape-approach-based routines enhanced) and the 3D shape of the receptor binding site. Our approach exploits the pharmacological preferences of a number of known active ligands to take advantage of the structural diversities and chemical similarities, using a linear combination of weighted molecular shape density. Furthermore, the algorithm generates a consensus molecular-shape pattern recognition that is used to filter and place the candidate structure into the binding pocket. The descriptor pool used to construct the consensus molecular-shape pattern consists of four dimensional (4D) fingerprints generated from the distribution of conformer states available to a molecule and the 3D shapes of a set of active ligands computed using SABRE software. The virtual screening efficiency of SABRE was validated using the Database of Useful Decoys (DUD) and the filtered version (WOMBAT) of 10 DUD targets. The ligand/structure shape-based similarity SABRE algorithm outperforms several other widely used virtual screening methods which uses the data fusion of multiscreening tools (2D and 3D fingerprints) and demonstrates a superior early retrieval rate of active compounds (EF(0.1%) = 69.0% and EF(1%) = 98.7%) from a large size of ligand database (∼95,000 structures). Therefore, our developed similarity approach can be of particular use for identifying active compounds that are similar to reference molecules and predicting activity against other targets (chemogenomics). An academic license of the SABRE program is available on request.

Development of purely structure-based pharmacophores for the topoisomerase I-DNA-ligand binding pocket

NASA Astrophysics Data System (ADS)

Drwal, Malgorzata N.; Agama, Keli; Pommier, Yves; Griffith, Renate

2013-12-01

Purely structure-based pharmacophores (SBPs) are an alternative method to ligand-based approaches and have the advantage of describing the entire interaction capability of a binding pocket. Here, we present the development of SBPs for topoisomerase I, an anticancer target with an unusual ligand binding pocket consisting of protein and DNA atoms. Different approaches to cluster and select pharmacophore features are investigated, including hierarchical clustering and energy calculations. In addition, the performance of SBPs is evaluated retrospectively and compared to the performance of ligand- and complex-based pharmacophores. SBPs emerge as a valid method in virtual screening and a complementary approach to ligand-focussed methods. The study further reveals that the choice of pharmacophore feature clustering and selection methods has a large impact on the virtual screening hit lists. A prospective application of the SBPs in virtual screening reveals that they can be used successfully to identify novel topoisomerase inhibitors.

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking.

PubMed

Chen, H F; Dong, X C; Zen, B S; Gao, K; Yuan, S G; Panaye, A; Doucet, J P; Fan, B T

2003-08-01

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

PubMed Central

Fang, Ye; Ding, Yun; Feinstein, Wei P.; Koppelman, David M.; Moreno, Juana; Jarrell, Mark; Ramanujam, J.; Brylinski, Michal

2016-01-01

Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249. PMID:27420300

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing.

PubMed

Fang, Ye; Ding, Yun; Feinstein, Wei P; Koppelman, David M; Moreno, Juana; Jarrell, Mark; Ramanujam, J; Brylinski, Michal

2016-01-01

Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249.

Statistical analysis of EGFR structures' performance in virtual screening

NASA Astrophysics Data System (ADS)

Li, Yan; Li, Xiang; Dong, Zigang

2015-11-01

In this work the ability of EGFR structures to distinguish true inhibitors from decoys in docking and MM-PBSA is assessed by statistical procedures. The docking performance depends critically on the receptor conformation and bound state. The enrichment of known inhibitors is well correlated with the difference between EGFR structures rather than the bound-ligand property. The optimal structures for virtual screening can be selected based purely on the complex information. And the mixed combination of distinct EGFR conformations is recommended for ensemble docking. In MM-PBSA, a variety of EGFR structures have identically good performance in the scoring and ranking of known inhibitors, indicating that the choice of the receptor structure has little effect on the screening.

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

PubMed Central

Gani, Osman A B S M; Narayanan, Dilip; Engh, Richard A

2013-01-01

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. ‘Dual active’ inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies. PMID:23746052

Virtual screening and pharmacophore design for a novel theoretical inhibitor of macrophage stimulating factor as a metastatic agent.

PubMed

Torktaz, Ibrahim; Mohamadhashem, Faezeh; Esmaeili, Abolghasem; Behjati, Mohaddeseh; Sharifzadeh, Sara

2013-01-01

Metastasis is a crucial aspect of cancer. Macrophage stimulating protein (MSP) is a single chain protein and can be cleaved by serum proteases. MSP has several roles in metastasis. In this in silico study, MSP as a metastatic agent was considered as a drug target. Crystallographic structure of MSP was retrieved from protein data bank. To find a chemical inhibitor of MSP, a library of KEGG compounds was screened and 1000 shape complemented ligands were retrieved with FindSite algorithm. Molegro Virtual Docker (MVD) software was used for docking simulation of shape complemented ligands against MSP. Moldock score was used as scoring function for virtual screening and potential inhibitors with more negative binding energy were obtained. PLANS scoring function was used for revaluation of virtual screening data. The top found chemical had binding affinity of -183.55 based on MolDock score and equal to -66.733 PLANTs score to MSP structure. Based on pharmacophore model of potential inhibitor, this study suggests that the chemical which was found in this research and its derivate can be used for subsequent laboratory studies.

Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set.

PubMed

Pei, Fen; Jin, Hongwei; Zhou, Xin; Xia, Jie; Sun, Lidan; Liu, Zhenming; Zhang, Liangren

2015-11-01

Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists. © 2015 John Wiley & Sons A/S.

When drug discovery meets web search: Learning to Rank for ligand-based virtual screening.

PubMed

Zhang, Wei; Ji, Lijuan; Chen, Yanan; Tang, Kailin; Wang, Haiping; Zhu, Ruixin; Jia, Wei; Cao, Zhiwei; Liu, Qi

2015-01-01

The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms. A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. Graphical AbstractThe analogy between web search and ligand-based drug discovery.

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Quantum probability ranking principle for ligand-based virtual screening

NASA Astrophysics Data System (ADS)

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Discovering Novel Alternaria solani Succinate Dehydrogenase Inhibitors by In Silico Modeling and Virtual Screening Strategies to Combat Early Blight

NASA Astrophysics Data System (ADS)

Iftikhar, Sehrish; Shahid, Ahmad A.; Halim, Sobia A.; Wolters, Pieter J.; Vleeshouwers, Vivianne G. A. A.; Khan, Ajmal; Al-Harrasi, Ahmed; Ahmad, Shahbaz

2017-11-01

Alternaria blight is an important foliage disease caused by Alternaria solani. The enzyme Succinate dehydrogenase (SDH) is a potential drug target because of its role in tricarboxylic acid cycle. Hence targeting Alternaria solani SDH enzyme could be efficient tool to design novel fungicides against A. solani. We employed computational methodologies to design new SDH inhibitors using homology modeling; pharmacophore modeling and structure based virtual screening protocol. The three dimensional SDH model showed good stereo-chemical and structural properties. Based on virtual screening results twelve commercially available compounds were purchased and tested in vitro and in vivo. The compounds were found to inhibit mycelial growth of A. solani. Moreover in vitro trials showed that inhibitory effects were enhanced with increase in concentrations. Similarly increased disease control was observed in pre-treated potato tubers. Hence the applied in silico strategy led us to identify new and novel fungicides.

Discovering Novel Alternaria solani Succinate Dehydrogenase Inhibitors by in Silico Modeling and Virtual Screening Strategies to Combat Early Blight

PubMed Central

Iftikhar, Sehrish; Shahid, Ahmad A.; Halim, Sobia A.; Wolters, Pieter J.; Vleeshouwers, Vivianne G. A. A.; Khan, Ajmal; Al-Harrasi, Ahmed; Ahmad, Shahbaz

2017-01-01

Alternaria blight is an important foliage disease caused by Alternaria solani. The enzyme Succinate dehydrogenase (SDH) is a potential drug target because of its role in tricarboxylic acid cycle. Hence targeting Alternaria solani SDH enzyme could be efficient tool to design novel fungicides against A. solani. We employed computational methodologies to design new SDH inhibitors using homology modeling; pharmacophore modeling and structure based virtual screening. The three dimensional SDH model showed good stereo-chemical and structural properties. Based on virtual screening results twelve commercially available compounds were purchased and tested in vitro and in vivo. The compounds were found to inhibit mycelial growth of A. solani. Moreover in vitro trials showed that inhibitory effects were enhanced with increase in concentrations. Similarly increased disease control was observed in pre-treated potato tubers. Hence the applied in silico strategy led us to identify novel fungicides. PMID:29204422

Condorcet and borda count fusion method for ligand-based virtual screening.

PubMed

Ahmed, Ali; Saeed, Faisal; Salim, Naomie; Abdo, Ammar

2014-01-01

It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database. The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.

Condorcet and borda count fusion method for ligand-based virtual screening

PubMed Central

2014-01-01

Background It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database. Results The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Conclusions Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought. PMID:24883114

A web-based platform for virtual screening.

PubMed

Watson, Paul; Verdonk, Marcel; Hartshorn, Michael J

2003-09-01

A fully integrated, web-based, virtual screening platform has been developed to allow rapid virtual screening of large numbers of compounds. ORACLE is used to store information at all stages of the process. The system includes a large database of historical compounds from high throughput screenings (HTS) chemical suppliers, ATLAS, containing over 3.1 million unique compounds with their associated physiochemical properties (ClogP, MW, etc.). The database can be screened using a web-based interface to produce compound subsets for virtual screening or virtual library (VL) enumeration. In order to carry out the latter task within ORACLE a reaction data cartridge has been developed. Virtual libraries can be enumerated rapidly using the web-based interface to the cartridge. The compound subsets can be seamlessly submitted for virtual screening experiments, and the results can be viewed via another web-based interface allowing ad hoc querying of the virtual screening data stored in ORACLE.

Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems.

PubMed

Kellenberger, Esther; Foata, Nicolas; Rognan, Didier

2008-05-01

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.

Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library.

PubMed

Shah, Falgun; Mukherjee, Prasenjit; Gut, Jiri; Legac, Jennifer; Rosenthal, Philip J; Tekwani, Babu L; Avery, Mitchell A

2011-04-25

Malaria, in particular that caused by Plasmodium falciparum , is prevalent across the tropics, and its medicinal control is limited by widespread drug resistance. Cysteine proteases of P. falciparum , falcipain-2 (FP-2) and falcipain-3 (FP-3), are major hemoglobinases, validated as potential antimalarial drug targets. Structure-based virtual screening of a focused cysteine protease inhibitor library built with soft rather than hard electrophiles was performed against an X-ray crystal structure of FP-2 using the Glide docking program. An enrichment study was performed to select a suitable scoring function and to retrieve potential candidates against FP-2 from a large chemical database. Biological evaluation of 50 selected compounds identified 21 diverse nonpeptidic inhibitors of FP-2 with a hit rate of 42%. Atomic Fukui indices were used to predict the most electrophilic center and its electrophilicity in the identified hits. Comparison of predicted electrophilicity of electrophiles in identified hits with those in known irreversible inhibitors suggested the soft-nature of electrophiles in the selected target compounds. The present study highlights the importance of focused libraries and enrichment studies in structure-based virtual screening. In addition, few compounds were screened against homologous human cysteine proteases for selectivity analysis. Further evaluation of structure-activity relationships around these nonpeptidic scaffolds could help in the development of selective leads for antimalarial chemotherapy.

Virtual Screening and Pharmacophore Design for a Novel Theoretical Inhibitor of Macrophage Stimulating Factor as a Metastatic Agent

PubMed Central

Torktaz, Ibrahim; Mohamadhashem, Faezeh; Esmaeili, Abolghasem; Behjati, Mohaddeseh; Sharifzadeh, Sara

2013-01-01

Introduction: Metastasis is a crucial aspect of cancer. Macrophage stimulating protein (MSP) is a single chain protein and can be cleaved by serum proteases. MSP has several roles in metastasis. In this in silico study, MSP as a metastatic agent was considered as a drug target. Methods: Crystallographic structure of MSP was retrieved from protein data bank. To find a chemical inhibitor of MSP, a library of KEGG compounds was screened and 1000 shape complemented ligands were retrieved with FindSite algorithm. Molegro Virtual Docker (MVD) software was used for docking simulation of shape complemented ligands against MSP. Moldock score was used as scoring function for virtual screening and potential inhibitors with more negative binding energy were obtained. PLANS scoring function was used for revaluation of virtual screening data. Results: The top found chemical had binding affinity of -183.55 based on MolDock score and equal to -66.733 PLANTs score to MSP structure. Conclusion: Based on pharmacophore model of potential inhibitor, this study suggests that the chemical which was found in this research and its derivate can be used for subsequent laboratory studies. PMID:24163807

Discovery of d-amino acid oxidase inhibitors based on virtual screening against the lid-open enzyme conformation.

PubMed

Szilágyi, Bence; Skok, Žiga; Rácz, Anita; Frlan, Rok; Ferenczy, György G; Ilaš, Janez; Keserű, György M

2018-06-01

d-Amino acid oxidase (DAAO) inhibitors are typically small polar compounds with often suboptimal pharmacokinetic properties. Features of the native binding site limit the operational freedom of further medicinal chemistry efforts. We therefore initiated a structure based virtual screening campaign based on the X-ray structures of DAAO complexes where larger ligands shifted the loop (lid opening) covering the native binding site. The virtual screening of our in-house collection followed by the in vitro test of the best ranked compounds led to the identification of a new scaffold with micromolar IC 50 . Subsequent SAR explorations enabled us to identify submicromolar inhibitors. Docking studies supported by in vitro activity measurements suggest that compounds bind to the active site with a salt-bridge characteristic to DAAO inhibitor binding. In addition, displacement of and interaction with the loop covering the active site contributes significantly to the activity of the most potent compounds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Virtual screening filters for the design of type II p38 MAP kinase inhibitors: a fragment based library generation approach.

PubMed

Badrinarayan, Preethi; Sastry, G Narahari

2012-04-01

In this work, we introduce the development and application of a three-step scoring and filtering procedure for the design of type II p38 MAP kinase leads using allosteric fragments extracted from virtual screening hits. The design of the virtual screening filters is based on a thorough evaluation of docking methods, DFG-loop conformation, binding interactions and chemotype specificity of the 138 p38 MAP kinase inhibitors from Protein Data Bank bound to DFG-in and DFG-out conformations using Glide, GOLD and CDOCKER. A 40 ns molecular dynamics simulation with the apo, type I with DFG-in and type II with DFG-out forms was carried out to delineate the effects of structural variations on inhibitor binding. The designed docking-score and sub-structure filters were first tested on a dataset of 249 potent p38 MAP kinase inhibitors from seven diverse series and 18,842 kinase inhibitors from PDB, to gauge their capacity to discriminate between kinase and non-kinase inhibitors and likewise to selectively filter-in target-specific inhibitors. The designed filters were then applied in the virtual screening of a database of ten million (10⁷) compounds resulting in the identification of 100 hits. Based on their binding modes, 98 allosteric fragments were extracted from the hits and a fragment library was generated. New type II p38 MAP kinase leads were designed by tailoring the existing type I ATP site binders with allosteric fragments using a common urea linker. Target specific virtual screening filters can thus be easily developed for other kinases based on this strategy to retrieve target selective compounds. Copyright © 2012 Elsevier Inc. All rights reserved.

Novel Mycosin Protease MycP1 Inhibitors Identified by Virtual Screening and 4D Fingerprints

PubMed Central

2015-01-01

The rise of drug-resistant Mycobacterium tuberculosis lends urgency to the need for new drugs for the treatment of tuberculosis (TB). The identification of a serine protease, mycosin protease-1 (MycP1), as the crucial agent in hydrolyzing the virulence factor, ESX-secretion-associated protein B (EspB), potentially opens the door to new tuberculosis treatment options. Using the crystal structure of mycobacterial MycP1 in the apo form, we performed an iterative ligand- and structure-based virtual screening (VS) strategy to identify novel, nonpeptide, small-molecule inhibitors against MycP1 protease. Screening of ∼485 000 ligands from databases at the Genomics Research Institute (GRI) at the University of Cincinnati and the National Cancer Institute (NCI) using our VS approach, which integrated a pharmacophore model and consensus molecular shape patterns of active ligands (4D fingerprints), identified 81 putative inhibitors, and in vitro testing subsequently confirmed two of them as active inhibitors. Thereafter, the lead structures of each VS round were used to generate a new 4D fingerprint that enabled virtual rescreening of the chemical libraries. Finally, the iterative process identified a number of diverse scaffolds as lead compounds that were tested and found to have micromolar IC50 values against the MycP1 target. This study validated the efficiency of the SABRE 4D fingerprints as a means of identifying novel lead compounds in each screening round of the databases. Together, these results underscored the value of using a combination of in silico iterative ligand- and structure-based virtual screening of chemical libraries with experimental validation for the identification of promising structural scaffolds, such as the MycP1 inhibitors. PMID:24628123

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

NASA Astrophysics Data System (ADS)

Kumar, Ashutosh; Zhang, Kam Y. J.

2012-05-01

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.

Virtual screening and optimization of Type II inhibitors of JAK2 from a natural product library.

PubMed

Ma, Dik-Lung; Chan, Daniel Shiu-Hin; Wei, Guo; Zhong, Hai-Jing; Yang, Hui; Leung, Lai To; Gullen, Elizabeth A; Chiu, Pauline; Cheng, Yung-Chi; Leung, Chung-Hang

2014-11-21

Amentoflavone has been identified as a JAK2 inhibitor by structure-based virtual screening of a natural product library. In silico optimization using the DOLPHIN model yielded analogues with enhanced potency against JAK2 activity and HCV activity in cellulo. Molecular modeling and kinetic experiments suggested that the analogues may function as Type II inhibitors of JAK2.

Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description.

PubMed

Spyrakis, Francesca; Cavasotto, Claudio N

2015-10-01

Structure-based virtual screening is currently an established tool in drug lead discovery projects. Although in the last years the field saw an impressive progress in terms of algorithm development, computational performance, and retrospective and prospective applications in ligand identification, there are still long-standing challenges where further improvement is needed. In this review, we consider the conceptual frame, state-of-the-art and recent developments of three critical "structural" issues in structure-based drug lead discovery: the use of homology modeling to accurately model the binding site when no experimental structures are available, the necessity of accounting for the dynamics of intrinsically flexible systems as proteins, and the importance of considering active site water molecules in lead identification and optimization campaigns. Copyright © 2015 Elsevier Inc. All rights reserved.

PyGOLD: a python based API for docking based virtual screening workflow generation.

PubMed

Patel, Hitesh; Brinkjost, Tobias; Koch, Oliver

2017-08-15

Molecular docking is one of the successful approaches in structure based discovery and development of bioactive molecules in chemical biology and medicinal chemistry. Due to the huge amount of computational time that is still required, docking is often the last step in a virtual screening approach. Such screenings are set as workflows spanned over many steps, each aiming at different filtering task. These workflows can be automatized in large parts using python based toolkits except for docking using the docking software GOLD. However, within an automated virtual screening workflow it is not feasible to use the GUI in between every step to change the GOLD configuration file. Thus, a python module called PyGOLD was developed, to parse, edit and write the GOLD configuration file and to automate docking based virtual screening workflows. The latest version of PyGOLD, its documentation and example scripts are available at: http://www.ccb.tu-dortmund.de/koch or http://www.agkoch.de. PyGOLD is implemented in Python and can be imported as a standard python module without any further dependencies. oliver.koch@agkoch.de, oliver.koch@tu-dortmund.de. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fragment-based virtual screening approach and molecular dynamics simulation studies for identification of BACE1 inhibitor leads.

PubMed

Manoharan, Prabu; Ghoshal, Nanda

2018-05-01

Traditional structure-based virtual screening method to identify drug-like small molecules for BACE1 is so far unsuccessful. Location of BACE1, poor Blood Brain Barrier permeability and P-glycoprotein (Pgp) susceptibility of the inhibitors make it even more difficult. Fragment-based drug design method is suitable for efficient optimization of initial hit molecules for target like BACE1. We have developed a fragment-based virtual screening approach to identify/optimize the fragment molecules as a starting point. This method combines the shape, electrostatic, and pharmacophoric features of known fragment molecules, bound to protein conjugate crystal structure, and aims to identify both chemically and energetically feasible small fragment ligands that bind to BACE1 active site. The two top-ranked fragment hits were subjected for a 53 ns MD simulation. Principle component analysis and free energy landscape analysis reveal that the new ligands show the characteristic features of established BACE1 inhibitors. The potent method employed in this study may serve for the development of potential lead molecules for BACE1-directed Alzheimer's disease therapeutics.

Pharmacophore Based 3D-QSAR, Virtual Screening and Docking Studies on Novel Series of HDAC Inhibitors with Thiophen Linker as Anticancer Agents.

PubMed

Patel, Preeti; Singh, Avineesh; Patel, Vijay K; Jain, Deepak K; Veerasamy, Ravichandran; Rajak, Harish

2016-01-01

Histone deacetylase (HDAC) inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. To identify the important pharmacophoric features and correlate 3Dchemical structure with biological activity using 3D-QSAR and Pharmacophore modeling studies. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with wellassigned HDAC inhibitory activity were used for 3D-QSAR model development. Best 3D-QSAR model, which is a five partial least square (PLS) factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811), cross-validated coefficient rcv 2=0.9807 and R2 pred=0.7147 with low standard deviation (0.0952). Additionally, the selected pharmacophore model DDRRR.419 was used as a 3D query for virtual screening against the ZINC database. In the virtual screening workflow, docking studies (HTVS, SP and XP) were carried out by selecting multiple receptors (PDB ID: 1T69, 1T64, 4LXZ, 4LY1, 3MAX, 2VQQ, 3C10, 1W22). Finally, six compounds were obtained based on high scoring function (dock score -11.2278-10.2222 kcal/mol) and diverse structures. The structure activity correlation was established using virtual screening, docking, energetic based pharmacophore modelling, pharmacophore, atom based 3D QSAR models and their validation. The outcomes of these studies could be further employed for the design of novel HDAC inhibitors for anticancer activity.

Virtual Screening and X-ray Crystallography for Human Kallikrein 6 Inhibitors with an Amidinothiophene P1 Group.

PubMed

Liang, Guyan; Chen, Xin; Aldous, Suzanne; Pu, Su-Fen; Mehdi, Shujaath; Powers, Elaine; Giovanni, Andrew; Kongsamut, Sathapana; Xia, Tianhui; Zhang, Ying; Wang, Rachel; Gao, Zhongli; Merriman, Gregory; McLean, Larry R; Morize, Isabelle

2012-02-09

A series of compounds with an amidinothiophene P1 group and a pyrrolidinone-sulphonamide scaffold linker was identified as potent inhibitors of human kallikrein 6 by structure-based virtual screening based on the union accessible binding space of serine proteases. As the first series of potent nonmechanism-based hK6 inhibitors, they may be used as tool compounds for target validation. An X-ray structure of a representative compound complexed with hK6, resolved at a resolution of 1.88 Å, revealed that the amidinothiophene moiety bound in the S1 pocket and the pyrrolidinone-sulphonamide linker projected the aromatic tail into the S' pocket.

Computational discovery of putative quorum sensing inhibitors against LasR and RhlR receptor proteins of Pseudomonas aeruginosa

NASA Astrophysics Data System (ADS)

Annapoorani, Angusamy; Umamageswaran, Venugopal; Parameswari, Radhakrishnan; Pandian, Shunmugiah Karutha; Ravi, Arumugam Veera

2012-09-01

Drugs have been discovered in the past mainly either by identification of active components from traditional remedies or by unpredicted discovery. A key motivation for the study of structure based virtual screening is the exploitation of such information to design targeted drugs. In this study, structure based virtual screening was used in search for putative quorum sensing inhibitors (QSI) of Pseudomonas aeruginosa. The virtual screening programme Glide version 5.5 was applied to screen 1,920 natural compounds/drugs against LasR and RhlR receptor proteins of P. aeruginosa. Based on the results of in silico docking analysis, five top ranking compounds namely rosmarinic acid, naringin, chlorogenic acid, morin and mangiferin were subjected to in vitro bioassays against laboratory strain PAO1 and two more antibiotic resistant clinical isolates, P. aeruginosa AS1 (GU447237) and P. aeruginosa AS2 (GU447238). Among the five compounds studied, except mangiferin other four compounds showed significant inhibition in the production of protease, elastase and hemolysin. Further, all the five compounds potentially inhibited the biofilm related behaviours. This interaction study provided promising ligands to inhibit the quorum sensing (QS) mediated virulence factors production in P. aeruginosa.

Structure-Based Virtual Screening and Biochemical Evaluation for the Identification of Novel Trypanosoma Brucei Aldolase Inhibitors.

PubMed

Ferreira, Leonardo L G; Ferreira, Rafaela S; Palomino, David L; Andricopulo, Adriano D

2018-04-27

The glycolytic enzyme fructose-1,6-bisphosphate aldolase is a validated molecular target in human African trypanosomiasis (HAT) drug discovery, a neglected tropical disease (NTD) caused by the protozoan Trypanosoma brucei. Herein, a structure-based virtual screening (SBVS) approach to the identification of novel T. brucei aldolase inhibitors is described. Distinct molecular docking algorithms were used to screen more than 500,000 compounds against the X-ray structure of the enzyme. This SBVS strategy led to the selection of a series of molecules which were evaluated for their activity on recombinant T. brucei aldolase. The effort led to the discovery of structurally new ligands able to inhibit the catalytic activity the enzyme. The predicted binding conformations were additionally investigated in molecular dynamics simulations, which provided useful insights into the enzyme-inhibitor intermolecular interactions. The molecular modeling results along with the enzyme inhibition data generated practical knowledge to be explored in further structure-based drug design efforts in HAT drug discovery. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Identification of New Human Malaria Parasite Plasmodium Falciparum Dihydroorotate Dehydrogenase Inhibitors by Pharmacophore and Structure-Based Virtual Screening

PubMed Central

Pavadai, Elumalai; El Mazouni, Farah; Wittlin, Sergio; de Kock, Carmen; Phillips, Margaret A.; Chibale, Kelly

2016-01-01

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH), a key enzyme in the de novo pyrimidine biosynthesis pathway, which the Plasmodium falciparum relies on exclusively for survival, has emerged as a promising target for antimalarial drugs. In an effort to discover new and potent PfDHODH inhibitors, 3D-QSAR pharmacophore models were developed based on the structures of known PfDHODH inhibitors and the validated Hypo1 model was used as a 3D search query for virtual screening of the National Cancer Institute database. The virtual hit compounds were further filtered based on molecular docking and Molecular Mechanics/Generalized Born Surface Area binding energy calculations. The combination of the pharmacophore and structure-based virtual screening resulted in the identification of nine new compounds that showed >25% inhibition of PfDHODH at a concentration of 10 μM, three of which exhibited IC50 values in the range of 0.38–20 μM. The most active compound, NSC336047, displayed species-selectivity for PfDHODH over human DHODH and inhibited parasite growth with an IC50 of 26 μM. In addition to this, thirteen compounds inhibited parasite growth with IC50 values of ≤ 50 μM, four of which showed IC50 values in the range of 5–12 μM. These compounds could be further explored in the identification and development of more potent PfDHODH and parasite growth inhibitors. PMID:26915022

A Novel Approach for Efficient Pharmacophore-based Virtual Screening: Method and Applications

PubMed Central

Dror, Oranit; Schneidman-Duhovny, Dina; Inbar, Yuval; Nussinov, Ruth; Wolfson, Haim J.

2009-01-01

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to: (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used dataset of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) dataset was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening. Availability The software is available for download. A user-friendly web interface for pharmacophore detection is available at http://bioinfo3d.cs.tau.ac.il/PharmaGist. PMID:19803502

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

PubMed

Ebalunode, Jerry O; Zheng, Weifan; Tropsha, Alexander

2011-01-01

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Exploration of natural product ingredients as inhibitors of human HMG-CoA reductase through structure-based virtual screening.

PubMed

Lin, Shih-Hung; Huang, Kao-Jean; Weng, Ching-Feng; Shiuan, David

2015-01-01

Cholesterol plays an important role in living cells. However, a very high level of cholesterol may lead to atherosclerosis. HMG-CoA (3-hydroxy-3-methylglutaryl coenzyme A) reductase is the key enzyme in the cholesterol biosynthesis pathway, and the statin-like drugs are inhibitors of human HMG-CoA reductase (hHMGR). The present study aimed to virtually screen for potential hHMGR inhibitors from natural product to discover hypolipidemic drug candidates with fewer side effects and lesser toxicities. We used the 3D structure 1HWK from the PDB (Protein Data Bank) database of hHMGR as the target to screen for the strongly bound compounds from the traditional Chinese medicine database. Many interesting molecules including polyphenolic compounds, polisubstituted heterocyclics, and linear lipophilic alcohols were identified and their ADMET (absorption, disrtibution, metabolism, excretion, toxicity) properties were predicted. Finally, four compounds were obtained for the in vitro validation experiments. The results indicated that curcumin and salvianolic acid C can effectively inhibit hHMGR, with IC50 (half maximal inhibitory concentration) values of 4.3 µM and 8 µM, respectively. The present study also demonstrated the feasibility of discovering new drug candidates through structure-based virtual screening.

Discovery of a fluorene class of compounds as inhibitors of botulinum neurotoxin serotype E by virtual screening.

PubMed

Kumar, Gyanendra; Agarwal, Rakhi; Swaminathan, Subramanyam

2012-02-28

Botulinum neurotoxins are one of the most poisonous biological substances known to humans and present a potential bioterrorism threat. There are no therapeutic interventions developed so far. Here, we report the first small molecule non-peptide inhibitor for botulinum neurotoxin serotype E discovered by structure-based virtual screening and propose a mechanism for its inhibitory activity. This journal is © The Royal Society of Chemistry 2012

A kinase-focused compound collection: compilation and screening strategy.

PubMed

Sun, Dongyu; Chuaqui, Claudio; Deng, Zhan; Bowes, Scott; Chin, Donovan; Singh, Juswinder; Cullen, Patrick; Hankins, Gretchen; Lee, Wen-Cherng; Donnelly, Jason; Friedman, Jessica; Josiah, Serene

2006-06-01

Lead identification by high-throughput screening of large compound libraries has been supplemented with virtual screening and focused compound libraries. To complement existing approaches for lead identification at Biogen Idec, a kinase-focused compound collection was designed, developed and validated. Two strategies were adopted to populate the compound collection: a ligand shape-based virtual screening and a receptor-based approach (structural interaction fingerprint). Compounds selected with the two approaches were cherry-picked from an existing high-throughput screening compound library, ordered from suppliers and supplemented with specific medicinal compounds from internal programs. Promising hits and leads have been generated from the kinase-focused compound collection against multiple kinase targets. The principle of the collection design and screening strategy was validated and the use of the kinase-focused compound collection for lead identification has been added to existing strategies.

Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries.

PubMed

Pham-The, H; Casañola-Martin, G; Diéguez-Santana, K; Nguyen-Hai, N; Ngoc, N T; Vu-Duc, L; Le-Thi-Thu, H

2017-03-01

Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.

Discovery of novel EGFR tyrosine kinase inhibitors by structure-based virtual screening.

PubMed

Li, Siyuan; Sun, Xianqiang; Zhao, Hongli; Tang, Yun; Lan, Minbo

2012-06-15

By using of structure-based virtual screening, 13 novel epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors were discovered from 197,116 compounds in the SPECS database here. Among them, 8 compounds significantly inhibited EGFR kinase activity with IC(50) values lower than 10 μM. 3-{[1-(3-Chloro-4-fluorophenyl)-3,5-dioxo-4-pyrazolidinylidene]methyl}phenyl 2-thiophenecarboxylate (13), particularly, was the most potent inhibitor possessing the IC(50) value of 3.5 μM. The docking studies also provide some useful information that the docking models of the 13 compounds are beneficial to find a new path for designing novel EGFR inhibitors. Copyright © 2012 Elsevier Ltd. All rights reserved.

LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening.

PubMed

Hu, Jun; Liu, Zi; Yu, Dong-Jun; Zhang, Yang

2018-02-15

Sequence-order independent structural comparison, also called structural alignment, of small ligand molecules is often needed for computer-aided virtual drug screening. Although many ligand structure alignment programs are proposed, most of them build the alignments based on rigid-body shape comparison which cannot provide atom-specific alignment information nor allow structural variation; both abilities are critical to efficient high-throughput virtual screening. We propose a novel ligand comparison algorithm, LS-align, to generate fast and accurate atom-level structural alignments of ligand molecules, through an iterative heuristic search of the target function that combines inter-atom distance with mass and chemical bond comparisons. LS-align contains two modules of Rigid-LS-align and Flexi-LS-align, designed for rigid-body and flexible alignments, respectively, where a ligand-size independent, statistics-based scoring function is developed to evaluate the similarity of ligand molecules relative to random ligand pairs. Large-scale benchmark tests are performed on prioritizing chemical ligands of 102 protein targets involving 1,415,871 candidate compounds from the DUD-E (Database of Useful Decoys: Enhanced) database, where LS-align achieves an average enrichment factor (EF) of 22.0 at the 1% cutoff and the AUC score of 0.75, which are significantly higher than other state-of-the-art methods. Detailed data analyses show that the advanced performance is mainly attributed to the design of the target function that combines structural and chemical information to enhance the sensitivity of recognizing subtle difference of ligand molecules and the introduces of structural flexibility that help capture the conformational changes induced by the ligand-receptor binding interactions. These data demonstrate a new avenue to improve the virtual screening efficiency through the development of sensitive ligand structural alignments. http://zhanglab.ccmb.med.umich.edu/LS-align/. njyudj@njust.edu.cn or zhng@umich.edu. Supplementary data are available at Bioinformatics online.

Discovery of Novel Human Epidermal Growth Factor Receptor-2 Inhibitors by Structure-based Virtual Screening.

PubMed

Shi, Zheng; Yu, Tian; Sun, Rong; Wang, Shan; Chen, Xiao-Qian; Cheng, Li-Jia; Liu, Rong

2016-01-01

Human epidermal growth factor receptor-2 (HER2) is a trans-membrane receptor like protein, and aberrant signaling of HER2 is implicated in many human cancers, such as ovarian cancer, gastric cancer, and prostate cancer, most notably breast cancer. Moreover, it has been in the spotlight in the recent years as a promising new target for therapy of breast cancer. Since virtual screening has become an integral part of the drug discovery process, it is of great significant to identify novel HER2 inhibitors by structure-based virtual screening. In this study, we carried out a series of elegant bioinformatics approaches, such as virtual screening and molecular dynamics (MD) simulations to identify HER2 inhibitors from Food and Drug Administration-approved small molecule drug as potential "new use" drugs. Molecular docking identified top 10 potential drugs which showed spectrum affinity to HER2. Moreover, MD simulations suggested that ZINC08214629 (Nonoxynol-9) and ZINC03830276 (Benzonatate) might exert potential inhibitory effects against HER2-targeted anti-breast cancer therapeutics. Together, our findings may provide successful application of virtual screening studies in the lead discovery process, and suggest that our discovered small molecules could be effective HER2 inhibitor candidates for further study. A series of elegant bioinformatics approaches, including virtual screening and molecular dynamics (MD) simulations were took advantage to identify human epidermal growth factor receptor-2 (HER2) inhibitors. Molecular docking recognized top 10 candidate compounds, which showed spectrum affinity to HER2. Further, MD simulations suggested that ZINC08214629 (Nonoxynol-9) and ZINC03830276 (Benzonatate) in candidate compounds were identified as potential "new use" drugs against HER2-targeted anti-breast cancer therapeutics. Abbreviations used: HER2: Human epidermal growth factor receptor-2, FDA: Food and Drug Administration, PDB: Protein Database Bank, RMSDs: Root mean square deviations, SPC: Single point charge, PME: Particle mesh Ewald, NVT: Constant volume, NPT: Constant pressure, RMSF: Root-mean-square fluctuation.

Docking and Virtual Screening Strategies for GPCR Drug Discovery.

PubMed

Beuming, Thijs; Lenselink, Bart; Pala, Daniele; McRobb, Fiona; Repasky, Matt; Sherman, Woody

2015-01-01

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis

PubMed Central

Wan, Minghui; Liao, Dongjiang; Peng, Guilin; Xu, Xin; Yin, Weiqiang; Guo, Guixin; Jiang, Funeng; Zhong, Weide

2017-01-01

Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by X-Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition. PMID:29147652

Structure Based Library Design (SBLD) for new 1,4-dihydropyrimidine scaffold as simultaneous COX-1/COX-2 and 5-LOX inhibitors.

PubMed

Lokwani, Deepak; Azad, Rajaram; Sarkate, Aniket; Reddanna, Pallu; Shinde, Devanand

2015-08-01

The various scaffolds containing 1,4-dihydropyrimidine ring were designed by considering the environment of the active site of COX-1/COX-2 and 5-LOX enzymes. The structure-based library design approach, including the focused library design (Virtual Combinatorial Library Design) and virtual screening was used to select the 1,4-dihydropyrimidine scaffold for simultaneous inhibition of both enzyme pathways (COX-1/COX-2 and 5-LOX). The virtual library on each 1,4-dihydropyrimidine scaffold was enumerated in two alternative ways. In first way, the chemical reagents at R groups were filtered by docking of scaffold with single position substitution, that is, only at R1, or R2, or R3, … Rn on COX-2 enzyme using Glide XP docking mode. The structures that do not dock well were removed and the library was enumerated with filtered chemical reagents. In second alternative way, the single position docking stage was bypassed, and the entire library was enumerated using all chemical reagents by docking on the COX-2 enzyme. The entire library of approximately 15,629 compounds obtained from both ways after screening for drug like properties, were further screened for their binding affinity against COX-1 and 5-LOX enzymes using Virtual Screening Workflow. Finally, 142 hits were obtained and divided into two groups based on their binding affinity for COX-1/COX-2 and for both enzyme pathways (COX-1/COX-2 and 5-LOX). The ten molecules were selected, synthesized and evaluated for their COX-1, COX-2 and 5-LOX inhibiting activity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discovery of thienoquinolone derivatives as selective and ATP non-competitive CDK5/p25 inhibitors by structure-based virtual screening

PubMed Central

Chatterjee, Arindam; Doerksen, Robert J.; Khan, Ikhlas A.

2014-01-01

Calpain mediated cleavage of CDK5 natural precursor p35 causes a stable complex formation of CDK5/p25, which leads to hyperphosphorylation of tau. Thus inhibition of this complex is a viable target for numerous acute and chronic neurodegenerative diseases involving tau protein, including Alzheimer’s disease. Since CDK5 has the highest sequence homology with its mitotic counterpart CDK2, our primary goal was to design selective CDK5/p25 inhibitors targeting neurodegeneration. A novel structure-based virtual screening protocol comprised of e-pharmacophore models and virtual screening work-flow was used to identify nine compounds from a commercial database containing 2.84 million compounds. An ATP non-competitive and selective thieno[3,2-c]quinolin-4(5H)-one inhibitor (10) with ligand efficiency (LE) of 0.3 was identified as the lead molecule. Further SAR optimization led to the discovery of several low micromolar inhibitors with good selectivity. The research represents a new class of potent ATP non-competitive CDK5/p25 inhibitors with good CDK2/E selectivity. PMID:25438765

SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library.

PubMed

Litfin, Thomas; Zhou, Yaoqi; Yang, Yuedong

2017-04-15

The high cost of drug discovery motivates the development of accurate virtual screening tools. Binding-homology, which takes advantage of known protein-ligand binding pairs, has emerged as a powerful discrimination technique. In order to exploit all available binding data, modelled structures of ligand-binding sequences may be used to create an expanded structural binding template library. SPOT-Ligand 2 has demonstrated significantly improved screening performance over its previous version by expanding the template library 15 times over the previous one. It also performed better than or similar to other binding-homology approaches on the DUD and DUD-E benchmarks. The server is available online at http://sparks-lab.org . yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery.

PubMed

Karthikeyan, Muthukumarasamy; Vyas, Renu

2015-01-01

Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products.

PubMed

Sun, Yunan; Zhou, Hui; Zhu, Hongmei; Leung, Siu-wai

2016-01-25

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

NASA Astrophysics Data System (ADS)

Sun, Yunan; Zhou, Hui; Zhu, Hongmei; Leung, Siu-Wai

2016-01-01

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

Consensus Induced Fit Docking (cIFD): methodology, validation, and application to the discovery of novel Crm1 inhibitors

NASA Astrophysics Data System (ADS)

Kalid, Ori; Toledo Warshaviak, Dora; Shechter, Sharon; Sherman, Woody; Shacham, Sharon

2012-11-01

We present the Consensus Induced Fit Docking (cIFD) approach for adapting a protein binding site to accommodate multiple diverse ligands for virtual screening. This novel approach results in a single binding site structure that can bind diverse chemotypes and is thus highly useful for efficient structure-based virtual screening. We first describe the cIFD method and its validation on three targets that were previously shown to be challenging for docking programs (COX-2, estrogen receptor, and HIV reverse transcriptase). We then demonstrate the application of cIFD to the challenging discovery of irreversible Crm1 inhibitors. We report the identification of 33 novel Crm1 inhibitors, which resulted from the testing of 402 purchased compounds selected from a screening set containing 261,680 compounds. This corresponds to a hit rate of 8.2 %. The novel Crm1 inhibitors reveal diverse chemical structures, validating the utility of the cIFD method in a real-world drug discovery project. This approach offers a pragmatic way to implicitly account for protein flexibility without the additional computational costs of ensemble docking or including full protein flexibility during virtual screening.

Ligand and structure based virtual screening strategies for hit-finding and optimization of hepatitis C virus (HCV) inhibitors.

PubMed

Melagraki, G; Afantitis, A

2011-01-01

Virtual Screening (VS) has experienced increased attention into the recent years due to the large datasets made available, the development of advanced VS techniques and the encouraging fact that VS has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. Hepatitis C Virus (HCV) nonstructural protein 5B (NS5B) has become an attractive target for the development of antiviral drugs and many small molecules have been explored as possible HCV NS5B inhibitors. In parallel with experimental practices, VS can serve as a valuable tool in the identification of novel effective inhibitors. Different techniques and workflows have been reported in literature with the goal to prioritize possible potent hits. In this context, different virtual screening strategies have been deployed for the identification of novel Hepatitis C Virus (HCV) inhibitors. This work reviews recent applications of virtual screening in an effort to identify novel potent HCV inhibitors.

Graph-based similarity concepts in virtual screening.

PubMed

Hutter, Michael C

2011-03-01

Applying similarity for finding new promising compounds is a key issue in drug design. Conversely, quantifying similarity between molecules has remained a difficult task despite the numerous approaches. Here, some general aspects along with recent developments regarding similarity criteria are collected. For the purpose of virtual screening, the compounds have to be encoded into a computer-readable format that permits a comparison, according to given similarity criteria, comprising the use of the 3D structure, fingerprints, graph-based and alignment-based approaches. Whereas finding the most common substructures is the most obvious method, more recent approaches take into account chemical modifications that appear throughout existing drugs, from various therapeutic categories and targets.

Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations

PubMed Central

2017-01-01

Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein–ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein–ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors. PMID:28884163

Free Energy-Based Virtual Screening and Optimization of RNase H Inhibitors of HIV-1 Reverse Transcriptase.

PubMed

Zhang, Baofeng; D'Erasmo, Michael P; Murelli, Ryan P; Gallicchio, Emilio

2016-09-30

We report the results of a binding free energy-based virtual screening campaign of a library of 77 α-hydroxytropolone derivatives against the challenging RNase H active site of the reverse transcriptase (RT) enzyme of human immunodeficiency virus-1. Multiple protonation states, rotamer states, and binding modalities of each compound were individually evaluated. The work involved more than 300 individual absolute alchemical binding free energy parallel molecular dynamics calculations and over 1 million CPU hours on national computing clusters and a local campus computational grid. The thermodynamic and structural measures obtained in this work rationalize a series of characteristics of this system useful for guiding future synthetic and biochemical efforts. The free energy model identified key ligand-dependent entropic and conformational reorganization processes difficult to capture using standard docking and scoring approaches. Binding free energy-based optimization of the lead compounds emerging from the virtual screen has yielded four compounds with very favorable binding properties, which will be the subject of further experimental investigations. This work is one of the few reported applications of advanced-binding free energy models to large-scale virtual screening and optimization projects. It further demonstrates that, with suitable algorithms and automation, advanced-binding free energy models can have a useful role in early-stage drug-discovery programs.

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy calculation studies.

PubMed

Zhang, Wen; Qiu, Kai-Xiong; Yu, Fang; Xie, Xiao-Guang; Zhang, Shu-Qun; Chen, Ya-Juan; Xie, Hui-Ding

2017-10-01

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG bind ) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC 50 <50μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs. Copyright © 2017. Published by Elsevier Ltd.

Discovery of new inhibitors of the bacterial peptidoglycan biosynthesis enzymes MurD and MurF by structure-based virtual screening.

PubMed

Turk, Samo; Kovac, Andreja; Boniface, Audrey; Bostock, Julieanne M; Chopra, Ian; Blanot, Didier; Gobec, Stanislav

2009-03-01

The ATP-dependent Mur ligases (MurC, MurD, MurE and MurF) successively add L-Ala, D-Glu, meso-A(2)pm or L-Lys, and D-Ala-D-Ala to the nucleotide precursor UDP-MurNAc, and they represent promising targets for antibacterial drug discovery. We have used the molecular docking programme eHiTS for the virtual screening of 1990 compounds from the National Cancer Institute 'Diversity Set' on MurD and MurF. The 50 top-scoring compounds from screening on each enzyme were selected for experimental biochemical evaluation. Our approach of virtual screening and subsequent in vitro biochemical evaluation of the best ranked compounds has provided four novel MurD inhibitors (best IC(50)=10 microM) and one novel MurF inhibitor (IC(50)=63 microM).

Identification of a New Isoindole-2-yl Scaffold as a Qo and Qi Dual Inhibitor of Cytochrome bc 1 Complex: Virtual Screening, Synthesis, and Biochemical Assay.

PubMed

Azizian, Homa; Bagherzadeh, Kowsar; Shahbazi, Sophia; Sharifi, Niusha; Amanlou, Massoud

2017-09-18

Respiratory chain ubiquinol-cytochrome (cyt) c oxidoreductase (cyt bc 1 or complex III) has been demonstrated as a promising target for numerous antibiotics and fungicide applications. In this study, a virtual screening of NCI diversity database was carried out in order to find novel Qo/Qi cyt bc 1 complex inhibitors. Structure-based virtual screening and molecular docking methodology were employed to further screen compounds with inhibition activity against cyt bc 1 complex after extensive reliability validation protocol with cross-docking method and identification of the best score functions. Subsequently, the application of rational filtering procedure over the target database resulted in the elucidation of a novel class of cyt bc 1 complex potent inhibitors with comparable binding energies and biological activities to those of the standard inhibitor, antimycin.

Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise.

PubMed

Kumar, Ashutosh; Zhang, Kam Y J

2016-06-27

To evaluate the applicability of shape similarity in docking-based pose selection and virtual screening, we participated in the CSARdock2014 benchmark exercise for identifying the correct docking pose of inhibitors targeting factor XA, spleen tyrosine kinase, and tRNA methyltransferase. This exercise provides a valuable opportunity for researchers to test their docking programs, methods, and protocols in a blind testing environment. In the CSARdock2014 benchmark exercise, we have implemented an approach that uses ligand 3D shape similarity to facilitate docking-based pose selection and virtual screening. We showed here that ligand 3D shape similarity between bound poses could be used to identify the native-like pose from an ensemble of docking-generated poses. Our method correctly identified the native pose as the top-ranking pose for 73% of test cases in a blind testing environment. Moreover, the pose selection results also revealed an excellent correlation between ligand 3D shape similarity scores and RMSD to X-ray crystal structure ligand. In the virtual screening exercise, the average RMSD for our pose prediction was found to be 1.02 Å, and it was one of the top performances achieved in CSARdock2014 benchmark exercise. Furthermore, the inclusion of shape similarity improved virtual screening performance of docking-based scoring and ranking. The coefficient of determination (r(2)) between experimental activities and docking scores for 276 spleen tyrosine kinase inhibitors was found to be 0.365 but reached 0.614 when the ligand 3D shape similarity was included.

PoLi: A Virtual Screening Pipeline Based On Template Pocket And Ligand Similarity

PubMed Central

Roy, Ambrish; Srinivasan, Bharath; Skolnick, Jeffrey

2015-01-01

Often in pharmaceutical research, the goal is to identify small molecules that can interact with and appropriately modify the biological behavior of a new protein target. Unfortunately, most proteins lack both known structures and small molecule binders, prerequisites of many virtual screening, VS, approaches. For such proteins, ligand homology modeling, LHM, that copies ligands from homologous and perhaps evolutionarily distant template proteins, has been shown to be a powerful VS approach to identify possible binding ligands. However, if we want to target a specific pocket for which there is no homologous holo template protein structure, then LHM will not work. To address this issue, in a new pocket based approach, PoLi, we generalize LHM by exploiting the fact that the number of distinct small molecule ligand binding pockets in proteins is small. PoLi identifies similar ligand binding pockets in a holo-template protein library, selectively copies relevant parts of template ligands and uses them for VS. In practice, PoLi is a hybrid structure and ligand based VS algorithm that integrates 2D fingerprint-based and 3D shape-based similarity metrics for improved virtual screening performance. On standard DUD and DUD-E benchmark databases, using modeled receptor structures, PoLi achieves an average enrichment factor of 13.4 and 9.6 respectively, in the top 1% of the screened library. In contrast, traditional docking based VS using AutoDock Vina and homology-based VS using FINDSITEfilt have an average enrichment of 1.6 (3.0) and 9.0 (7.9) on the DUD (DUD-E) sets respectively. Experimental validation of PoLi predictions on dihydrofolate reductase, DHFR, using differential scanning fluorimetry, DSF, identifies multiple ligands with diverse molecular scaffolds, thus demonstrating the advantage of PoLi over current state-of-the-art VS methods. PMID:26225536

Structure-Based Virtual Screening of Protein Tyrosine Phosphatase Inhibitors: Significance, Challenges, and Solutions.

PubMed

Reddy, Rallabandi Harikrishna; Kim, Hackyoung; Cha, Seungbin; Lee, Bongsoo; Kim, Young Jun

2017-05-28

Phosphorylation, a critical mechanism in biological systems, is estimated to be indispensable for about 30% of key biological activities, such as cell cycle progression, migration, and division. It is synergistically balanced by kinases and phosphatases, and any deviation from this balance leads to disease conditions. Pathway or biological activity-based abnormalities in phosphorylation and the type of involved phosphatase influence the outcome, and cause diverse diseases ranging from diabetes, rheumatoid arthritis, and numerous cancers. Protein tyrosine phosphatases (PTPs) are of prime importance in the process of dephosphorylation and catalyze several biological functions. Abnormal PTP activities are reported to result in several human diseases. Consequently, there is an increased demand for potential PTP inhibitory small molecules. Several strategies in structure-based drug designing techniques for potential inhibitory small molecules of PTPs have been explored along with traditional drug designing methods in order to overcome the hurdles in PTP inhibitor discovery. In this review, we discuss druggable PTPs and structure-based virtual screening efforts for successful PTP inhibitor design.

CSBB-ConeExclusion, adapting structure based solution virtual screening to libraries on solid support.

PubMed

Shave, Steven; Auer, Manfred

2013-12-23

Combinatorial chemical libraries produced on solid support offer fast and cost-effective access to a large number of unique compounds. If such libraries are screened directly on-bead, the speed at which chemical space can be explored by chemists is much greater than that addressable using solution based synthesis and screening methods. Solution based screening has a large supporting body of software such as structure-based virtual screening tools which enable the prediction of protein-ligand complexes. Use of these techniques to predict the protein bound complexes of compounds synthesized on solid support neglects to take into account the conjugation site on the small molecule ligand. This may invalidate predicted binding modes, the linker may be clashing with protein atoms. We present CSBB-ConeExclusion, a methodology and computer program which provides a measure of the applicability of solution dockings to solid support. Output is given in the form of statistics for each docking pose, a unique 2D visualization method which can be used to determine applicability at a glance, and automatically generated PyMol scripts allowing visualization of protein atom incursion into a defined exclusion volume. CSBB-ConeExclusion is then exemplarically used to determine the optimum attachment point for a purine library targeting cyclin-dependent kinase 2 CDK2.

Ultrafast protein structure-based virtual screening with Panther

NASA Astrophysics Data System (ADS)

Niinivehmas, Sanna P.; Salokas, Kari; Lätti, Sakari; Raunio, Hannu; Pentikäinen, Olli T.

2015-10-01

Molecular docking is by far the most common method used in protein structure-based virtual screening. This paper presents Panther, a novel ultrafast multipurpose docking tool. In Panther, a simple shape-electrostatic model of the ligand-binding area of the protein is created by utilizing the protein crystal structure. The features of the possible ligands are then compared to the model by using a similarity search algorithm. On average, one ligand can be processed in a few minutes by using classical docking methods, whereas using Panther processing takes <1 s. The presented Panther protocol can be used in several applications, such as speeding up the early phases of drug discovery projects, reducing the number of failures in the clinical phase of the drug development process, and estimating the environmental toxicity of chemicals. Panther-code is available in our web pages (http://www.jyu.fi/panther) free of charge after registration.

Ultrafast protein structure-based virtual screening with Panther.

PubMed

Niinivehmas, Sanna P; Salokas, Kari; Lätti, Sakari; Raunio, Hannu; Pentikäinen, Olli T

2015-10-01

Molecular docking is by far the most common method used in protein structure-based virtual screening. This paper presents Panther, a novel ultrafast multipurpose docking tool. In Panther, a simple shape-electrostatic model of the ligand-binding area of the protein is created by utilizing the protein crystal structure. The features of the possible ligands are then compared to the model by using a similarity search algorithm. On average, one ligand can be processed in a few minutes by using classical docking methods, whereas using Panther processing takes <1 s. The presented Panther protocol can be used in several applications, such as speeding up the early phases of drug discovery projects, reducing the number of failures in the clinical phase of the drug development process, and estimating the environmental toxicity of chemicals. Panther-code is available in our web pages (http://www.jyu.fi/panther) free of charge after registration.

Best Matching Protein Conformations and Docking Programs for a Virtual Screening Campaign Against SMO Receptor.

PubMed

Amendola, Giorgio; Di Maio, Danilo; La Pietra, Valeria; Cosconati, Sandro

2016-09-01

SMO receptor is one of the main components of the Hedgehog biochemical pathway. In the last decades compelling body of evidence demonstrated that this receptor is a pertinent target for the treatment of various types of solid tumors. Recently, the X-ray determination of the three-dimensional structure of SMO in complex with different antagonists opened up the way for the structure-based design of new antagonists for this receptor that could possibly overcome the limitations connected with the induction of acquired tumor resistance. Herein, taking advantage of three different docking software (namely Glide, PLANTS, and Vina) and of the available SMO structures we set up a retrospective virtual screening (VS) protocol. A database, made up by known SMO antagonists and compounds with no alleged activity against the receptor was created and screened against the different SMO structures. To evaluate the performance of the ranking in VS calculations different statistical metrics (EF, AUAC and BEDROC) were employed allowing to identify the best performing VS docking protocol. Results of these studies will serve as a platform for the application of structure-based VS against the pharmaceutically relevant SMO receptor. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluation of a focused virtual library of heterobifunctional ligands for Clostridium difficile toxins.

PubMed

Sanhueza, Carlos A; Cartmell, Jonathan; El-Hawiet, Amr; Szpacenko, Adam; Kitova, Elena N; Daneshfar, Rambod; Klassen, John S; Lang, Dean E; Eugenio, Luiz; Ng, Kenneth K-S; Kitov, Pavel I; Bundle, David R

2015-01-07

A focused library of virtual heterobifunctional ligands was generated in silico and a set of ligands with recombined fragments was synthesized and evaluated for binding to Clostridium difficile toxins. The position of the trisaccharide fragment was used as a reference for filtering docked poses during virtual screening to match the trisaccharide ligand in a crystal structure. The peptoid, a diversity fragment probing the protein surface area adjacent to a known binding site, was generated by a multi-component Ugi reaction. Our approach combines modular fragment-based design with in silico screening of synthetically feasible compounds and lays the groundwork for future efforts in development of composite bifunctional ligands for large clostridial toxins.

PhAST: pharmacophore alignment search tool.

PubMed

Hähnke, Volker; Hofmann, Bettina; Grgat, Tomislav; Proschak, Ewgenij; Steinhilber, Dieter; Schneider, Gisbert

2009-04-15

We present a ligand-based virtual screening technique (PhAST) for rapid hit and lead structure searching in large compound databases. Molecules are represented as strings encoding the distribution of pharmacophoric features on the molecular graph. In contrast to other text-based methods using SMILES strings, we introduce a new form of text representation that describes the pharmacophore of molecules. This string representation opens the opportunity for revealing functional similarity between molecules by sequence alignment techniques in analogy to homology searching in protein or nucleic acid sequence databases. We favorably compared PhAST with other current ligand-based virtual screening methods in a retrospective analysis using the BEDROC metric. In a prospective application, PhAST identified two novel inhibitors of 5-lipoxygenase product formation with minimal experimental effort. This outcome demonstrates the applicability of PhAST to drug discovery projects and provides an innovative concept of sequence-based compound screening with substantial scaffold hopping potential. 2008 Wiley Periodicals, Inc.

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

NASA Astrophysics Data System (ADS)

Fayaz, S. M.; Rajanikant, G. K.

2014-07-01

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.

Avalanche for shape and feature-based virtual screening with 3D alignment

NASA Astrophysics Data System (ADS)

Diller, David J.; Connell, Nancy D.; Welsh, William J.

2015-11-01

This report introduces a new ligand-based virtual screening tool called Avalanche that incorporates both shape- and feature-based comparison with three-dimensional (3D) alignment between the query molecule and test compounds residing in a chemical database. Avalanche proceeds in two steps. The first step is an extremely rapid shape/feature based comparison which is used to narrow the focus from potentially millions or billions of candidate molecules and conformations to a more manageable number that are then passed to the second step. The second step is a detailed yet still rapid 3D alignment of the remaining candidate conformations to the query conformation. Using the 3D alignment, these remaining candidate conformations are scored, re-ranked and presented to the user as the top hits for further visualization and evaluation. To provide further insight into the method, the results from two prospective virtual screens are presented which show the ability of Avalanche to identify hits from chemical databases that would likely be missed by common substructure-based or fingerprint-based search methods. The Avalanche method is extended to enable patent landscaping, i.e., structural refinements to improve the patentability of hits for deployment in drug discovery campaigns.

Virtual screening of mandelate racemase mutants with enhanced activity based on binding energy in the transition state.

PubMed

Gu, Jiali; Liu, Min; Guo, Fei; Xie, Wenping; Lu, Wenqiang; Ye, Lidan; Chen, Zhirong; Yuan, Shenfeng; Yu, Hongwei

2014-02-05

Mandelate racemase (MR) is a promising candidate for the dynamic kinetic resolution of racemates. However, the poor activity of MR towards most of its non-natural substrates limits its widespread application. In this work, a virtual screening method based on the binding energy in the transition state was established to assist in the screening of MR mutants with enhanced catalytic efficiency. Using R-3-chloromandelic acid as a model substrate, a total of 53 mutants were constructed based on rational design in the two rounds of screening. The number of mutants for experimental validation was brought down to 17 by the virtual screening method, among which 14 variants turned out to possess improved catalytic efficiency. The variant V26I/Y54V showed 5.2-fold higher catalytic efficiency (k(cat)/K(m)) towards R-3-chloromandelic acid than that observed for the wild-type enzyme. Using this strategy, mutants were successfully obtained for two other substrates, R-mandelamide and R-2-naphthylglycolate (V26I and V29L, respectively), both with a 2-fold improvement in catalytic efficiency. These results demonstrated that this method could effectively predict the trend of mutational effects on catalysis. Analysis from the energetic and structural assays indicated that the enhanced interactions between the active sites and the substrate in the transition state led to improved catalytic efficiency. It was concluded that this virtual screening method based on the binding energy in the transition state was beneficial in enzyme rational redesign and helped to better understand the catalytic properties of the enzyme. Copyright © 2013 Elsevier Inc. All rights reserved.

Exploiting PubChem for Virtual Screening

PubMed Central

Xie, Xiang-Qun

2011-01-01

Importance of the field PubChem is a public molecular information repository, a scientific showcase of the NIH Roadmap Initiative. The PubChem database holds over 27 million records of unique chemical structures of compounds (CID) derived from nearly 70 million substance depositions (SID), and contains more than 449,000 bioassay records with over thousands of in vitro biochemical and cell-based screening bioassays established, with targeting more than 7000 proteins and genes linking to over 1.8 million of substances. Areas covered in this review This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling. What the reader will gain These publicly available datasets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the datasets, in particular the bioassay-associated false positives/negatives and highly imbalanced datasets in PubChem, also creates major challenges. Several approaches regarding the modeling of PubChem datasets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed. Take home message Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design. PMID:21691435

An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs

PubMed Central

2015-01-01

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD. PMID:24749745

An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.

PubMed

Xia, Jie; Jin, Hongwei; Liu, Zhenming; Zhang, Liangren; Wang, Xiang Simon

2014-05-27

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the "artificial enrichment" and "analogue bias" of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.

Congestion game scheduling for virtual drug screening optimization

NASA Astrophysics Data System (ADS)

Nikitina, Natalia; Ivashko, Evgeny; Tchernykh, Andrei

2018-02-01

In virtual drug screening, the chemical diversity of hits is an important factor, along with their predicted activity. Moreover, interim results are of interest for directing the further research, and their diversity is also desirable. In this paper, we consider a problem of obtaining a diverse set of virtual screening hits in a short time. To this end, we propose a mathematical model of task scheduling for virtual drug screening in high-performance computational systems as a congestion game between computational nodes to find the equilibrium solutions for best balancing the number of interim hits with their chemical diversity. The model considers the heterogeneous environment with workload uncertainty, processing time uncertainty, and limited knowledge about the input dataset structure. We perform computational experiments and evaluate the performance of the developed approach considering organic molecules database GDB-9. The used set of molecules is rich enough to demonstrate the feasibility and practicability of proposed solutions. We compare the algorithm with two known heuristics used in practice and observe that game-based scheduling outperforms them by the hit discovery rate and chemical diversity at earlier steps. Based on these results, we use a social utility metric for assessing the efficiency of our equilibrium solutions and show that they reach greatest values.

Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads

PubMed Central

Reynolds, Christopher R; Muggleton, Stephen H; Sternberg, Michael J E

2015-01-01

The use of virtual screening has become increasingly central to the drug development pipeline, with ligand-based virtual screening used to screen databases of compounds to predict their bioactivity against a target. These databases can only represent a small fraction of chemical space, and this paper describes a method of exploring synthetic space by applying virtual reactions to promising compounds within a database, and generating focussed libraries of predicted derivatives. A ligand-based virtual screening tool Investigational Novel Drug Discovery by Example (INDDEx) is used as the basis for a system of virtual reactions. The use of virtual reactions is estimated to open up a potential space of 1.21×1012 potential molecules. A de novo design algorithm known as Partial Logical-Rule Reactant Selection (PLoRRS) is introduced and incorporated into the INDDEx methodology. PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products. The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016. Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs. PMID:26583052

A cross docking pipeline for improving pose prediction and virtual screening performance

NASA Astrophysics Data System (ADS)

Kumar, Ashutosh; Zhang, Kam Y. J.

2018-01-01

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

Discovery of Novel ROCK1 Inhibitors via Integrated Virtual Screening Strategy and Bioassays

PubMed Central

Shen, Mingyun; Tian, Sheng; Pan, Peichen; Sun, Huiyong; Li, Dan; Li, Youyong; Zhou, Hefeng; Li, Chuwen; Lee, Simon Ming-Yuen; Hou, Tingjun

2015-01-01

Rho-associated kinases (ROCKs) have been regarded as promising drug targets for the treatment of cardiovascular diseases, nervous system diseases and cancers. In this study, a novel integrated virtual screening protocol by combining molecular docking and pharmacophore mapping based on multiple ROCK1 crystal structures was utilized to screen the ChemBridge database for discovering potential inhibitors of ROCK1. Among the 38 tested compounds, seven of them exhibited significant inhibitory activities of ROCK1 (IC50 < 10 μM) and the most potent one (compound TS-f22) with the novel scaffold of 4-Phenyl-1H-pyrrolo [2,3-b] pyridine had an IC50 of 480 nM. Then, the structure-activity relationships of 41 analogues of TS-f22 were examined. Two potent inhibitors were proven effective in inhibiting the phosphorylation of the downstream target in the ROCK signaling pathway in vitro and protecting atorvastatin-induced cerebral hemorrhage in vivo. The high hit rate (28.95%) suggested that the integrated virtual screening strategy was quite reliable and could be used as a powerful tool for identifying promising active compounds for targets of interest. PMID:26568382

Discovery of Novel ROCK1 Inhibitors via Integrated Virtual Screening Strategy and Bioassays.

PubMed

Shen, Mingyun; Tian, Sheng; Pan, Peichen; Sun, Huiyong; Li, Dan; Li, Youyong; Zhou, Hefeng; Li, Chuwen; Lee, Simon Ming-Yuen; Hou, Tingjun

2015-11-16

Rho-associated kinases (ROCKs) have been regarded as promising drug targets for the treatment of cardiovascular diseases, nervous system diseases and cancers. In this study, a novel integrated virtual screening protocol by combining molecular docking and pharmacophore mapping based on multiple ROCK1 crystal structures was utilized to screen the ChemBridge database for discovering potential inhibitors of ROCK1. Among the 38 tested compounds, seven of them exhibited significant inhibitory activities of ROCK1 (IC50 < 10 μM) and the most potent one (compound TS-f22) with the novel scaffold of 4-Phenyl-1H-pyrrolo [2,3-b] pyridine had an IC50 of 480 nM. Then, the structure-activity relationships of 41 analogues of TS-f22 were examined. Two potent inhibitors were proven effective in inhibiting the phosphorylation of the downstream target in the ROCK signaling pathway in vitro and protecting atorvastatin-induced cerebral hemorrhage in vivo. The high hit rate (28.95%) suggested that the integrated virtual screening strategy was quite reliable and could be used as a powerful tool for identifying promising active compounds for targets of interest.

μ Opioid receptor: novel antagonists and structural modeling

NASA Astrophysics Data System (ADS)

Kaserer, Teresa; Lantero, Aquilino; Schmidhammer, Helmut; Spetea, Mariana; Schuster, Daniela

2016-02-01

The μ opioid receptor (MOR) is a prominent member of the G protein-coupled receptor family and the molecular target of morphine and other opioid drugs. Despite the long tradition of MOR-targeting drugs, still little is known about the ligand-receptor interactions and structure-function relationships underlying the distinct biological effects upon receptor activation or inhibition. With the resolved crystal structure of the β-funaltrexamine-MOR complex, we aimed at the discovery of novel agonists and antagonists using virtual screening tools, i.e. docking, pharmacophore- and shape-based modeling. We suggest important molecular interactions, which active molecules share and distinguish agonists and antagonists. These results allowed for the generation of theoretically validated in silico workflows that were employed for prospective virtual screening. Out of 18 virtual hits evaluated in in vitro pharmacological assays, three displayed antagonist activity and the most active compound significantly inhibited morphine-induced antinociception. The new identified chemotypes hold promise for further development into neurochemical tools for studying the MOR or as potential therapeutic lead candidates.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases.

PubMed

Kaserer, Teresa; Beck, Katharina R; Akram, Muhammad; Odermatt, Alex; Schuster, Daniela

2015-12-19

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models.

PubMed

Boppana, Kiran; Dubey, P K; Jagarlapudi, Sarma A R P; Vadivelan, S; Rambabu, G

2009-09-01

Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best model, Hypo2 consists of three pharmacophore features, i.e., one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic. The Hypo2 model was used to screen an in-house database of 80,000 molecules and have resulted in 5500 compounds. Docking studies were performed, subsequently, on the cluster representatives of 530 hits from 5500 compounds. Based on the structural novelty and selectivity index, we have suggested 15 selective MAO-B inhibitors for further synthesis and pharmacological screening.

Position specific interaction dependent scoring technique for virtual screening based on weighted protein--ligand interaction fingerprint profiles.

PubMed

Nandigam, Ravi K; Kim, Sangtae; Singh, Juswinder; Chuaqui, Claudio

2009-05-01

The desire to exploit structural information to aid structure based design and virtual screening led to the development of the interaction fingerprint for analyzing, mining, and filtering the binding patterns underlying the complex 3D data. In this paper we introduce a new approach, weighted SIFt (or w-SIFt), extending the concept of SIFt to capture the relative importance of different binding interactions. The methodology presented here for determining the weights in w-SIFt involves utilizing a dimensionality reduction technique for eliminating linear redundancies in the data followed by a stochastic optimization. We find that the relative weights of the fingerprint bits provide insight into what interactions are critical in determining inhibitor potency. Moreover, the weighted interaction fingerprint can serve as an interpretable position dependent scoring function for ligand protein interactions.

Rational approach to identify newer caspase-1 inhibitors using pharmacophore based virtual screening, docking and molecular dynamic simulation studies.

PubMed

Patel, Shivani; Modi, Palmi; Chhabria, Mahesh

2018-05-01

Caspase-1 is a key endoprotease responsible for the post-translational processing of pro-inflammatory cytokines IL-1β, 18 & 33. Excessive secretion of IL-1β leads to numerous inflammatory and autoimmune diseases. Thus caspase-1 inhibition would be considered as an important therapeutic strategy for development of newer anti-inflammatory agents. Here we have employed an integrated virtual screening by combining pharmacophore mapping and docking to identify small molecules as caspase-1 inhibitors. The ligand based 3D pharmacophore model was generated having the essential structural features of (HBA, HY & RA) using a data set of 27 compounds. A validated pharmacophore hypothesis (Hypo 1) was used to screen ZINC and Minimaybridge chemical databases. The retrieved virtual hits were filtered by ADMET properties and molecular docking analysis. Subsequently, the cross-docking study was also carried out using crystal structure of caspase-1, 3, 7 and 8 to identify the key residual interaction for specific caspase-1 inhibition. Finally, the best mapped and top scored (ZINC00885612, ZINC72003647, BTB04175 and BTB04410) molecules were subjected to molecular dynamics simulation for accessing the dynamic structure of protein after ligand binding. This study identifies the most promising hits, which can be leads for the development of novel caspase-1 inhibitors as anti-inflammatory agents. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision

PubMed Central

2011-01-01

Background Data fusion methods are widely used in virtual screening, and make the implicit assumption that the more often a molecule is retrieved in multiple similarity searches, the more likely it is to be active. This paper tests the correctness of this assumption. Results Sets of 25 searches using either the same reference structure and 25 different similarity measures (similarity fusion) or 25 different reference structures and the same similarity measure (group fusion) show that large numbers of unique molecules are retrieved by just a single search, but that the numbers of unique molecules decrease very rapidly as more searches are considered. This rapid decrease is accompanied by a rapid increase in the fraction of those retrieved molecules that are active. There is an approximately log-log relationship between the numbers of different molecules retrieved and the number of searches carried out, and a rationale for this power-law behaviour is provided. Conclusions Using multiple searches provides a simple way of increasing the precision of a similarity search, and thus provides a justification for the use of data fusion methods in virtual screening. PMID:21824430

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

PubMed

Jayaraj, P B; Ajay, Mathias K; Nufail, M; Gopakumar, G; Jaleel, U C A

2016-01-01

In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

Virtual screening of compound libraries.

PubMed

Cerqueira, Nuno M F S A; Sousa, Sérgio F; Fernandes, Pedro A; Ramos, Maria João

2009-01-01

During the last decade, Virtual Screening (VS) has definitively established itself as an important part of the drug discovery and development process. VS involves the selection of likely drug candidates from large libraries of chemical structures by using computational methodologies, but the generic definition of VS encompasses many different methodologies. This chapter provides an introduction to the field by reviewing a variety of important aspects, including the different types of virtual screening methods, and the several steps required for a successful virtual screening campaign within a state-of-the-art approach, from target selection to postfilter application. This analysis is further complemented with a small collection important VS success stories.

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

PubMed Central

Lionta, Evanthia; Spyrou, George; Vassilatis, Demetrios K.; Cournia, Zoe

2014-01-01

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process. PMID:25262799

An Integrated In Silico Method to Discover Novel Rock1 Inhibitors: Multi- Complex-Based Pharmacophore, Molecular Dynamics Simulation and Hybrid Protocol Virtual Screening.

PubMed

Chen, Haining; Li, Sijia; Hu, Yajiao; Chen, Guo; Jiang, Qinglin; Tong, Rongsheng; Zang, Zhihe; Cai, Lulu

2016-01-01

Rho-associated, coiled-coil containing protein kinase 1 (ROCK1) is an important regulator of focal adhesion, actomyosin contraction and cell motility. In this manuscript, a combination of the multi-complex-based pharmacophore (MCBP), molecular dynamics simulation and a hybrid protocol of a virtual screening method, comprised of multipharmacophore- based virtual screening (PBVS) and ensemble docking-based virtual screening (DBVS) methods were used for retrieving novel ROCK1 inhibitors from the natural products database embedded in the ZINC database. Ten hit compounds were selected from the hit compounds, and five compounds were tested experimentally. Thus, these results may provide valuable information for further discovery of more novel ROCK1 inhibitors.

HPPD: ligand- and target-based virtual screening on a herbicide target.

PubMed

López-Ramos, Miriam; Perruccio, Francesca

2010-05-24

Hydroxyphenylpyruvate dioxygenase (HPPD) has proven to be a very successful target for the development of herbicides with bleaching properties, and today HPPD inhibitors are well established in the agrochemical market. Syngenta has a long history of HPPD-inhibitor research, and HPPD was chosen as a case study for the validation of diverse ligand- and target-based virtual screening approaches to identify compounds with inhibitory properties. Two-dimensional extended connectivity fingerprints, three-dimensional shape-based tools (ROCS, EON, and Phase-shape) and a pharmacophore approach (Phase) were used as ligand-based methods; Glide and Gold were used as target-based. Both the virtual screening utility and the scaffold-hopping ability of the screening tools were assessed. Particular emphasis was put on the specific pitfalls to take into account for the design of a virtual screening campaign in an agrochemical context, as compared to a pharmaceutical environment.

Ligand-based virtual screening under partial shape constraints.

PubMed

von Behren, Mathias M; Rarey, Matthias

2017-04-01

Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).

Ligand-based virtual screening under partial shape constraints

NASA Astrophysics Data System (ADS)

von Behren, Mathias M.; Rarey, Matthias

2017-04-01

Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise).

DEC Ada interface to Screen Management Guidelines (SMG)

NASA Technical Reports Server (NTRS)

Laomanachareon, Somsak; Lekkos, Anthony A.

1986-01-01

DEC's Screen Management Guidelines are the Run-Time Library procedures that perform terminal-independent screen management functions on a VT100-class terminal. These procedures assist users in designing, composing, and keeping track of complex images on a video screen. There are three fundamental elements in the screen management model: the pasteboard, the virtual display, and the virtual keyboard. The pasteboard is like a two-dimensional area on which a user places and manipulates screen displays. The virtual display is a rectangular part of the terminal screen to which a program writes data with procedure calls. The virtual keyboard is a logical structure for input operation associated with a physical keyboard. SMG can be called by all major VAX languages. Through Ada, predefined language Pragmas are used to interface with SMG. These features and elements of SMG are briefly discussed.

Data Resources for the Computer-Guided Discovery of Bioactive Natural Products.

PubMed

Chen, Ya; de Bruyn Kops, Christina; Kirchmair, Johannes

2017-09-25

Natural products from plants, animals, marine life, fungi, bacteria, and other organisms are an important resource for modern drug discovery. Their biological relevance and structural diversity make natural products good starting points for drug design. Natural product-based drug discovery can benefit greatly from computational approaches, which are a valuable precursor or supplementary method to in vitro testing. We present an overview of 25 virtual and 31 physical natural product libraries that are useful for applications in cheminformatics, in particular virtual screening. The overview includes detailed information about each library, the extent of its structural information, and the overlap between different sources of natural products. In terms of chemical structures, there is a large overlap between freely available and commercial virtual natural product libraries. Of particular interest for drug discovery is that at least ten percent of known natural products are readily purchasable and many more natural products and derivatives are available through on-demand sourcing, extraction and synthesis services. Many of the readily purchasable natural products are of small size and hence of relevance to fragment-based drug discovery. There are also an increasing number of macrocyclic natural products and derivatives becoming available for screening.

Probing the structure of Leishmania major DHFR TS and structure based virtual screening of peptide library for the identification of anti-leishmanial leads.

PubMed

Rajasekaran, Rajalakshmi; Chen, Yi-Ping Phoebe

2012-09-01

Leishmaniasis, a multi-faceted ethereal disease is considered to be one of the World's major communicable diseases that demands exhaustive research and control measures. The substantial data on these protozoan parasites has not been utilized completely to develop potential therapeutic strategies against Leishmaniasis. Dihydrofolate reductase thymidylate synthase (DHFR-TS) plays a major role in the infective state of the parasite and hence the DHFR-TS based drugs remains of much interest to researchers working on Leishmaniasis. Although, crystal structures of DHFR-TS from different species including Plasmodium falciparum and Trypanosoma cruzi are available, the experimentally determined structure of the Leishmania major DHFR-TS has not yet been reported in the Protein Data Bank. A high quality three dimensional structure of L.major DHFR-TS has been modeled through the homology modeling approach. Carefully refined and the energy minimized structure of the modeled protein was validated using a number of structure validation programs to confirm its structure quality. The modeled protein structure was used in the process of structure based virtual screening to figure out a potential lead structure against DHFR TS. The lead molecule identified has a binding affinity of 0.51 nM and clearly follows drug like properties.

Discovery of novel mGluR1 antagonists: a multistep virtual screening approach based on an SVM model and a pharmacophore hypothesis significantly increases the hit rate and enrichment factor.

PubMed

Li, Guo-Bo; Yang, Ling-Ling; Feng, Shan; Zhou, Jian-Ping; Huang, Qi; Xie, Huan-Zhang; Li, Lin-Li; Yang, Sheng-Yong

2011-03-15

Development of glutamate non-competitive antagonists of mGluR1 (Metabotropic glutamate receptor subtype 1) has increasingly attracted much attention in recent years due to their potential therapeutic application for various nervous disorders. Since there is no crystal structure reported for mGluR1, ligand-based virtual screening (VS) methods, typically pharmacophore-based VS (PB-VS), are often used for the discovery of mGluR1 antagonists. Nevertheless, PB-VS usually suffers a lower hit rate and enrichment factor. In this investigation, we established a multistep ligand-based VS approach that is based on a support vector machine (SVM) classification model and a pharmacophore model. Performance evaluation of these methods in virtual screening against a large independent test set, M-MDDR, show that the multistep VS approach significantly increases the hit rate and enrichment factor compared with the individual SB-VS and PB-VS methods. The multistep VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally a total of 20 compounds were selected from the top ranking compounds, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future. Copyright © 2011 Elsevier Ltd. All rights reserved.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

PubMed

Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

NASA Astrophysics Data System (ADS)

Voet, Arnout R. D.; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y. J.

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Hierarchical virtual screening of the dual MMP-2/HDAC-6 inhibitors from natural products based on pharmacophore models and molecular docking.

PubMed

Wang, Yijun; Yang, Limei; Hou, Jiaying; Zou, Qing; Gao, Qi; Yao, Wenhui; Yao, Qizheng; Zhang, Ji

2018-02-12

The dual-target inhibitors tend to improve the response rate in treating tumors, comparing with the single-target inhibitors. Matrix metalloproteinase-2 (MMP-2) and histone deacetylase-6 (HDAC-6) are attractive targets for cancer therapy. In this study, the hierarchical virtual screening of dual MMP-2/HDAC-6 inhibitors from natural products is investigated. The pharmacophore model of MMP-2 inhibitors is built based on ligands, but the pharmacophore model of HDAC-6 inhibitors is built based on the experimental crystal structures of multiple receptor-ligand complexes. The reliability of these two pharmacophore models is validated subsequently. The hierarchical virtual screening, combining these two different pharmacophore models of MMP-2 and HDAC-6 inhibitors with molecular docking, is carried out to identify the dual MMP-2/HDAC-6 inhibitors from a database of natural products. The four potential dual MMP-2/HDAC-6 inhibitors of natural products, STOCK1 N-46177, STOCK1 N-52245, STOCK1 N-55477, and STOCK1 N-69706, are found. The studies of binding modes show that the screened four natural products can simultaneously well bind with the MMP-2 and HDAC-6 active sites by different kinds of interactions, to inhibit the MMP-2 and HDAC-6 activities. In addition, the ADMET properties of screened four natural products are assessed. These found dual MMP-2/HDAC-6 inhibitors of natural products could serve as the lead compounds for designing the new dual MMP-2/HDAC-6 inhibitors having higher biological activities by carrying out structural modifications and optimizations in the future studies.

Identification of PPARgamma Partial Agonists of Natural Origin (I): Development of a Virtual Screening Procedure and In Vitro Validation

PubMed Central

Guasch, Laura; Sala, Esther; Castell-Auví, Anna; Cedó, Lidia; Liedl, Klaus R.; Wolber, Gerhard; Muehlbacher, Markus; Mulero, Miquel; Pinent, Montserrat; Ardévol, Anna; Valls, Cristina; Pujadas, Gerard; Garcia-Vallvé, Santiago

2012-01-01

Background Although there are successful examples of the discovery of new PPARγ agonists, it has recently been of great interest to identify new PPARγ partial agonists that do not present the adverse side effects caused by PPARγ full agonists. Consequently, the goal of this work was to design, apply and validate a virtual screening workflow to identify novel PPARγ partial agonists among natural products. Methodology/Principal Findings We have developed a virtual screening procedure based on structure-based pharmacophore construction, protein-ligand docking and electrostatic/shape similarity to discover novel scaffolds of PPARγ partial agonists. From an initial set of 89,165 natural products and natural product derivatives, 135 compounds were identified as potential PPARγ partial agonists with good ADME properties. Ten compounds that represent ten new chemical scaffolds for PPARγ partial agonists were selected for in vitro biological testing, but two of them were not assayed due to solubility problems. Five out of the remaining eight compounds were confirmed as PPARγ partial agonists: they bind to PPARγ, do not or only moderately stimulate the transactivation activity of PPARγ, do not induce adipogenesis of preadipocyte cells and stimulate the insulin-induced glucose uptake of adipocytes. Conclusions/Significance We have demonstrated that our virtual screening protocol was successful in identifying novel scaffolds for PPARγ partial agonists. PMID:23226391

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors.

PubMed

Fu, Ying; Sun, Yi-Na; Yi, Ke-Han; Li, Ming-Qiang; Cao, Hai-Feng; Li, Jia-Zhong; Ye, Fei

2017-06-09

p -Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.

Automated Inference of Chemical Discriminants of Biological Activity.

PubMed

Raschka, Sebastian; Scott, Anne M; Huertas, Mar; Li, Weiming; Kuhn, Leslie A

2018-01-01

Ligand-based virtual screening has become a standard technique for the efficient discovery of bioactive small molecules. Following assays to determine the activity of compounds selected by virtual screening, or other approaches in which dozens to thousands of molecules have been tested, machine learning techniques make it straightforward to discover the patterns of chemical groups that correlate with the desired biological activity. Defining the chemical features that generate activity can be used to guide the selection of molecules for subsequent rounds of screening and assaying, as well as help design new, more active molecules for organic synthesis.The quantitative structure-activity relationship machine learning protocols we describe here, using decision trees, random forests, and sequential feature selection, take as input the chemical structure of a single, known active small molecule (e.g., an inhibitor, agonist, or substrate) for comparison with the structure of each tested molecule. Knowledge of the atomic structure of the protein target and its interactions with the active compound are not required. These protocols can be modified and applied to any data set that consists of a series of measured structural, chemical, or other features for each tested molecule, along with the experimentally measured value of the response variable you would like to predict or optimize for your project, for instance, inhibitory activity in a biological assay or ΔG binding . To illustrate the use of different machine learning algorithms, we step through the analysis of a dataset of inhibitor candidates from virtual screening that were tested recently for their ability to inhibit GPCR-mediated signaling in a vertebrate.

Identification of novel monoamine oxidase B inhibitors by structure-based virtual screening.

PubMed

Geldenhuys, Werner J; Darvesh, Altaf S; Funk, Max O; Van der Schyf, Cornelis J; Carroll, Richard T

2010-09-01

Parkinson's disease is a severe debilitating neurodegenerative disorder. Recently, it was shown that the peroxisome proliferating-activator receptor-gamma agonist pioglitazone protected mice from 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine neurotoxicity due to its ability to inhibit monoamine oxidase B (MAO-B). Docking studies were initiated to investigate pioglitazone's interactions within the substrate cavity of MAO-B. Modeling studies indicated that the thiazolidinedione (TZD) moiety was a likely candidate for its specificity to MAO-B. To explore this potential novel MAO-B scaffold, we performed a structure-based virtual screen to identify additional MAO-B inhibitors. Our search identified eight novel compounds containing the TZD-moiety that allowed for a limited study to identify structural requirements for binding to MAO-B. Inhibition assays identified two TZDs (A6355 and L136662) which were found to inhibit recombinant human MAO-B with IC(50) values of 82 and 195 nM, respectively. Copyright 2010 Elsevier Ltd. All rights reserved.

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

PubMed

Zhang, Yanmin; Jiao, Yu; Xiong, Xiao; Liu, Haichun; Ran, Ting; Xu, Jinxing; Lu, Shuai; Xu, Anyang; Pan, Jing; Qiao, Xin; Shi, Zhihao; Lu, Tao; Chen, Yadong

2015-11-01

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

mRAISE: an alternative algorithmic approach to ligand-based virtual screening

NASA Astrophysics Data System (ADS)

von Behren, Mathias M.; Bietz, Stefan; Nittinger, Eva; Rarey, Matthias

2016-08-01

Ligand-based virtual screening is a well established method to find new lead molecules in todays drug discovery process. In order to be applicable in day to day practice, such methods have to face multiple challenges. The most important part is the reliability of the results, which can be shown and compared in retrospective studies. Furthermore, in the case of 3D methods, they need to provide biologically relevant molecular alignments of the ligands, that can be further investigated by a medicinal chemist. Last but not least, they have to be able to screen large databases in reasonable time. Many algorithms for ligand-based virtual screening have been proposed in the past, most of them based on pairwise comparisons. Here, a new method is introduced called mRAISE. Based on structural alignments, it uses a descriptor-based bitmap search engine (RAISE) to achieve efficiency. Alignments created on the fly by the search engine get evaluated with an independent shape-based scoring function also used for ranking of compounds. The correct ranking as well as the alignment quality of the method are evaluated and compared to other state of the art methods. On the commonly used Directory of Useful Decoys dataset mRAISE achieves an average area under the ROC curve of 0.76, an average enrichment factor at 1 % of 20.2 and an average hit rate at 1 % of 55.5. With these results, mRAISE is always among the top performing methods with available data for comparison. To access the quality of the alignments calculated by ligand-based virtual screening methods, we introduce a new dataset containing 180 prealigned ligands for 11 diverse targets. Within the top ten ranked conformations, the alignment closest to X-ray structure calculated with mRAISE has a root-mean-square deviation of less than 2.0 Å for 80.8 % of alignment pairs and achieves a median of less than 2.0 Å for eight of the 11 cases. The dataset used to rate the quality of the calculated alignments is freely available at http://www.zbh.uni-hamburg.de/mraise-dataset.html. The table of all PDB codes contained in the ensembles can be found in the supplementary material. The software tool mRAISE is freely available for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise).

Structure-Guided Screening for Functionally Selective D2 Dopamine Receptor Ligands from a Virtual Chemical Library.

PubMed

Männel, Barbara; Jaiteh, Mariama; Zeifman, Alexey; Randakova, Alena; Möller, Dorothee; Hübner, Harald; Gmeiner, Peter; Carlsson, Jens

2017-10-20

Functionally selective ligands stabilize conformations of G protein-coupled receptors (GPCRs) that induce a preference for signaling via a subset of the intracellular pathways activated by the endogenous agonists. The possibility to fine-tune the functional activity of a receptor provides opportunities to develop drugs that selectively signal via pathways associated with a therapeutic effect and avoid those causing side effects. Animal studies have indicated that ligands displaying functional selectivity at the D 2 dopamine receptor (D 2 R) could be safer and more efficacious drugs against neuropsychiatric diseases. In this work, computational design of functionally selective D 2 R ligands was explored using structure-based virtual screening. Molecular docking of known functionally selective ligands to a D 2 R homology model indicated that such compounds were anchored by interactions with the orthosteric site and extended into a common secondary pocket. A tailored virtual library with close to 13 000 compounds bearing 2,3-dichlorophenylpiperazine, a privileged orthosteric scaffold, connected to diverse chemical moieties via a linker was docked to the D 2 R model. Eighteen top-ranked compounds that occupied both the orthosteric and allosteric site were synthesized, leading to the discovery of 16 partial agonists. A majority of the ligands had comparable maximum effects in the G protein and β-arrestin recruitment assays, but a subset displayed preference for a single pathway. In particular, compound 4 stimulated β-arrestin recruitment (EC 50 = 320 nM, E max = 16%) but had no detectable G protein signaling. The use of structure-based screening and virtual libraries to discover GPCR ligands with tailored functional properties will be discussed.

Computer-aided drug design of falcipain inhibitors: virtual screening, structure-activity relationships, hydration site thermodynamics, and reactivity analysis.

PubMed

Shah, Falgun; Gut, Jiri; Legac, Jennifer; Shivakumar, Devleena; Sherman, Woody; Rosenthal, Philip J; Avery, Mitchell A

2012-03-26

Falcipains (FPs) are hemoglobinases of Plasmodium falciparum that are validated targets for the development of antimalarial chemotherapy. A combined ligand- and structure-based virtual screening of commercial databases was performed to identify structural analogs of virtual screening hits previously discovered in our laboratory. A total of 28 low micromolar inhibitors of FP-2 and FP-3 were identified and the structure-activity relationship (SAR) in each series was elaborated. The SAR of the compounds was unusually steep in some cases and could not be explained by a traditional analysis of the ligand-protein interactions (van der Waals, electrostatics, and hydrogen bonds). To gain further insights, a statistical thermodynamic analysis of explicit solvent in the ligand binding domains of FP-2 and FP-3 was carried out to understand the roles played by water molecules in binding of these inhibitors. Indeed, the energetics associated with the displacement of water molecules upon ligand binding explained some of the complex trends in the SAR. Furthermore, low potency of a subset of FP-2 inhibitors that could not be understood by the water energetics was explained in the context of poor chemical reactivity of the reactive centers of these compounds. The present study highlights the importance of considering energetic contributors to binding beyond traditional ligand-protein interactions. © 2012 American Chemical Society

In silico identification of novel ligands for G-quadruplex in the c- MYC promoter

NASA Astrophysics Data System (ADS)

Kang, Hyun-Jin; Park, Hyun-Ju

2015-04-01

G-quadruplex DNA formed in NHEIII1 region of oncogene promoter inhibits transcription of the genes. In this study, virtual screening combining pharmacophore-based search and structure-based docking screening was conducted to discover ligands binding to G-quadruplex in promoter region of c- MYC. Several hit ligands showed the selective PCR-arresting effects for oligonucleotide containing c- MYC G-quadruplex forming sequence. Among them, three hits selectively inhibited cell proliferation and decreased c- MYC mRNA level in Ramos cells, where NHEIII1 is included in translocated c- MYC gene for overexpression. Promoter assay using two kinds of constructs with wild-type and mutant sequences showed that interaction of these ligands with the G-quadruplex resulted in turning-off of the reporter gene. In conclusion, combined virtual screening methods were successfully used for discovery of selective c- MYC promoter G-quadruplex binders with anticancer activity.

Identification of potential glutaminyl cyclase inhibitors from lead-like libraries by in silico and in vitro fragment-based screening.

PubMed

Szaszkó, Mária; Hajdú, István; Flachner, Beáta; Dobi, Krisztina; Magyar, Csaba; Simon, István; Lőrincz, Zsolt; Kapui, Zoltán; Pázmány, Tamás; Cseh, Sándor; Dormán, György

2017-02-01

A glutaminyl cyclase (QC) fragment library was in silico selected by disconnection of the structure of known QC inhibitors and by lead-like 2D virtual screening of the same set. The resulting fragment library (204 compounds) was acquired from commercial suppliers and pre-screened by differential scanning fluorimetry followed by functional in vitro assays. In this way, 10 fragment hits were identified ([Formula: see text]5 % hit rate, best inhibitory activity: 16 [Formula: see text]). The in vitro hits were then docked to the active site of QC, and the best scoring compounds were analyzed for binding interactions. Two fragments bound to different regions in a complementary manner, and thus, linking those fragments offered a rational strategy to generate novel QC inhibitors. Based on the structure of the virtual linked fragment, a 77-membered QC target focused library was selected from vendor databases and docked to the active site of QC. A PubChem search confirmed that the best scoring analogues are novel, potential QC inhibitors.

Structure-Based Virtual Screening for Dopamine D2 Receptor Ligands as Potential Antipsychotics.

PubMed

Kaczor, Agnieszka A; Silva, Andrea G; Loza, María I; Kolb, Peter; Castro, Marián; Poso, Antti

2016-04-05

Structure-based virtual screening using a D2 receptor homology model was performed to identify dopamine D2 receptor ligands as potential antipsychotics. From screening a library of 6.5 million compounds, 21 were selected and were subjected to experimental validation. From these 21 compounds tested, ten D2 ligands were identified (47.6% success rate, among them D2 receptor antagonists, as expected) that have additional affinity for other receptors tested, in particular 5-HT2A receptors. The affinity (Ki values) of the compounds ranged from 58 nm to about 24 μM. Similarity and fragment analysis indicated a significant degree of structural novelty among the identified compounds. We found one D2 receptor antagonist that did not have a protonatable nitrogen atom, which is a key structural element of the classical D2 pharmacophore model necessary for interaction with the conserved Asp(3.32) residue. This compound exhibited greater than 20-fold binding selectivity for the D2 receptor over the D3 receptor. We provide additional evidence that the amide hydrogen atom of this compound forms a hydrogen bond with Asp(3.32), as determined by tests of its derivatives that cannot maintain this interaction. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility.

PubMed

Tian, Sheng; Sun, Huiyong; Pan, Peichen; Li, Dan; Zhen, Xuechu; Li, Youyong; Hou, Tingjun

2014-10-27

In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naïve Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitors from non-inhibitors. Finally, by using the naïve Bayesian classification technique, an integrated VS strategy was developed to combine the prediction results of molecular docking based on different representative conformations chosen from crystal structures and MD trajectories. It was encouraging to observe that the integrated VS strategy yields better performance than the docking-based VS based on any single rigid conformation. This novel protocol may provide an improvement over existing strategies to search for more diverse and promising active compounds for a target of interest.

Novel fatty acid binding protein 4 (FABP4) inhibitors: virtual screening, synthesis and crystal structure determination.

PubMed

Cai, Haiyan; Liu, Qiufeng; Gao, Dingding; Wang, Ting; Chen, Tiantian; Yan, Guirui; Chen, Kaixian; Xu, Yechun; Wang, Heyao; Li, Yingxia; Zhu, Weiliang

2015-01-27

Fatty acid binding protein 4 (FABP4) is a potential drug target for diabetes and atherosclerosis. For discovering new chemical entities as FABP4 inhibitors, structure-based virtual screening (VS) was performed, bioassay demonstrated that 16 of 251 tested compounds are FABP4 inhibitors, among which compound m1 are more active than endogenous ligand linoleic acid (LA). Based on the structure of m1, new derivatives were designed and prepared, leading to the discovery of two more potent inhibitors, compounds 9 and 10. To further explore the binding mechanisms of these new inhibitors, we determined the X-ray structures of the complexes of FABP4-9 and FABP4-10, which revealed similar binding conformations of the two compounds. Residue Ser53 and Arg126 formed direct hydrogen bonding with the ligands. We also found that 10 could significantly reduce the levels of lipolysis on mouse 3T3-L1 adipocytes. Taken together, in silico, in vitro and crystallographic data provide useful hints for future development of novel inhibitors against FABP4. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Curcumin Based Drug Screening for Inhibitors of NF kappa B in a Cell Model of Prostate Cancer Progression

DTIC Science & Technology

2008-02-01

West Society of Toxicology in Breckenridge, CO in September 2007: “Identification of Curcumin Analogs Toxic against Prostate Cancer Cells Through...quantitative structure-activity relationship ( QSAR ) and ligand-based virtual screening (LBVS) to explore the possibility of improving their efficacy...Student in my laboratory has presented part of this data at the 25th Annual Meeting of the Mountain West Society of Toxicology in Breckenridge, CO in

Comparative Analysis of Virtual Screening Approaches in the Search for Novel EphA2 Receptor Antagonists.

PubMed

Callegari, Donatella; Pala, Daniele; Scalvini, Laura; Tognolini, Massimiliano; Incerti, Matteo; Rivara, Silvia; Mor, Marco; Lodola, Alessio

2015-09-17

The EphA2 receptor and its ephrin-A1 ligand form a key cell communication system, which has been found overexpressed in many cancer types and involved in tumor growth. Recent medicinal chemistry efforts have identified bile acid derivatives as low micromolar binders of the EphA2 receptor. However, these compounds suffer from poor physicochemical properties, hampering their use in vivo. The identification of compounds able to disrupt the EphA2-ephrin-A1 complex lacking the bile acid scaffold may lead to new pharmacological tools suitable for in vivo studies. To identify the most promising virtual screening (VS) protocol aimed at finding novel EphA2 antagonists, we investigated the ability of both ligand-based and structure-based approaches to retrieve known EphA2 antagonists from libraries of decoys with similar molecular properties. While ligand-based VSs were conducted using UniPR129 and ephrin-A1 ligand as reference structures, structure-based VSs were performed with Glide, using the X-ray structure of the EphA2 receptor/ephrin-A1 complex. A comparison of enrichment factors showed that ligand-based approaches outperformed the structure-based ones, suggesting ligand-based methods using the G-H loop of ephrin-A1 ligand as template as the most promising protocols to search for novel EphA2 antagonists.

ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions.

PubMed

Sushko, Iurii; Salmina, Elena; Potemkin, Vladimir A; Poda, Gennadiy; Tetko, Igor V

2012-08-27

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.

ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions

PubMed Central

2012-01-01

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing. PMID:22876798

[Crystal structure of SMU.2055 protein from Streptococcus mutans and its small molecule inhibitors design and selection].

PubMed

Xiaodan, Chen; Xiurong, Zhan; Xinyu, Wu; Chunyan, Zhao; Wanghong, Zhao

2015-04-01

The aim of this study is to analyze the three-dimensional crystal structure of SMU.2055 protein, a putative acetyltransferase from the major caries pathogen Streptococcus mutans (S. mutans). The design and selection of the structure-based small molecule inhibitors are also studied. The three-dimensional crystal structure of SMU.2055 protein was obtained by structural genomics research methods of gene cloning and expression, protein purification with Ni²⁺-chelating affinity chromatography, crystal screening, and X-ray diffraction data collection. An inhibitor virtual model matching with its target protein structure was set up using computer-aided drug design methods, virtual screening and fine docking, and Libdock and Autodock procedures. The crystal of SMU.2055 protein was obtained, and its three-dimensional crystal structure was analyzed. This crystal was diffracted to a resolution of 0.23 nm. It belongs to orthorhombic space group C222(1), with unit cell parameters of a = 9.20 nm, b = 9.46 nm, and c = 19.39 nm. The asymmetric unit contained four molecules, with a solvent content of 56.7%. Moreover, five small molecule compounds, whose structure matched with that of the target protein in high degree, were designed and selected. Protein crystallography research of S. mutans SMU.2055 helps to understand the structures and functions of proteins from S. mutans at the atomic level. These five compounds may be considered as effective inhibitors to SMU.2055. The virtual model of small molecule inhibitors we built will lay a foundation to the anticaries research based on the crystal structure of proteins.

LIGSIFT: an open-source tool for ligand structural alignment and virtual screening.

PubMed

Roy, Ambrish; Skolnick, Jeffrey

2015-02-15

Shape-based alignment of small molecules is a widely used approach in computer-aided drug discovery. Most shape-based ligand structure alignment applications, both commercial and freely available ones, use the Tanimoto coefficient or similar functions for evaluating molecular similarity. Major drawbacks of using such functions are the size dependence of the score and the fact that the statistical significance of the molecular match using such metrics is not reported. We describe a new open-source ligand structure alignment and virtual screening (VS) algorithm, LIGSIFT, that uses Gaussian molecular shape overlay for fast small molecule alignment and a size-independent scoring function for efficient VS based on the statistical significance of the score. LIGSIFT was tested against the compounds for 40 protein targets available in the Directory of Useful Decoys and the performance was evaluated using the area under the ROC curve (AUC), the Enrichment Factor (EF) and Hit Rate (HR). LIGSIFT-based VS shows an average AUC of 0.79, average EF values of 20.8 and a HR of 59% in the top 1% of the screened library. LIGSIFT software, including the source code, is freely available to academic users at http://cssb.biology.gatech.edu/LIGSIFT. Supplementary data are available at Bioinformatics online. skolnick@gatech.edu. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

How to Achieve Better Results Using Pass-Based Virtual Screening: Case Study for Kinase Inhibitors

NASA Astrophysics Data System (ADS)

Pogodin, Pavel V.; Lagunin, Alexey A.; Rudik, Anastasia V.; Filimonov, Dmitry A.; Druzhilovskiy, Dmitry S.; Nicklaus, Mark C.; Poroikov, Vladimir V.

2018-04-01

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of “active” and “inactive” compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening.

PubMed

Kumar, Ashutosh; Ito, Akihiro; Hirohama, Mikako; Yoshida, Minoru; Zhang, Kam Y J

2013-04-22

SUMO activating enzyme 1 (SUMO E1) is responsible for the activation of SUMO in the first step of the sumoylation cascade. SUMO E1 is linked to many human diseases including cancer, thus making it a potential therapeutic target. There are few reported SUMO E1 inhibitors including several natural products. To identify small molecule inhibitors of SUMO E1 with better drug-like properties for potential therapeutic studies, we have used structure-based virtual screening to identify hits from the Maybridge small molecule library for biological assay. Our virtual screening protocol involves fast docking of the entire small molecule library with rigid protein and ligands followed by redocking of top hits using a method that incorporates both ligand and protein flexibility. Subsequently, the top-ranking compounds were prioritized using the molecular dynamics simulation-based binding free energy calculation. Out of 24 compounds that were acquired and tested using in vitro sumoylation assay, four of them showed more than 85% inhibition of sumoylation with the most active compound showing an IC50 of 14.4 μM. A similarity search with the most active compound in the ZINC database has identified three more compounds with improved potency. These compounds share a common phenyl urea scaffold and have been confirmed to inhibit SUMO E1 by in vitro SUMO-1 thioester bond formation assay. Our study suggests that these phenyl urea compounds could be used as a starting point for the development of novel therapeutic agents.

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.

PubMed

Xia, Jie; Tilahun, Ermias Lemma; Reid, Terry-Elinor; Zhang, Liangren; Wang, Xiang Simon

2015-01-01

Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs. Copyright © 2014 Elsevier Inc. All rights reserved.

Benchmarking Methods and Data Sets for Ligand Enrichment Assessment in Virtual Screening

PubMed Central

Xia, Jie; Tilahun, Ermias Lemma; Reid, Terry-Elinor; Zhang, Liangren; Wang, Xiang Simon

2014-01-01

Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. “analogue bias”, “artificial enrichment” and “false negative”. In addition, we introduced our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylase (HDAC) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The Leave-One-Out Cross-Validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased in terms of property matching, ROC curves and AUCs. PMID:25481478

Combination of virtual screening protocol by in silico towards the discovery of novel 4-hydroxyphenylpyruvate dioxygenase inhibitors

NASA Astrophysics Data System (ADS)

Fu, Ying; Sun, Yi-Na; Yi, Ke-Han; Li, Ming-Qiang; Cao, Hai-Feng; Li, Jia-Zhong; Ye, Fei

2018-02-01

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) is a potent new bleaching herbicide target. Therefore, in silico structure-based virtual screening was performed in order to speed up the identification of promising HPPD inhibitors. In this study, an integrated virtual screening protocol by combining 3D-pharmacophore model, molecular docking and molecular dynamics (MD) simulation was established to find novel HPPD inhibitors from four commercial databases. 3D-pharmacophore Hypo1 model was applied to efficiently narrow potential hits. The hit compounds were subsequently submitted to molecular docking studies, showing four compounds as potent inhibitor with the mechanism of the Fe(II) coordination and interaction with Phe360, Phe403 and Phe398. MD result demonstrated that nonpolar term of compound 3881 made great contributions to binding affinities. It showed an IC50 being 2.49 µM against AtHPPD in vitro. The results provided useful information for developing novel HPPD inhibitors, leading to further understanding of the interaction mechanism of HPPD inhibitors.

Performance Studies on Distributed Virtual Screening

PubMed Central

Krüger, Jens; de la Garza, Luis; Kohlbacher, Oliver; Nagel, Wolfgang E.

2014-01-01

Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly. PMID:25032219

Sense of presence and anxiety during virtual social interactions between a human and virtual humans.

PubMed

Morina, Nexhmedin; Brinkman, Willem-Paul; Hartanto, Dwi; Emmelkamp, Paul M G

2014-01-01

Virtual reality exposure therapy (VRET) has been shown to be effective in treatment of anxiety disorders. Yet, there is lack of research on the extent to which interaction between the individual and virtual humans can be successfully implanted to increase levels of anxiety for therapeutic purposes. This proof-of-concept pilot study aimed at examining levels of the sense of presence and anxiety during exposure to virtual environments involving social interaction with virtual humans and using different virtual reality displays. A non-clinical sample of 38 participants was randomly assigned to either a head-mounted display (HMD) with motion tracker and sterescopic view condition or a one-screen projection-based virtual reality display condition. Participants in both conditions engaged in free speech dialogues with virtual humans controlled by research assistants. It was hypothesized that exposure to virtual social interactions will elicit moderate levels of sense of presence and anxiety in both groups. Further it was expected that participants in the HMD condition will report higher scores of sense of presence and anxiety than participants in the one-screen projection-based display condition. Results revealed that in both conditions virtual social interactions were associated with moderate levels of sense of presence and anxiety. Additionally, participants in the HMD condition reported significantly higher levels of presence than those in the one-screen projection-based display condition (p = .001). However, contrary to the expectations neither the average level of anxiety nor the highest level of anxiety during exposure to social virtual environments differed between the groups (p = .97 and p = .75, respectively). The findings suggest that virtual social interactions can be successfully applied in VRET to enhance sense of presence and anxiety. Furthermore, our results indicate that one-screen projection-based displays can successfully activate levels of anxiety in social virtual environments. The outcome can prove helpful in using low-cost projection-based virtual reality environments for treating individuals with social phobia.

Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance.

PubMed

Chaput, Ludovic; Martinez-Sanz, Juan; Saettel, Nicolas; Mouawad, Liliane

2016-01-01

In a structure-based virtual screening, the choice of the docking program is essential for the success of a hit identification. Benchmarks are meant to help in guiding this choice, especially when undertaken on a large variety of protein targets. Here, the performance of four popular virtual screening programs, Gold, Glide, Surflex and FlexX, is compared using the Directory of Useful Decoys-Enhanced database (DUD-E), which includes 102 targets with an average of 224 ligands per target and 50 decoys per ligand, generated to avoid biases in the benchmarking. Then, a relationship between these program performances and the properties of the targets or the small molecules was investigated. The comparison was based on two metrics, with three different parameters each. The BEDROC scores with α = 80.5, indicated that, on the overall database, Glide succeeded (score > 0.5) for 30 targets, Gold for 27, FlexX for 14 and Surflex for 11. The performance did not depend on the hydrophobicity nor the openness of the protein cavities, neither on the families to which the proteins belong. However, despite the care in the construction of the DUD-E database, the small differences that remain between the actives and the decoys likely explain the successes of Gold, Surflex and FlexX. Moreover, the similarity between the actives of a target and its crystal structure ligand seems to be at the basis of the good performance of Glide. When all targets with significant biases are removed from the benchmarking, a subset of 47 targets remains, for which Glide succeeded for only 5 targets, Gold for 4 and FlexX and Surflex for 2. The performance dramatic drop of all four programs when the biases are removed shows that we should beware of virtual screening benchmarks, because good performances may be due to wrong reasons. Therefore, benchmarking would hardly provide guidelines for virtual screening experiments, despite the tendency that is maintained, i.e., Glide and Gold display better performance than FlexX and Surflex. We recommend to always use several programs and combine their results. Graphical AbstractSummary of the results obtained by virtual screening with the four programs, Glide, Gold, Surflex and FlexX, on the 102 targets of the DUD-E database. The percentage of targets with successful results, i.e., with BDEROC(α = 80.5) > 0.5, when the entire database is considered are in Blue, and when targets with biased chemical libraries are removed are in Red.

Shape based virtual screening and molecular docking towards designing novel pancreatic lipase inhibitors

PubMed Central

Veeramachaneni, Ganesh Kumar; Raj, K Kranthi; Chalasani, Leela Madhuri; Annamraju, Sai Krishna; JS, Bondili; Talluri, Venkateswara Rao

2015-01-01

Increase in obesity rates and obesity associated health issues became one of the greatest health concerns in the present world population. With alarming increase in obese percentage there is a need to design new drugs related to the obesity targets. Among the various targets linked to obesity, pancreatic lipase was one of the promising targets for obesity treatment. Using the in silico methods like structure based virtual screening, QikProp, docking studies and binding energy calculations three molecules namely zinc85531017, zinc95919096 and zinc33963788 from the natural database were reported as the potential inhibitors for the pancreatic lipase. Among them zinc95919096 presented all the interactions matching to both standard and crystal ligand and hence it can be further proceeded to drug discovery process. PMID:26770027

Stepping into the virtual unknown: feasibility study of a virtual reality-based test of ocular misalignment.

PubMed

Nesaratnam, N; Thomas, P; Vivian, A

2017-10-01

IntroductionDissociated tests of strabismus provide valuable information for diagnosis and monitoring of ocular misalignment in patients with normal retinal correspondence. However, they are vulnerable to operator error and rely on a fixed head position. Virtual reality headsets obviate the need for head fixation, while providing other clear theoretical advantages, including complete control over the illumination and targets presented for the patient's interaction.PurposeWe compared the performance of a virtual reality-based test of ocular misalignment to that of the traditional Lees screen, to establish the feasibility of using virtual reality technology in ophthalmic settings in the future.MethodsThree patients underwent a traditional Lees screen test, and a virtual reality headset-based test of ocular motility. The virtual reality headset-based programme consisted of an initial test to measure horizontal and vertical deviation, followed by a test for torsion.ResultsThe pattern of deviation obtained using the virtual reality-based test showed agreement with that obtained from the Lees screen for patients with a fourth nerve palsy, comitant esotropia, and restrictive thyroid eye disease.ConclusionsThis study reports the first use of a virtual reality headset in assessing ocular misalignment, and demonstrates that it is a feasible dissociative test of strabismus.

Identification of Direct Activator of Adenosine Monophosphate-Activated Protein Kinase (AMPK) by Structure-Based Virtual Screening and Molecular Docking Approach.

PubMed

Huang, Tonghui; Sun, Jie; Zhou, Shanshan; Gao, Jian; Liu, Yi

2017-06-30

Adenosine monophosphate-activated protein kinase (AMPK) plays a critical role in the regulation of energy metabolism and has been targeted for drug development of therapeutic intervention in Type II diabetes and related diseases. Recently, there has been renewed interest in the development of direct β1-selective AMPK activators to treat patients with diabetic nephropathy. To investigate the details of AMPK domain structure, sequence alignment and structural comparison were used to identify the key amino acids involved in the interaction with activators and the structure difference between β1 and β2 subunits. Additionally, a series of potential β1-selective AMPK activators were identified by virtual screening using molecular docking. The retrieved hits were filtered on the basis of Lipinski's rule of five and drug-likeness. Finally, 12 novel compounds with diverse scaffolds were obtained as potential starting points for the design of direct β1-selective AMPK activators.

Homology modeling and virtual screening of inhibitors against TEM- and SHV-type-resistant mutants: A multilayer filtering approach.

PubMed

Baig, Mohammad H; Balaramnavar, Vishal M; Wadhwa, Gulshan; Khan, Asad U

2015-01-01

TEM and SHV are class-A-type β-lactamases commonly found in Escherichia coli and Klebsiella pneumoniae. Previous studies reported S130G and K234R mutations in SHVs to be 41- and 10-fold more resistant toward clavulanic acid than SHV-1, respectively, whereas TEM S130G and R244S also showed the same level of resistance. These selected mutants confer higher level of resistance against clavulanic acid. They also show little susceptibility against other commercially available β-lactamase inhibitors. In this study, we have used docking-based virtual screening approach in order to screen potential inhibitors against some of the major resistant mutants of SHV and TEM types β-lactamase. Two different inhibitor-resistant mutants from SHV and TEM were selected. Moreover, we have retained the active site water molecules within each enzyme. Active site water molecules were placed within modeled structure of the mutant whose structure was unavailable with protein databank. The novelty of this work lies in the use of multilayer virtual screening approach for the prediction of best and accurate results. We are reporting five inhibitors on the basis of their efficacy against all the selected resistant mutants. These inhibitors were selected on the basis of their binding efficacies and pharmacophore features. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

ChemScreener: A Distributed Computing Tool for Scaffold based Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Pandit, Deepak; Vyas, Renu

2015-01-01

In this work we present ChemScreener, a Java-based application to perform virtual library generation combined with virtual screening in a platform-independent distributed computing environment. ChemScreener comprises a scaffold identifier, a distinct scaffold extractor, an interactive virtual library generator as well as a virtual screening module for subsequently selecting putative bioactive molecules. The virtual libraries are annotated with chemophore-, pharmacophore- and toxicophore-based information for compound prioritization. The hits selected can then be further processed using QSAR, docking and other in silico approaches which can all be interfaced within the ChemScreener framework. As a sample application, in this work scaffold selectivity, diversity, connectivity and promiscuity towards six important therapeutic classes have been studied. In order to illustrate the computational power of the application, 55 scaffolds extracted from 161 anti-psychotic compounds were enumerated to produce a virtual library comprising 118 million compounds (17 GB) and annotated with chemophore, pharmacophore and toxicophore based features in a single step which would be non-trivial to perform with many standard software tools today on libraries of this size.

Virtual screening on an α-helix to β-strand switchable region of the FGFR2 extracellular domain revealed positive and negative modulators.

PubMed

Diaz, Constantino; Corentin, Herbert; Thierry, Vermat; Chantal, Alcouffe; Tanguy, Bozec; David, Sibrac; Jean-Marc, Herbert; Pascual, Ferrara; Françoise, Bono; Edgardo, Ferran

2014-11-01

The secondary structure of some protein segments may vary between α-helix and β-strand. To predict these switchable segments, we have developed an algorithm, Switch-P, based solely on the protein sequence. This algorithm was used on the extracellular parts of FGF receptors. For FGFR2, it predicted that β4 and β5 strands of the third Ig-like domain were highly switchable. These two strands possess a high number of somatic mutations associated with cancer. Analysis of PDB structures of FGF receptors confirmed the switchability prediction for β5. We thus evaluated if compound-driven α-helix/β-strand switching of β5 could modulate FGFR2 signaling. We performed the virtual screening of a library containing 1.4 million of chemical compounds with two models of the third Ig-like domain of FGFR2 showing different secondary structures for β5, and we selected 32 compounds. Experimental testing using proliferation assays with FGF7-stimulated SNU-16 cells and a FGFR2-dependent Erk1/2 phosphorylation assay with FGFR2-transfected L6 cells, revealed activators and inhibitors of FGFR2. Our method for the identification of switchable proteinic regions, associated with our virtual screening approach, provides an opportunity to discover new generation of drugs with under-explored mechanism of action. © 2014 Wiley Periodicals, Inc.

TS-Chemscore, a Target-Specific Scoring Function, Significantly Improves the Performance of Scoring in Virtual Screening.

PubMed

Wang, Wen-Jing; Huang, Qi; Zou, Jun; Li, Lin-Li; Yang, Sheng-Yong

2015-07-01

Most of the scoring functions currently used in structure-based drug design belong to 'universal' scoring functions, which often give a poor correlation between the calculated scores and experimental binding affinities. In this investigation, we proposed a simple strategy to construct target-specific scoring functions based on known 'universal' scoring functions. This strategy was applied to Chemscore, a widely used empirical scoring function, which led to a new scoring function, termed TS-Chemscore. TS-Chemscore was validated on 14 protein targets, which cover a wide range of biological target categories. The results showed that TS-Chemscore significantly improved the correlation between the calculated scores and experimental binding affinities compared with the original Chemscore. TS-Chemscore was then applied in virtual screening to retrieve novel JAK3 and YopH inhibitors. Top 30 compounds for each target were selected for experimental validation. Six active compounds for JAK3 and four for YopH were obtained. These compounds were out of the lists of top 30 compounds sorted by Chemscore. Collectively, TS-Chemscore established in this study showed a better performance in virtual screening than its counterpart Chemscore. © 2014 John Wiley & Sons A/S.

Discovery of covalent inhibitors for MIF tautomerase via cocrystal structures with phantom hits from virtual screening

DOE Office of Scientific and Technical Information (OSTI.GOV)

McLean, Larry R.; Zhang, Ying; Li, Hua

Biochemical and X-ray crystallographic studies confirmed that hydroxyquinoline derivatives identified by virtual screening were actually covalent inhibitors of the MIF tautomerase. Adducts were formed by N-alkylation of the Pro-1 at the catalytic site with a loss of an amino group of the inhibitor.

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

PubMed Central

2016-01-01

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2N). A recursive approximation to the optimal solution scales as O(N2), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets. PMID:27097522

In Silico Identification of a Novel Hinge-Binding Scaffold for Kinase Inhibitor Discovery.

PubMed

Wang, Yanli; Sun, Yuze; Cao, Ran; Liu, Dan; Xie, Yuting; Li, Li; Qi, Xiangbing; Huang, Niu

2017-10-26

To explore novel kinase hinge-binding scaffolds, we carried out structure-based virtual screening against p38α MAPK as a model system. With the assistance of developed kinase-specific structural filters, we identify a novel lead compound that selectively inhibits a panel of kinases with threonine as the gatekeeper residue, including BTK and LCK. These kinases play important roles in lymphocyte activation, which encouraged us to design novel kinase inhibitors as drug candidates for ameliorating inflammatory diseases and cancers. Therefore, we chemically modified our substituted triazole-class lead compound to improve the binding affinity and selectivity via a "minimal decoration" strategy, which resulted in potent and selective kinase inhibitors against LCK (18 nM) and BTK (8 nM). Subsequent crystallographic experiments validated our design. These rationally designed compounds exhibit potent on-target inhibition against BTK in B cells or LCK in T cells, respectively. Our work demonstrates that structure-based virtual screening can be applied to facilitate the development of novel chemical entities in crowded chemical space in the field of kinase inhibitor discovery.

Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach

NASA Astrophysics Data System (ADS)

Neves, Marco A. C.; Simões, Sérgio; Sá e Melo, M. Luisa

2010-12-01

CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure-function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists.

Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition.

PubMed

Chakraborty, Sandipan; Ramachandran, Balaji; Basu, Soumalee

2014-10-01

Mimicking receptor flexibility during receptor-ligand binding is a challenging task in computational drug design since it is associated with a large increase in the conformational search space. In the present study, we have devised an in silico design strategy incorporating receptor flexibility in virtual screening to identify potential lead compounds as inhibitors for flexible proteins. We have considered BACE1 (β-secretase), a key target protease from a therapeutic perspective for Alzheimer's disease, as the highly flexible receptor. The protein undergoes significant conformational transitions from open to closed form upon ligand binding, which makes it a difficult target for inhibitor design. We have designed a hybrid structure-activity model containing both ligand based descriptors and energetic descriptors obtained from molecular docking based on a dataset of structurally diverse BACE1 inhibitors. An ensemble of receptor conformations have been used in the docking study, further improving the prediction ability of the model. The designed model that shows significant prediction ability judged by several statistical parameters has been used to screen an in house developed 3-D structural library of 731 phytochemicals. 24 highly potent, novel BACE1 inhibitors with predicted activity (Ki) ≤ 50 nM have been identified. Detailed analysis reveals pharmacophoric features of these novel inhibitors required to inhibit BACE1.

Rational Design of a New Class of Toll-Like Receptor 4 (TLR4) Tryptamine Related Agonists by Means of the Structure- and Ligand-Based Virtual Screening for Vaccine Adjuvant Discovery.

PubMed

Honegr, Jan; Dolezal, Rafael; Malinak, David; Benkova, Marketa; Soukup, Ondrej; Almeida, Joyce S F D de; Franca, Tanos C C; Kuca, Kamil; Prymula, Roman

2018-01-04

In order to identify novel lead structures for human toll-like receptor 4 ( h TLR4) modulation virtual high throughput screening by a peta-flops-scale supercomputer has been performed. Based on the in silico studies, a series of 12 compounds related to tryptamine was rationally designed to retain suitable molecular geometry for interaction with the h TLR4 binding site as well as to satisfy general principles of drug-likeness. The proposed compounds were synthesized, and tested by in vitro and ex vivo experiments, which revealed that several of them are capable to stimulate h TLR4 in vitro up to 25% activity of Monophosphoryl lipid A. The specific affinity of the in vitro most potent substance was confirmed by surface plasmon resonance direct-binding experiments. Moreover, two compounds from the series show also significant ability to elicit production of interleukin 6.

Docking and scoring in virtual screening for drug discovery: methods and applications.

PubMed

Kitchen, Douglas B; Decornez, Hélène; Furr, John R; Bajorath, Jürgen

2004-11-01

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.

A review on PARP1 inhibitors: Pharmacophore modeling, virtual and biological screening studies to identify novel PARP1 inhibitors.

PubMed

Singh, Sardar Shamshair; Sarma, Jagarlapudi A R P; Narasu, Lakshmi; Dayam, Raveendra; Xu, Shili; Neamati, Nouri

2014-01-01

A tremendous research on Poly (ADP-ribose) polymerase (PARP) pertaining to cancer and ischemia is in very rapid progress. PARP's are a specific class of enzymes that repairs the damaged DNA. Recent findings suggest also that PARP-1 is the most abundantly expressed nuclear enzyme which involves in various therapeutic areas like inflammation, stroke, cardiac ischemia, cancer and diabetes. The current review describes the overview on clinical candidates of PARP1 and its current status in clinical trials. This paper also covers identification of potent PARP1 inhibitors using structure and ligand based pharmacophore models. Finally 36 potential hits were identified from the virtual screening of pharmacophore models and screened for PARP1 activity. 15 actives were identified as potent PARP1 inhibitors and further optimization of these analogues are in progress.

Pharmacophore screening of the protein data bank for specific binding site chemistry.

PubMed

Campagna-Slater, Valérie; Arrowsmith, Andrew G; Zhao, Yong; Schapira, Matthieu

2010-03-22

A simple computational approach was developed to screen the Protein Data Bank (PDB) for putative pockets possessing a specific binding site chemistry and geometry. The method employs two commonly used 3D screening technologies, namely identification of cavities in protein structures and pharmacophore screening of chemical libraries. For each protein structure, a pocket finding algorithm is used to extract potential binding sites containing the correct types of residues, which are then stored in a large SDF-formatted virtual library; pharmacophore filters describing the desired binding site chemistry and geometry are then applied to screen this virtual library and identify pockets matching the specified structural chemistry. As an example, this approach was used to screen all human protein structures in the PDB and identify sites having chemistry similar to that of known methyl-lysine binding domains that recognize chromatin methylation marks. The selected genes include known readers of the histone code as well as novel binding pockets that may be involved in epigenetic signaling. Putative allosteric sites were identified on the structures of TP53BP1, L3MBTL3, CHEK1, KDM4A, and CREBBP.

Pharmit: interactive exploration of chemical space.

PubMed

Sunseri, Jocelyn; Koes, David Ryan

2016-07-08

Pharmit (http://pharmit.csb.pitt.edu) provides an online, interactive environment for the virtual screening of large compound databases using pharmacophores, molecular shape and energy minimization. Users can import, create and edit virtual screening queries in an interactive browser-based interface. Queries are specified in terms of a pharmacophore, a spatial arrangement of the essential features of an interaction, and molecular shape. Search results can be further ranked and filtered using energy minimization. In addition to a number of pre-built databases of popular compound libraries, users may submit their own compound libraries for screening. Pharmit uses state-of-the-art sub-linear algorithms to provide interactive screening of millions of compounds. Queries typically take a few seconds to a few minutes depending on their complexity. This allows users to iteratively refine their search during a single session. The easy access to large chemical datasets provided by Pharmit simplifies and accelerates structure-based drug design. Pharmit is available under a dual BSD/GPL open-source license. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

[Selection of a melanine concentrating hormone receptor-1 (MCHR1) antagonists' focused library and its biological screening with AequoScreen].

PubMed

Flachner, Beáta; Hajdú, István; Dobi, Krisztina; Lorincz, Zsolt; Cseh, Sándor; Dormán, György

2013-01-01

Target focused libraries can be rapidly selected by 2D virtual screening methods from multimillion compounds' repositories if structures of active compounds are available. In the present study a multi-step virtual and in vitro screening cascade is reported to select Melanin Concentrating Hormone Receptor-1 (MCHR1) antagonists. The 2D similarity search combined with physicochemical parameter filtering is suitable for selecting candidates from multimillion compounds' repository. The seeds of the first round virtual screening were collected from the literature and commercial databases, while the seeds of the second round were the hits of the first round. In vitro screening underlined the efficiency of our approach, as in the second screening round the hit rate (8.6 %) significantly improved compared to the first round (1.9%), reaching the antagonist activity even below 10 nM.

Teaching Basic Field Skills Using Screen-Based Virtual Reality Landscapes

NASA Astrophysics Data System (ADS)

Houghton, J.; Robinson, A.; Gordon, C.; Lloyd, G. E. E.; Morgan, D. J.

2016-12-01

We are using screen-based virtual reality landscapes, created using the Unity 3D game engine, to augment the training geoscience students receive in preparing for fieldwork. Students explore these landscapes as they would real ones, interacting with virtual outcrops to collect data, determine location, and map the geology. Skills for conducting field geological surveys - collecting, plotting and interpreting data; time management and decision making - are introduced interactively and intuitively. As with real landscapes, the virtual landscapes are open-ended terrains with embedded data. This means the game does not structure student interaction with the information as it is through experience the student learns the best methods to work successfully and efficiently. These virtual landscapes are not replacements for geological fieldwork rather virtual spaces between classroom and field in which to train and reinforcement essential skills. Importantly, these virtual landscapes offer accessible parallel provision for students unable to visit, or fully partake in visiting, the field. The project has received positive feedback from both staff and students. Results show students find it easier to focus on learning these basic field skills in a classroom, rather than field setting, and make the same mistakes as when learning in the field, validating the realistic nature of the virtual experience and providing opportunity to learn from these mistakes. The approach also saves time, and therefore resources, in the field as basic skills are already embedded. 70% of students report increased confidence with how to map boundaries and 80% have found the virtual training a useful experience. We are also developing landscapes based on real places with 3D photogrammetric outcrops, and a virtual urban landscape in which Engineering Geology students can conduct a site investigation. This project is a collaboration between the University of Leeds and Leeds College of Art, UK, and all our virtual landscapes are freely available online at www.see.leeds.ac.uk/virtual-landscapes/.

On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

PubMed

Roy, Kunal; Mitra, Indrani

2011-07-01

Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

Vitual screening and binding mode elucidation of curcumin analogues on Cyclooxygenase-2 using AYO_COX2_V1.1 protocol

NASA Astrophysics Data System (ADS)

Mulatsari, E.; Mumpuni, E.; Herfian, A.

2017-05-01

Curcumin is yellow colored phenolic compounds contained in Curcuma longa. Curcumin is known to have biological activities as anti-inflammatory, antiviral, antioxidant, and anti-infective agent [1]. Synthesis of curcumin analogue compounds has been done and some of them had biological activity like curcumin. In this research, the virtual screening of curcumin analogue compounds has been conducted. The purpose of this research was to determine the activity of these compounds as selective Cyclooxygenase-2inhibitors in in-silico. Binding mode elucidation was made by active and inactive representative compounds to see the interaction of the amino acids in the binding site of the compounds. This research used AYO_COX2_V.1.1, a structure-based virtual screening protocol (SBVS) that has been validated by Mumpuni E et al, 2014 [2]. AYO_COX2_V.1.1 protocol using a variety of integrated applications such as SPORES, PLANTS, BKchem, OpenBabel and PyMOL. The results of virtual screening conducted on 49 curcumin analogue compounds obtained 8 compounds with 4 active amino acid residues (GLY340, ILE503, PHE343, and PHE367) that were considered active as COX-2 inhibitor.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh

2013-10-01

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα bindingmore » affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function. • The results have potential applications to green chemistry. • Models are publicly available for virtual screening via a web portal.« less

Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension.

PubMed

Su, Hao; Yan, Ji; Xu, Jian; Fan, Xi-Zhen; Sun, Xian-Lin; Chen, Kang-Yu

2015-08-01

Pulmonary hypertension (PH) is a devastating disease characterized by progressive elevation of pulmonary arterial pressure and vascular resistance due to pulmonary vasoconstriction and vessel remodeling. The activation of RhoA/Rho-kinase (ROCK) pathway plays a central role in the pathologic progression of PH and thus the Rho kinase, an essential effector of the ROCK pathway, is considered as a potential therapeutic target to attenuate PH. In the current study, a synthetic pipeline is used to discover new potent Rho inhibitors from various natural products. In the pipeline, the stepwise high-throughput virtual screening, quantitative structure-activity relationship (QSAR)-based rescoring, and kinase assay were integrated. The screening was performed against a structurally diverse, drug-like natural product library, from which six identified compounds were tested to determine their inhibitory potencies agonist Rho by using a standard kinase assay protocol. With this scheme, we successfully identified two potent Rho inhibitors, namely phloretin and baicalein, with activity values of IC50 = 0.22 and 0.95 μM, respectively. Structural examination suggested that complicated networks of non-bonded interactions such as hydrogen bonding, hydrophobic forces, and van der Waals contacts across the complex interfaces of Rho kinase are formed with the screened compounds.

Searching for new leads to treat epilepsy. Target-based virtual screening for the discovery of anticonvulsant agents.

PubMed

Palestro, Pablo; Enrique, Nicolas; Goicoechea, Sofia; Villalba, María Luisa; Sabatier, Laureano Leonel; Martin, Pedro; Milesi, Veronica; Bruno-Blanch, Luis E; Gavernet, Luciana

2018-06-05

The purpose of this investigation is to contribute to the development of new anticonvulsant drugs to treat patients with refractory epilepsy. We applied a virtual screening protocol that involved the search into molecular databases of new compounds and known drugs to find small molecules that interact with the open conformation of the Nav1.2 pore. As the 3D structure of human Nav1.2 is not available, we first assembled 3D models of the target, in closed and open conformations. After the virtual screening, the resulting candidates were submitted to a second virtual filter, to find compounds with better chances of being effective for the treatment of P-glycoprotein (P-gp) mediated resistant epilepsy. Again, we built a model of the 3D structure of human P-gp and we validated the docking methodology selected to propose the best candidates, which were experimentally tested on Nav1.2 channels by patch clamp techniques and in vivo by MES-test. Patch clamp studies allowed us to corroborate that our candidates, drugs used for the treatment of other pathologies like Ciprofloxacin, Losartan and Valsartan, exhibit inhibitory effects on Nav1.2 channel activity. Additionally, a compound synthesized in our lab, N,N´-diphenethylsulfamide, interacts with the target and also triggers significant Na1.2 channel inhibitory action. Finally, in-vivo studies confirmed the anticonvulsant action of Valsartan, Ciprofloxacin and N.N´-diphenethylsulfamide.

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

PubMed Central

Johnson, David K.; Karanicolas, John

2016-01-01

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased “pocket optimization” simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its “exemplar”: a perfect, but non-physical, pseudo-ligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 minutes on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a “pocket-optimized” ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target. PMID:26726827

Identification of novel Trypanosoma cruzi prolyl oligopeptidase inhibitors by structure-based virtual screening

NASA Astrophysics Data System (ADS)

de Almeida, Hugo; Leroux, Vincent; Motta, Flávia Nader; Grellier, Philippe; Maigret, Bernard; Santana, Jaime M.; Bastos, Izabela Marques Dourado

2016-12-01

We have previously demonstrated that the secreted prolyl oligopeptidase of Trypanosoma cruzi (POPTc80) is involved in the infection process by facilitating parasite migration through the extracellular matrix. We have built a 3D structural model where POPTc80 is formed by a catalytic α/β-hydrolase domain and a β-propeller domain, and in which the substrate docks at the inter-domain interface, suggesting a "jaw opening" gating access mechanism. This preliminary model was refined by molecular dynamics simulations and next used for a virtual screening campaign, whose predictions were tested by standard binding assays. This strategy was successful as all 13 tested molecules suggested from the in silico calculations were found out to be active POPTc80 inhibitors in the micromolar range (lowest K i at 667 nM). This work paves the way for future development of innovative drugs against Chagas disease.

Development of α-glucosidase inhibitors by room temperature C-C cross couplings of quinazolinones.

PubMed

Garlapati, Ramesh; Pottabathini, Narender; Gurram, Venkateshwarlu; Kasani, Kumara Swamy; Gundla, Rambabu; Thulluri, Chiranjeevi; Machiraju, Pavan Kumar; Chaudhary, Avinash B; Addepally, Uma; Dayam, Raveendra; Chunduri, Venkata Rao; Patro, Balaram

2013-08-07

Novel quinazolinone based α-glucosidase inhibitors have been developed. For this purpose a virtual screening model has been generated and validated utilizing acarbose as a α-glucosidase inhibitor. Homology modeling, docking, and virtual screening were successfully employed to discover a set of structurally diverse compounds active against α-glucosidase. A search of a 3D database containing 22,500 small molecules using the structure based virtual model yielded ten possible candidates. All ten candidates were N-3-pyridyl-2-cyclopropyl quinazolinone-4-one derivatives, varying at the 6 position. This position was modified by Suzuki-Miyaura cross coupling with aryl, heteroaryl, and alkyl boronic acids. A catalyst screen was performed, and using the best optimal conditions, a series of twenty five compounds was synthesized. Notably, the C-C cross coupling reactions of the 6-bromo-2-cyclopropyl-3-(pyridyl-3-ylmethyl)quinazolin-4(3H)-one precursor have been accomplished at room temperature. A comparison of the relative reactivities of 6-bromo and 6-chloro-2,3-disubstituted quinazolinones with phenyl boronic acid was conducted. An investigation of pre-catalyst loading for the reaction of the 6-bromo-2-cyclopropyl-3-(pyridyl-3-ylmethyl)quinazolin-4(3H)-one substrate was also carried out. Finally, we submitted our compounds to biological assays against α-glucosidase inhibitors. Of these, three hits (compounds 4a, 4t and 4r) were potentially active as α-glucosidase inhibitors and showed activity with IC50 values <20 μM. Based on structural novelty and desirable drug-like properties, 4a was selected for structure-activity relationship study, and thirteen analogs were synthesized. Nine out of thirteen analogs acted as α-glucosidase inhibitors with IC50 values <10 μM. These lead compounds have desirable physicochemical properties and are excellent candidates for further optimization.

Discovery of nonsteroidal 17beta-hydroxysteroid dehydrogenase 1 inhibitors by pharmacophore-based screening of virtual compound libraries.

PubMed

Schuster, Daniela; Nashev, Lyubomir G; Kirchmair, Johannes; Laggner, Christian; Wolber, Gerhard; Langer, Thierry; Odermatt, Alex

2008-07-24

17Beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) plays a pivotal role in the local synthesis of the most potent estrogen estradiol. Its expression is a prognostic marker for the outcome of patients with breast cancer and inhibition of 17beta-HSD1 is currently under consideration for breast cancer prevention and treatment. We aimed to identify nonsteroidal 17beta-HSD1 inhibitor scaffolds by virtual screening with pharmacophore models built from crystal structures containing steroidal compounds. The most promising model was validated by comparing predicted and experimentally determined inhibitory activities of several flavonoids. Subsequently, a virtual library of nonsteroidal compounds was screened against the 3D pharmacophore. Analysis of 14 selected compounds yielded four that inhibited the activity of human 17beta-HSD1 (IC 50 below 50 microM). Specificity assessment of identified 17beta-HSD1 inhibitors emphasized the importance of including related short-chain dehydrogenase/reductase (SDR) members to analyze off-target effects. Compound 29 displayed at least 10-fold selectivity over the related SDR enzymes tested.

Virtual Screening Approach of Bacterial Peptide Deformylase Inhibitors Results in New Antibiotics.

PubMed

Merzoug, Amina; Chikhi, Abdelouahab; Bensegueni, Abderrahmane; Boucherit, Hanane; Okay, Sezer

2018-03-01

The increasing resistance of bacteria to antibacterial therapy poses an enormous health problem, it renders the development of new antibacterial agents with novel mechanism of action an urgent need. Peptide deformylase, a metalloenzyme which catalytically removes N-formyl group from N-terminal methionine of newly synthesized polypeptides, is an important target in antibacterial drug discovery. In this study, we report the structure-based virtual screening of ZINC database in order to discover potential hits as bacterial peptide deformylase enzyme inhibitors with more affinity as compared to GSK1322322, previously known inhibitor. After virtual screening, fifteen compounds of the top hits predicted were purchased and evaluated in vitro for their antibacterial activities against one Gram positive (Staphylococcus aureus) and three Gram negative (Escherichia coli, Pseudomonas aeruginosa and Klebsiella. pneumoniae) bacteria in different concentrations by disc diffusion method. Out of these, three compounds, ZINC00039650, ZINC03872971 and ZINC00126407, exhibited significant zone of inhibition. The results obtained were confirmed using the dilution method. Thus, these proposed compounds may aid the development of more efficient antibacterial agents. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

PubMed

Ivanciuc, Ovidiu

2013-06-01

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

Discovery and study of novel protein tyrosine phosphatase 1B inhibitors

NASA Astrophysics Data System (ADS)

Zhang, Qian; Chen, Xi; Feng, Changgen

2017-10-01

Protein tyrosine phosphatase 1B (PTP1B) is considered to be a target for therapy of type II diabetes and obesity. So it is of great significance to take advantage of a computer aided drug design protocol involving the structured-based virtual screening with docking simulations for fast searching small molecule PTP1B inhibitors. Based on optimized complex structure of PTP1B bound with specific inhibitor of IX1, structured-based virtual screening against a library of natural products containing 35308 molecules, which was constructed based on Traditional Chinese Medicine database@ Taiwan (TCM database@ Taiwan), was conducted to determine the occurrence of PTP1B inhibitors using the Lubbock module and CDOCKER module from Discovery Studio 3.1 software package. The results were further filtered by predictive ADME simulation and predictive toxic simulation. As a result, 2 good drug-like molecules, namely para-benzoquinone compound 1 and Clavepictine analogue 2 were identified ultimately with the dock score of original inhibitor (IX1) and the receptor as a threshold. Binding model analyses revealed that these two candidate compounds have good interactions with PTP1B. The PTP1B inhibitory activity of compound 2 hasn't been reported before. The optimized compound 2 has higher scores and deserves further study.

Discovery of novel inhibitors of the NorA multidrug transporter of Staphylococcus aureus.

PubMed

Brincat, Jean Pierre; Carosati, Emanuele; Sabatini, Stefano; Manfroni, Giuseppe; Fravolini, Arnaldo; Raygada, Jose L; Patel, Diixa; Kaatz, Glenn W; Cruciani, Gabriele

2011-01-13

Four novel inhibitors of the NorA efflux pump of Staphylococcus aureus, discovered through a virtual screening process, are reported. The four compounds belong to different chemical classes and were tested for their in vitro ability to block the efflux of a well-known NorA substrate, as well as for their ability to potentiate the effect of ciprofloxacin (CPX) on several strains of S. aureus, including a NorA overexpressing strain. Additionally, the MIC values of each of the compounds individually are reported. A structure-activity relationship study was also performed on these novel chemotypes, revealing three new compounds that are also potent NorA inhibitors. The virtual screening procedure employed FLAP, a new methodology based on GRID force field descriptors.

Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening.

PubMed

Hsieh, Jui-Hua; Yin, Shuangye; Wang, Xiang S; Liu, Shubin; Dokholyan, Nikolay V; Tropsha, Alexander

2012-01-23

Poor performance of scoring functions is a well-known bottleneck in structure-based virtual screening (VS), which is most frequently manifested in the scoring functions' inability to discriminate between true ligands vs known nonbinders (therefore designated as binding decoys). This deficiency leads to a large number of false positive hits resulting from VS. We have hypothesized that filtering out or penalizing docking poses recognized as non-native (i.e., pose decoys) should improve the performance of VS in terms of improved identification of true binders. Using several concepts from the field of cheminformatics, we have developed a novel approach to identifying pose decoys from an ensemble of poses generated by computational docking procedures. We demonstrate that the use of target-specific pose (scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in VS studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD). This new hybrid scoring function outperforms several conventional structure-based scoring functions, including XSCORE::HMSCORE, ChemScore, PLP, and Chemgauss3, in 6 out of 13 data sets at early stage of VS (up 1% decoys of the screening database). We compare our hybrid method with several novel VS methods that were recently reported to have good performances on the same DUD data sets. We find that the retrieved ligands using our method are chemically more diverse in comparison with two ligand-based methods (FieldScreen and FLAP::LBX). We also compare our method with FLAP::RBLB, a high-performance VS method that also utilizes both the receptor and the cognate ligand structures. Interestingly, we find that the top ligands retrieved using our method are highly complementary to those retrieved using FLAP::RBLB, hinting effective directions for best VS applications. We suggest that this integrative VS approach combining cheminformatics and molecular mechanics methodologies may be applied to a broad variety of protein targets to improve the outcome of structure-based drug discovery studies.

Discovery of novel SERCA inhibitors by virtual screening of a large compound library.

PubMed

Elam, Christopher; Lape, Michael; Deye, Joel; Zultowsky, Jodie; Stanton, David T; Paula, Stefan

2011-05-01

Two screening protocols based on recursive partitioning and computational ligand docking methodologies, respectively, were employed for virtual screens of a compound library with 345,000 entries for novel inhibitors of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA), a potential target for cancer chemotherapy. A total of 72 compounds that were predicted to be potential inhibitors of SERCA were tested in bioassays and 17 displayed inhibitory potencies at concentrations below 100 μM. The majority of these inhibitors were composed of two phenyl rings tethered to each other by a short link of one to three atoms. Putative interactions between SERCA and the inhibitors were identified by inspection of docking-predicted poses and some of the structural features required for effective SERCA inhibition were determined by analysis of the classification pattern employed by the recursive partitioning models. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation.

PubMed

Li, Liwei; Khanna, May; Jo, Inha; Wang, Fang; Ashpole, Nicole M; Hudmon, Andy; Meroueh, Samy O

2011-04-25

We assess the performance of our previously reported structure-based support vector machine target-specific scoring function across 41 targets, 40 among them from the Directory of Useful Decoys (DUD). The area under the curve of receiver operating characteristic plots (ROC-AUC) revealed that scoring with SVM-SP resulted in consistently better enrichment over all target families, outperforming Glide and other scoring functions, most notably among kinases. In addition, SVM-SP performance showed little variation among protein classes, exhibited excellent performance in a test case using a homology model, and in some cases showed high enrichment even with few structures used to train a model. We put SVM-SP to the test by virtual screening 1125 compounds against two kinases, EGFR and CaMKII. Among the top 25 EGFR compounds, three compounds (1-3) inhibited kinase activity in vitro with IC₅₀ of 58, 2, and 10 μM. In cell cultures, compounds 1-3 inhibited nonsmall cell lung carcinoma (H1299) cancer cell proliferation with similar IC₅₀ values for compound 3. For CaMKII, one compound inhibited kinase activity in a dose-dependent manner among 20 tested with an IC₅₀ of 48 μM. These results are encouraging given that our in-house library consists of compounds that emerged from virtual screening of other targets with pockets that are different from typical ATP binding sites found in kinases. In light of the importance of kinases in chemical biology, these findings could have implications in future efforts to identify chemical probes of kinases within the human kinome.

Structure-based virtual screening and characterization of a novel IL-6 antagonistic compound from synthetic compound database.

PubMed

Wang, Jing; Qiao, Chunxia; Xiao, He; Lin, Zhou; Li, Yan; Zhang, Jiyan; Shen, Beifen; Fu, Tinghuan; Feng, Jiannan

2016-01-01

According to the three-dimensional (3D) complex structure of (hIL-6⋅hIL-6R⋅gp 130) 2 and the binding orientation of hIL-6, three compounds with high affinity to hIL-6R and bioactivity to block hIL-6 in vitro were screened theoretically from the chemical databases, including 3D-Available Chemicals Directory (ACD) and MDL Drug Data Report (MDDR), by means of the computer-guided virtual screening method. Using distance geometry, molecular modeling and molecular dynamics trajectory analysis methods, the binding mode and binding energy of the three compounds were evaluated theoretically. Enzyme-linked immunosorbent assay analysis demonstrated that all the three compounds could block IL-6 binding to IL-6R specifically. However, only compound 1 could effectively antagonize the function of hIL-6 and inhibit the proliferation of XG-7 cells in a dose-dependent manner, whereas it showed no cytotoxicity to SP2/0 or L929 cells. These data demonstrated that the compound 1 could be a promising candidate of hIL-6 antagonist.

Discovery of potent and novel smoothened antagonists via structure-based virtual screening and biological assays.

PubMed

Lu, Wenfeng; Zhang, Dihua; Ma, Haikuo; Tian, Sheng; Zheng, Jiyue; Wang, Qin; Luo, Lusong; Zhang, Xiaohu

2018-05-23

The Hedgehog (Hh) signaling pathway plays a critical role in controlling patterning, growth and cell migration during embryonic development. Aberrant activation of Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell carcinoma (BCC) and medulloblastoma. As a key member of the Hh pathway, the Smoothened (Smo) receptor, a member of the G protein-coupled receptor (GPCR) family, has emerged as an attractive therapeutic target for the treatment and prevention of human cancers. The recent determination of several crystal structures of Smo in complex with different antagonists offers the possibility to perform structure-based virtual screening for discovering potent Smo antagonists with distinct chemical scaffolds. In this study, based on the two Smo crystal complexes with the best capacity to distinguish the known Smo antagonists from decoys, the molecular docking-based virtual screening was conducted to identify promising Smo antagonists from ChemDiv library. A total of 21 structurally novel and diverse compounds were selected for experimental testing, and six of them exhibited significant inhibitory activity against the Hh pathway activation (IC 50  < 10 μM) in a GRE (Gli-responsive element) reporter gene assay. Specifically, the most potent compound (compound 20: 47 nM) showed comparable Hh signaling inhibition to vismodegib (46 nM). Compound 20 was further confirmed to be a potent Smo antagonist in a fluorescence based competitive binding assay. Optimization using substructure searching method led to the discovery of 12 analogues of compound 20 with decent Hh pathway inhibition activity, including four compounds with IC 50 lower than 1 μM. The important residues uncovered by binding free energy calculation (MM/GBSA) and binding free energy decomposition were highlighted and discussed. These findings suggest that the novel scaffold afforded by compound 20 can be used as a good starting point for further modification/optimization and the clarified interaction patterns may also guide us to find more potent Smo antagonists. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Probing voltage sensing domain of KCNQ2 channel as a potential target to combat epilepsy: a comparative study.

PubMed

Mehta, Pakhuri; Srivastava, Shubham; Choudhary, Bhanwar Singh; Sharma, Manish; Malik, Ruchi

2017-12-01

Multidrug resistance along with side-effects of available anti-epileptic drugs and unavailability of potent and effective agents in submicromolar quantities presents the biggest therapeutic challenges in anti-epileptic drug discovery. The molecular modeling techniques allow us to identify agents with novel structures to match the continuous urge for its discovery. KCNQ2 channel represents one of the validated targets for its therapy. The present study involves identification of newer anti-epileptic agents by means of a computer-aided drug design adaptive protocol involving both structure-based virtual screening of Asinex library using homology model of KCNQ2 and 3D-QSAR based virtual screening with docking analysis, followed by dG bind and ligand efficiency calculations with ADMET studies, of which 20 hits qualified all the criterions. The best ligands of both screenings with least potential for toxicity predicted computationally were then taken for molecular dynamic simulations. All the crucial amino acid interactions were observed in hits of both screenings such as Glu130, Arg207, Arg210 and Phe137. Robustness of docking protocol was analyzed through Receiver operating characteristic (ROC) curve values 0.88 (Area under curve AUC = 0.87) in Standard Precision and 0.84 (AUC = 0.82) in Extra Precision modes. Novelty analysis indicates that these compounds have not been reported previously as anti-epileptic agents.

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

PubMed

Steindl, Theodora M; Crump, Carolyn E; Hayden, Frederick G; Langer, Thierry

2005-10-06

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

WarpEngine, a Flexible Platform for Distributed Computing Implemented in the VEGA Program and Specially Targeted for Virtual Screening Studies.

PubMed

Pedretti, Alessandro; Mazzolari, Angelica; Vistoli, Giulio

2018-05-21

The manuscript describes WarpEngine, a novel platform implemented within the VEGA ZZ suite of software for performing distributed simulations both in local and wide area networks. Despite being tailored for structure-based virtual screening campaigns, WarpEngine possesses the required flexibility to carry out distributed calculations utilizing various pieces of software, which can be easily encapsulated within this platform without changing their source codes. WarpEngine takes advantages of all cheminformatics features implemented in the VEGA ZZ program as well as of its largely customizable scripting architecture thus allowing an efficient distribution of various time-demanding simulations. To offer an example of the WarpEngine potentials, the manuscript includes a set of virtual screening campaigns based on the ACE data set of the DUD-E collections using PLANTS as the docking application. Benchmarking analyses revealed a satisfactory linearity of the WarpEngine performances, the speed-up values being roughly equal to the number of utilized cores. Again, the computed scalability values emphasized that a vast majority (i.e., >90%) of the performed simulations benefit from the distributed platform presented here. WarpEngine can be freely downloaded along with the VEGA ZZ program at www.vegazz.net .

Identifying potential selective fluorescent probes for cancer-associated protein carbonic anhydrase IX using a computational approach.

PubMed

Kamstra, Rhiannon L; Floriano, Wely B

2014-11-01

Carbonic anhydrase IX (CAIX) is a biomarker for tumor hypoxia. Fluorescent inhibitors of CAIX have been used to study hypoxic tumor cell lines. However, these inhibitor-based fluorescent probes may have a therapeutic effect that is not appropriate for monitoring treatment efficacy. In the search for novel fluorescent probes that are not based on known inhibitors, a database of 20,860 fluorescent compounds was virtually screened against CAIX using hierarchical virtual ligand screening (HierVLS). The screening database contained 14,862 compounds tagged with the ATTO680 fluorophore plus an additional 5998 intrinsically fluorescent compounds. Overall ranking of compounds to identify hit molecular probe candidates utilized a principal component analysis (PCA) approach. Four potential binding sites, including the catalytic site, were identified within the structure of the protein and targeted for virtual screening. Available sequence information for 23 carbonic anhydrase isoforms was used to prioritize the four sites based on the estimated "uniqueness" of each site in CAIX relative to the other isoforms. A database of 32 known inhibitors and 478 decoy compounds was used to validate the methodology. A receiver-operating characteristic (ROC) analysis using the first principal component (PC1) as predictive score for the validation database yielded an area under the curve (AUC) of 0.92. AUC is interpreted as the probability that a binder will have a better score than a non-binder. The use of first component analysis of binding energies for multiple sites is a novel approach for hit selection. The very high prediction power for this approach increases confidence in the outcome from the fluorescent library screening. Ten of the top scoring candidates for isoform-selective putative binding sites are suggested for future testing as fluorescent molecular probe candidates. Copyright © 2014 Elsevier Inc. All rights reserved.

Footprinting of Inhibitor Interactions of In Silico Identified Inhibitors of Trypanothione Reductase of Leishmania Parasite

PubMed Central

Venkatesan, Santhosh K.; Dubey, Vikash Kumar

2012-01-01

Structure-based virtual screening of NCI Diversity set II compounds was performed to indentify novel inhibitor scaffolds of trypanothione reductase (TR) from Leishmania infantum. The top 50 ranked hits were clustered using the AuPoSOM tool. Majority of the top-ranked compounds were Tricyclic. Clustering of hits yielded four major clusters each comprising varying number of subclusters differing in their mode of binding and orientation in the active site. Moreover, for the first time, we report selected alkaloids and dibenzothiazepines as inhibitors of Leishmania infantum TR. The mode of binding observed among the clusters also potentiates the probable in vitro inhibition kinetics and aids in defining key interaction which might contribute to the inhibition of enzymatic reduction of T[S] 2. The method provides scope for automation and integration into the virtual screening process employing docking softwares, for clustering the small molecule inhibitors based upon protein-ligand interactions. PMID:22550471

Discovery of a quorum-sensing inhibitor of drug-resistant staphylococcal infections by structure-based virtual screening.

PubMed

Kiran, Madanahally D; Adikesavan, Nallini Vijayarangan; Cirioni, Oscar; Giacometti, Andrea; Silvestri, Carmela; Scalise, Giorgio; Ghiselli, Roberto; Saba, Vittorio; Orlando, Fiorenza; Shoham, Menachem; Balaban, Naomi

2008-05-01

Staphylococci are a major health threat because of increasing resistance to antibiotics. An alternative to antibiotic treatment is preventing virulence by inhibition of bacterial cell-to-cell communication using the quorum-sensing inhibitor RNAIII-inhibiting peptide (RIP). In this work, we identified 2',5-di-O-galloyl-d-hamamelose (hamamelitannin) as a nonpeptide analog of RIP by virtual screening of a RIP-based pharmacophore against a database of commercially available small-molecule compounds. Hamamelitannin is a natural product found in the bark of Hamamelis virginiana (witch hazel), and it has no effect on staphylococcal growth in vitro; but like RIP, it does inhibit the quorum-sensing regulator RNAIII. In a rat graft model, hamamelitannin prevented device-associated infections in vivo, including infections caused by methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis strains. These findings suggest that hamamelitannin may be used as a suppressor to staphylococcal infections.

Virtual screening of cocrystal formers for CL-20

NASA Astrophysics Data System (ADS)

Zhou, Jun-Hong; Chen, Min-Bo; Chen, Wei-Ming; Shi, Liang-Wei; Zhang, Chao-Yang; Li, Hong-Zhen

2014-08-01

According to the structure characteristics of 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) and the kinetic mechanism of the cocrystal formation, the method of virtual screening CL-20 cocrystal formers by the criterion of the strongest intermolecular site pairing energy (ISPE) was proposed. In this method the strongest ISPE was thought to determine the first step of the cocrystal formation. The prediction results for four sets of common drug molecule cocrystals by this method were compared with those by the total ISPE method from the reference (Musumeci et al., 2011), and the experimental results. This method was then applied to virtually screen the CL-20 cocrystal formers, and the prediction results were compared with the experimental results.

Virtual Screening of Receptor Sites for Molecularly Imprinted Polymers.

PubMed

Bates, Ferdia; Cela-Pérez, María Concepción; Karim, Kal; Piletsky, Sergey; López-Vilariño, José Manuel

2016-08-01

Molecularly Imprinted Polymers (MIPs) are highly advantageous in the field of analytical chemistry. However, interference from secondary molecules can also impede capture of a target by a MIP receptor. This greatly complicates the design process and often requires extensive laboratory screening which is time consuming, costly, and creates substantial waste products. Herein, is presented a new technique for screening of "virtually imprinted receptors" for rebinding of the molecular template as well as secondary structures, correlating the virtual predictions with experimentally acquired data in three case studies. This novel technique is particularly applicable to the evaluation and prediction of MIP receptor specificity and efficiency in complex aqueous systems. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors.

PubMed

Mizutani, Miho Yamada; Itai, Akiko

2004-09-23

A method of easily finding ligands, with a variety of core structures, for a given target macromolecule would greatly contribute to the rapid identification of novel lead compounds for drug development. We have developed an efficient method for discovering ligand candidates from a number of flexible compounds included in databases, when the three-dimensional (3D) structure of the drug target is available. The method, named ADAM&EVE, makes use of our automated docking method ADAM, which has already been reported. Like ADAM, ADAM&EVE takes account of the flexibility of each molecule in databases, by exploring the conformational space fully and continuously. Database screening has been made much faster than with ADAM through the tuning of parameters, so that computational screening of several hundred thousand compounds is possible in a practical time. Promising ligand candidates can be selected according to various criteria based on the docking results and characteristics of compounds. Furthermore, we have developed a new tool, EVE-MAKE, for automatically preparing the additional compound data necessary for flexible docking calculation, prior to 3D database screening. Among several successful cases of lead discovery by ADAM&EVE, the finding of novel acetylcholinesterase (AChE) inhibitors is presented here. We performed a virtual screening of about 160 000 commercially available compounds against the X-ray crystallographic structure of AChE. Among 114 compounds that could be purchased and assayed, 35 molecules with various core structures showed inhibitory activities with IC(50) values less than 100 microM. Thirteen compounds had IC(50) values between 0.5 and 10 microM, and almost all their core structures are very different from those of known inhibitors. The results demonstrate the effectiveness and validity of the ADAM&EVE approach and provide a starting point for development of novel drugs to treat Alzheimer's disease.

Electronic Delivery of Lectures in the University Environment: An Empirical Comparison of Three Delivery Styles

ERIC Educational Resources Information Center

Stephenson, Julia E.; Brown, Clifford; Griffin, Darren K.

2008-01-01

The purpose of this study was to consider the efficacy and popularity of "Virtual Lectures" (text-based, structured electronic courseware with information presented in manageable "chunks", interaction and multimedia) and "e-Lectures" (on-screen synchrony of PowerPoint slides and recorded voice) as alternatives to traditional lectures. We…

Structure-based discovery of inhibitors of the YycG histidine kinase: New chemical leads to combat Staphylococcus epidermidis infections

PubMed Central

Qin, Zhiqiang; Zhang, Jian; Xu, Bin; Chen, Lili; Wu, Yang; Yang, Xiaomei; Shen, Xu; Molin, Soeren; Danchin, Antoine; Jiang, Hualiang; Qu, Di

2006-01-01

Background Coagulase-negative Staphylococcus epidermidis has become a major frequent cause of infections in relation to the use of implanted medical devices. The pathogenicity of S. epidermidis has been attributed to its capacity to form biofilms on surfaces of medical devices, which greatly increases its resistance to many conventional antibiotics and often results in chronic infection. It has an urgent need to design novel antibiotics against staphylococci infections, especially those can kill cells embedded in biofilm. Results In this report, a series of novel inhibitors of the histidine kinase (HK) YycG protein of S. epidermidis were discovered first using structure-based virtual screening (SBVS) from a small molecular lead-compound library, followed by experimental validation. Of the 76 candidates derived by SBVS targeting of the homolog model of the YycG HATPase_c domain of S. epidermidis, seven compounds displayed significant activity in inhibiting S. epidermidis growth. Furthermore, five of them displayed bactericidal effects on both planktonic and biofilm cells of S. epidermidis. Except for one, the compounds were found to bind to the YycG protein and to inhibit its auto-phosphorylation in vitro, indicating that they are potential inhibitors of the YycG/YycF two-component system (TCS), which is essential in S. epidermidis. Importantly, all these compounds did not affect the stability of mammalian cells nor hemolytic activities at the concentrations used in our study. Conclusion These novel inhibitors of YycG histidine kinase thus are of potential value as leads for developing new antibiotics against infecting staphylococci. The structure-based virtual screening (SBVS) technology can be widely used in screening potential inhibitors of other bacterial TCSs, since it is more rapid and efficacious than traditional screening technology. PMID:17094812

Inhibitors of SARS-3CLpro: Virtual Screening, Biological Evaluation and Molecular Dynamics Simulation Studies

PubMed Central

Mukherjee, Prasenjit; Shah, Falgun; Desai, Prashant; Avery, Mitchell

2011-01-01

SARS-CoV from the coronaviridae family has been identified as the etiological agent of Severe Acute Respiratory Syndrome (SARS), a highly contagious upper respiratory disease that reached epidemic status in 2002. SARS-3CLpro, a cysteine protease indispensible to the viral life cycle, has been identified as one of the key therapeutic target against SARS. A combined ligand and structure based virtual screening was carried out against the Asinex Platinum collection. Multiple low micromolar inhibitors of the enzyme were identified through this search, one of which also showed activity against SARS-CoV in a whole cell CPE assay. Furthermore, multi nanosecond explicit solvent simulations were carried out using the docking poses of the identified hits to study the overall stability of the binding site interactions as well as identify important changes in the interaction profile that were not apparent from the docking study. Cumulative analysis of the evaluated compounds and the simulation studies led to the identification of certain protein-ligand interaction patterns which would be useful in further structure based design efforts. PMID:21604711

Discovery of a Small-Molecule Inhibitor of Interleukin 15: Pharmacophore-Based Virtual Screening and Hit Optimization.

PubMed

Quéméner, Agnès; Maillasson, Mike; Arzel, Laurence; Sicard, Benoit; Vomiandry, Romy; Mortier, Erwan; Dubreuil, Didier; Jacques, Yannick; Lebreton, Jacques; Mathé-Allainmat, Monique

2017-07-27

Interleukin (IL)-15 is a pleiotropic cytokine, which is structurally close to IL-2 and shares with it the IL-2 β and γ receptor (R) subunits. By promoting the activation and proliferation of NK, NK-T, and CD8+ T cells, IL-15 plays important roles in innate and adaptative immunity. Moreover, the association of high levels of IL-15 expression with inflammatory and autoimmune diseases has led to the development of various antagonistic approaches targeting IL-15. This study is an original approach aimed at discovering small-molecule inhibitors impeding IL-15/IL-15R interaction. A pharmacophore and docking-based virtual screening of compound libraries led to the selection of 240 high-scoring compounds, 36 of which were found to bind IL-15, to inhibit the binding of IL-15 to the IL-2Rβ chain or the proliferation of IL-15-dependent cells or both. One of them was selected as a hit and optimized by a structure-activity relationship approach, leading to the first small-molecule IL-15 inhibitor with sub-micromolar activity.

Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets.

PubMed

Bauer, Matthias R; Ibrahim, Tamer M; Vogel, Simon M; Boeckler, Frank M

2013-06-24

The application of molecular benchmarking sets helps to assess the actual performance of virtual screening (VS) workflows. To improve the efficiency of structure-based VS approaches, the selection and optimization of various parameters can be guided by benchmarking. With the DEKOIS 2.0 library, we aim to further extend and complement the collection of publicly available decoy sets. Based on BindingDB bioactivity data, we provide 81 new and structurally diverse benchmark sets for a wide variety of different target classes. To ensure a meaningful selection of ligands, we address several issues that can be found in bioactivity data. We have improved our previously introduced DEKOIS methodology with enhanced physicochemical matching, now including the consideration of molecular charges, as well as a more sophisticated elimination of latent actives in the decoy set (LADS). We evaluate the docking performance of Glide, GOLD, and AutoDock Vina with our data sets and highlight existing challenges for VS tools. All DEKOIS 2.0 benchmark sets will be made accessible at http://www.dekois.com.

The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.

PubMed

Xia, Jie; Hsieh, Jui-Hua; Hu, Huabin; Wu, Song; Wang, Xiang Simon

2017-06-26

Structure-based virtual screening (SBVS) has become an indispensable technique for hit identification at the early stage of drug discovery. However, the accuracy of current scoring functions is not high enough to confer success to every target and thus remains to be improved. Previously, we had developed binary pose filters (PFs) using knowledge derived from the protein-ligand interface of a single X-ray structure of a specific target. This novel approach had been validated as an effective way to improve ligand enrichment. Continuing from it, in the present work we attempted to incorporate knowledge collected from diverse protein-ligand interfaces of multiple crystal structures of the same target to build PF ensembles (PFEs). Toward this end, we first constructed a comprehensive data set to meet the requirements of ensemble modeling and validation. This set contains 10 diverse targets, 118 well-prepared X-ray structures of protein-ligand complexes, and large benchmarking actives/decoys sets. Notably, we designed a unique workflow of two-layer classifiers based on the concept of ensemble learning and applied it to the construction of PFEs for all of the targets. Through extensive benchmarking studies, we demonstrated that (1) coupling PFE with Chemgauss4 significantly improves the early enrichment of Chemgauss4 itself and (2) PFEs show greater consistency in boosting early enrichment and larger overall enrichment than our prior PFs. In addition, we analyzed the pairwise topological similarities among cognate ligands used to construct PFEs and found that it is the higher chemical diversity of the cognate ligands that leads to the improved performance of PFEs. Taken together, the results so far prove that the incorporation of knowledge from diverse protein-ligand interfaces by ensemble modeling is able to enhance the screening competence of SBVS scoring functions.

Discovery of Anti-Hypertensive Oligopeptides from Adlay Based on In Silico Proteolysis and Virtual Screening

PubMed Central

Qiao, Liansheng; Li, Bin; Chen, Yankun; Li, Lingling; Chen, Xi; Wang, Lingzhi; Lu, Fang; Luo, Ganggang; Li, Gongyu; Zhang, Yanling

2016-01-01

Adlay (Coix larchryma-jobi L.) was the commonly used Traditional Chinese Medicine (TCM) with high content of seed storage protein. The hydrolyzed bioactive oligopeptides of adlay have been proven to be anti-hypertensive effective components. However, the structures and anti-hypertensive mechanism of bioactive oligopeptides from adlay were not clear. To discover the definite anti-hypertensive oligopeptides from adlay, in silico proteolysis and virtual screening were implemented to obtain potential oligopeptides, which were further identified by biochemistry assay and molecular dynamics simulation. In this paper, ten sequences of adlay prolamins were collected and in silico hydrolyzed to construct the oligopeptide library with 134 oligopeptides. This library was reverse screened by anti-hypertensive pharmacophore database, which was constructed by our research team and contained ten anti-hypertensive targets. Angiotensin-I converting enzyme (ACE) was identified as the main potential target for the anti-hypertensive activity of adlay oligopeptides. Three crystal structures of ACE were utilized for docking studies and 19 oligopeptides were finally identified with potential ACE inhibitory activity. According to mapping features and evaluation indexes of pharmacophore and docking, three oligopeptides were selected for biochemistry assay. An oligopeptide sequence, NPATY (IC50 = 61.88 ± 2.77 µM), was identified as the ACE inhibitor by reverse-phase high performance liquid chromatography (RP-HPLC) assay. Molecular dynamics simulation of NPATY was further utilized to analyze interactive bonds and key residues. ALA354 was identified as a key residue of ACE inhibitors. Hydrophobic effect of VAL518 and electrostatic effects of HIS383, HIS387, HIS513 and Zn2+ were also regarded as playing a key role in inhibiting ACE activities. This study provides a research strategy to explore the pharmacological mechanism of Traditional Chinese Medicine (TCM) proteins based on in silico proteolysis and virtual screening, which could be beneficial to reveal the pharmacological action of TCM proteins and provide new lead compounds for peptides-based drug design. PMID:27983650

Visualization of reservoir simulation data with an immersive virtual reality system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, B.K.

1996-10-01

This paper discusses an investigation into the use of an immersive virtual reality (VR) system to visualize reservoir simulation output data. The hardware and software configurations of the test-immersive VR system are described and compared to a nonimmersive VR system and to an existing workstation screen-based visualization system. The structure of 3D reservoir simulation data and the actions to be performed on the data within the VR system are discussed. The subjective results of the investigation are then presented, followed by a discussion of possible future work.

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

PubMed Central

Zhou, Nannan; Xu, Yuan; Liu, Xian; Wang, Yulan; Peng, Jianlong; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

2015-01-01

The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors. PMID:26110383

Cognitive evaluation for the diagnosis of Alzheimer's disease based on Turing Test and Virtual Environments.

PubMed

Fernandez Montenegro, Juan Manuel; Argyriou, Vasileios

2017-05-01

Alzheimer's screening tests are commonly used by doctors to diagnose the patient's condition and stage as early as possible. Most of these tests are based on pen-paper interaction and do not embrace the advantages provided by new technologies. This paper proposes novel Alzheimer's screening tests based on virtual environments and game principles using new immersive technologies combined with advanced Human Computer Interaction (HCI) systems. These new tests are focused on the immersion of the patient in a virtual room, in order to mislead and deceive the patient's mind. In addition, we propose two novel variations of Turing Test proposed by Alan Turing as a method to detect dementia. As a result, four tests are introduced demonstrating the wide range of screening mechanisms that could be designed using virtual environments and game concepts. The proposed tests are focused on the evaluation of memory loss related to common objects, recent conversations and events; the diagnosis of problems in expressing and understanding language; the ability to recognize abnormalities; and to differentiate between virtual worlds and reality, or humans and machines. The proposed screening tests were evaluated and tested using both patients and healthy adults in a comparative study with state-of-the-art Alzheimer's screening tests. The results show the capacity of the new tests to distinguish healthy people from Alzheimer's patients. Copyright © 2017. Published by Elsevier Inc.

Surflex-Dock: Docking benchmarks and real-world application

NASA Astrophysics Data System (ADS)

Spitzer, Russell; Jain, Ajay N.

2012-06-01

Benchmarks for molecular docking have historically focused on re-docking the cognate ligand of a well-determined protein-ligand complex to measure geometric pose prediction accuracy, and measurement of virtual screening performance has been focused on increasingly large and diverse sets of target protein structures, cognate ligands, and various types of decoy sets. Here, pose prediction is reported on the Astex Diverse set of 85 protein ligand complexes, and virtual screening performance is reported on the DUD set of 40 protein targets. In both cases, prepared structures of targets and ligands were provided by symposium organizers. The re-prepared data sets yielded results not significantly different than previous reports of Surflex-Dock on the two benchmarks. Minor changes to protein coordinates resulting from complex pre-optimization had large effects on observed performance, highlighting the limitations of cognate ligand re-docking for pose prediction assessment. Docking protocols developed for cross-docking, which address protein flexibility and produce discrete families of predicted poses, produced substantially better performance for pose prediction. Performance on virtual screening performance was shown to benefit by employing and combining multiple screening methods: docking, 2D molecular similarity, and 3D molecular similarity. In addition, use of multiple protein conformations significantly improved screening enrichment.

CycloPs: generating virtual libraries of cyclized and constrained peptides including nonnatural amino acids.

PubMed

Duffy, Fergal J; Verniere, Mélanie; Devocelle, Marc; Bernard, Elise; Shields, Denis C; Chubb, Anthony J

2011-04-25

We introduce CycloPs, software for the generation of virtual libraries of constrained peptides including natural and nonnatural commercially available amino acids. The software is written in the cross-platform Python programming language, and features include generating virtual libraries in one-dimensional SMILES and three-dimensional SDF formats, suitable for virtual screening. The stand-alone software is capable of filtering the virtual libraries using empirical measurements, including peptide synthesizability by standard peptide synthesis techniques, stability, and the druglike properties of the peptide. The software and accompanying Web interface is designed to enable the rapid generation of large, structurally diverse, synthesizable virtual libraries of constrained peptides quickly and conveniently, for use in virtual screening experiments. The stand-alone software, and the Web interface for evaluating these empirical properties of a single peptide, are available at http://bioware.ucd.ie .

Exploration of multiple Sortase A protein conformations in virtual screening

NASA Astrophysics Data System (ADS)

Gao, Chunxia; Uzelac, Ivana; Gottfries, Johan; Eriksson, Leif A.

2016-02-01

Methicillin resistant Staphylococcus aureus (MRSA) has become a major health concern which has brought about an urgent need for new therapeutic agents. As the S. aureus Sortase A (SrtA) enzyme contributes to the adherence of the bacteria to the host cells, inhibition thereof by small molecules could be employed as potential antivirulence agents, also towards resistant strains. Albeit several virtual docking SrtA campaigns have been reported, no strongly inhibitatory non-covalent binders have as yet emerged therefrom. In order to better understand the binding modes of small molecules, and the effect of different receptor structures employed in the screening, we herein report on an exploratory study employing 10 known binders and 500 decoys on 100 SrtA structures generated from regular or steered molecular dynamics simulations on four different SrtA crystal/NMR structures. The results suggest a correlation between the protein structural flexibility and the virtual screening performance, and confirm the noted immobilization of the β6/β7 loop upon substrate binding. The NMR structures reported appear to perform slightly better than the Xray-crystal structures, but the binding modes fluctuate tremendously, and it might be suspected that the catalytic site is not necessarily the preferred site of binding for some of the reported active compounds.

Exploration of multiple Sortase A protein conformations in virtual screening

PubMed Central

Gao, Chunxia; Uzelac, Ivana; Gottfries, Johan; Eriksson, Leif A.

2016-01-01

Methicillin resistant Staphylococcus aureus (MRSA) has become a major health concern which has brought about an urgent need for new therapeutic agents. As the S. aureus Sortase A (SrtA) enzyme contributes to the adherence of the bacteria to the host cells, inhibition thereof by small molecules could be employed as potential antivirulence agents, also towards resistant strains. Albeit several virtual docking SrtA campaigns have been reported, no strongly inhibitatory non-covalent binders have as yet emerged therefrom. In order to better understand the binding modes of small molecules, and the effect of different receptor structures employed in the screening, we herein report on an exploratory study employing 10 known binders and 500 decoys on 100 SrtA structures generated from regular or steered molecular dynamics simulations on four different SrtA crystal/NMR structures. The results suggest a correlation between the protein structural flexibility and the virtual screening performance, and confirm the noted immobilization of the β6/β7 loop upon substrate binding. The NMR structures reported appear to perform slightly better than the Xray-crystal structures, but the binding modes fluctuate tremendously, and it might be suspected that the catalytic site is not necessarily the preferred site of binding for some of the reported active compounds. PMID:26846342

Approaches to virtual screening and screening library selection.

PubMed

Wildman, Scott A

2013-01-01

The ease of access to virtual screening (VS) software in recent years has resulted in a large increase in literature reports. Over 300 publications in the last year report the use of virtual screening techniques to identify new chemical matter or present the development of new virtual screening techniques. The increased use is accompanied by a corresponding increase in misuse and misinterpretation of virtual screening results. This review aims to identify many of the common difficulties associated with virtual screening and allow researchers to better assess the reliability of their virtual screening effort.

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

PubMed

Rohrer, Sebastian G; Baumann, Knut

2009-02-01

Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.

Novel Computational Approaches to Drug Discovery

NASA Astrophysics Data System (ADS)

Skolnick, Jeffrey; Brylinski, Michal

2010-01-01

New approaches to protein functional inference based on protein structure and evolution are described. First, FINDSITE, a threading based approach to protein function prediction, is summarized. Then, the results of large scale benchmarking of ligand binding site prediction, ligand screening, including applications to HIV protease, and GO molecular functional inference are presented. A key advantage of FINDSITE is its ability to use low resolution, predicted structures as well as high resolution experimental structures. Then, an extension of FINDSITE to ligand screening in GPCRs using predicted GPCR structures, FINDSITE/QDOCKX, is presented. This is a particularly difficult case as there are few experimentally solved GPCR structures. Thus, we first train on a subset of known binding ligands for a set of GPCRs; this is then followed by benchmarking against a large ligand library. For the virtual ligand screening of a number of Dopamine receptors, encouraging results are seen, with significant enrichment in identified ligands over those found in the training set. Thus, FINDSITE and its extensions represent a powerful approach to the successful prediction of a variety of molecular functions.

A Fragment-Based Ligand Screen Against Part of a Large Protein Machine: The ND1 Domains of the AAA+ ATPase p97/VCP.

PubMed

Chimenti, Michael S; Bulfer, Stacie L; Neitz, R Jeffrey; Renslo, Adam R; Jacobson, Matthew P; James, Thomas L; Arkin, Michelle R; Kelly, Mark J S

2015-07-01

The ubiquitous AAA+ ATPase p97 functions as a dynamic molecular machine driving several cellular processes. It is essential in regulating protein homeostasis, and it represents a potential drug target for cancer, particularly when there is a greater reliance on the endoplasmic reticulum-associated protein degradation pathway and ubiquitin-proteasome pathway to degrade an overabundance of secreted proteins. Here, we report a case study for using fragment-based ligand design approaches against this large and dynamic hexamer, which has multiple potential binding sites for small molecules. A screen of a fragment library was conducted by surface plasmon resonance (SPR) and followed up by nuclear magnetic resonance (NMR), two complementary biophysical techniques. Virtual screening was also carried out to examine possible binding sites for the experimental hits and evaluate the potential utility of fragment docking for this target. Out of this effort, 13 fragments were discovered that showed reversible binding with affinities between 140 µM and 1 mM, binding stoichiometries of 1:1 or 2:1, and good ligand efficiencies. Structural data for fragment-protein interactions were obtained with residue-specific [U-(2)H] (13)CH3-methyl-labeling NMR strategies, and these data were compared to poses from docking. The combination of virtual screening, SPR, and NMR enabled us to find and validate a number of interesting fragment hits and allowed us to gain an understanding of the structural nature of fragment binding. © 2015 Society for Laboratory Automation and Screening.

A Pipeline To Enhance Ligand Virtual Screening: Integrating Molecular Dynamics and Fingerprints for Ligand and Proteins.

PubMed

Spyrakis, Francesca; Benedetti, Paolo; Decherchi, Sergio; Rocchia, Walter; Cavalli, Andrea; Alcaro, Stefano; Ortuso, Francesco; Baroni, Massimo; Cruciani, Gabriele

2015-10-26

The importance of taking into account protein flexibility in drug design and virtual ligand screening (VS) has been widely debated in the literature, and molecular dynamics (MD) has been recognized as one of the most powerful tools for investigating intrinsic protein dynamics. Nevertheless, deciphering the amount of information hidden in MD simulations and recognizing a significant minimal set of states to be used in virtual screening experiments can be quite complicated. Here we present an integrated MD-FLAP (molecular dynamics-fingerprints for ligand and proteins) approach, comprising a pipeline of molecular dynamics, clustering and linear discriminant analysis, for enhancing accuracy and efficacy in VS campaigns. We first extracted a limited number of representative structures from tens of nanoseconds of MD trajectories by means of the k-medoids clustering algorithm as implemented in the BiKi Life Science Suite ( http://www.bikitech.com [accessed July 21, 2015]). Then, instead of applying arbitrary selection criteria, that is, RMSD, pharmacophore properties, or enrichment performances, we allowed the linear discriminant analysis algorithm implemented in FLAP ( http://www.moldiscovery.com [accessed July 21, 2015]) to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD-FLAP approach as a valuable tool for improving VS performances.

A structure-based virtual screening approach toward the discovery of histone deacetylase inhibitors: identification of promising zinc-chelating groups.

PubMed

Park, Hwangseo; Kim, Sukyoung; Kim, Yong Eun; Lim, Soo-Jeong

2010-04-06

The inhibitors of histone deacetylases (HDACs) have drawn a great deal of attention due to their promising potential as small-molecule therapeutics for the treatment of cancer. By means of virtual screening with docking simulations under consideration of the effects of ligand solvation, we were able to identify six novel HDAC inhibitors with IC(50) values ranging from 1 to 100 muM. These newly identified inhibitors are structurally diverse and have various chelating groups for the active site zinc ion, including N-[1,3,4]thiadiazol-2-yl sulfonamide, N-thiazol-2-yl sulfonamide, and hydroxamic acid moieties. The former two groups are included in many drugs in current clinical use and have not yet been reported as HDAC inhibitors. Therefore, they can be considered as new inhibitor scaffolds for the development of anticancer drugs by structure-activity relationship studies to improve the inhibitory activities against HDACs. Interactions with the HDAC1 active site residues responsible for stabilizing these new inhibitors are addressed in detail.

NALDB: nucleic acid ligand database for small molecules targeting nucleic acid

PubMed Central

Kumar Mishra, Subodh; Kumar, Amit

2016-01-01

Nucleic acid ligand database (NALDB) is a unique database that provides detailed information about the experimental data of small molecules that were reported to target several types of nucleic acid structures. NALDB is the first ligand database that contains ligand information for all type of nucleic acid. NALDB contains more than 3500 ligand entries with detailed pharmacokinetic and pharmacodynamic information such as target name, target sequence, ligand 2D/3D structure, SMILES, molecular formula, molecular weight, net-formal charge, AlogP, number of rings, number of hydrogen bond donor and acceptor, potential energy along with their Ki, Kd, IC50 values. All these details at single platform would be helpful for the development and betterment of novel ligands targeting nucleic acids that could serve as a potential target in different diseases including cancers and neurological disorders. With maximum 255 conformers for each ligand entry, our database is a multi-conformer database and can facilitate the virtual screening process. NALDB provides powerful web-based search tools that make database searching efficient and simplified using option for text as well as for structure query. NALDB also provides multi-dimensional advanced search tool which can screen the database molecules on the basis of molecular properties of ligand provided by database users. A 3D structure visualization tool has also been included for 3D structure representation of ligands. NALDB offers an inclusive pharmacological information and the structurally flexible set of small molecules with their three-dimensional conformers that can accelerate the virtual screening and other modeling processes and eventually complement the nucleic acid-based drug discovery research. NALDB can be routinely updated and freely available on bsbe.iiti.ac.in/bsbe/naldb/HOME.php. Database URL: http://bsbe.iiti.ac.in/bsbe/naldb/HOME.php PMID:26896846

Discovery of novel inhibitors of Mycobacterium tuberculosis MurG: homology modelling, structure based pharmacophore, molecular docking, and molecular dynamics simulations.

PubMed

Saxena, Shalini; Abdullah, Maaged; Sriram, Dharmarajan; Guruprasad, Lalitha

2017-10-17

MurG (Rv2153c) is a key player in the biosynthesis of the peptidoglycan layer in Mycobacterium tuberculosis (Mtb). This work is an attempt to highlight the structural and functional relationship of Mtb MurG, the three-dimensional (3D) structure of protein was constructed by homology modelling using Discovery Studio 3.5 software. The quality and consistency of generated model was assessed by PROCHECK, ProSA and ERRAT. Later, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with substrate Uridine-diphosphate-N-acetylglucosamine (UD1) facilitated us to employ structure-based virtual screening approach to obtain new hits from Asinex database using energy-optimized pharmacophore modelling (e-pharmacophore). The pharmacophore model was validated using enrichment calculations, and finally, validated model was employed for high-throughput virtual screening and molecular docking to identify novel Mtb MurG inhibitors. This study led to the identification of 10 potential compounds with good fitness, docking score, which make important interactions with the protein active site. The 25 ns MD simulations of three potential lead compounds with protein confirmed that the structure was stable and make several non-bonding interactions with amino acids, such as Leu290, Met310 and Asn167. Hence, we concluded that the identified compounds may act as new leads for the design of Mtb MurG inhibitors.

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PubMed Central

Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

2015-01-01

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

NASA Astrophysics Data System (ADS)

Maffucci, Irene; Hu, Xiao; Fumagalli, Valentina; Contini, Alessandro

2018-03-01

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 ns or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20% and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

A new insight into mushroom tyrosinase inhibitors: docking, pharmacophore-based virtual screening, and molecular modeling studies.

PubMed

Bagherzadeh, Kowsar; Shirgahi Talari, Faezeh; Sharifi, Amirhossein; Ganjali, Mohammad Reza; Saboury, Ali Akbar; Amanlou, Massoud

2015-01-01

Tyrosinase, a widely spread enzyme in micro-organisms, animals, and plants, participates in two rate-limiting steps in melanin formation pathway which is responsible for skin protection against UV lights' harm whose functional deficiency result in serious dermatological diseases. This enzyme seems to be responsible for neuromelanin formation in human brain as well. In plants, the enzyme leads the browning pathway which is commonly observed in injured tissues that is economically very unfavorable. Among different types of tyrosinase, mushroom tyrosinase has the highest homology with the mammalian tyrosinase and the only commercial tyrosinase available. In this study, ligand-based pharmacophore drug discovery method was applied to rapidly identify mushroom tyrosinase enzyme inhibitors using virtual screening. The model pharmacophore of essential interactions was developed and refined studying already experimentally discovered potent inhibitors employing Docking analysis methodology. After pharmacophore virtual screening and binding modes prediction, 14 compounds from ZINC database were identified as potent inhibitors of mushroom tyrosinase which were classified into five groups according to their chemical structures. The inhibition behavior of the discovered compounds was further studied through Classical Molecular Dynamic Simulations and the conformational changes induced by the presence of the studied ligands were discussed and compared to those of the substrate, tyrosine. According to the obtained results, five novel leads are introduced to be further optimized or directly used as potent inhibitors of mushroom tyrosinase.

A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents

PubMed Central

Sánchez-Rodríguez, Aminael; Tejera, Eduardo; Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M. Natália D. S.; Le-Thi-Thu, Huong; Pham-The, Hai

2018-01-01

Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents. PMID:29420638

(Z)-2-(3-Chlorobenzylidene)-3,4-dihydro-N-(2-methoxyethyl)-3-oxo-2H-benzo[b][1,4]oxazine-6-carboxamide as GSK-3β inhibitor: Identification by virtual screening and its validation in enzyme- and cell-based assay.

PubMed

Joshi, Prashant; Gupta, Mehak; Vishwakarma, Ram A; Kumar, Ajay; Bharate, Sandip B

2017-06-01

Glycogen synthase kinase 3β (GSK-3β) is a widely investigated molecular target for numerous diseases including Alzheimer's disease, cancer, and diabetes mellitus. The present study was aimed to discover new scaffolds for GSK-3β inhibition, through protein structure-guided virtual screening approach. With the availability of large number of GSK-3β crystal structures with varying degree of RMSD in protein backbone and RMSF in side chain geometry, herein appropriate crystal structures were selected based on the characteristic ROC curve and percentage enrichment of actives. The validated docking protocol was employed to screen a library of 50,000 small molecules using molecular docking and binding affinity calculations. Based on the GLIDE docking score, Prime MMGB/SA binding affinity, and interaction pattern analysis, the top 50 ligands were selected for GSK-3β inhibition. (Z)-2-(3-chlorobenzylidene)-3,4-dihydro-N-(2-methoxyethyl)-3-oxo-2H-benzo[b][1,4]oxazine-6-carboxamide (F389-0663, 7) was identified as a potent inhibitor of GSK-3β with an IC 50 value of 1.6 μm. Further, GSK-3β inhibition activity was then investigated in cell-based assay. The treatment of neuroblastoma N2a cells with 12.5 μm of F389-0663 resulted in the significant increase in GSK-3β Ser9 levels, which is indicative of the GSK-3β inhibitory activity of a compound. The molecular dynamic simulations were carried out to understand the interactions of F389-0663 with GSK-3β protein. © 2016 John Wiley & Sons A/S.

Pharmacophore-based virtual screening, biological evaluation and binding mode analysis of a novel protease-activated receptor 2 antagonist

NASA Astrophysics Data System (ADS)

Cho, Nam-Chul; Seo, Seoung-Hwan; Kim, Dohee; Shin, Ji-Sun; Ju, Jeongmin; Seong, Jihye; Seo, Seon Hee; Lee, Iiyoun; Lee, Kyung-Tae; Kim, Yun Kyung; No, Kyoung Tai; Pae, Ae Nim

2016-08-01

Protease-activated receptor 2 (PAR2) is a G protein-coupled receptor, mediating inflammation and pain signaling in neurons, thus it is considered to be a potential therapeutic target for inflammatory diseases. In this study, we performed a ligand-based virtual screening of 1.6 million compounds by employing a common-feature pharmacophore model and two-dimensional similarity search to identify a new PAR2 antagonist. The common-feature pharmacophore model was established based on the biological screening results of our in-house library. The initial virtual screening yielded a total number of 47 hits, and additional biological activity tests including PAR2 antagonism and anti-inflammatory effects resulted in a promising candidate, compound 43, which demonstrated an IC50 value of 8.22 µM against PAR2. In next step, a PAR2 homology model was constructed using the crystal structure of the PAR1 as a template to explore the binding mode of the identified ligands. A molecular docking method was optimized by comparing the binding modes of a known PAR2 agonist GB110 and antagonist GB83, and applied to predict the binding mode of our hit compound 43. In-depth docking analyses revealed that the hydrophobic interaction with Phe2435.39 is crucial for PAR2 ligands to exert antagonistic activity. MD simulation results supported the predicted docking poses that PAR2 antagonist blocked a conformational rearrangement of Na+ allosteric site in contrast to PAR2 agonist that showed Na+ relocation upon GPCR activation. In conclusion, we identified new a PAR2 antagonist together with its binding mode, which provides useful insights for the design and development of PAR2 ligands.

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

PubMed

Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

2016-01-01

Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Structure-based drug design: docking and scoring.

PubMed

Kroemer, Romano T

2007-08-01

This review gives an introduction into ligand - receptor docking and illustrates the basic underlying concepts. An overview of different approaches and algorithms is provided. Although the application of docking and scoring has led to some remarkable successes, there are still some major challenges ahead, which are outlined here as well. Approaches to address some of these challenges and the latest developments in the area are presented. Some aspects of the assessment of docking program performance are discussed. A number of successful applications of structure-based virtual screening are described.

Creating and virtually screening databases of fluorescently-labelled compounds for the discovery of target-specific molecular probes

NASA Astrophysics Data System (ADS)

Kamstra, Rhiannon L.; Dadgar, Saedeh; Wigg, John; Chowdhury, Morshed A.; Phenix, Christopher P.; Floriano, Wely B.

2014-11-01

Our group has recently demonstrated that virtual screening is a useful technique for the identification of target-specific molecular probes. In this paper, we discuss some of our proof-of-concept results involving two biologically relevant target proteins, and report the development of a computational script to generate large databases of fluorescence-labelled compounds for computer-assisted molecular design. The virtual screening of a small library of 1,153 fluorescently-labelled compounds against two targets, and the experimental testing of selected hits reveal that this approach is efficient at identifying molecular probes, and that the screening of a labelled library is preferred over the screening of base compounds followed by conjugation of confirmed hits. The automated script for library generation explores the known reactivity of commercially available dyes, such as NHS-esters, to create large virtual databases of fluorescence-tagged small molecules that can be easily synthesized in a laboratory. A database of 14,862 compounds, each tagged with the ATTO680 fluorophore was generated with the automated script reported here. This library is available for downloading and it is suitable for virtual ligand screening aiming at the identification of target-specific fluorescent molecular probes.

VSDMIP 1.5: an automated structure- and ligand-based virtual screening platform with a PyMOL graphical user interface.

PubMed

Cabrera, Álvaro Cortés; Gil-Redondo, Rubén; Perona, Almudena; Gago, Federico; Morreale, Antonio

2011-09-01

A graphical user interface (GUI) for our previously published virtual screening (VS) and data management platform VSDMIP (Gil-Redondo et al. J Comput Aided Mol Design, 23:171-184, 2009) that has been developed as a plugin for the popular molecular visualization program PyMOL is presented. In addition, a ligand-based VS module (LBVS) has been implemented that complements the already existing structure-based VS (SBVS) module and can be used in those cases where the receptor's 3D structure is not known or for pre-filtering purposes. This updated version of VSDMIP is placed in the context of similar available software and its LBVS and SBVS capabilities are tested here on a reduced set of the Directory of Useful Decoys database. Comparison of results from both approaches confirms the trend found in previous studies that LBVS outperforms SBVS. We also show that by combining LBVS and SBVS, and using a cluster of ~100 modern processors, it is possible to perform complete VS studies of several million molecules in less than a month. As the main processes in VSDMIP are 100% scalable, more powerful processors and larger clusters would notably decrease this time span. The plugin is distributed under an academic license upon request from the authors. © Springer Science+Business Media B.V. 2011

Structure-based Virtual Screening and Identification of a Novel Androgen Receptor Antagonist*

PubMed Central

Song, Chin-Hee; Yang, Su Hui; Park, Eunsook; Cho, Suk Hee; Gong, Eun-Yeung; Khadka, Daulat Bikram; Cho, Won-Jea; Lee, Keesook

2012-01-01

Hormonal therapies, mainly combinations of anti-androgens and androgen deprivation, have been the mainstay treatment for advanced prostate cancer because the androgen-androgen receptor (AR) system plays a pivotal role in the development and progression of prostate cancers. However, the emergence of androgen resistance, largely due to inefficient anti-hormone action, limits the therapeutic usefulness of these therapies. Here, we report that 6-(3,4-dihydro-1H-isoquinolin-2-yl)-N-(6-methylpyridin-2-yl)nicotinamide (DIMN) acts as a novel anti-androgenic compound that may be effective in the treatment of both androgen-dependent and androgen-independent prostate cancers. Through AR structure-based virtual screening using the FlexX docking model, fifty-four compounds were selected and further screened for AR antagonism via cell-based tests. One compound, DIMN, showed an antagonistic effect specific to AR with comparable potency to that of the classical AR antagonists, hydroxyflutamide and bicalutamide. Consistent with their anti-androgenic activity, DIMN inhibited the growth of androgen-dependent LNCaP prostate cancer cells. Interestingly, the compound also suppressed the growth of androgen-independent C4–2 and CWR22rv prostate cancer cells, which express a functional AR, but did not suppress the growth of the AR-negative prostate cancer cells PPC-1, DU145, and R3327-AT3.1. Taken together, the results suggest that the synthetic compound DIMN is a novel anti-androgen and strong candidate for useful therapeutic agent against early stage to advanced prostate cancer. PMID:22798067

Modeling and Deorphanization of Orphan GPCRs.

PubMed

Diaz, Constantino; Angelloz-Nicoud, Patricia; Pihan, Emilie

2018-01-01

Despite tremendous efforts, approximately 120 GPCRs remain orphan. Their physiological functions and their potential roles in diseases are poorly understood. Orphan GPCRs are extremely important because they may provide novel therapeutic targets for unmet medical needs. As a complement to experimental approaches, molecular modeling and virtual screening are efficient techniques to discover synthetic surrogate ligands which can help to elucidate the role of oGPCRs. Constitutively activated mutants and recently published active structures of GPCRs provide stimulating opportunities for building active molecular models for oGPCRs and identifying activators using virtual screening of compound libraries. We describe the molecular modeling and virtual screening process we have applied in the discovery of surrogate ligands, and provide examples for CCKA, a simulated oGPCR, and for two oGPCRs, GPR52 and GPR34.

Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor.

PubMed

Weiss, Dahlia R; Ahn, SeungKirl; Sassano, Maria F; Kleist, Andrew; Zhu, Xiao; Strachan, Ryan; Roth, Bryan L; Lefkowitz, Robert J; Shoichet, Brian K

2013-05-17

A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.

Small molecule correctors of F508del-CFTR discovered by structure-based virtual screening

NASA Astrophysics Data System (ADS)

Kalid, Ori; Mense, Martin; Fischman, Sharon; Shitrit, Alina; Bihler, Hermann; Ben-Zeev, Efrat; Schutz, Nili; Pedemonte, Nicoletta; Thomas, Philip J.; Bridges, Robert J.; Wetmore, Diana R.; Marantz, Yael; Senderowitz, Hanoch

2010-12-01

Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability. In silico structure-based screening was performed at the putative binding-sites and a total of 496 candidate compounds from all three sites were tested in functional assays. A total of 15 compounds, representing diverse chemotypes, were identified as F508del folding correctors. This corresponds to a 3% hit rate, tenfold higher than hit rates obtained in corresponding high-throughput screening campaigns. The same binding sites also yielded potentiators and, most notably, compounds with a dual corrector-potentiator activity (dual-acting). Compounds harboring both activity types may prove to be better leads for the development of CF therapeutics than either pure correctors or pure potentiators. To the best of our knowledge this is the first report of structure-based discovery of CFTR modulators.

Identification of ligand efficient, fragment-like hits from an HTS library: structure-based virtual screening and docking investigations of 2H- and 3H-pyrazolo tautomers for Aurora kinase A selectivity.

PubMed

Sarvagalla, Sailu; Singh, Vivek Kumar; Ke, Yi-Yu; Shiao, Hui-Yi; Lin, Wen-Hsing; Hsieh, Hsing-Pang; Hsu, John T A; Coumar, Mohane Selvaraj

2015-01-01

Furanopyrimidine 1 (IC50 = 273 nM, LE = 0.36, LELP = 10.28) was recently identified by high-throughput screening (HTS) of an in-house library (125,000 compounds) as an Aurora kinase inhibitor. Structure-based hit optimization resulted in lead molecules with in vivo efficacy in a mouse tumour xenograft model, but no oral bioavailability. This is attributed to "molecular obesity", a common problem during hit to lead evolution during which degradation of important molecular properties such as molecular weight (MW) and lipophilicity occurs. This could be effectively tackled by the right choice of hit compounds for optimization. In this regard, ligand efficiency (LE) and ligand efficiency dependent lipophilicity (LELP) indices are more often used to choose fragment-like hits for optimization. To identify hits with appropriate LE, we used a MW cut-off <250, and pyrazole structure to filter HTS library. Next, structure-based virtual screening using software (Libdock and Glide) in the Aurora A crystal structure (PDB ID: 3E5A) was carried out, and the top scoring 18 compounds tested for Aurora A enzyme inhibition. This resulted in the identification of a novel tetrahydro-pyrazolo-isoquinoline hit 7 (IC50 = 852 nM, LE = 0.44, LELP = 8.36) with fragment-like properties suitable for further hit optimization. Moreover, hit 7 was found to be selective for Aurora A (Aurora B IC50 = 35,150 nM) and the possible reasons for selectivity investigated by docking two tautomeric forms (2H- and 3H-pyrazole) of 7 in Auroras A and B (PDB ID: 4AF3) crystal structures. This docking study shows that the major 3H-pyrazole tautomer of 7 binds in Aurora A stronger than in Aurora B.

Identification of ligand efficient, fragment-like hits from an HTS library: structure-based virtual screening and docking investigations of 2 H- and 3 H-pyrazolo tautomers for Aurora kinase A selectivity

NASA Astrophysics Data System (ADS)

Sarvagalla, Sailu; Singh, Vivek Kumar; Ke, Yi-Yu; Shiao, Hui-Yi; Lin, Wen-Hsing; Hsieh, Hsing-Pang; Hsu, John T. A.; Coumar, Mohane Selvaraj

2015-01-01

Furanopyrimidine 1 (IC50 = 273 nM, LE = 0.36, LELP = 10.28) was recently identified by high-throughput screening (HTS) of an in-house library (125,000 compounds) as an Aurora kinase inhibitor. Structure-based hit optimization resulted in lead molecules with in vivo efficacy in a mouse tumour xenograft model, but no oral bioavailability. This is attributed to "molecular obesity", a common problem during hit to lead evolution during which degradation of important molecular properties such as molecular weight (MW) and lipophilicity occurs. This could be effectively tackled by the right choice of hit compounds for optimization. In this regard, ligand efficiency (LE) and ligand efficiency dependent lipophilicity (LELP) indices are more often used to choose fragment-like hits for optimization. To identify hits with appropriate LE, we used a MW cut-off <250, and pyrazole structure to filter HTS library. Next, structure-based virtual screening using software (Libdock and Glide) in the Aurora A crystal structure (PDB ID: 3E5A) was carried out, and the top scoring 18 compounds tested for Aurora A enzyme inhibition. This resulted in the identification of a novel tetrahydro-pyrazolo-isoquinoline hit 7 (IC50 = 852 nM, LE = 0.44, LELP = 8.36) with fragment-like properties suitable for further hit optimization. Moreover, hit 7 was found to be selective for Aurora A (Aurora B IC50 = 35,150 nM) and the possible reasons for selectivity investigated by docking two tautomeric forms (2 H- and 3 H-pyrazole) of 7 in Auroras A and B (PDB ID: 4AF3) crystal structures. This docking study shows that the major 3 H-pyrazole tautomer of 7 binds in Aurora A stronger than in Aurora B.

Macromolecular Modelling and Docking Simulations for the Discovery of Selective GPER Ligands.

PubMed

Rosano, Camillo; Ponassi, Marco; Santolla, Maria Francesca; Pisano, Assunta; Felli, Lamberto; Vivacqua, Adele; Maggiolini, Marcello; Lappano, Rosamaria

2016-01-01

Estrogens influence multiple physiological processes and are implicated in many diseases as well. Cellular responses to estrogens are mainly mediated by the estrogen receptors (ER)α and ERβ, which act as ligand-activated transcription factors. Recently, a member of the G protein-coupled receptor (GPCR) superfamily, namely GPER/GPR30, has been identified as a further mediator of estrogen signalling in different pathophysiological conditions, including cancer. Today, computational methods are commonly used in all areas of health science research. Among these methods, virtual ligand screening has become an established technique for hit discovery and optimization. The absence of an established three-dimensional structure of GPER promoted studies of structure-based drug design in order to build reliable molecular models of this receptor. Here, we discuss the results obtained through the structure-based virtual ligand screening for GPER, which allowed the identification and synthesis of different selective agonist and antagonist moieties. These compounds led significant advances in our understanding of the GPER function at the cellular, tissue, and organismal levels. In particular, selective GPER ligands were critical toward the evaluation of the role elicited by this receptor in several pathophysiological conditions, including cancer. Considering that structure-based approaches are fundamental in drug discovery, future research breakthroughs with the aid of computer-aided molecular design and chemo-bioinformatics could generate a new class of drugs that, acting through GPER, would be useful in a variety of diseases as well as in innovative anticancer strategies.

Identification of promising DNA GyrB inhibitors for Tuberculosis using pharmacophore-based virtual screening, molecular docking and molecular dynamics studies.

PubMed

Islam, Md Ataul; Pillay, Tahir S

2017-08-01

In this study, we searched for potential DNA GyrB inhibitors using pharmacophore-based virtual screening followed by molecular docking and molecular dynamics simulation approaches. For this purpose, a set of 248 DNA GyrB inhibitors was collected from the literature and a well-validated pharmacophore model was generated. The best pharmacophore model explained that two each of hydrogen bond acceptors and hydrophobicity regions were critical for inhibition of DNA GyrB. Good statistical results of the pharmacophore model indicated that the model was robust in nature. Virtual screening of molecular databases revealed three molecules as potential antimycobacterial agents. The final screened promising compounds were evaluated in molecular docking and molecular dynamics simulation studies. In the molecular dynamics studies, RMSD and RMSF values undoubtedly explained that the screened compounds formed stable complexes with DNA GyrB. Therefore, it can be concluded that the compounds identified may have potential for the treatment of TB. © 2017 John Wiley & Sons A/S.

Discovery of Novel Anti-prion Compounds Using In Silico and In Vitro Approaches

PubMed Central

Hyeon, Jae Wook; Choi, Jiwon; Kim, Su Yeon; Govindaraj, Rajiv Gandhi; Jam Hwang, Kyu; Lee, Yeong Seon; An, Seong Soo A.; Lee, Myung Koo; Joung, Jong Young; No, Kyoung Tai; Lee, Jeongmin

2015-01-01

Prion diseases are associated with the conformational conversion of the physiological form of cellular prion protein (PrPC) to the pathogenic form, PrPSc. Compounds that inhibit this process by blocking conversion to the PrPSc could provide useful anti-prion therapies. However, no suitable drugs have been identified to date. To identify novel anti-prion compounds, we developed a combined structure- and ligand-based virtual screening system in silico. Virtual screening of a 700,000-compound database, followed by cluster analysis, identified 37 compounds with strong interactions with essential hotspot PrP residues identified in a previous study of PrPC interaction with a known anti-prion compound (GN8). These compounds were tested in vitro using a multimer detection system, cell-based assays, and surface plasmon resonance. Some compounds effectively reduced PrPSc levels and one of these compounds also showed a high binding affinity for PrPC. These results provide a promising starting point for the development of anti-prion compounds. PMID:26449325

Novel small molecule inhibitors targeting the "switch region" of bacterial RNAP: structure-based optimization of a virtual screening hit.

PubMed

Sahner, J Henning; Groh, Matthias; Negri, Matthias; Haupenthal, Jörg; Hartmann, Rolf W

2013-07-01

Rising resistance against current antibiotics necessitates the development of antibacterial agents with alternative targets. The "switch region" of RNA polymerase (RNAP), addressed by the myxopyronins, could be such a novel target site. Based on a hit candidate discovered by virtual screening, a small library of 5-phenyl-3-ureidothiophene-2-carboxylic acids was synthesized resulting in compounds with increased RNAP inhibition. Hansch analysis revealed π (lipophilicity constant) and σ (Hammet substituent constant) of the substituents at the 5-phenyl moiety to be crucial for activity. The binding mode was proven by the targeted introduction of a moiety mimicking the enecarbamate side chain of myxopyronin into the hit compound, accompanied by enhanced RNAP inhibitory potency. The new compounds displayed good antibacterial activities against Gram positive bacteria and Gram negative Escherichia coli TolC and a reduced resistance frequency compared to the established antibiotic rifampicin. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Virtual Screening as a Strategy for the Identification of Xenobiotics Disrupting Corticosteroid Action

PubMed Central

Praxmarer, Lukas; Chantong, Boonrat; Cereghetti, Diego; Winiger, Rahel; Schuster, Daniela; Odermatt, Alex

2012-01-01

Background Impaired corticosteroid action caused by genetic and environmental influence, including exposure to hazardous xenobiotics, contributes to the development and progression of metabolic diseases, cardiovascular complications and immune disorders. Novel strategies are thus needed for identifying xenobiotics that interfere with corticosteroid homeostasis. 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) and mineralocorticoid receptors (MR) are major regulators of corticosteroid action. 11β-HSD2 converts the active glucocorticoid cortisol to the inactive cortisone and protects MR from activation by glucocorticoids. 11β-HSD2 has also an essential role in the placenta to protect the fetus from high maternal cortisol concentrations. Methods and Principal Findings We employed a previously constructed 3D-structural library of chemicals with proven and suspected endocrine disrupting effects for virtual screening using a chemical feature-based 11β-HSD pharmacophore. We tested several in silico predicted chemicals in a 11β-HSD2 bioassay. The identified antibiotic lasalocid and the silane-coupling agent AB110873 were found to concentration-dependently inhibit 11β-HSD2. Moreover, the silane AB110873 was shown to activate MR and stimulate mitochondrial ROS generation and the production of the proinflammatory cytokine interleukin-6 (IL-6). Finally, we constructed a MR pharmacophore, which successfully identified the silane AB110873. Conclusions Screening of virtual chemical structure libraries can facilitate the identification of xenobiotics inhibiting 11β-HSD2 and/or activating MR. Lasalocid and AB110873 belong to new classes of 11β-HSD2 inhibitors. The silane AB110873 represents to the best of our knowledge the first industrial chemical shown to activate MR. Furthermore, the MR pharmacophore can now be used for future screening purposes. PMID:23056542

Virtual Screening of Phytochemicals to Novel Target (HAT) Rtt109 in Pneumocystis Jirovecii using Bioinformatics Tools.

PubMed

Sugumar, Ramya; Adithavarman, Abhinand Ponneri; Dakshinamoorthi, Anusha; David, Darling Chellathai; Ragunath, Padmavathi Kannan

2016-03-01

Pneumocystis jirovecii is a fungus that causes Pneumocystis pneumonia in HIV and other immunosuppressed patients. Treatment of Pneumocystis pneumonia with the currently available antifungals is challenging and associated with considerable adverse effects. There is a need to develop drugs against novel targets with minimal human toxicities. Histone Acetyl Transferase (HAT) Rtt109 is a potential therapeutic target in Pneumocystis jirovecii species. HAT is linked to transcription and is required to acetylate conserved lysine residues on histone proteins by transferring an acetyl group from acetyl CoA to form e-N-acetyl lysine. Therefore, inhibitors of HAT can be useful therapeutic options in Pneumocystis pneumonia. To screen phytochemicals against (HAT) Rtt109 using bioinformatics tool. The tertiary structure of Pneumocystis jirovecii (HAT) Rtt109 was modeled by Homology Modeling. The ideal template for modeling was obtained by performing Psi BLAST of the protein sequence. Rtt109-AcCoA/Vps75 protein from Saccharomyces cerevisiae (PDB structure 3Q35) was chosen as the template. The target protein was modeled using Swiss Modeler and validated using Ramachandran plot and Errat 2. Comprehensive text mining was performed to identify phytochemical compounds with antipneumonia and fungicidal properties and these compounds were filtered based on Lipinski's Rule of 5. The chosen compounds were subjected to virtual screening against the target protein (HAT) Rtt109 using Molegro Virtual Docker 4.5. Osiris Property Explorer and Open Tox Server were used to predict ADME-T properties of the chosen phytochemicals. Tertiary structure model of HAT Rtt 109 had a ProSA score of -6.57 and Errat 2 score of 87.34. Structure validation analysis by Ramachandran plot for the model revealed 97% of amino acids were in the favoured region. Of all the phytochemicals subjected to virtual screening against the target protein (HAT) Rtt109, baicalin exhibited highest binding affinity towards the target protein as indicated by the Molegro score of 130.68 and formed 16 H-bonds. The ADME-T property prediction revealed that baicalin was non-mutagenic, non-tumorigenic and had a drug likeness score of 0.87. Baicalin has good binding with Rtt 109 in Pneumocystis jirovecii and can be considered as a novel and valuable treatment option for Pneumocystis pneumonia patients after subjecting it to invivo and invitro studies.

Virtual Screening of Phytochemicals to Novel Target (HAT) Rtt109 in Pneumocystis Jirovecii using Bioinformatics Tools

PubMed Central

Adithavarman, Abhinand Ponneri; Dakshinamoorthi, Anusha; David, Darling Chellathai; Ragunath, Padmavathi Kannan

2016-01-01

Introduction Pneumocystis jirovecii is a fungus that causes Pneumocystis pneumonia in HIV and other immunosuppressed patients. Treatment of Pneumocystis pneumonia with the currently available antifungals is challenging and associated with considerable adverse effects. There is a need to develop drugs against novel targets with minimal human toxicities. Histone Acetyl Transferase (HAT) Rtt109 is a potential therapeutic target in Pneumocystis jirovecii species. HAT is linked to transcription and is required to acetylate conserved lysine residues on histone proteins by transferring an acetyl group from acetyl CoA to form e-N-acetyl lysine. Therefore, inhibitors of HAT can be useful therapeutic options in Pneumocystis pneumonia. Aim To screen phytochemicals against (HAT) Rtt109 using bioinformatics tool. Materials and Methods The tertiary structure of Pneumocystis jirovecii (HAT) Rtt109 was modeled by Homology Modeling. The ideal template for modeling was obtained by performing Psi BLAST of the protein sequence. Rtt109-AcCoA/Vps75 protein from Saccharomyces cerevisiae (PDB structure 3Q35) was chosen as the template. The target protein was modeled using Swiss Modeler and validated using Ramachandran plot and Errat 2. Comprehensive text mining was performed to identify phytochemical compounds with antipneumonia and fungicidal properties and these compounds were filtered based on Lipinski’s Rule of 5. The chosen compounds were subjected to virtual screening against the target protein (HAT) Rtt109 using Molegro Virtual Docker 4.5. Osiris Property Explorer and Open Tox Server were used to predict ADME-T properties of the chosen phytochemicals. Results Tertiary structure model of HAT Rtt 109 had a ProSA score of -6.57 and Errat 2 score of 87.34. Structure validation analysis by Ramachandran plot for the model revealed 97% of amino acids were in the favoured region. Of all the phytochemicals subjected to virtual screening against the target protein (HAT) Rtt109, baicalin exhibited highest binding affinity towards the target protein as indicated by the Molegro score of 130.68 and formed 16 H-bonds. The ADME-T property prediction revealed that baicalin was non-mutagenic, non-tumorigenic and had a drug likeness score of 0.87. Conclusion Baicalin has good binding with Rtt 109 in Pneumocystis jirovecii and can be considered as a novel and valuable treatment option for Pneumocystis pneumonia patients after subjecting it to invivo and invitro studies. PMID:27134887

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

PubMed Central

Simms, John; Christopoulos, Arthur; Wootten, Denise

2017-01-01

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state. PMID:29131821

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer.

PubMed

Ren, Ji-Xia; Li, Cheng-Ping; Zhou, Xiu-Ling; Cao, Xue-Song; Xie, Yong

2017-08-22

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using 'Receptor-Ligand Pharmacophore Generation' method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r 2 of 0.996; for the test set, the correlation coefficient r 2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening

NASA Astrophysics Data System (ADS)

Zavodszky, Maria I.; Sanschagrin, Paul C.; Kuhn, Leslie A.; Korde, Rajesh S.

2002-12-01

For the successful identification and docking of new ligands to a protein target by virtual screening, the essential features of the protein and ligand surfaces must be captured and distilled in an efficient representation. Since the running time for docking increases exponentially with the number of points representing the protein and each ligand candidate, it is important to place these points where the best interactions can be made between the protein and the ligand. This definition of favorable points of interaction can also guide protein structure-based ligand design, which typically focuses on which chemical groups provide the most energetically favorable contacts. In this paper, we present an alternative method of protein template and ligand interaction point design that identifies the most favorable points for making hydrophobic and hydrogen-bond interactions by using a knowledge base. The knowledge-based protein and ligand representations have been incorporated in version 2.0 of SLIDE and resulted in dockings closer to the crystal structure orientations when screening a set of 57 known thrombin and glutathione S-transferase (GST) ligands against the apo structures of these proteins. There was also improved scoring enrichment of the dockings, meaning better differentiation between the chemically diverse known ligands and a ˜15,000-molecule dataset of randomly-chosen small organic molecules. This approach for identifying the most important points of interaction between proteins and their ligands can equally well be used in other docking and design techniques. While much recent effort has focused on improving scoring functions for protein-ligand docking, our results indicate that improving the representation of the chemistry of proteins and their ligands is another avenue that can lead to significant improvements in the identification, docking, and scoring of ligands.

Fragment-Based Docking: Development of the CHARMMing Web User Interface as a Platform for Computer-Aided Drug Design

PubMed Central

2015-01-01

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser.1 One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing’s capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of “re-dockings” with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing’s docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening. PMID:25151852

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design.

PubMed

Pevzner, Yuri; Frugier, Emilie; Schalk, Vinushka; Caflisch, Amedeo; Woodcock, H Lee

2014-09-22

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment.

PubMed

Minovski, Nikola; Perdih, Andrej; Solmajer, Tom

2012-05-01

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

Discovery of Dual ETA/ETB Receptor Antagonists from Traditional Chinese Herbs through in Silico and in Vitro Screening

PubMed Central

Wang, Xing; Zhang, Yuxin; Liu, Qing; Ai, Zhixin; Zhang, Yanling; Xiang, Yuhong; Qiao, Yanjiang

2016-01-01

Endothelin-1 receptors (ETAR and ETBR) act as a pivotal regulator in the biological effects of ET-1 and represent a potential drug target for the treatment of multiple cardiovascular diseases. The purpose of the study is to discover dual ETA/ETB receptor antagonists from traditional Chinese herbs. Ligand- and structure-based virtual screening was performed to screen an in-house database of traditional Chinese herbs, followed by a series of in vitro bioassay evaluation. Aristolochic acid A (AAA) was first confirmed to be a dual ETA/ETB receptor antagonist based intracellular calcium influx assay and impedance-based assay. Dose-response curves showed that AAA can block both ETAR and ETBR with IC50 of 7.91 and 7.40 μM, respectively. Target specificity and cytotoxicity bioassay proved that AAA is a selective dual ETA/ETB receptor antagonist and has no significant cytotoxicity on HEK293/ETAR and HEK293/ETBR cells within 24 h. It is a feasible and effective approach to discover bioactive compounds from traditional Chinese herbs using in silico screening combined with in vitro bioassay evaluation. The structural characteristic of AAA for its activity was especially interpreted, which could provide valuable reference for the further structural modification of AAA. PMID:26999111

Virtual Screening on MMP-13 Led to Discovering New Inhibitors Including a Non-Zinc Binding and a Micro Molar One: A Successful Example of Receptor Selection According to Cross-Docking Results for a Flexible Enzyme.

PubMed

Ramezani, Mohammad; Shamsara, Jamal

2017-01-01

MMP-13 belongs to a large family of proteases called matrix metalloproteinases (MMPs) that degrades type II collagen, the main structural protein of articular cartilage. The main pathologic role of MMP-13 over expression is to contribute to the development of osteoarthritis. To find new inhibitors with possible selectivity for MMP-13 a structure based virtual screening was employed. The inhibitory activities of 11 inhibitors among 19 purchased compounds were approved by enzyme inhibition assay. Our results demonstrated that the CADD (computer aided drug design) could be successfully applied to find new MMP-13 inhibitors using a receptor structure (PDB code: 3O2X) which had been demonstrated a good performance in a cross-docking study. We discovered inhibitors with new scaffolds for inhibition of MMP-13 and some selectivity features such as proper S1' occupancy and interactions with S1' pocket that could be subjected to a future lead optimization study. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The effect of degree of immersion upon learning performance in virtual reality simulations for medical education.

PubMed

Gutiérrez, Fátima; Pierce, Jennifer; Vergara, Víctor M; Coulter, Robert; Saland, Linda; Caudell, Thomas P; Goldsmith, Timothy E; Alverson, Dale C

2007-01-01

Simulations are being used in education and training to enhance understanding, improve performance, and assess competence. However, it is important to measure the performance of these simulations as learning and training tools. This study examined and compared knowledge acquisition using a knowledge structure design. The subjects were first-year medical students at The University of New Mexico School of Medicine. One group used a fully immersed virtual reality (VR) environment using a head mounted display (HMD) and another group used a partially immersed (computer screen) VR environment. The study aims were to determine whether there were significant differences between the two groups as measured by changes in knowledge structure before and after the VR simulation experience. The results showed that both groups benefited from the VR simulation training as measured by the significant increased similarity to the expert knowledge network after the training experience. However, the immersed group showed a significantly higher gain than the partially immersed group. This study demonstrated a positive effect of VR simulation on learning as reflected by improvements in knowledge structure but an enhanced effect of full-immersion using a HMD vs. a screen-based VR system.

Comparative analyses of structural features and scaffold diversity for purchasable compound libraries.

PubMed

Shang, Jun; Sun, Huiyong; Liu, Hui; Chen, Fu; Tian, Sheng; Pan, Peichen; Li, Dan; Kong, Dexin; Hou, Tingjun

2017-04-21

Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign is quite important to improve the success rates and avoid wasting resources in later experimental phases. Analysis of the structural features and molecular diversity for different screening libraries can provide valuable information to the decision making process when selecting screening libraries for VS. In this study, the structural features and scaffold diversity of eleven purchasable screening libraries and Traditional Chinese Medicine Compound Database (TCMCD) were analyzed and compared. Their scaffold diversity represented by the Murcko frameworks and Level 1 scaffolds was characterized by the scaffold counts and cumulative scaffold frequency plots, and visualized by Tree Maps and SAR Maps. The analysis demonstrates that, based on the standardized subsets with similar molecular weight distributions, Chembridge, ChemicalBlock, Mucle, TCMCD and VitasM are more structurally diverse than the others. Compared with all purchasable screening libraries, TCMCD has the highest structural complexity indeed but more conservative molecular scaffolds. Moreover, we found that some representative scaffolds were important components of drug candidates against different drug targets, such as kinases and guanosine-binding protein coupled receptors, and therefore the molecules containing pharmacologically important scaffolds found in screening libraries might be potential inhibitors against the relevant targets. This study may provide valuable perspective on which purchasable compound libraries are better for you to screen. Graphical abstract Selecting diverse compound libraries with scaffold analyses.

DOVIS 2.0: An Efficient and Easy to Use Parallel Virtual Screening Tool Based on AutoDock 4.0

DTIC Science & Technology

2008-09-08

under the GNU General Public License. Background Molecular docking is a computational method that pre- dicts how a ligand interacts with a receptor...Hence, it is an important tool in studying receptor-ligand interactions and plays an essential role in drug design. Particularly, molecular docking has...libraries from OpenBabel and setup a molecular data structure as a C++ object in our program. This makes handling of molecular structures (e.g., atoms

Molecular graph convolutions: moving beyond fingerprints

NASA Astrophysics Data System (ADS)

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Molecular graph convolutions: moving beyond fingerprints.

PubMed

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-08-01

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Novel approaches for targeting the adenosine A2A receptor.

PubMed

Yuan, Gengyang; Gedeon, Nicholas G; Jankins, Tanner C; Jones, Graham B

2015-01-01

The adenosine A2A receptor (A2AR) represents a drug target for a wide spectrum of diseases. Approaches for targeting this membrane-bound protein have been greatly advanced by new stabilization techniques. The resulting X-ray crystal structures and subsequent analyses provide deep insight to the A2AR from both static and dynamic perspectives. Application of this, along with other biophysical methods combined with fragment-based drug design (FBDD), has become a standard approach in targeting A2AR. Complementarities of in silico screening based- and biophysical screening assisted- FBDD are likely to feature in future approaches in identifying novel ligands against this key receptor. This review describes evolution of the above approaches for targeting A2AR and highlights key modulators identified. It includes a review of: adenosine receptor structures, homology modeling, X-ray structural analysis, rational drug design, biophysical methods, FBDD and in silico screening. As a drug target, the A2AR is attractive as its function plays a role in a wide spectrum of diseases including oncologic, inflammatory, Parkinson's and cardiovascular diseases. Although traditional approaches such as high-throughput screening and homology model-based virtual screening (VS) have played a role in targeting A2AR, numerous shortcomings have generally restricted their applications to specific ligand families. Using stabilization methods for crystallization, X-ray structures of A2AR have greatly accelerated drug discovery and influenced development of biophysical-in silico hybrid screening methods. Application of these new methods to other ARs and G-protein-coupled receptors is anticipated in the future.

Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex.

PubMed

Mishra, Vinita; Pathak, Chandramani

2018-05-29

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

Tautomer preference in PDB complexes and its impact on structure-based drug discovery.

PubMed

Milletti, Francesca; Vulpetti, Anna

2010-06-28

Tautomer enrichment is a key step of ligand preparation prior to virtual screening. In this paper, we have investigated how tautomer preference in various media (water, gas phase, and crystal) compares to tautomer preference at the active site of the protein by analyzing the different possible H-bonding contacts for a set of 13 tautomeric structures. In addition, we have explored the impact of four different protocols for the enumeration of tautomers in virtual screening by using Flap, Glide, and Gold as docking tools on seven targets of the DUD data set. Excluding targets in which the binding does not involve tautomeric atoms (HSP90, p38, and VEGFR2), we found that the average receiver operating characteristic curve enrichment at 10% was 0.25 (Gold), 0.24 (Glide), and 0.50 (Flap) by considering only tautomers predicted to be unstable in water versus 0.41 (Gold), 0.56 (Glide), 0.51 (Flap) by limiting the enumeration process only to the predicted most stable tautomer. The inclusion of all tautomers (stable and unstable) yielded slightly poorer results than considering only the most stable form in water.

Ligand efficiency based approach for efficient virtual screening of compound libraries.

PubMed

Ke, Yi-Yu; Coumar, Mohane Selvaraj; Shiao, Hui-Yi; Wang, Wen-Chieh; Chen, Chieh-Wen; Song, Jen-Shin; Chen, Chun-Hwa; Lin, Wen-Hsing; Wu, Szu-Huei; Hsu, John T A; Chang, Chung-Ming; Hsieh, Hsing-Pang

2014-08-18

Here we report for the first time the use of fit quality (FQ), a ligand efficiency (LE) based measure for virtual screening (VS) of compound libraries. The LE based VS protocol was used to screen an in-house database of 125,000 compounds to identify aurora kinase A inhibitors. First, 20 known aurora kinase inhibitors were docked to aurora kinase A crystal structure (PDB ID: 2W1C); and the conformations of docked ligand were used to create a pharmacophore (PH) model. The PH model was used to screen the database compounds, and rank (PH rank) them based on the predicted IC50 values. Next, LE_Scale, a weight-dependant LE function, was derived from 294 known aurora kinase inhibitors. Using the fit quality (FQ = LE/LE_Scale) score derived from the LE_Scale function, the database compounds were reranked (PH_FQ rank) and the top 151 (0.12% of database) compounds were assessed for aurora kinase A inhibition biochemically. This VS protocol led to the identification of 7 novel hits, with compound 5 showing aurora kinase A IC50 = 1.29 μM. Furthermore, testing of 5 against a panel of 31 kinase reveals that it is selective toward aurora kinase A & B, with <50% inhibition for other kinases at 10 μM concentrations and is a suitable candidate for further development. Incorporation of FQ score in the VS protocol not only helped identify a novel aurora kinase inhibitor, 5, but also increased the hit rate of the VS protocol by improving the enrichment factor (EF) for FQ based screening (EF = 828), compared to PH based screening (EF = 237) alone. The LE based VS protocol disclosed here could be applied to other targets for hit identification in an efficient manner. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Discovery of novel and cardioselective diltiazem-like calcium channel blockers via virtual screening.

PubMed

Carosati, Emanuele; Budriesi, Roberta; Ioan, Pierfranco; Ugenti, Maria P; Frosini, Maria; Fusi, Fabio; Corda, Gaetano; Cosimelli, Barbara; Spinelli, Domenico; Chiarini, Alberto; Cruciani, Gabriele

2008-09-25

With the effort to discover new chemotypes blocking L-type calcium channels (LTCCs), ligand-based virtual screening was applied with a specific interest toward the diltiazem binding site. Roughly 50000 commercially available compounds served as a database for screening. The filtering through predicted pharmacokinetic properties and structural requirements reduced the initial database to a few compounds for which the similarity was calculated toward two template molecules, diltiazem and 4-chloro-Ncyclopropyl- N-(4-piperidinyl)benzene-sulfonamide, the most interesting hit of a previous screening experiment. For 18 compounds, inotropic and chronotropic activity as well as the vasorelaxant effect on guinea pig were studied "in vitro", and for the most promising, binding studies to the diltiazem site were carried out. The procedure yielded several hits, confirming in silico techniques to be useful for finding new chemotypes. In particular, N-[2-(dimethylamino)ethyl]-3-hydroxy-2-naphthamide, N,Ndimethyl- N'-(2-pyridin-3-ylquinolin-4-yl)ethane-1,2-diamine, 2-[(4-chlorophenyl)(pyridin-2-yl)methoxy]- N,N-dimethylethanamine (carbinoxamine), and 7-[2-(diethylamino)ethoxy]-2H-chromen-2-one revealed interesting activity and binding to the benzothiazepine site.

Discovery of novel Trypanosoma brucei phosphodiesterase B1 inhibitors by virtual screening against the unliganded TbrPDEB1 crystal structure

PubMed Central

Jansen, Chimed; Wang, Huanchen; Kooistra, Albert J.; de Graaf, Chris; Orrling, Kristina; Tenor, Hermann; Seebeck, Thomas; Bailey, David; de Esch, Iwan J.P.; Ke, Hengming; Leurs, Rob

2013-01-01

Trypanosoma brucei cyclic nucleotide phosphodiesterase B1 (TbrPDEB1) and TbrPDEB2 have recently been validated as new therapeutic targets for human African Trypanosomiasis by both genetic and pharmacological means. In this study we report the crystal structure of the catalytic domain of the unliganded TbrPDEB1 and its use for the in silico screening for new TbrPDEB1 inhibitors with novel scaffolds. The TbrPDEB1 crystal structure shows the characteristic folds of human PDE enzymes, but also contains the parasite-specific P-pocket found in the structures of Leishmania major PDEB1 and Trypanosoma cruzi PDEC. The unliganded TbrPDEB1 X-ray structure was subjected to a structure-based in silico screening approach that combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. This approach identified, six novel TbrPDEB1 inhibitors with IC50 values of 10–80 μM, which may be further optimized as potential selective TbrPDEB inhibitors. PMID:23409953

Pharmacophore based approach to design inhibitors in crustaceans: an insight into the molt inhibition response to the receptor guanylyl cyclase.

PubMed

Shrivastava, Sajal; Princy, S Adline

2014-04-01

The first set of competitive inhibitors of molt inhibiting hormone (MIH) has been developed using the effective approaches such as Hip-Hop, virtual screening and manual alterations. Moreover, the conserved residues at 71 and 72 positions in the molt inhibiting hormone is known to be significant for selective inhibition of ecdysteroidogenesis; thus, the information from mutation and solution structure were used to generate common pharmacophore features. The geometry of the final six-feature pharmacophore was also found to be consistent with the homology-modeled MIH structures from various other decapod crustaceans. The Hypo-1, comprising six features hypothesis was carefully selected as a best pharmacophore model for virtual screening created on the basis of rank score and cluster processes. The hypothesis was validated and the database was virtually screened using this 3D query and the compounds were then manually altered to enhance the fit value. The hits obtained were further filtered for drug-likeness, which is expressed as physicochemical properties that contribute to favorable ADME/Tox profiles to eliminate the molecules exhibit toxicity and poor pharmacokinetics. In conclusion, the higher fit values of CI-1 (4.6), CI-4 (4.9) and CI-7 (4.2) in conjunction with better pharmacokinetic profile made these molecules practically helpful tool to increase production by accelerating molt in crustaceans. The use of feeding sub-therapeutic dosages of these growth enhancers can be very effectively implemented and certainly turn out to be a vital part of emerging nutritional strategies for economically important crustacean livestock.

[Computational chemistry in structure-based drug design].

PubMed

Cao, Ran; Li, Wei; Sun, Han-Zi; Zhou, Yu; Huang, Niu

2013-07-01

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.

Tandem application of ligand-based virtual screening and G4-OAS assay to identify novel G-quadruplex-targeting chemotypes.

PubMed

Musumeci, Domenica; Amato, Jussara; Zizza, Pasquale; Platella, Chiara; Cosconati, Sandro; Cingolani, Chiara; Biroccio, Annamaria; Novellino, Ettore; Randazzo, Antonio; Giancola, Concetta; Pagano, Bruno; Montesarchio, Daniela

2017-05-01

G-quadruplex (G4) structures are key elements in the regulation of cancer cell proliferation and their targeting is deemed to be a promising strategy in anticancer therapy. A tandem application of ligand-based virtual screening (VS) calculations together with the experimental G-quadruplex on Oligo Affinity Support (G4-OAS) assay was employed to discover novel G4-targeting compounds. The interaction of the selected compounds with the investigated G4 in solution was analysed through a series of biophysical techniques and their biological activity investigated by immunofluorescence and MTT assays. A focused library of 60 small molecules, designed as putative G4 groove binders, was identified through the VS. The G4-OAS experimental screening led to the selection of 7 ligands effectively interacting with the G4-forming human telomeric DNA. Evaluation of the biological activity of the selected compounds showed that 3 ligands of this sub-library induced a marked telomere-localized DNA damage response in human tumour cells. The combined application of virtual and experimental screening tools proved to be a successful strategy to identify new bioactive chemotypes able to target the telomeric G4 DNA. These compounds may represent useful leads for the development of more potent and selective G4 ligands. Expanding the repertoire of the available G4-targeting chemotypes with improved physico-chemical features, in particular aiming at the discovery of novel, selective G4 telomeric ligands, can help in developing effective anti-cancer drugs with fewer side effects. This article is part of a Special Issue entitled "G-quadruplex" Guest Editor: Dr. Concetta Giancola and Dr. Daniela Montesarchio. Copyright © 2017 Elsevier B.V. All rights reserved.

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2.

PubMed

Lu, Pinyi; Hontecillas, Raquel; Horne, William T; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R; Lewis, Stephanie N; Bassaganya-Riera, Josep

2012-01-01

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

PubMed Central

Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep

2012-01-01

Background Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates. PMID:22509338

Graph Kernels for Molecular Similarity.

PubMed

Rupp, Matthias; Schneider, Gisbert

2010-04-12

Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase-a potential target to reduce Fusarium head blight disease.

PubMed

Bresso, E; Leroux, V; Urban, M; Hammond-Kosack, K E; Maigret, B; Martins, N F

2016-07-01

Fusarium head blight (FHB) is one of the most destructive diseases of wheat and other cereals worldwide. During infection, the Fusarium fungi produce mycotoxins that represent a high risk to human and animal health. Developing small-molecule inhibitors to specifically reduce mycotoxin levels would be highly beneficial since current treatments unspecifically target the Fusarium pathogen. Culmorin possesses a well-known important synergistically virulence role among mycotoxins, and longiborneol synthase appears to be a key enzyme for its synthesis, thus making longiborneol synthase a particularly interesting target. This study aims to discover potent and less toxic agrochemicals against FHB. These compounds would hamper culmorin synthesis by inhibiting longiborneol synthase. In order to select starting molecules for further investigation, we have conducted a structure-based virtual screening investigation. A longiborneol synthase structural model is first built using homology modeling, followed by molecular dynamics simulations that provided the required input for a protein-ligand ensemble docking procedure. From this strategy, the three most interesting compounds (hits) were selected among the 25 top-ranked docked compounds from a library of 15,000 drug-like compounds. These putative inhibitors of longiborneol synthase provide a sound starting point for further studies involving molecular modeling coupled to biochemical experiments. This process could eventually lead to the development of novel approaches to reduce mycotoxin contamination in harvested grain.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs.

PubMed

Dai, Shao-Xing; Li, Gong-Hua; Gao, Yue-Dong; Huang, Jing-Fei

2016-02-01

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

DG-AMMOS: a new tool to generate 3d conformation of small molecules using distance geometry and automated molecular mechanics optimization for in silico screening.

PubMed

Lagorce, David; Pencheva, Tania; Villoutreix, Bruno O; Miteva, Maria A

2009-11-13

Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats. Here, we describe the new open source program DG-AMMOS which allows the generation of the 3D conformation of small molecules using Distance Geometry and their energy minimization via Automated Molecular Mechanics Optimization. The program is validated on the Astex dataset, the ChemBridge Diversity database and on a number of small molecules with known crystal structures extracted from the Cambridge Structural Database. A comparison with the free program Balloon and the well-known commercial program Omega generating the 3D of small molecules is carried out. The results show that the new free program DG-AMMOS is a very efficient 3D structure generator engine. DG-AMMOS provides fast, automated and reliable access to the generation of 3D conformation of small molecules and facilitates the preparation of a compound collection prior to high-throughput virtual screening computations. The validation of DG-AMMOS on several different datasets proves that generated structures are generally of equal quality or sometimes better than structures obtained by other tested methods.

Evaluation of a novel virtual screening strategy using receptor decoy binding sites.

PubMed

Patel, Hershna; Kukol, Andreas

2016-08-23

Virtual screening is used in biomedical research to predict the binding affinity of a large set of small organic molecules to protein receptor targets. This report shows the development and evaluation of a novel yet straightforward attempt to improve this ranking in receptor-based molecular docking using a receptor-decoy strategy. This strategy includes defining a decoy binding site on the receptor and adjusting the ranking of the true binding-site virtual screen based on the decoy-site screen. The results show that by docking against a receptor-decoy site with Autodock Vina, improved Receiver Operator Characteristic Enrichment (ROCE) was achieved for 5 out of fifteen receptor targets investigated, when up to 15 % of a decoy site rank list was considered. No improved enrichment was seen for 7 targets, while for 3 targets the ROCE was reduced. The extent to which this strategy can effectively improve ligand prediction is dependent on the target receptor investigated.

Targeting Dengue Virus NS-3 Helicase by Ligand based Pharmacophore Modeling and Structure based Virtual Screening

NASA Astrophysics Data System (ADS)

Halim, Sobia A.; Khan, Shanza; Khan, Ajmal; Wadood, Abdul; Mabood, Fazal; Hussain, Javid; Al-Harrasi, Ahmed

2017-10-01

Dengue fever is an emerging public health concern, with several million viral infections occur annually, for which no effective therapy currently exist. Non-structural protein 3 (NS-3) Helicase encoded by the dengue virus (DENV) is considered as a potential drug target to design new and effective drugs against dengue. Helicase is involved in unwinding of dengue RNA. This study was conducted to design new NS-3 Helicase inhibitor by in silico ligand- and structure based approaches. Initially ligand-based pharmacophore model was generated that was used to screen a set of 1201474 compounds collected from ZINC Database. The compounds matched with the pharmacophore model were docked into the active site of NS-3 helicase. Based on docking scores and binding interactions, twenty five compounds are suggested to be potential inhibitors of NS3 Helicase. The pharmacokinetic properties of these hits were predicted. The selected hits revealed acceptable ADMET properties. This study identified potential inhibitors of NS-3 Helicase in silico, and can be helpful in the treatment of Dengue.

Assessment of wheelchair driving performance in a virtual reality-based simulator

PubMed Central

Mahajan, Harshal P.; Dicianno, Brad E.; Cooper, Rory A.; Ding, Dan

2013-01-01

Objective To develop a virtual reality (VR)-based simulator that can assist clinicians in performing standardized wheelchair driving assessments. Design A completely within-subjects repeated measures design. Methods Participants drove their wheelchairs along a virtual driving circuit modeled after the Power Mobility Road Test (PMRT) and in a hallway with decreasing width. The virtual simulator was displayed on computer screen and VR screens and participants interacted with it using a set of instrumented rollers and a wheelchair joystick. Driving performances of participants were estimated and compared using quantitative metrics from the simulator. Qualitative ratings from two experienced clinicians were used to estimate intra- and inter-rater reliability. Results Ten regular wheelchair users (seven men, three women; mean age ± SD, 39.5 ± 15.39 years) participated. The virtual PMRT scores from the two clinicians show high inter-rater reliability (78–90%) and high intra-rater reliability (71–90%) for all test conditions. More research is required to explore user preferences and effectiveness of the two control methods (rollers and mathematical model) and the display screens. Conclusions The virtual driving simulator seems to be a promising tool for wheelchair driving assessment that clinicians can use to supplement their real-world evaluations. PMID:23820148

Computational redesign of bacterial biotin carboxylase inhibitors using structure-based virtual screening of combinatorial libraries.

PubMed

Brylinski, Michal; Waldrop, Grover L

2014-04-02

As the spread of antibiotic resistant bacteria steadily increases, there is an urgent need for new antibacterial agents. Because fatty acid synthesis is only used for membrane biogenesis in bacteria, the enzymes in this pathway are attractive targets for antibacterial agent development. Acetyl-CoA carboxylase catalyzes the committed and regulated step in fatty acid synthesis. In bacteria, the enzyme is composed of three distinct protein components: biotin carboxylase, biotin carboxyl carrier protein, and carboxyltransferase. Fragment-based screening revealed that amino-oxazole inhibits biotin carboxylase activity and also exhibits antibacterial activity against Gram-negative organisms. In this report, we redesigned previously identified lead inhibitors to expand the spectrum of bacteria sensitive to the amino-oxazole derivatives by including Gram-positive species. Using 9,411 small organic building blocks, we constructed a diverse combinatorial library of 1.2×10⁸ amino-oxazole derivatives. A subset of 9×10⁶ of these compounds were subjected to structure-based virtual screening against seven biotin carboxylase isoforms using similarity-based docking by eSimDock. Potentially broad-spectrum antibiotic candidates were selected based on the consensus ranking by several scoring functions including non-linear statistical models implemented in eSimDock and traditional molecular mechanics force fields. The analysis of binding poses of the top-ranked compounds docked to biotin carboxylase isoforms suggests that: (1) binding of the amino-oxazole anchor is stabilized by a network of hydrogen bonds to residues 201, 202 and 204; (2) halogenated aromatic moieties attached to the amino-oxazole scaffold enhance interactions with a hydrophobic pocket formed by residues 157, 169, 171 and 203; and (3) larger substituents reach deeper into the binding pocket to form additional hydrogen bonds with the side chains of residues 209 and 233. These structural insights into drug-biotin carboxylase interactions will be tested experimentally in in vitro and in vivo systems to increase the potency of amino-oxazole inhibitors towards both Gram-negative as well as Gram-positive species.

Visualizing vascular structures in virtual environments

NASA Astrophysics Data System (ADS)

Wischgoll, Thomas

2013-01-01

In order to learn more about the cause of coronary heart diseases and develop diagnostic tools, the extraction and visualization of vascular structures from volumetric scans for further analysis is an important step. By determining a geometric representation of the vasculature, the geometry can be inspected and additional quantitative data calculated and incorporated into the visualization of the vasculature. To provide a more user-friendly visualization tool, virtual environment paradigms can be utilized. This paper describes techniques for interactive rendering of large-scale vascular structures within virtual environments. This can be applied to almost any virtual environment configuration, such as CAVE-type displays. Specifically, the tools presented in this paper were tested on a Barco I-Space and a large 62x108 inch passive projection screen with a Kinect sensor for user tracking.

Multicomplex-based pharmacophore-guided 3D-QSAR studies of N-substituted 2'-(aminoaryl)benzothiazoles as Aurora-A inhibitors.

PubMed

He, Gu; Qiu, Minghua; Li, Rui; Ouyang, Liang; Wu, Fengbo; Song, Xiangrong; Cheng, Li; Xiang, Mingli; Yu, Luoting

2012-06-01

Aurora-A has been known as one of the most important targets for cancer therapy, and some Aurora-A inhibitors have entered clinical trails. In this study, combination of the ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship of known Aurora-A inhibitors, and multicomplex-based pharmacophore-guided method has been suggested to generate a comprehensive pharmacophore of Aurora-A kinase based on a collection of crystal structures of Aurora-A-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 37 structurally diverse N-substituted 2'-(aminoaryl)benzothiazoles derivatives. The quantitative structure-activity relationship analyses have been performed on these Aurora-A inhibitors based on multicomplex-based pharmacophore-guided alignment. These results may provide important information for further design and virtual screening of novel Aurora-A inhibitors. © 2012 John Wiley & Sons A/S.

Carboxylic acid derivatives display potential selectivity for human histone deacetylase 6: Structure-based virtual screening, molecular docking and dynamics simulation studies.

PubMed

Uba, Abdullahi Ibrahim; Yelekçi, Kemal

2018-08-01

Human histone deacetylase 6 (HDAC6) has been shown to play a major role in oncogenic cell transformation via deacetylation of α-tubulin, making it a viable target of anticancer drug design and development. The crystal structure of HDAC6 catalytic domain 2 has been recently made available, providing avenues for structure-based drug design campaign. Here, in our continuous effort to identify potentially selective HDAC6 inhibitors, structure-based virtual screening of ∼72 461 compounds was carried out using Autodock Vina. The top 100 compounds with calculated ΔG < -10 kcal/mol were manually inspected for binding mode orientation. Furthermore, the top 20 compounds with reasonable binding modes were evaluated for selectivity by further docking against HDAC6 and HDAC7 using Autodock4. Four compounds with a carboxylic fragment, displayed potential selectivity for HDAC6 over HDAC7, and were found to have good druglike and ADMET properties. Their docking complexes were then submitted to 10 ns-molecular dynamics (MD) simulation using nanoscale MD (NAMD) software, to examine the stability of ligand binding modes. These predicted inhibitors remained bound to HDAC6 in the presence of water and ions, and the root-mean-square deviation (RMSD), radius of gyration (Rg) and nonbond distance (protein-ligand) profiles suggested that they might be stable over time of the simulation. This study may provide scaffolds for further lead optimization towards the design of HDAC6 inhibitors with improved selectivity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

PubMed

Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

2018-02-12

We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets.

PubMed

Feinstein, Wei P; Brylinski, Michal

2015-01-01

Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated. In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite. A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins. This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size. Graphical AbstractWe developed a procedure to optimize the box size in molecular docking calculations. Left panel shows the predicted binding pose of NADP (green sticks) compared to the experimental complex structure of human aldose reductase (blue sticks) using a default protocol. Right panel shows the docking accuracy using an optimized box size.

NALDB: nucleic acid ligand database for small molecules targeting nucleic acid.

PubMed

Kumar Mishra, Subodh; Kumar, Amit

2016-01-01

Nucleic acid ligand database (NALDB) is a unique database that provides detailed information about the experimental data of small molecules that were reported to target several types of nucleic acid structures. NALDB is the first ligand database that contains ligand information for all type of nucleic acid. NALDB contains more than 3500 ligand entries with detailed pharmacokinetic and pharmacodynamic information such as target name, target sequence, ligand 2D/3D structure, SMILES, molecular formula, molecular weight, net-formal charge, AlogP, number of rings, number of hydrogen bond donor and acceptor, potential energy along with their Ki, Kd, IC50 values. All these details at single platform would be helpful for the development and betterment of novel ligands targeting nucleic acids that could serve as a potential target in different diseases including cancers and neurological disorders. With maximum 255 conformers for each ligand entry, our database is a multi-conformer database and can facilitate the virtual screening process. NALDB provides powerful web-based search tools that make database searching efficient and simplified using option for text as well as for structure query. NALDB also provides multi-dimensional advanced search tool which can screen the database molecules on the basis of molecular properties of ligand provided by database users. A 3D structure visualization tool has also been included for 3D structure representation of ligands. NALDB offers an inclusive pharmacological information and the structurally flexible set of small molecules with their three-dimensional conformers that can accelerate the virtual screening and other modeling processes and eventually complement the nucleic acid-based drug discovery research. NALDB can be routinely updated and freely available on bsbe.iiti.ac.in/bsbe/naldb/HOME.php. Database URL: http://bsbe.iiti.ac.in/bsbe/naldb/HOME.php. © The Author(s) 2016. Published by Oxford University Press.

West Nile Virus Drug Discovery

PubMed Central

Lim, Siew Pheng; Shi, Pei-Yong

2013-01-01

The outbreak of West Nile virus (WNV) in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development. PMID:24300672

West Nile virus drug discovery.

PubMed

Lim, Siew Pheng; Shi, Pei-Yong

2013-12-03

The outbreak of West Nile virus (WNV) in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development.

A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors.

PubMed

Hu, Huabin; Xia, Jie; Wang, Dongmei; Wang, Xiang Simon; Wu, Song

2017-01-18

Histone deacetylase 3 (HDAC3) has been recently identified as a potential target for the treatment of cancer and other diseases, such as chronic inflammation, neurodegenerative diseases, and diabetes. Virtual screening (VS) is currently a routine technique for hit identification, but its success depends on rational development of VS strategies. To facilitate this process, we applied our previously released benchmarking dataset, i.e., MUBD-HDAC3 to the evaluation of structure-based VS (SBVS) and ligand-based VS (LBVS) combinatorial approaches. We have identified FRED (Chemgauss4) docking against a structural model of HDAC3, i.e., SAHA-3 generated by a computationally inexpensive "flexible docking", as the best SBVS approach and a common feature pharmacophore model, i.e., Hypo1 generated by Catalyst/HipHop as the optimal model for LBVS. We then developed a pipeline that was composed of Hypo1, FRED (Chemgauss4), and SAHA-3 sequentially, and demonstrated that it was superior to other combinations in terms of ligand enrichment. In summary, we present the first highly-validated, rationally-designed VS strategy specific to HDAC3 inhibitor discovery. The constructed pipeline is publicly accessible for the scientific community to identify novel HDAC3 inhibitors in a time-efficient and cost-effective way.

A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors

PubMed Central

Hu, Huabin; Xia, Jie; Wang, Dongmei; Wang, Xiang Simon; Wu, Song

2017-01-01

Histone deacetylase 3 (HDAC3) has been recently identified as a potential target for the treatment of cancer and other diseases, such as chronic inflammation, neurodegenerative diseases, and diabetes. Virtual screening (VS) is currently a routine technique for hit identification, but its success depends on rational development of VS strategies. To facilitate this process, we applied our previously released benchmarking dataset, i.e., MUBD-HDAC3 to the evaluation of structure-based VS (SBVS) and ligand-based VS (LBVS) combinatorial approaches. We have identified FRED (Chemgauss4) docking against a structural model of HDAC3, i.e., SAHA-3 generated by a computationally inexpensive “flexible docking”, as the best SBVS approach and a common feature pharmacophore model, i.e., Hypo1 generated by Catalyst/HipHop as the optimal model for LBVS. We then developed a pipeline that was composed of Hypo1, FRED (Chemgauss4), and SAHA-3 sequentially, and demonstrated that it was superior to other combinations in terms of ligand enrichment. In summary, we present the first highly-validated, rationally-designed VS strategy specific to HDAC3 inhibitor discovery. The constructed pipeline is publicly accessible for the scientific community to identify novel HDAC3 inhibitors in a time-efficient and cost-effective way. PMID:28106794

Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition.

PubMed

Didziapetris, Remigijus; Dapkunas, Justas; Sazonovas, Andrius; Japertas, Pranas

2010-11-01

A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.

Developing Hypothetical Inhibition Mechanism of Novel Urea Transporter B Inhibitor

NASA Astrophysics Data System (ADS)

Li, Min; Tou, Weng Ieong; Zhou, Hong; Li, Fei; Ren, Huiwen; Chen, Calvin Yu-Chian; Yang, Baoxue

2014-07-01

Urea transporter B (UT-B) is a membrane channel protein that specifically transports urea. UT-B null mouse exhibited urea selective urine concentrating ability deficiency, which suggests the potential clinical applications of the UT-B inhibitors as novel diuretics. Primary high-throughput virtual screening (HTVS) of 50000 small-molecular drug-like compounds identified 2319 hit compounds. These 2319 compounds were screened by high-throughput screening using an erythrocyte osmotic lysis assay. Based on the pharmacological data, putative UT-B binding sites were identified by structure-based drug design and validated by ligand-based and QSAR model. Additionally, UT-B structural and functional characteristics under inhibitors treated and untreated conditions were simulated by molecular dynamics (MD). As the result, we identified four classes of compounds with UT-B inhibitory activity and predicted a human UT-B model, based on which computative binding sites were identified and validated. A novel potential mechanism of UT-B inhibitory activity was discovered by comparing UT-B from different species. Results suggest residue PHE198 in rat and mouse UT-B might block the inhibitor migration pathway. Inhibitory mechanisms of UT-B inhibitors and the functions of key residues in UT-B were proposed. The binding site analysis provides a structural basis for lead identification and optimization of UT-B inhibitors.

Scoring ligand similarity in structure-based virtual screening.

PubMed

Zavodszky, Maria I; Rohatgi, Anjali; Van Voorst, Jeffrey R; Yan, Honggao; Kuhn, Leslie A

2009-01-01

Scoring to identify high-affinity compounds remains a challenge in virtual screening. On one hand, protein-ligand scoring focuses on weighting favorable and unfavorable interactions between the two molecules. Ligand-based scoring, on the other hand, focuses on how well the shape and chemistry of each ligand candidate overlay on a three-dimensional reference ligand. Our hypothesis is that a hybrid approach, using ligand-based scoring to rank dockings selected by protein-ligand scoring, can ensure that high-ranking molecules mimic the shape and chemistry of a known ligand while also complementing the binding site. Results from applying this approach to screen nearly 70 000 National Cancer Institute (NCI) compounds for thrombin inhibitors tend to support the hypothesis. EON ligand-based ranking of docked molecules yielded the majority (4/5) of newly discovered, low to mid-micromolar inhibitors from a panel of 27 assayed compounds, whereas ranking docked compounds by protein-ligand scoring alone resulted in one new inhibitor. Since the results depend on the choice of scoring function, an analysis of properties was performed on the top-scoring docked compounds according to five different protein-ligand scoring functions, plus EON scoring using three different reference compounds. The results indicate that the choice of scoring function, even among scoring functions measuring the same types of interactions, can have an unexpectedly large effect on which compounds are chosen from screening. Furthermore, there was almost no overlap between the top-scoring compounds from protein-ligand versus ligand-based scoring, indicating the two approaches provide complementary information. Matchprint analysis, a new addition to the SLIDE (Screening Ligands by Induced-fit Docking, Efficiently) screening toolset, facilitated comparison of docked molecules' interactions with those of known inhibitors. The majority of interactions conserved among top-scoring compounds for a given scoring function, and from the different scoring functions, proved to be conserved interactions in known inhibitors. This was particularly true in the S1 pocket, which was occupied by all the docked compounds. (c) 2009 John Wiley & Sons, Ltd.

Enhancing the Sensitivity of Pharmacophore-Based Virtual Screening by Incorporating Customized ZBG Features: A Case Study Using Histone Deacetylase 8.

PubMed

Hou, Xuben; Du, Jintong; Liu, Renshuai; Zhou, Yi; Li, Minyong; Xu, Wenfang; Fang, Hao

2015-04-27

As key regulators of epigenetic regulation, human histone deacetylases (HDACs) have been identified as drug targets for the treatment of several cancers. The proper recognition of zinc-binding groups (ZBGs) will help improve the accuracy of virtual screening for novel HDAC inhibitors. Here, we developed a high-specificity ZBG-based pharmacophore model for HDAC8 inhibitors by incorporating customized ZBG features. Subsequently, pharmacophore-based virtual screening led to the discovery of three novel HDAC8 inhibitors with low micromole IC50 values (1.8-1.9 μM). Further studies demonstrated that compound H8-A5 was selective for HDAC8 over HDAC 1/4 and showed antiproliferation activity in MDA-MB-231 cancer cells. Molecular docking and molecular dynamic studies suggested a possible binding mode for H8-A5, which provides a good starting point for the development of HDAC8 inhibitors in cancer treatment.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

PubMed

Speck-Planche, Alejandro; Cordeiro, M N D S

2014-02-10

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Performance of machine-learning scoring functions in structure-based virtual screening.

PubMed

Wójcikowski, Maciej; Ballester, Pedro J; Siedlecki, Pawel

2017-04-25

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and -0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).

Molecular graph convolutions: moving beyond fingerprints

PubMed Central

Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

2016-01-01

Molecular “fingerprints” encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement. PMID:27558503

Dockres: a computer program that analyzes the output of virtual screening of small molecules

PubMed Central

2010-01-01

Background This paper describes a computer program named Dockres that is designed to analyze and summarize results of virtual screening of small molecules. The program is supplemented with utilities that support the screening process. Foremost among these utilities are scripts that run the virtual screening of a chemical library on a large number of processors in parallel. Methods Dockres and some of its supporting utilities are written Fortran-77; other utilities are written as C-shell scripts. They support the parallel execution of the screening. The current implementation of the program handles virtual screening with Autodock-3 and Autodock-4, but can be extended to work with the output of other programs. Results Analysis of virtual screening by Dockres led to both active and selective lead compounds. Conclusions Analysis of virtual screening was facilitated and enhanced by Dockres in both the authors' laboratories as well as laboratories elsewhere. PMID:20205801

Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis.

PubMed

Zhu, Tian; Cao, Shuyi; Su, Pin-Chih; Patel, Ram; Shah, Darshan; Chokshi, Heta B; Szukala, Richard; Johnson, Michael E; Hevener, Kirk E

2013-09-12

A critical analysis of virtual screening results published between 2007 and 2011 was performed. The activity of reported hit compounds from over 400 studies was compared to their hit identification criteria. Hit rates and ligand efficiencies were calculated to assist in these analyses, and the results were compared with factors such as the size of the virtual library and the number of compounds tested. A series of promiscuity, druglike, and ADMET filters were applied to the reported hits to assess the quality of compounds reported, and a careful analysis of a subset of the studies that presented hit optimization was performed. These data allowed us to make several practical recommendations with respect to selection of compounds for experimental testing, definition of hit identification criteria, and general virtual screening hit criteria to allow for realistic hit optimization. A key recommendation is the use of size-targeted ligand efficiency values as hit identification criteria.

Design and Development of ChemInfoCloud: An Integrated Cloud Enabled Platform for Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Pandit, Deepak; Bhavasar, Arvind; Vyas, Renu

2015-01-01

The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach.

PubMed

Liu, X H; Song, H Y; Zhang, J X; Han, B C; Wei, X N; Ma, X H; Cui, W K; Chen, Y Z

2010-05-17

Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Role of Chemical Reactivity and Transition State Modeling for Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Vyas, Renu; Tambe, Sanjeev S; Radhamohan, Deepthi; Kulkarni, Bhaskar D

2015-01-01

Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product- like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.

Automated Protocol for Large-Scale Modeling of Gene Expression Data.

PubMed

Hall, Michelle Lynn; Calkins, David; Sherman, Woody

2016-11-28

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

Protocols for the Design of Kinase-focused Compound Libraries.

PubMed

Jacoby, Edgar; Wroblowski, Berthold; Buyck, Christophe; Neefs, Jean-Marc; Meyer, Christophe; Cummings, Maxwell D; van Vlijmen, Herman

2018-05-01

Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A ranking method for the concurrent learning of compounds with various activity profiles.

PubMed

Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

2015-01-01

In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

Identification of new benzamide inhibitor against α-subunit of tryptophan synthase from Mycobacterium tuberculosis through structure-based virtual screening, anti-tuberculosis activity and molecular dynamics simulations.

PubMed

Naz, Sadia; Farooq, Umar; Ali, Sajid; Sarwar, Rizwana; Khan, Sara; Abagyan, Ruben

2018-03-13

Multi-drug-resistant tuberculosis and extensively drug-resistant tuberculosis has emerged as global health threat, causing millions of deaths worldwide. Identification of new drug candidates for tuberculosis (TB) by targeting novel and less explored protein targets will be invaluable for antituberculosis drug discovery. We performed structure-based virtual screening of eMolecules database against a homology model of relatively unexplored protein target: the α-subunit of tryptophan synthase (α-TRPS) from Mycobacterium tuberculosis essential for bacterial survival. Based on physiochemical properties analysis and molecular docking, the seven candidate compounds were selected and evaluated through whole cell-based activity against the H37Rv strain of M. tuberculosis. A new Benzamide inhibitor against α-subunit of tryptophan synthase (α-TRPS) from M. tuberculosis has been identified causing 100% growth inhibition at 25 μg/ml and visible bactericidal activity at 6 μg/ml. This benzamide inhibitor displayed a good predicted binding score (-48.24 kcal/mol) with the α-TRPS binding pocket and has logP value (2.95) comparable to Rifampicin. Further refinement of docking results and evaluation of inhibitor-protein complex stability were investigated through Molecular dynamic (MD) simulations studies. Following MD simulations, Root mean square deviation, Root mean square fluctuation and secondary structure analysis confirmed that protein did not unfold and ligand stayed inside the active pocket of protein during the explored time scale. This identified benzamide inhibitor against the α-subunit of TRPS from M. tuberculosis could be considered as candidate for drug discovery against TB and will be further evaluated for enzyme-based inhibition in future studies.

Study on the Mechanisms of Active Compounds in Traditional Chinese Medicine for the Treatment of Influenza Virus by Virtual Screening.

PubMed

Ai, Haixin; Wu, Xuewei; Qi, Mengyuan; Zhang, Li; Hu, Huan; Zhao, Qi; Zhao, Jian; Liu, Hongsheng

2018-06-01

In recent years, new strains of influenza virus such as H7N9, H10N8, H5N6 and H5N8 had continued to emerge. There was an urgent need for discovery of new anti-influenza virus drugs as well as accurate and efficient large-scale inhibitor screening methods. In this study, we focused on six influenza virus proteins that could be anti-influenza drug targets, including neuraminidase (NA), hemagglutinin (HA), matrix protein 1 (M1), M2 proton channel (M2), nucleoprotein (NP) and non-structural protein 1 (NS1). Structure-based molecular docking was utilized to identify potential inhibitors for these drug targets from 13144 compounds in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The results showed that 56 compounds could inhibit more than two drug targets simultaneously. Further, we utilized reverse docking to study the interaction of these compounds with host targets. Finally, the 22 compound inhibitors could stably bind to host targets with high binding free energy. The results showed that the Chinese herbal medicines had a multi-target effect, which could directly inhibit influenza virus by the target viral protein and indirectly inhibit virus by the human target protein. This method was of great value for large-scale virtual screening of new anti-influenza virus compounds.

Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules.

PubMed

Wassermann, Anne Mai; Lounkine, Eugen; Glick, Meir

2013-03-25

Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.

Identification of novel antilipogenic agents targeting fatty acid biosynthesis through structure-based virtual screening.

PubMed

Soulère, Laurent; Alix, Pascaline M; Croze, Marine L; Soulage, Christophe O

2018-04-10

An Asinex Gold Platinium chemical library subset of 12 055 compounds was screened employing docking simulations in the active site of the human FAS KS domain. Among them, 13 compounds were further evaluated for their ability to inhibit fatty acid biosynthesis. Four compounds were found to be active in particular ASN05064661 and ASN05374526 with IC50 values of 6.6 and 10.5 μm, respectively. A binding mode study was further conducted with these two compounds structurally related to benzene sulfonamide and aromatic polyamide. This study showed that they fit tightly with the active site with several interactions, notably with the key residues Cys161, His293, and His331. © 2018 John Wiley & Sons A/S.

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives.

PubMed

Lagarde, Nathalie; Zagury, Jean-François; Montes, Matthieu

2015-07-27

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

Lead identification for the K-Ras protein: virtual screening and combinatorial fragment-based approaches

PubMed Central

Pathan, Akbar Ali Khan; Panthi, Bhavana; Khan, Zahid; Koppula, Purushotham Reddy; Alanazi, Mohammed Saud; Sachchidanand; Parine, Narasimha Reddy; Chourasia, Mukesh

2016-01-01

Objective Kirsten rat sarcoma (K-Ras) protein is a member of Ras family belonging to the small guanosine triphosphatases superfamily. The members of this family share a conserved structure and biochemical properties, acting as binary molecular switches. The guanosine triphosphate-bound active K-Ras interacts with a range of effectors, resulting in the stimulation of downstream signaling pathways regulating cell proliferation, differentiation, and apoptosis. Efforts to target K-Ras have been unsuccessful until now, placing it among high-value molecules against which developing a therapy would have an enormous impact. K-Ras transduces signals when it binds to guanosine triphosphate by directly binding to downstream effector proteins, but in case of guanosine diphosphate-bound conformation, these interactions get disrupted. Methods In the present study, we targeted the nucleotide-binding site in the “on” and “off” state conformations of the K-Ras protein to find out suitable lead compounds. A structure-based virtual screening approach has been used to screen compounds from different databases, followed by a combinatorial fragment-based approach to design the apposite lead for the K-Ras protein. Results Interestingly, the designed compounds exhibit a binding preference for the “off” state over “on” state conformation of K-Ras protein. Moreover, the designed compounds’ interactions are similar to guanosine diphosphate and, thus, could presumably act as a potential lead for K-Ras. The predicted drug-likeness properties of these compounds suggest that these compounds follow the Lipinski’s rule of five and have tolerable absorption, distribution, metabolism, excretion and toxicity values. Conclusion Thus, through the current study, we propose targeting only “off” state conformations as a promising strategy for the design of reversible inhibitors to pharmacologically inhibit distinct conformations of K-Ras protein. PMID:27217775

Discovery of a novel and potent class of F. tularensis enoyl-reductase (FabI) inhibitors by molecular shape and electrostatic matching

PubMed Central

Hevener, Kirk E.; Mehboob, Shahila; Su, Pin-Chih; Truong, Kent; Boci, Teuta; Deng, Jiangping; Ghassemi, Mahmood; Cook, James L.; Johnson, Michael E.

2011-01-01

Enoyl-acyl carrier protein (ACP) reductase, FabI, is a key enzyme in the bacterial fatty acid biosynthesis pathway (FAS II). FabI is an NADH-dependent oxidoreductase that acts to reduce enoyl-ACP substrates in a final step of the pathway. The absence of this enzyme in humans makes it an attractive target for the development of new antibacterial agents. FabI is known to be unresponsive to structure-based design efforts due to a high degree of induced fit and a mobile flexible loop encompassing the active site. Here we discuss the development, validation, and careful application of a ligand-based virtual screen used for the identification of novel inhibitors of the Francisella tularensis FabI target. In this study, four known classes of FabI inhibitors were used as templates for virtual screens that involved molecular shape and electrostatic matching. The program ROCS was used to search a high-throughput screening library for compounds that matched any of the four molecular shape queries. Matching compounds were further refined using the program EON, which compares and scores compounds by matching electrostatic properties. Using these techniques, 50 compounds were selected, ordered, and tested. The tested compounds possessed novel chemical scaffolds when compared to the input query compounds. Several hits with low micromolar activity were identified and follow-up scaffold-based searches resulted in the identification of a lead series with sub-micromolar enzyme inhibition, high ligand efficiency, and a novel scaffold. Additionally, one of the most active compounds showed promising whole-cell antibacterial activity against several Gram-positive and Gram-negative species, including the target pathogen. The results of a preliminary structure-activity relationship analysis are presented. PMID:22098466

Serious games for screening pre-dementia conditions: from virtuality to reality? A pilot project.

PubMed

Zucchella, Chiara; Sinforiani, Elena; Tassorelli, Cristina; Cavallini, Elena; Tost-Pardell, Daniela; Grau, Sergi; Pazzi, Stefania; Puricelli, Stefano; Bernini, Sara; Bottiroli, Sara; Vecchi, Tomaso; Sandrini, Giorgio; Nappi, Giuseppe

2014-01-01

Conventional cognitive assessment is based on a pencil-and-paper neuropsychological evaluation, which is time consuming, expensive and requires the involvement of several professionals. Information and communication technology could be exploited to allow the development of tools that are easy to use, reduce the amount of data processing, and provide controllable test conditions. Serious games (SGs) have the potential to be new and effective tools in the management and treatment of cognitive impairments Serious games for screening pre-dementia conditions: from virtuality to reality? A pilot project in the elderly. Moreover, by adopting SGs in 3D virtual reality settings, cognitive functions might be evaluated using tasks that simulate daily activities, increasing the "ecological validity" of the assessment. In this commentary we report our experience in the creation of the Smart Aging platform, a 3D SGand virtual environment-based platform for the early identification and characterization of mild cognitive impairment.

Multiple e-pharmacophore modelling pooled with high-throughput virtual screening, docking and molecular dynamics simulations to discover potential inhibitors of Plasmodium falciparum lactate dehydrogenase (PfLDH).

PubMed

Saxena, Shalini; Durgam, Laxman; Guruprasad, Lalitha

2018-05-14

Development of new antimalarial drugs continues to be of huge importance because of the resistance of malarial parasite towards currently used drugs. Due to the reliance of parasite on glycolysis for energy generation, glycolytic enzymes have played important role as potential targets for the development of new drugs. Plasmodium falciparum lactate dehydrogenase (PfLDH) is a key enzyme for energy generation of malarial parasites and is considered to be a potential antimalarial target. Presently, there are nearly 15 crystal structures bound with inhibitors and substrate that are available in the protein data bank (PDB). In the present work, we attempted to consider multiple crystal structures with bound inhibitors showing affinity in the range of 1.4 × 10 2 -1.3 × 10 6  nM efficacy and optimized the pharmacophore based on the energy involved in binding termed as e-pharmacophore mapping. A high throughput virtual screening (HTVS) combined with molecular docking, ADME predictions and molecular dynamics simulation led to the identification of 20 potential compounds which could be further developed as novel inhibitors for PfLDH.

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease.

PubMed

Bresso, Emmanuel; Togawa, Roberto; Hammond-Kosack, Kim; Urban, Martin; Maigret, Bernard; Martins, Natalia Florencio

2016-12-15

Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination. In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation. This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.

Target specific proteochemometric model development for BACE1 - protein flexibility and structural water are critical in virtual screening.

PubMed

Manoharan, Prabu; Chennoju, Kiranmai; Ghoshal, Nanda

2015-07-01

BACE1 is an attractive target in Alzheimer's disease (AD) treatment. A rational drug design effort for the inhibition of BACE1 is actively pursued by researchers in both academic and pharmaceutical industries. This continued effort led to the steady accumulation of BACE1 crystal structures, co-complexed with different classes of inhibitors. This wealth of information is used in this study to develop target specific proteochemometric models and these models are exploited for predicting the prospective BACE1 inhibitors. The models developed in this study have performed excellently in predicting the computationally generated poses, separately obtained from single and ensemble docking approaches. The simple protein-ligand contact (SPLC) model outperforms other sophisticated high end models, in virtual screening performance, developed during this study. In an attempt to account for BACE1 protein active site flexibility information in predictive models, we included the change in the area of solvent accessible surface and the change in the volume of solvent accessible surface in our models. The ensemble and single receptor docking results obtained from this study indicate that the structural water mediated interactions improve the virtual screening results. Also, these waters are essential for recapitulating bioactive conformation during docking study. The proteochemometric models developed in this study can be used for the prediction of BACE1 inhibitors, during the early stage of AD drug discovery.

Search for β2 Adrenergic Receptor Ligands by Virtual Screening via Grid Computing and Investigation of Binding Modes by Docking and Molecular Dynamics Simulations

PubMed Central

Bai, Qifeng; Shao, Yonghua; Pan, Dabo; Zhang, Yang; Liu, Huanxiang; Yao, Xiaojun

2014-01-01

We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD) simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for β2 adrenergic receptor (β2AR) from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of β2AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551). The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and β2AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at http://molgridcal.codeplex.com. PMID:25229694

The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability.

PubMed

Lopes, Julio Cesar Dias; Dos Santos, Fábio Mendes; Martins-José, Andrelly; Augustyns, Koen; De Winter, Hans

2017-01-01

A new metric for the evaluation of model performance in the field of virtual screening and quantitative structure-activity relationship applications is described. This metric has been termed the power metric and is defined as the fraction of the true positive rate divided by the sum of the true positive and false positive rates, for a given cutoff threshold. The performance of this metric is compared with alternative metrics such as the enrichment factor, the relative enrichment factor, the receiver operating curve enrichment factor, the correct classification rate, Matthews correlation coefficient and Cohen's kappa coefficient. The performance of this new metric is found to be quite robust with respect to variations in the applied cutoff threshold and ratio of the number of active compounds to the total number of compounds, and at the same time being sensitive to variations in model quality. It possesses the correct characteristics for its application in early-recognition virtual screening problems.

Docking and scoring with ICM: the benchmarking results and strategies for improvement

PubMed Central

Neves, Marco A. C.; Totrov, Maxim; Abagyan, Ruben

2012-01-01

Flexible docking and scoring using the Internal Coordinate Mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91% and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC= 82.2 and ROC(2%)= 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target. PMID:22569591

A Quantum-Based Similarity Method in Virtual Screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2015-10-02

One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.

DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina.

PubMed

Di Muzio, Elena; Toti, Daniele; Polticelli, Fabio

2017-02-01

Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.

Discovery of small molecules binding to the normal conformation of prion by combining virtual screening and multiple biological activity evaluation methods

NASA Astrophysics Data System (ADS)

Li, Lanlan; Wei, Wei; Jia, Wen-Juan; Zhu, Yongchang; Zhang, Yan; Chen, Jiang-Huai; Tian, Jiaqi; Liu, Huanxiang; He, Yong-Xing; Yao, Xiaojun

2017-12-01

Conformational conversion of the normal cellular prion protein, PrPC, into the misfolded isoform, PrPSc, is considered to be a central event in the development of fatal neurodegenerative diseases. Stabilization of prion protein at the normal cellular form (PrPC) with small molecules is a rational and efficient strategy for treatment of prion related diseases. However, few compounds have been identified as potent prion inhibitors by binding to the normal conformation of prion. In this work, to rational screening of inhibitors capable of stabilizing cellular form of prion protein, multiple approaches combining docking-based virtual screening, steady-state fluorescence quenching, surface plasmon resonance and thioflavin T fluorescence assay were used to discover new compounds interrupting PrPC to PrPSc conversion. Compound 3253-0207 that can bind to PrPC with micromolar affinity and inhibit prion fibrillation was identified from small molecule databases. Molecular dynamics simulation indicated that compound 3253-0207 can bind to the hotspot residues in the binding pocket composed by β1, β2 and α2, which are significant structure moieties in conversion from PrPC to PrPSc.

Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking.

PubMed

Modi, Palmi; Patel, Shivani; Chhabria, Mahesh T

2018-05-04

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, 1 H-NMR, 13 C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina

NASA Astrophysics Data System (ADS)

Di Muzio, Elena; Toti, Daniele; Polticelli, Fabio

2017-02-01

Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.

DockoMatic: automated peptide analog creation for high throughput virtual screening.

PubMed

Jacob, Reed B; Bullock, Casey W; Andersen, Tim; McDougal, Owen M

2011-10-01

The purpose of this manuscript is threefold: (1) to describe an update to DockoMatic that allows the user to generate cyclic peptide analog structure files based on protein database (pdb) files, (2) to test the accuracy of the peptide analog structure generation utility, and (3) to evaluate the high throughput capacity of DockoMatic. The DockoMatic graphical user interface interfaces with the software program Treepack to create user defined peptide analogs. To validate this approach, DockoMatic produced cyclic peptide analogs were tested for three-dimensional structure consistency and binding affinity against four experimentally determined peptide structure files available in the Research Collaboratory for Structural Bioinformatics database. The peptides used to evaluate this new functionality were alpha-conotoxins ImI, PnIA, and their published analogs. Peptide analogs were generated by DockoMatic and tested for their ability to bind to X-ray crystal structure models of the acetylcholine binding protein originating from Aplysia californica. The results, consisting of more than 300 simulations, demonstrate that DockoMatic predicts the binding energy of peptide structures to within 3.5 kcal mol(-1), and the orientation of bound ligand compares to within 1.8 Å root mean square deviation for ligand structures as compared to experimental data. Evaluation of high throughput virtual screening capacity demonstrated that Dockomatic can collect, evaluate, and summarize the output of 10,000 AutoDock jobs in less than 2 hours of computational time, while 100,000 jobs requires approximately 15 hours and 1,000,000 jobs is estimated to take up to a week. Copyright © 2011 Wiley Periodicals, Inc.

Options in virtual 3D, optical-impression-based planning of dental implants.

PubMed

Reich, Sven; Kern, Thomas; Ritter, Lutz

2014-01-01

If a 3D radiograph, which in today's dentistry often consists of a CBCT dataset, is available for computerized implant planning, the 3D planning should also consider functional prosthetic aspects. In a conventional workflow, the CBCT is done with a specially produced radiopaque prosthetic setup that makes the desired prosthetic situation visible during virtual implant planning. If an exclusively digital workflow is chosen, intraoral digital impressions are taken. On these digital models, the desired prosthetic suprastructures are designed. The entire datasets are virtually superimposed by a "registration" process on the corresponding structures (teeth) in the CBCTs. Thus, both the osseous and prosthetic structures are visible in one single 3D application and make it possible to consider surgical and prosthetic aspects. After having determined the implant positions on the computer screen, a drilling template is designed digitally. According to this design (CAD), a template is printed or milled in CAM process. This template is the first physically extant product in the entire workflow. The article discusses the options and limitations of this workflow.

Improving the accuracy of ultrafast ligand-based screening: incorporating lipophilicity into ElectroShape as an extra dimension.

PubMed

Armstrong, M Stuart; Finn, Paul W; Morris, Garrett M; Richards, W Graham

2011-08-01

In a previous paper, we presented the ElectroShape method, which we used to achieve successful ligand-based virtual screening. It extended classical shape-based methods by applying them to the four-dimensional shape of the molecule where partial charge was used as the fourth dimension to capture electrostatic information. This paper extends the approach by using atomic lipophilicity (alogP) as an additional molecular property and validates it using the improved release 2 of the Directory of Useful Decoys (DUD). When alogP replaced partial charge, the enrichment results were slightly below those of ElectroShape, though still far better than purely shape-based methods. However, when alogP was added as a complement to partial charge, the resulting five-dimensional enrichments shows a clear improvement in performance. This demonstrates the utility of extending the ElectroShape virtual screening method by adding other atom-based descriptors.

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge

NASA Astrophysics Data System (ADS)

Perryman, Alexander L.; Santiago, Daniel N.; Forli, Stefano; Santos-Martins, Diogo; Olson, Arthur J.

2014-04-01

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge.

PubMed

Perryman, Alexander L; Santiago, Daniel N; Forli, Stefano; Martins, Diogo Santos; Olson, Arthur J

2014-04-01

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".

From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library.

PubMed

Bietz, Stefan; Inhester, Therese; Lauck, Florian; Sommer, Kai; von Behren, Mathias M; Fährrolfes, Rainer; Flachsenberg, Florian; Meyder, Agnes; Nittinger, Eva; Otto, Thomas; Hilbig, Matthias; Schomburg, Karen T; Volkamer, Andrea; Rarey, Matthias

2017-11-10

Nowadays, computational approaches are an integral part of life science research. Problems related to interpretation of experimental results, data analysis, or visualization tasks highly benefit from the achievements of the digital era. Simulation methods facilitate predictions of physicochemical properties and can assist in understanding macromolecular phenomena. Here, we will give an overview of the methods developed in our group that aim at supporting researchers from all life science areas. Based on state-of-the-art approaches from structural bioinformatics and cheminformatics, we provide software covering a wide range of research questions. Our all-in-one web service platform ProteinsPlus (http://proteins.plus) offers solutions for pocket and druggability prediction, hydrogen placement, structure quality assessment, ensemble generation, protein-protein interaction classification, and 2D-interaction visualization. Additionally, we provide a software package that contains tools targeting cheminformatics problems like file format conversion, molecule data set processing, SMARTS editing, fragment space enumeration, and ligand-based virtual screening. Furthermore, it also includes structural bioinformatics solutions for inverse screening, binding site alignment, and searching interaction patterns across structure libraries. The software package is available at http://software.zbh.uni-hamburg.de. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods

PubMed Central

2015-01-01

Background Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology. Methods In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins. Results We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy. Conclusions Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy. PMID:26680552

Virtual fragment preparation for computational fragment-based drug design.

PubMed

Ludington, Jennifer L

2015-01-01

Fragment-based drug design (FBDD) has become an important component of the drug discovery process. The use of fragments can accelerate both the search for a hit molecule and the development of that hit into a lead molecule for clinical testing. In addition to experimental methodologies for FBDD such as NMR and X-ray Crystallography screens, computational techniques are playing an increasingly important role. The success of the computational simulations is due in large part to how the database of virtual fragments is prepared. In order to prepare the fragments appropriately it is necessary to understand how FBDD differs from other approaches and the issues inherent in building up molecules from smaller fragment pieces. The ultimate goal of these calculations is to link two or more simulated fragments into a molecule that has an experimental binding affinity consistent with the additive predicted binding affinities of the virtual fragments. Computationally predicting binding affinities is a complex process, with many opportunities for introducing error. Therefore, care should be taken with the fragment preparation procedure to avoid introducing additional inaccuracies.This chapter is focused on the preparation process used to create a virtual fragment database. Several key issues of fragment preparation which affect the accuracy of binding affinity predictions are discussed. The first issue is the selection of the two-dimensional atomic structure of the virtual fragment. Although the particular usage of the fragment can affect this choice (i.e., whether the fragment will be used for calibration, binding site characterization, hit identification, or lead optimization), general factors such as synthetic accessibility, size, and flexibility are major considerations in selecting the 2D structure. Other aspects of preparing the virtual fragments for simulation are the generation of three-dimensional conformations and the assignment of the associated atomic point charges.

Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor.

PubMed

Lenselink, Eelke B; Beuming, Thijs; Sherman, Woody; van Vlijmen, Herman W T; IJzerman, Adriaan P

2014-06-23

A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation - without any knowledge of crystallographic waters - can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein-ligand interactions.

Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening.

PubMed

Gladysz, Rafaela; Dos Santos, Fabio Mendes; Langenaeker, Wilfried; Thijs, Gert; Augustyns, Koen; De Winter, Hans

2018-03-07

Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990's by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure-activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.

Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

NASA Astrophysics Data System (ADS)

Wingert, Bentley M.; Oerlemans, Rick; Camacho, Carlos J.

2018-01-01

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.

Identification of small molecules capable of regulating conformational changes of telomeric G-quadruplex

NASA Astrophysics Data System (ADS)

Chen, Shuo-Bin; Liu, Guo-Cai; Gu, Lian-Quan; Huang, Zhi-Shu; Tan, Jia-Heng

2018-02-01

Design of small molecules targeted at human telomeric G-quadruplex DNA is an extremely active research area. Interestingly, the telomeric G-quadruplex is a highly polymorphic structure. Changes in its conformation upon small molecule binding may be a powerful method to achieve a desired biological effect. However, the rational development of small molecules capable of regulating conformational change of telomeric G-quadruplex structures is still challenging. In this study, we developed a reliable ligand-based pharmacophore model based on isaindigotone derivatives with conformational change activity toward telomeric G-quadruplex DNA. Furthermore, virtual screening of database was conducted using this pharmacophore model and benzopyranopyrimidine derivatives in the database were identified as a strong inducer of the telomeric G-quadruplex DNA conformation, transforming it from hybrid-type structure to parallel structure.

Performance of machine-learning scoring functions in structure-based virtual screening

PubMed Central

Wójcikowski, Maciej; Ballester, Pedro J.; Siedlecki, Pawel

2017-01-01

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and −0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary). PMID:28440302

Knowledge-driven lead discovery.

PubMed

Pirard, Bernard

2005-11-01

Virtual screening encompasses several computational approaches which have proven valuable for identifying novel leads. These approaches rely on available information. Herein, we review recent successful applications of virtual screening. The extension of virtual screening methodologies to target families is also briefly discussed.

Hit Identification and Optimization in Virtual Screening: Practical Recommendations Based Upon a Critical Literature Analysis

PubMed Central

Zhu, Tian; Cao, Shuyi; Su, Pin-Chih; Patel, Ram; Shah, Darshan; Chokshi, Heta B.; Szukala, Richard; Johnson, Michael E.; Hevener, Kirk E.

2013-01-01

A critical analysis of virtual screening results published between 2007 and 2011 was performed. The activity of reported hit compounds from over 400 studies was compared to their hit identification criteria. Hit rates and ligand efficiencies were calculated to assist in these analyses and the results were compared with factors such as the size of the virtual library and the number of compounds tested. A series of promiscuity, drug-like, and ADMET filters were applied to the reported hits to assess the quality of compounds reported and a careful analysis of a subset of the studies which presented hit optimization was performed. This data allowed us to make several practical recommendations with respect to selection of compounds for experimental testing, defining hit identification criteria, and general virtual screening hit criteria to allow for realistic hit optimization. A key recommendation is the use of size-targeted ligand efficiency values as hit identification criteria. PMID:23688234

Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition

NASA Astrophysics Data System (ADS)

Patil, Sachin P.; Ballester, Pedro J.; Kerezsi, Cassidy R.

2014-02-01

The p53 protein, known as the guardian of genome, is mutated or deleted in approximately 50 % of human tumors. In the rest of the cancers, p53 is expressed in its wild-type form, but its function is inhibited by direct binding with the murine double minute 2 (MDM2) protein. Therefore, inhibition of the p53-MDM2 interaction, leading to the activation of tumor suppressor p53 protein presents a fundamentally novel therapeutic strategy against several types of cancers. The present study utilized ultrafast shape recognition (USR), a virtual screening technique based on ligand-receptor 3D shape complementarity, to screen DrugBank database for novel p53-MDM2 inhibitors. Specifically, using 3D shape of one of the most potent crystal ligands of MDM2, MI-63, as the query molecule, six compounds were identified as potential p53-MDM2 inhibitors. These six USR hits were then subjected to molecular modeling investigations through flexible receptor docking followed by comparative binding energy analysis. These studies suggested a potential role of the USR-selected molecules as p53-MDM2 inhibitors. This was further supported by experimental tests showing that the treatment of human colon tumor cells with the top USR hit, telmisartan, led to a dose-dependent cell growth inhibition in a p53-dependent manner. It is noteworthy that telmisartan has a long history of safe human use as an approved anti-hypertension drug and thus may present an immediate clinical potential as a cancer therapeutic. Furthermore, it could also serve as a structurally-novel lead molecule for the development of more potent, small-molecule p53-MDM2 inhibitors against variety of cancers. Importantly, the present study demonstrates that the adopted USR-based virtual screening protocol is a useful tool for hit identification in the domain of small molecule p53-MDM2 inhibitors.

Virtual daily living test to screen for mild cognitive impairment using kinematic movement analysis

PubMed Central

Seo, Kyoungwon; Kim, Jae-kwan; Oh, Dong Hoon

2017-01-01

Questionnaires or computer-based tests for assessing activities of daily living are well-known approaches to screen for mild cognitive impairment (MCI). However, questionnaires are subjective and computerized tests only collect simple performance data with conventional input devices such as a mouse and keyboard. This study explored the validity and discriminative power of a virtual daily living test as a new diagnostic approach to assess MCI. Twenty-two healthy controls and 20 patients with MCI were recruited. The virtual daily living test presents two complex daily living tasks in an immersive virtual reality environment. The tasks were conducted based on subject body movements and detailed behavioral data (i.e., kinematic measures) were collected. Performance in both the proposed virtual daily living test and conventional neuropsychological tests for patients with MCI was compared to healthy controls. Kinematic measures considered in this study, such as body movement trajectory, time to completion, and speed, classified patients with MCI from healthy controls, F(8, 33) = 5.648, p < 0.001, η2 = 0.578. When both hand and head speed were employed in conjunction with the immediate free-recall test, a conventional neuropsychological test, the discrimination power for screening MCI was significantly improved to 90% sensitivity and 95.5% specificity (cf. the immediate free-recall test alone has 80% sensitivity and 77.3% specificity). Inclusion of the kinematic measures in screening for MCI significantly improved the classification of patients with MCI compared to the healthy control group, Wilks’ Lambda = 0.451, p < 0.001. PMID:28738088

Discovery of Natural Products as Novel and Potent FXR Antagonists by Virtual Screening

NASA Astrophysics Data System (ADS)

Diao, Yanyan; Jiang, Jing; Zhang, Shoude; Li, Shiliang; Shan, Lei; Huang, Jin; Zhang, Weidong; Li, Honglin

2018-04-01

Farnesoid X receptor (FXR) is a member of nuclear receptor family involved in multiple physiological processes through regulating specific target genes. The critical role of FXR as a transcriptional regulator makes it a promising target for diverse diseases, especially those related to metabolic disorders such as diabetes and cholestasis. However, the underlying activation mechanism of FXR is still a blur owing to the absence of proper FXR modulators. To identify potential FXR modulators, an in-house natural product database (NPD) containing over 4000 compounds was screened by structure-based virtual screening strategy and subsequent hit-based similarity searching method. After the yeast two-hybrid (Y2H) assay, six natural products were identified as FXR antagonists which blocked the CDCA-induced SRC-1 association. The IC50 values of compounds 2a, a diterpene bearing polycyclic skeleton, and 3a, named daphneone with chain scaffold, are as low as 1.29 μM and 1.79 μM, respectively. Compared to the control compound guggulsterone (IC50 = 6.47 μM), compounds 2a and 3a displayed 5-fold and 3-fold higher antagonistic activities against FXR, respectively. Remarkably, the two representative compounds shared low topological similarities with other reported FXR antagonists. According to the putative binding poses, the molecular basis of these antagonists against FXR was also elucidated in this report.

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents.

PubMed

Balakumar, Chandrasekaran; Ramesh, Muthusamy; Tham, Chuin Lean; Khathi, Samukelisiwe Pretty; Kozielski, Frank; Srinivasulu, Cherukupalli; Hampannavar, Girish A; Sayyad, Nisar; Soliman, Mahmoud E; Karpoormath, Rajshekhar

2017-11-29

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.

Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery

PubMed Central

Sun, Huiyong; Pan, Peichen; Tian, Sheng; Xu, Lei; Kong, Xiaotian; Li, Youyong; Dan Li; Hou, Tingjun

2016-01-01

The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF). As for the investigated targets, the optimized MIEC-SVM models performed much better than the models based on the default SVM parameters and Autodock for the tested datasets. Then, the proposed strategy was utilized to screen the Specs database for discovering potential inhibitors of the ALK kinase. The experimental results showed that the optimized MIEC-SVM model, which identified 7 actives with IC50 < 10 μM from 50 purchased compounds (namely hit rate of 14%, and 4 in nM level) and performed much better than Autodock (3 actives with IC50 < 10 μM from 50 purchased compounds, namely hit rate of 6%, and 2 in nM level), suggesting that the proposed strategy is a powerful tool in structure-based virtual screening. PMID:27102549

Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery.

PubMed

Sun, Huiyong; Pan, Peichen; Tian, Sheng; Xu, Lei; Kong, Xiaotian; Li, Youyong; Dan Li; Hou, Tingjun

2016-04-22

The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF). As for the investigated targets, the optimized MIEC-SVM models performed much better than the models based on the default SVM parameters and Autodock for the tested datasets. Then, the proposed strategy was utilized to screen the Specs database for discovering potential inhibitors of the ALK kinase. The experimental results showed that the optimized MIEC-SVM model, which identified 7 actives with IC50 < 10 μM from 50 purchased compounds (namely hit rate of 14%, and 4 in nM level) and performed much better than Autodock (3 actives with IC50 < 10 μM from 50 purchased compounds, namely hit rate of 6%, and 2 in nM level), suggesting that the proposed strategy is a powerful tool in structure-based virtual screening.

Small molecule inhibitors of ERCC1-XPF protein-protein interaction synergize alkylating agents in cancer cells.

PubMed

Jordheim, Lars Petter; Barakat, Khaled H; Heinrich-Balard, Laurence; Matera, Eva-Laure; Cros-Perrial, Emeline; Bouledrak, Karima; El Sabeh, Rana; Perez-Pineiro, Rolando; Wishart, David S; Cohen, Richard; Tuszynski, Jack; Dumontet, Charles

2013-07-01

The benefit of cancer chemotherapy based on alkylating agents is limited because of the action of DNA repair enzymes, which mitigate the damage induced by these agents. The interaction between the proteins ERCC1 and XPF involves two major components of the nucleotide excision repair pathway. Here, novel inhibitors of this interaction were identified by virtual screening based on available structures with use of the National Cancer Institute diversity set and a panel of DrugBank small molecules. Subsequently, experimental validation of the in silico screening was undertaken. Top hits were evaluated on A549 and HCT116 cancer cells. In particular, the compound labeled NSC 130813 [4-[(6-chloro-2-methoxy-9-acridinyl)amino]-2-[(4-methyl-1-piperazinyl)methyl

Immersive Virtual Moon Scene System Based on Panoramic Camera Data of Chang'E-3

NASA Astrophysics Data System (ADS)

Gao, X.; Liu, J.; Mu, L.; Yan, W.; Zeng, X.; Zhang, X.; Li, C.

2014-12-01

The system "Immersive Virtual Moon Scene" is used to show the virtual environment of Moon surface in immersive environment. Utilizing stereo 360-degree imagery from panoramic camera of Yutu rover, the system enables the operator to visualize the terrain and the celestial background from the rover's point of view in 3D. To avoid image distortion, stereo 360-degree panorama stitched by 112 images is projected onto inside surface of sphere according to panorama orientation coordinates and camera parameters to build the virtual scene. Stars can be seen from the Moon at any time. So we render the sun, planets and stars according to time and rover's location based on Hipparcos catalogue as the background on the sphere. Immersing in the stereo virtual environment created by this imaged-based rendering technique, the operator can zoom, pan to interact with the virtual Moon scene and mark interesting objects. Hardware of the immersive virtual Moon system is made up of four high lumen projectors and a huge curve screen which is 31 meters long and 5.5 meters high. This system which take all panoramic camera data available and use it to create an immersive environment, enable operator to interact with the environment and mark interesting objects contributed heavily to establishment of science mission goals in Chang'E-3 mission. After Chang'E-3 mission, the lab with this system will be open to public. Besides this application, Moon terrain stereo animations based on Chang'E-1 and Chang'E-2 data will be showed to public on the huge screen in the lab. Based on the data of lunar exploration，we will made more immersive virtual moon scenes and animations to help the public understand more about the Moon in the future.

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

PubMed Central

Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

2015-01-01

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained. PMID:26035757

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

PubMed

Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

2015-05-29

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

Discovery of Novel Inhibitors of Indoleamine 2,3-Dioxygenase 1 Through Structure-Based Virtual Screening

PubMed Central

Zhang, Guoqing; Xing, Jing; Wang, Yulan; Wang, Lihao; Ye, Yan; Lu, Dong; Zhao, Jihui; Luo, Xiaomin; Zheng, Mingyue; Yan, Shiying

2018-01-01

Indoleamine 2,3-dioxygenase 1 (IDO1) is an intracellular monomeric heme-containing enzyme that catalyzes the first and the rate limiting step in catabolism of tryptophan via the kynurenine (KYN) pathway, which plays a significant role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for anticancer therapy and chronic viral infections. In the present study, a class of IDO1 inhibitors with novel scaffolds were identified by virtual screening and biochemical validation, in which the compound DC-I028 shows moderate IDO1 inhibitory activity with an IC50 of 21.61 μM on enzymatic level and 89.11 μM on HeLa cell. In the following hit expansion stage, DC-I02806, an analog of DC-I028, showed better inhibitory activity with IC50 about 18 μM on both enzymatic level and cellular level. The structure–activity relationship (SAR) of DC-I028 and its analogs was then discussed based on the molecular docking result. The novel IDO1 inhibitors of DC-I028 and its analogs may provide useful clues for IDO1 inhibitor development. PMID:29651242

Engineering another class of anti-tubercular lead: Hit to lead optimization of an intriguing class of gyrase ATPase inhibitors.

PubMed

Jeankumar, Variam Ullas; Reshma, Rudraraju Srilakshmi; Vats, Rahul; Janupally, Renuka; Saxena, Shalini; Yogeeswari, Perumal; Sriram, Dharmarajan

2016-10-21

A structure based medium throughput virtual screening campaign of BITS-Pilani in house chemical library to identify novel binders of Mycobacterium tuberculosis gyrase ATPase domain led to the discovery of a quinoline scaffold. Further medicinal chemistry explorations on the right hand core of the early hit, engendered a potent lead demonstrating superior efficacy both in the enzyme and whole cell screening assay. The binding affinity shown at the enzyme level was further corroborated by biophysical characterization techniques. Early pharmacokinetic evaluation of the optimized analogue was encouraging and provides interesting potential for further optimization. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Selected approaches for rational drug design and high throughput screening to identify anti-cancer molecules.

PubMed

Hedvat, Michael; Emdad, Luni; Das, Swadesh K; Kim, Keetae; Dasgupta, Santanu; Thomas, Shibu; Hu, Bin; Zhu, Shan; Dash, Rupesh; Quinn, Bridget A; Oyesanya, Regina A; Kegelman, Timothy P; Sokhi, Upneet K; Sarkar, Siddik; Erdogan, Eda; Menezes, Mitchell E; Bhoopathi, Praveen; Wang, Xiang-Yang; Pomper, Martin G; Wei, Jun; Wu, Bainan; Stebbins, John L; Diaz, Paul W; Reed, John C; Pellecchia, Maurizio; Sarkar, Devanand; Fisher, Paul B

2012-11-01

Structure-based modeling combined with rational drug design, and high throughput screening approaches offer significant potential for identifying and developing lead compounds with therapeutic potential. The present review focuses on these two approaches using explicit examples based on specific derivatives of Gossypol generated through rational design and applications of a cancer-specificpromoter derived from Progression Elevated Gene-3. The Gossypol derivative Sabutoclax (BI-97C1) displays potent anti-tumor activity against a diverse spectrum of human tumors. The model of the docked structure of Gossypol bound to Bcl-XL provided a virtual structure-activity-relationship where appropriate modifications were predicted on a rational basis. These structure-based studies led to the isolation of Sabutoclax, an optically pure isomer of Apogossypol displaying superior efficacy and reduced toxicity. These studies illustrate the power of combining structure-based modeling with rational design to predict appropriate derivatives of lead compounds to be empirically tested and evaluated for bioactivity. Another approach to cancer drug discovery utilizes a cancer-specific promoter as readouts of the transformed state. The promoter region of Progression Elevated Gene-3 is such a promoter with cancer-specific activity. The specificity of this promoter has been exploited as a means of constructing cancer terminator viruses that selectively kill cancer cells and as a systemic imaging modality that specifically visualizes in vivo cancer growth with no background from normal tissues. Screening of small molecule inhibitors that suppress the Progression Elevated Gene-3-promoter may provide relevant lead compounds for cancer therapy that can be combined with further structure-based approaches leading to the development of novel compounds for cancer therapy.

Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening

PubMed Central

Bottegoni, Giovanni; Rocchia, Walter; Rueda, Manuel; Abagyan, Ruben; Cavalli, Andrea

2011-01-01

The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario. PMID:21625529

iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan

NASA Astrophysics Data System (ADS)

Tsai, Tsung-Ying; Chang, Kai-Wei; Chen, Calvin Yu-Chian

2011-06-01

The rapidly advancing researches on traditional Chinese medicine (TCM) have greatly intrigued pharmaceutical industries worldwide. To take initiative in the next generation of drug development, we constructed a cloud-computing system for TCM intelligent screening system (iScreen) based on TCM Database@Taiwan. iScreen is compacted web server for TCM docking and followed by customized de novo drug design. We further implemented a protein preparation tool that both extract protein of interest from a raw input file and estimate the size of ligand bind site. In addition, iScreen is designed in user-friendly graphic interface for users who have less experience with the command line systems. For customized docking, multiple docking services, including standard, in-water, pH environment, and flexible docking modes are implemented. Users can download first 200 TCM compounds of best docking results. For TCM de novo drug design, iScreen provides multiple molecular descriptors for a user's interest. iScreen is the world's first web server that employs world's largest TCM database for virtual screening and de novo drug design. We believe our web server can lead TCM research to a new era of drug development. The TCM docking and screening server is available at http://iScreen.cmu.edu.tw/.

iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan.

PubMed

Tsai, Tsung-Ying; Chang, Kai-Wei; Chen, Calvin Yu-Chian

2011-06-01

The rapidly advancing researches on traditional Chinese medicine (TCM) have greatly intrigued pharmaceutical industries worldwide. To take initiative in the next generation of drug development, we constructed a cloud-computing system for TCM intelligent screening system (iScreen) based on TCM Database@Taiwan. iScreen is compacted web server for TCM docking and followed by customized de novo drug design. We further implemented a protein preparation tool that both extract protein of interest from a raw input file and estimate the size of ligand bind site. In addition, iScreen is designed in user-friendly graphic interface for users who have less experience with the command line systems. For customized docking, multiple docking services, including standard, in-water, pH environment, and flexible docking modes are implemented. Users can download first 200 TCM compounds of best docking results. For TCM de novo drug design, iScreen provides multiple molecular descriptors for a user's interest. iScreen is the world's first web server that employs world's largest TCM database for virtual screening and de novo drug design. We believe our web server can lead TCM research to a new era of drug development. The TCM docking and screening server is available at http://iScreen.cmu.edu.tw/.

Computer Aided Drug Design: Success and Limitations.

PubMed

Baig, Mohammad Hassan; Ahmad, Khurshid; Roy, Sudeep; Ashraf, Jalaluddin Mohammad; Adil, Mohd; Siddiqui, Mohammad Haris; Khan, Saif; Kamal, Mohammad Amjad; Provazník, Ivo; Choi, Inho

2016-01-01

Over the last few decades, computer-aided drug design has emerged as a powerful technique playing a crucial role in the development of new drug molecules. Structure-based drug design and ligand-based drug design are two methods commonly used in computer-aided drug design. In this article, we discuss the theory behind both methods, as well as their successful applications and limitations. To accomplish this, we reviewed structure based and ligand based virtual screening processes. Molecular dynamics simulation, which has become one of the most influential tool for prediction of the conformation of small molecules and changes in their conformation within the biological target, has also been taken into account. Finally, we discuss the principles and concepts of molecular docking, pharmacophores and other methods used in computer-aided drug design.

Small molecule inhibitors of mesotrypsin from a structure-based docking screen

DOE PAGES

Kayode, Olumide; Huang, Zunnan; Soares, Alexei S.; ...

2017-05-02

PRSS3/mesotrypsin is an atypical isoform of trypsin, the upregulation of which has been implicated in promoting tumor progression. To date there are no mesotrypsin-selective pharmacological inhibitors which could serve as tools for deciphering the pathological role of this enzyme, and could potentially form the basis for novel therapeutic strategies targeting mesotrypsin. A virtual screen of the Natural Product Database (NPD) and Food and Drug Administration (FDA) approved Drug Database was conducted by high-throughput molecular docking utilizing crystal structures of mesotrypsin. Twelve high-scoring compounds were selected for testing based on lowest free energy docking scores, interaction with key mesotrypsin active sitemore » residues, and commercial availability. Diminazene (C1D22956468), along with two similar compounds presenting the bis-benzamidine substructure, was validated as a competitive inhibitor of mesotrypsin and other human trypsin isoforms. Diminazene is the most potent small molecule inhibitor of mesotrypsin reported to date with an inhibitory constant (K i) of 3.6±0.3 pM. Diminazene was subsequently co-crystalized with mesotrypsin and the crystal structure was solved and refined to 1.25 Å resolution. This high resolution crystal structure can now offer a foundation for structure-guided efforts to develop novel and potentially more selective mesotrypsin inhibitors based on similar molecular substructures.« less

Small molecule inhibitors of mesotrypsin from a structure-based docking screen

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kayode, Olumide; Huang, Zunnan; Soares, Alexei S.

PRSS3/mesotrypsin is an atypical isoform of trypsin, the upregulation of which has been implicated in promoting tumor progression. To date there are no mesotrypsin-selective pharmacological inhibitors which could serve as tools for deciphering the pathological role of this enzyme, and could potentially form the basis for novel therapeutic strategies targeting mesotrypsin. A virtual screen of the Natural Product Database (NPD) and Food and Drug Administration (FDA) approved Drug Database was conducted by high-throughput molecular docking utilizing crystal structures of mesotrypsin. Twelve high-scoring compounds were selected for testing based on lowest free energy docking scores, interaction with key mesotrypsin active sitemore » residues, and commercial availability. Diminazene (C1D22956468), along with two similar compounds presenting the bis-benzamidine substructure, was validated as a competitive inhibitor of mesotrypsin and other human trypsin isoforms. Diminazene is the most potent small molecule inhibitor of mesotrypsin reported to date with an inhibitory constant (K i) of 3.6±0.3 pM. Diminazene was subsequently co-crystalized with mesotrypsin and the crystal structure was solved and refined to 1.25 Å resolution. This high resolution crystal structure can now offer a foundation for structure-guided efforts to develop novel and potentially more selective mesotrypsin inhibitors based on similar molecular substructures.« less

Screening for lead compounds and herbal extracts with potential anti-influenza viral activity.

PubMed

Klaywong, Konrapob; Khutrakul, Gachagorn; Choowongkomon, Kiattawee; Lekcharoensuk, Chalermpol; Petcharat, Nantawan; Leckcharoensuk, Porntippa; Ramasoota, Pongrama

2014-01-01

Nonstructural protein 1 (NS1) of the highly pathogenic avian influenza virus (H5N1) contains a conserved RNA binding domain (RBD) that inhibits antiviral functions of host-innate immune response. Dimerization of NS1 forms a central groove and binds to double stranded (ds) RNA. This region might serve as a potential drug target. In this study, three dimensional structure model of NS1 RBD protein was constructed and virtual screening was performed to identify lead compounds that bound within and around the central groove. The virtual screening showed that 5 compounds bound within the central groove with binding energy ranging between -16.05 and -17.36 Kcal/mol. Two commercially available compounds, estradiol and veratridine, were selected for using in an in vitro screening assay. The results showed that neither of the compounds could inhibit the association between dsRNA and NS1 RBD protein. In addition, 34 herbal extracts were examined for their inhibitory effects. Five of them were able to inhibit association between NS1 RBD and dsRNA in electrophoresis mobility shift assay. Four herbs, Terminalia belirica, Salacia chinensis, Zingiber montanum and Peltophorum pterocarpum, could reduce > 50% of infectivity of H5N1 in a cell-based assay, and it is worth further studying their potential use as source of antiviral drugs.

[Chemical databases and virtual screening].

PubMed

Rognan, Didier; Bonnet, Pascal

2014-12-01

A prerequisite to any virtual screening is the definition of compound libraries to be screened. As we describe here, various sources are available. The selection of the proper library is usually project-dependent but at least as important as the screening method itself. This review details the main compound libraries that are available for virtual screening and guide the reader to the best possible selection according to its needs. © 2014 médecine/sciences – Inserm.

Identification of potential isoform-selective histone deacetylase inhibitors for cancer therapy: a combined approach of structure-based virtual screening, ADMET prediction and molecular dynamics simulation assay.

PubMed

Uba, Abdullahi Ibrahim; Yelekçi, Kemal

2017-10-23

Histone deacetylases (HDACs) have gained increased attention as targets for anticancer drug design and development. HDAC inhibitors have proven to be effective for reversing the malignant phenotype in HDAC-dependent cancer cases. However, lack of selectivity of the many HDAC inhibitors in clinical use and trials contributes to toxicities to healthy cells. It is believed that, the continued identification of isoform-selective inhibitors will eliminate these undesirable adverse effects - a task that remains a major challenge to HDAC inhibitor designs. Here, in an attempt to identify isoform-selective inhibitors, a large compound library containing 2,703,000 compounds retrieved from Otava database was screened against class I HDACs by exhaustive approach of structure-based virtual screening using rDOCK and Autodock Vina. A total of 41 compounds were found to show high-isoform selectivity and were further redocked into their respective targets using Autodock4. Thirty-six compounds showed remarkable isoform selectivity and passed drug-likeness and absorption, distribution, metabolism, elimination and toxicity prediction tests using ADMET Predictor™ and admetSAR. Furthermore, to study the stability of ligand binding modes, 10 ns-molecular dynamics (MD) simulations of the free HDAC isoforms and their complexes with respective best-ranked ligands were performed using nanoscale MD software. The inhibitors remained bound to their respective targets over time of the simulation and the overall potential energy, root-mean-square deviation, root-mean-square fluctuation profiles suggested that the detected compounds may be potential isoform-selective HDAC inhibitors or serve as promising scaffolds for further optimization towards the design of selective inhibitors for cancer therapy.

Docking based virtual screening and molecular dynamics study to identify potential monoacylglycerol lipase inhibitors.

PubMed

Afzal, Obaid; Kumar, Suresh; Kumar, Rajiv; Firoz, Ahmad; Jaggi, Manu; Bawa, Sandhya

2014-08-15

Monoacylglycerol lipase (MAGL) is one of the key enzymes of the endocannabinoid system (ECS). It hydrolyzes one of the major endocannabinoid, 2-arachidonoylglycerol (2-AG), an endogenous full agonist at G protein coupled cannabinoid receptors CB1 and CB2. Numerous studies showed that MGL inhibitors are potentially useful for the treatment of pain, inflammation, cancer and CNS disorders. These provocative findings suggested that pharmacological inhibition of MAGL function may confer significant therapeutic benefits. In this study, we presented hybrid ligand and structure-based approaches to obtain a novel set of virtual leads as MAGL inhibitors. The constraints used in this study, were Glide score, binding free energy estimates and ADME properties to screen the ZINC database, containing approximately 21 million compounds. A total of seven virtual hits were obtained, which showed significant binding affinity towards MAGL protein. Ligand, ZINC24092691 was employed in complex form with the protein MAGL, for molecular dynamics simulation study, because of its excellent glide score, binding free energy and ADME properties. The RMSD of ZINC24092691 was observed to stay at 0.1 nm (1 Å) in most of the trajectories, which further confirmed its ability to inhibit the protein MAGL. The hits were then evaluated for their ability to inhibit human MAGL. The compound ZINC24092691 displayed the noteworthy inhibitory activity reducing MAGL activity to 21.15% at 100 nM concentration, with an IC50 value of 10 nM. Copyright © 2014 Elsevier Ltd. All rights reserved.

Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.

PubMed

Zhang, Changsheng; Tang, Bo; Wang, Qian; Lai, Luhua

2014-10-01

Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets. © 2014 Wiley Periodicals, Inc.

bcl::Cluster : A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System.

PubMed

Alexander, Nathan; Woetzel, Nils; Meiler, Jens

2011-02-01

Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring.

PubMed

Onawole, Abdulmujeeb T; Kolapo, Temitope U; Sulaiman, Kazeem O; Adegoke, Rukayat O

2018-02-01

Ebola virus (EBOV) causes zoonotic viral infection with a potential risk of global spread and a highly fatal effect on humans. Till date, no drug has gotten market approval for the treatment of Ebola virus disease (EVD), and this perhaps allows the use of both experimental and computational approaches in the antiviral drug discovery process. The main target of potential vaccines that are recently undergoing clinical trials is trimeric glycoprotein (GP) of the EBOV and its exact crystal structure was used in this structure based virtual screening study, with the aid of consensus scoring to select three possible hit compounds from about 36 million compounds in MCULE's database. Amongst these three compounds, (5R)-5-[[5-(4-chlorophenyl)-1,2,4-oxadiazol-3-yl]methyl]-N-[(4-methoxyphenyl)methyl]-4,5-dihydroisoxazole-3-carboxamide (SC-2, C 21 H 19 ClN 4 O 4 ) showed good features with respect to drug likeness, ligand efficiency metrics, solubility, absorption and distribution properties and non-carcinogenicity to emerge as the most promising compound that can be optimized to lead compound against the GP EBOV. The binding mode showed that SC-2 is well embedded within the trimeric chains of the GP EBOV with molecular interactions with some amino acids. The SC-2 hit compound, upon its optimization to lead, might be a good potential candidate with efficacy against the EBOV pathogen and subsequently receive necessary approval to be used as antiviral drug for the treatment of EVD. Copyright © 2017 Elsevier Ltd. All rights reserved.

Low-cost, smartphone based frequency doubling technology visual field testing using virtual reality (Conference Presentation)

NASA Astrophysics Data System (ADS)

Alawa, Karam A.; Sayed, Mohamed; Arboleda, Alejandro; Durkee, Heather A.; Aguilar, Mariela C.; Lee, Richard K.

2017-02-01

Glaucoma is the leading cause of irreversible blindness worldwide. Due to its wide prevalence, effective screening tools are necessary. The purpose of this project is to design and evaluate a system that enables portable, cost effective, smartphone based visual field screening based on frequency doubling technology. The system is comprised of an Android smartphone to display frequency doubling stimuli and handle processing, a Bluetooth remote for user input, and a virtual reality headset to simulate the exam. The LG Nexus 5 smartphone and BoboVR Z3 virtual reality headset were used for their screen size and lens configuration, respectively. The system is capable of running the C-20, N-30, 24-2, and 30-2 testing patterns. Unlike the existing system, the smartphone FDT tests both eyes concurrently by showing the same background to both eyes but only displaying the stimulus to one eye at a time. Both the Humphrey Zeiss FDT and the smartphone FDT were tested on five subjects without a history of ocular disease with the C-20 testing pattern. The smartphone FDT successfully produced frequency doubling stimuli at the correct spatial and temporal frequency. Subjects could not tell which eye was being tested. All five subjects preferred the smartphone FDT to the Humphrey Zeiss FDT due to comfort and ease of use. The smartphone FDT is a low-cost, portable visual field screening device that can be used as a screening tool for glaucoma.

Homology modeling, molecular dynamics, and virtual screening of NorA efflux pump inhibitors of Staphylococcus aureus

PubMed Central

Bhaskar, Baki Vijaya; Babu, Tirumalasetty Muni Chandra; Reddy, Netala Vasudeva; Rajendra, Wudayagiri

2016-01-01

Emerging drug resistance in clinical isolates of Staphylococcus aureus might be implicated to the overexpression of NorA efflux pump which is capable of extruding numerous structurally diverse compounds. However, NorA efflux pump is considered as a potential drug target for the development of efflux pump inhibitors. In the present study, NorA model was constructed based on the crystal structure of glycerol-3-phosphate transporter (PDBID: 1PW4). Molecular dynamics (MD) simulation was performed using NAMD2.7 for NorA which is embedded in the hydrated lipid bilayer. Structural design of NorA unveils amino (N)- and carboxyl (C)-terminal domains which are connected by long cytoplasmic loop. N and C domains are composed of six transmembrane α-helices (TM) which exhibits pseudo-twofold symmetry and possess voluminous substrate binding cavity between TM helices. Molecular docking of reserpine, totarol, ferruginol, salvin, thioxanthene, phenothiazine, omeprazole, verapamil, nalidixic acid, ciprofloxacin, levofloxacin, and acridine to NorA found that all the molecules were bound at the large hydrophobic cleft and indicated significant interactions with the key residues. In addition, structure-based virtual screening was employed which indicates that 14 potent novel lead molecules such as CID58685302, CID58685367, CID5799283, CID5578487, CID60028372, ZINC12196383, ZINC72140751, ZINC72137843, ZINC39227983, ZINC43742707, ZINC12196375, ZINC66166948, ZINC39228014, and ZINC14616160 have highest binding affinity for NorA. These lead molecules displayed considerable pharmacological properties as evidenced by Lipinski rule of five and prophecy of toxicity risk assessment. Thus, the present study will be helpful in designing and synthesis of a novel class of NorA efflux pump inhibitors that restore the susceptibilities of drug compounds. PMID:27757014

Homology modeling, molecular dynamics, and virtual screening of NorA efflux pump inhibitors of Staphylococcus aureus.

PubMed

Bhaskar, Baki Vijaya; Babu, Tirumalasetty Muni Chandra; Reddy, Netala Vasudeva; Rajendra, Wudayagiri

2016-01-01

Emerging drug resistance in clinical isolates of Staphylococcus aureus might be implicated to the overexpression of NorA efflux pump which is capable of extruding numerous structurally diverse compounds. However, NorA efflux pump is considered as a potential drug target for the development of efflux pump inhibitors. In the present study, NorA model was constructed based on the crystal structure of glycerol-3-phosphate transporter (PDBID: 1PW4). Molecular dynamics (MD) simulation was performed using NAMD2.7 for NorA which is embedded in the hydrated lipid bilayer. Structural design of NorA unveils amino (N)- and carboxyl (C)-terminal domains which are connected by long cytoplasmic loop. N and C domains are composed of six transmembrane α-helices (TM) which exhibits pseudo-twofold symmetry and possess voluminous substrate binding cavity between TM helices. Molecular docking of reserpine, totarol, ferruginol, salvin, thioxanthene, phenothiazine, omeprazole, verapamil, nalidixic acid, ciprofloxacin, levofloxacin, and acridine to NorA found that all the molecules were bound at the large hydrophobic cleft and indicated significant interactions with the key residues. In addition, structure-based virtual screening was employed which indicates that 14 potent novel lead molecules such as CID58685302, CID58685367, CID5799283, CID5578487, CID60028372, ZINC12196383, ZINC72140751, ZINC72137843, ZINC39227983, ZINC43742707, ZINC12196375, ZINC66166948, ZINC39228014, and ZINC14616160 have highest binding affinity for NorA. These lead molecules displayed considerable pharmacological properties as evidenced by Lipinski rule of five and prophecy of toxicity risk assessment. Thus, the present study will be helpful in designing and synthesis of a novel class of NorA efflux pump inhibitors that restore the susceptibilities of drug compounds.

Building a virtual ligand screening pipeline using free software: a survey.

PubMed

Glaab, Enrico

2016-03-01

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems. © The Author 2015. Published by Oxford University Press.

Building a virtual ligand screening pipeline using free software: a survey

PubMed Central

2016-01-01

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems. PMID:26094053

An investigation of the efficacy of collaborative virtual reality systems for moderated remote usability testing.

PubMed

Chalil Madathil, Kapil; Greenstein, Joel S

2017-11-01

Collaborative virtual reality-based systems have integrated high fidelity voice-based communication, immersive audio and screen-sharing tools into virtual environments. Such three-dimensional collaborative virtual environments can mirror the collaboration among usability test participants and facilitators when they are physically collocated, potentially enabling moderated usability tests to be conducted effectively when the facilitator and participant are located in different places. We developed a virtual collaborative three-dimensional remote moderated usability testing laboratory and employed it in a controlled study to evaluate the effectiveness of moderated usability testing in a collaborative virtual reality-based environment with two other moderated usability testing methods: the traditional lab approach and Cisco WebEx, a web-based conferencing and screen sharing approach. Using a mixed methods experimental design, 36 test participants and 12 test facilitators were asked to complete representative tasks on a simulated online shopping website. The dependent variables included the time taken to complete the tasks; the usability defects identified and their severity; and the subjective ratings on the workload index, presence and satisfaction questionnaires. Remote moderated usability testing methodology using a collaborative virtual reality system performed similarly in terms of the total number of defects identified, the number of high severity defects identified and the time taken to complete the tasks with the other two methodologies. The overall workload experienced by the test participants and facilitators was the least with the traditional lab condition. No significant differences were identified for the workload experienced with the virtual reality and the WebEx conditions. However, test participants experienced greater involvement and a more immersive experience in the virtual environment than in the WebEx condition. The ratings for the virtual environment condition were not significantly different from those for the traditional lab condition. The results of this study suggest that participants were productive and enjoyed the virtual lab condition, indicating the potential of a virtual world based approach as an alternative to conventional approaches for synchronous usability testing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2.

PubMed

Kaserer, Teresa; Temml, Veronika; Kutil, Zsofia; Vanek, Tomas; Landa, Premysl; Schuster, Daniela

2015-01-01

Computational methods can be applied in drug development for the identification of novel lead candidates, but also for the prediction of pharmacokinetic properties and potential adverse effects, thereby aiding to prioritize and identify the most promising compounds. In principle, several techniques are available for this purpose, however, which one is the most suitable for a specific research objective still requires further investigation. Within this study, the performance of several programs, representing common virtual screening methods, was compared in a prospective manner. First, we selected top-ranked virtual screening hits from the three methods pharmacophore modeling, shape-based modeling, and docking. For comparison, these hits were then additionally predicted by external pharmacophore- and 2D similarity-based bioactivity profiling tools. Subsequently, the biological activities of the selected hits were assessed in vitro, which allowed for evaluating and comparing the prospective performance of the applied tools. Although all methods performed well, considerable differences were observed concerning hit rates, true positive and true negative hits, and hitlist composition. Our results suggest that a rational selection of the applied method represents a powerful strategy to maximize the success of a research project, tightly linked to its aims. We employed cyclooxygenase as application example, however, the focus of this study lied on highlighting the differences in the virtual screening tool performances and not in the identification of novel COX-inhibitors. Copyright © 2015 The Authors. Published by Elsevier Masson SAS.. All rights reserved.

In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport.

PubMed

Ribeiro, Taisa Pereira Piacentini; Manarin, Flávia Giovana; Borges de Melo, Eduardo

2018-05-30

To address the rising global demand for food, it is necessary to search for new herbicides that can control resistant weeds. We performed a 2D-quantitative structure-activity relationship (QSAR) study to predict compounds with photosynthesis-inhibitory activity. A data set of 44 compounds (quinolines and naphthalenes), which are described as photosynthetic electron transport (PET) inhibitors, was used. The obtained model was approved in internal and external validation tests. 2D Similarity-based virtual screening was performed and 64 compounds were selected from the ZINC database. By using the VEGA QSAR software, 48 compounds were shown to have potential toxic effects (mutagenicity and carcinogenicity). Therefore, the model was also tested using a set of 16 molecules obtained by a similarity search of the ZINC database. Six compounds showed good predicted inhibition of PET. The obtained model shows potential utility in the design of new PET inhibitors, and the hit compounds found by virtual screening are novel bicyclic scaffolds of this class. Copyright © 2018 Elsevier Inc. All rights reserved.

Development and Application of a Virtual Screening Protocol for the Identification of Multitarget Fragments.

PubMed

Bottegoni, Giovanni; Veronesi, Marina; Bisignano, Paola; Kacker, Puneet; Favia, Angelo D; Cavalli, Andrea

2016-06-20

In this study, we report on a virtual ligand screening protocol optimized to identify fragments endowed with activity at multiple targets. Thanks to this protocol, we were able to identify a fragment that displays activity in the low-micromolar range at both β-secretase 1 (BACE-1) and glycogen synthase kinase 3β (GSK-3β). These two structurally and physiologically unrelated enzymes likely contribute, through different pathways, to the onset of Alzheimer's disease (AD). Therefore, their simultaneous inhibition holds great potential in exerting a profound effect on AD. In perspective, the strategy outlined herein can be adapted to other target combinations. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

ILP-2 modeling and virtual screening of an FDA-approved library:a possible anticancer therapy.

PubMed

Khalili, Saeed; Mohammadpour, Hemn; Shokrollahi Barough, Mahideh; Kokhaei, Parviz

2016-06-23

The members of the inhibitors of apoptosis protein (IAP) family inhibit diverse components of the caspase signaling pathway, notably caspase 3, 7, and 9. ILP-2 (BIRC-8) is the most recently identified member of the IAPs, mainly interacting with caspase 9. This interaction would eventually lead to death resistance in the case of cancerous cells. Therefore, structural modeling of ILP-2 and finding applicable inhibitors of its interaction with caspase 9 are a compelling challenge. Three main protein modeling approaches along with various model refinement measures were harnessed to achieve a reliable 3D model, using state-of-the-art software. Thereafter, the selected model was employed to perform virtual screening of an FDA approved library. A model built by a combinatorial approach (homology and ab initio approaches) was chosen as the best model. Model refinement processes successfully bolstered the model quality. Virtual screening of the compound library introduced several high affinity inhibitor candidates that interact with functional residues of ILP2. Given the 3D structure of the ILP2 molecule, we found promising inhibitory molecules. In addition to high affinity towards the ILP2 molecule, these molecules interact with residues that play pivotal rules in ILP2-caspase interaction. These molecules would inhibit ILP2-caspase interaction and consequently would lead to reactivated cell apoptosis through the caspases pathway.

From genome to drug lead: identification of a small-molecule inhibitor of the SARS virus.

PubMed

Dooley, Andrea J; Shindo, Nice; Taggart, Barbara; Park, Jewn-Giew; Pang, Yuan-Ping

2006-02-15

Virtual screening, a fast, computational approach to identify drug leads [Perola, E.; Xu, K.; Kollmeyer, T. M.; Kaufmann, S. H.; Prendergast, F. G. J. Med. Chem.2000, 43, 401; Miller, M. A. Nat. Rev. Drug Disc.2002, 1 220], is limited by a known challenge in crystallographically determining flexible regions of proteins. This approach has not been able to identify active inhibitors of the severe acute respiratory syndrome-associated coronavirus (SARS-CoV) using solely the crystal structures of a SARS-CoV cysteine proteinase with a flexible loop in the active site [Yang, H. T.; Yang, M. J.; Ding, Y.; Liu, Y. W.; Lou, Z. Y. Proc. Natl. Acad. Sci. U.S.A.2003, 100, 13190; Jenwitheesuk, E.; Samudrala, R. Bioorg. Med. Chem. Lett.2003, 13, 3989; Rajnarayanan, R. V.; Dakshanamurthy, S.; Pattabiraman, N. Biochem. Biophys. Res. Commun.2004, 321, 370; Du, Q.; Wang, S.; Wei, D.; Sirois, S.; Chou, K. Anal. Biochem.2005, 337, 262; Du, Q.; Wang, S.; Zhu, Y.; Wei, D.; Guo, H. Peptides2004, 25, 1857; Lee, V.; Wittayanarakul, K.; Remsungenen, T.; Parasuk, V.; Sompornpisut, P. Science (Asia)2003, 29, 181; Toney, J.; Navas-Martin, S.; Weiss, S.; Koeller, A. J. Med. Chem.2004, 47, 1079; Zhang, X. W.; Yap, Y. L. Bioorg. Med. Chem.2004, 12, 2517]. This article demonstrates a genome-to-drug-lead approach that uses terascale computing to model flexible regions of proteins, thus permitting the utilization of genetic information to identify drug leads expeditiously. A small-molecule inhibitor of SARS-CoV, exhibiting an effective concentration (EC50) of 23 microM in cell-based assays, was identified through virtual screening against a computer-predicted model of the cysteine proteinase. Screening against two crystal structures of the same proteinase failed to identify the 23-microM inhibitor. This study suggests that terascale computing can complement crystallography, broaden the scope of virtual screening, and accelerate the development of therapeutics to treat emerging infectious diseases such as SARS and Bird Flu.

Graph wavelet alignment kernels for drug virtual screening.

PubMed

Smalter, Aaron; Huan, Jun; Lushington, Gerald

2009-06-01

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.

Discovery of a small-molecule inhibitor of Dvl-CXXC5 interaction by computational approaches

NASA Astrophysics Data System (ADS)

Ma, Songling; Choi, Jiwon; Jin, Xuemei; Kim, Hyun-Yi; Yun, Ji-Hye; Lee, Weontae; Choi, Kang-Yell; No, Kyoung Tai

2018-05-01

The Wnt/β-catenin signaling pathway plays a significant role in the control of osteoblastogenesis and bone formation. CXXC finger protein 5 (CXXC5) has been recently identified as a negative feedback regulator of osteoblast differentiation through a specific interaction with Dishevelled (Dvl) protein. It was reported that targeting the Dvl-CXXC5 interaction could be a novel anabolic therapeutic target for osteoporosis. In this study, complex structure of Dvl PDZ domain and CXXC5 peptide was simulated with molecular dynamics (MD). Based on the structural analysis of binding modes of MD-simulated Dvl PDZ domain with CXXC5 peptide and crystal Dvl PDZ domain with synthetic peptide-ligands, we generated two different pharmacophore models and applied pharmacophore-based virtual screening to discover potent inhibitors of the Dvl-CXXC5 interaction for the anabolic therapy of osteoporosis. Analysis of 16 compounds selected by means of a virtual screening protocol yielded four compounds that effectively disrupted the Dvl-CXXC5 interaction in the fluorescence polarization assay. Potential compounds were validated by fluorescence spectroscopy and nuclear magnetic resonance. We successfully identified a highly potent inhibitor, BMD4722, which directly binds to the Dvl PDZ domain and disrupts the Dvl-CXXC5 interaction. Overall, CXXC5-Dvl PDZ domain complex based pharmacophore combined with various traditional and simple computational methods is a promising approach for the development of modulators targeting the Dvl-CXXC5 interaction, and the potent inhibitor BMD4722 could serve as a starting point to discover or design more potent and specific the Dvl-CXXC5 interaction disruptors.

Discovery of a small-molecule inhibitor of Dvl-CXXC5 interaction by computational approaches.

PubMed

Ma, Songling; Choi, Jiwon; Jin, Xuemei; Kim, Hyun-Yi; Yun, Ji-Hye; Lee, Weontae; Choi, Kang-Yell; No, Kyoung Tai

2018-05-01

The Wnt/β-catenin signaling pathway plays a significant role in the control of osteoblastogenesis and bone formation. CXXC finger protein 5 (CXXC5) has been recently identified as a negative feedback regulator of osteoblast differentiation through a specific interaction with Dishevelled (Dvl) protein. It was reported that targeting the Dvl-CXXC5 interaction could be a novel anabolic therapeutic target for osteoporosis. In this study, complex structure of Dvl PDZ domain and CXXC5 peptide was simulated with molecular dynamics (MD). Based on the structural analysis of binding modes of MD-simulated Dvl PDZ domain with CXXC5 peptide and crystal Dvl PDZ domain with synthetic peptide-ligands, we generated two different pharmacophore models and applied pharmacophore-based virtual screening to discover potent inhibitors of the Dvl-CXXC5 interaction for the anabolic therapy of osteoporosis. Analysis of 16 compounds selected by means of a virtual screening protocol yielded four compounds that effectively disrupted the Dvl-CXXC5 interaction in the fluorescence polarization assay. Potential compounds were validated by fluorescence spectroscopy and nuclear magnetic resonance. We successfully identified a highly potent inhibitor, BMD4722, which directly binds to the Dvl PDZ domain and disrupts the Dvl-CXXC5 interaction. Overall, CXXC5-Dvl PDZ domain complex based pharmacophore combined with various traditional and simple computational methods is a promising approach for the development of modulators targeting the Dvl-CXXC5 interaction, and the potent inhibitor BMD4722 could serve as a starting point to discover or design more potent and specific the Dvl-CXXC5 interaction disruptors.

Discovery of a small-molecule inhibitor of Dvl-CXXC5 interaction by computational approaches

NASA Astrophysics Data System (ADS)

Ma, Songling; Choi, Jiwon; Jin, Xuemei; Kim, Hyun-Yi; Yun, Ji-Hye; Lee, Weontae; Choi, Kang-Yell; No, Kyoung Tai

2018-04-01

The Wnt/β-catenin signaling pathway plays a significant role in the control of osteoblastogenesis and bone formation. CXXC finger protein 5 (CXXC5) has been recently identified as a negative feedback regulator of osteoblast differentiation through a specific interaction with Dishevelled (Dvl) protein. It was reported that targeting the Dvl-CXXC5 interaction could be a novel anabolic therapeutic target for osteoporosis. In this study, complex structure of Dvl PDZ domain and CXXC5 peptide was simulated with molecular dynamics (MD). Based on the structural analysis of binding modes of MD-simulated Dvl PDZ domain with CXXC5 peptide and crystal Dvl PDZ domain with synthetic peptide-ligands, we generated two different pharmacophore models and applied pharmacophore-based virtual screening to discover potent inhibitors of the Dvl-CXXC5 interaction for the anabolic therapy of osteoporosis. Analysis of 16 compounds selected by means of a virtual screening protocol yielded four compounds that effectively disrupted the Dvl-CXXC5 interaction in the fluorescence polarization assay. Potential compounds were validated by fluorescence spectroscopy and nuclear magnetic resonance. We successfully identified a highly potent inhibitor, BMD4722, which directly binds to the Dvl PDZ domain and disrupts the Dvl-CXXC5 interaction. Overall, CXXC5-Dvl PDZ domain complex based pharmacophore combined with various traditional and simple computational methods is a promising approach for the development of modulators targeting the Dvl-CXXC5 interaction, and the potent inhibitor BMD4722 could serve as a starting point to discover or design more potent and specific the Dvl-CXXC5 interaction disruptors.

Identification of Mycobacterium tuberculosis BioA inhibitors by using structure-based virtual screening

PubMed Central

Singh, Swati; Khare, Garima; Bahal, Ritika Kar; Ghosh, Prahlad C; Tyagi, Anil K

2018-01-01

Background 7,8-Diaminopelargonic acid synthase (BioA), an enzyme of biotin biosynthesis pathway, is a well-known promising target for anti-tubercular drug development. Methods In this study, structure-based virtual screening was employed against the active site of BioA to identify new chemical entities for BioA inhibition and top ranking compounds were evaluated for their ability to inhibit BioA enzymatic activity. Results Seven compounds inhibited BioA enzymatic activity by greater than 60% at 100 μg/mL with most potent compounds being A36, A35 and A65, displaying IC50 values of 10.48 μg/mL (28.94 μM), 33.36 μg/mL (88.16 μM) and 39.17 μg/mL (114.42 μM), respectively. Compounds A65 and A35 inhibited Mycobacterium tuberculosis (M. tuberculosis) growth with MIC90 of 20 μg/mL and 80 μg/mL, respectively, whereas compound A36 exhibited relatively weak inhibition of M. tuberculosis growth (83% inhibition at 200 μg/mL). Compound A65 emerged as the most potent compound identified in our study that inhibited BioA enzymatic activity and growth of the pathogen and possessed drug-like properties. Conclusion Our study has identified a few hit molecules against M. tuberculosis BioA that can act as potential candidates for further development of potent anti-tubercular therapeutic agents. PMID:29750019

Searching Fragment Spaces with feature trees.

PubMed

Lessel, Uta; Wellenzohn, Bernd; Lilienthal, Markus; Claussen, Holger

2009-02-01

Virtual combinatorial chemistry easily produces billions of compounds, for which conventional virtual screening cannot be performed even with the fastest methods available. An efficient solution for such a scenario is the generation of Fragment Spaces, which encode huge numbers of virtual compounds by their fragments/reagents and rules of how to combine them. Similarity-based searches can be performed in such spaces without ever fully enumerating all virtual products. Here we describe the generation of a huge Fragment Space encoding about 5 * 10(11) compounds based on established in-house synthesis protocols for combinatorial libraries, i.e., we encode practically evaluated combinatorial chemistry protocols in a machine readable form, rendering them accessible to in silico search methods. We show how such searches in this Fragment Space can be integrated as a first step in an overall workflow. It reduces the extremely huge number of virtual products by several orders of magnitude so that the resulting list of molecules becomes more manageable for further more elaborated and time-consuming analysis steps. Results of a case study are presented and discussed, which lead to some general conclusions for an efficient expansion of the chemical space to be screened in pharmaceutical companies.

Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces.

PubMed

Boehm, Markus; Wu, Tong-Ying; Claussen, Holger; Lemmen, Christian

2008-04-24

Large collections of combinatorial libraries are an integral element in today's pharmaceutical industry. It is of great interest to perform similarity searches against all virtual compounds that are synthetically accessible by any such library. Here we describe the successful application of a new software tool CoLibri on 358 combinatorial libraries based on validated reaction protocols to create a single chemistry space containing over 10 (12) possible products. Similarity searching with FTrees-FS allows the systematic exploration of this space without the need to enumerate all product structures. The search result is a set of virtual hits which are synthetically accessible by one or more of the existing reaction protocols. Grouping these virtual hits by their synthetic protocols allows the rapid design and synthesis of multiple follow-up libraries. Such library ideas support hit-to-lead design efforts for tasks like follow-up from high-throughput screening hits or scaffold hopping from one hit to another attractive series.

Distributed attitude synchronization of formation flying via consensus-based virtual structure

NASA Astrophysics Data System (ADS)

Cong, Bing-Long; Liu, Xiang-Dong; Chen, Zhen

2011-06-01

This paper presents a general framework for synchronized multiple spacecraft rotations via consensus-based virtual structure. In this framework, attitude control systems for formation spacecrafts and virtual structure are designed separately. Both parametric uncertainty and external disturbance are taken into account. A time-varying sliding mode control (TVSMC) algorithm is designed to improve the robustness of the actual attitude control system. As for the virtual attitude control system, a behavioral consensus algorithm is presented to accomplish the attitude maneuver of the entire formation and guarantee a consistent attitude among the local virtual structure counterparts during the attitude maneuver. A multiple virtual sub-structures (MVSSs) system is introduced to enhance current virtual structure scheme when large amounts of spacecrafts are involved in the formation. The attitude of spacecraft is represented by modified Rodrigues parameter (MRP) for its non-redundancy. Finally, a numerical simulation with three synchronization situations is employed to illustrate the effectiveness of the proposed strategy.

MOLA: a bootable, self-configuring system for virtual screening using AutoDock4/Vina on computer clusters.

PubMed

Abreu, Rui Mv; Froufe, Hugo Jc; Queiroz, Maria João Rp; Ferreira, Isabel Cfr

2010-10-28

Virtual screening of small molecules using molecular docking has become an important tool in drug discovery. However, large scale virtual screening is time demanding and usually requires dedicated computer clusters. There are a number of software tools that perform virtual screening using AutoDock4 but they require access to dedicated Linux computer clusters. Also no software is available for performing virtual screening with Vina using computer clusters. In this paper we present MOLA, an easy-to-use graphical user interface tool that automates parallel virtual screening using AutoDock4 and/or Vina in bootable non-dedicated computer clusters. MOLA automates several tasks including: ligand preparation, parallel AutoDock4/Vina jobs distribution and result analysis. When the virtual screening project finishes, an open-office spreadsheet file opens with the ligands ranked by binding energy and distance to the active site. All results files can automatically be recorded on an USB-flash drive or on the hard-disk drive using VirtualBox. MOLA works inside a customized Live CD GNU/Linux operating system, developed by us, that bypass the original operating system installed on the computers used in the cluster. This operating system boots from a CD on the master node and then clusters other computers as slave nodes via ethernet connections. MOLA is an ideal virtual screening tool for non-experienced users, with a limited number of multi-platform heterogeneous computers available and no access to dedicated Linux computer clusters. When a virtual screening project finishes, the computers can just be restarted to their original operating system. The originality of MOLA lies on the fact that, any platform-independent computer available can he added to the cluster, without ever using the computer hard-disk drive and without interfering with the installed operating system. With a cluster of 10 processors, and a potential maximum speed-up of 10x, the parallel algorithm of MOLA performed with a speed-up of 8,64× using AutoDock4 and 8,60× using Vina.

Combination of Pharmacophore Matching, 2D Similarity Search, and In Vitro Biological Assays in the Selection of Potential 5-HT6 Antagonists from Large Commercial Repositories.

PubMed

Dobi, Krisztina; Flachner, Beáta; Pukáncsik, Mária; Máthé, Enikő; Bognár, Melinda; Szaszkó, Mária; Magyar, Csaba; Hajdú, István; Lőrincz, Zsolt; Simon, István; Fülöp, Ferenc; Cseh, Sándor; Dormán, György

2015-10-01

Rapid in silico selection of target-focused libraries from commercial repositories is an attractive and cost-effective approach. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compound databases, but the generated library requires further focusing. We report here a combination of the 2D approach with pharmacophore matching which was used for selecting 5-HT6 antagonists. In the first screening round, 12 compounds showed >85% antagonist efficacy of the 91 screened. For the second-round (hit validation) screening phase, pharmacophore models were built, applied, and compared with the routine 2D similarity search. Three pharmacophore models were created based on the structure of the reference compounds and the first-round hit compounds. The pharmacophore search resulted in a high hit rate (40%) and led to novel chemotypes, while 2D similarity search had slightly better hit rate (51%), but lacking the novelty. To demonstrate the power of the virtual screening cascade, ligand efficiency indices were also calculated and their steady improvement was confirmed. © 2015 John Wiley & Sons A/S.

Discovery of new erbB4 inhibitors: Repositioning an orphan chemical library by inverse virtual screening.

PubMed

Giordano, Assunta; Forte, Giovanni; Massimo, Luigia; Riccio, Raffaele; Bifulco, Giuseppe; Di Micco, Simone

2018-04-12

Inverse Virtual Screening (IVS) is a docking based approach aimed to the evaluation of the virtual ability of a single compound to interact with a library of proteins. For the first time, we applied this methodology to a library of synthetic compounds, which proved to be inactive towards the target they were initially designed for. Trifluoromethyl-benzenesulfonamides 3-21 were repositioned by means of IVS identifying new lead compounds (14-16, 19 and 20) for the inhibition of erbB4 in the low micromolar range. Among these, compound 20 exhibited an interesting value of IC 50 on MCF7 cell lines, thus validating IVS in lead repurposing. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

NASA Astrophysics Data System (ADS)

Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

2008-09-01

The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries.

A combination of 2D similarity search, pharmacophore, and molecular docking techniques for the identification of vascular endothelial growth factor receptor-2 inhibitors.

PubMed

Ai, Guanhua; Tian, Caiping; Deng, Dawei; Fida, Guissi; Chen, Haiyan; Ma, Yuxiang; Ding, Li; Gu, Yueqing

2015-04-01

The human vascular endothelial growth factor receptor-2 (VEGFR-2) has been an attractive target for the inhibition of angiogenesis. In the current study, we used a hybrid protocol of virtual screening methods to retrieve new VEGFR-2 inhibitors from the Zinc-Specs Database (441 574 compounds). The hybrid protocol included the initial screening of candidates by comparing the 2D similarity to five reported top active inhibitors of 13 VEGFR-2 X-ray crystallography structures, followed by the pharmacophore modeling of virtual screening on the basis of receptor-ligand interactions and further narrowing by LibDOCK to obtain the final hits. Two compounds (AN-919/41439526 and AK-968/40939851) with a high libscore were selected as the final hits for a subsequent cell cytotoxicity study. The two compounds screened exerted significant inhibitory effects on the proliferation of cancer cells (U87 and MCF-7). The results indicated that the hybrid procedure is an effective approach for screening specific receptor inhibitors.

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

PubMed Central

Kobayashi, Hajime; Ohkubo, Masaki; Narita, Akihiro; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Sone, Shusuke

2017-01-01

Objective: We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using low-dose CT. Methods: The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n = 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (ΔCT) were inserted into clinical images (n = 10) and submitted for CAD analysis. Results: In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient = 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a ΔCT > 220 HU. Conclusion: Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy. PMID:27897029

A Method to Predict the Structure and Stability of RNA/RNA Complexes.

PubMed

Xu, Xiaojun; Chen, Shi-Jie

2016-01-01

RNA/RNA interactions are essential for genomic RNA dimerization and regulation of gene expression. Intermolecular loop-loop base pairing is a widespread and functionally important tertiary structure motif in RNA machinery. However, computational prediction of intermolecular loop-loop base pairing is challenged by the entropy and free energy calculation due to the conformational constraint and the intermolecular interactions. In this chapter, we describe a recently developed statistical mechanics-based method for the prediction of RNA/RNA complex structures and stabilities. The method is based on the virtual bond RNA folding model (Vfold). The main emphasis in the method is placed on the evaluation of the entropy and free energy for the loops, especially tertiary kissing loops. The method also uses recursive partition function calculations and two-step screening algorithm for large, complicated structures of RNA/RNA complexes. As case studies, we use the HIV-1 Mal dimer and the siRNA/HIV-1 mutant (T4) to illustrate the method.

A rotation-translation invariant molecular descriptor of partial charges and its use in ligand-based virtual screening

PubMed Central

2014-01-01

Background Measures of similarity for chemical molecules have been developed since the dawn of chemoinformatics. Molecular similarity has been measured by a variety of methods including molecular descriptor based similarity, common molecular fragments, graph matching and 3D methods such as shape matching. Similarity measures are widespread in practice and have proven to be useful in drug discovery. Because of our interest in electrostatics and high throughput ligand-based virtual screening, we sought to exploit the information contained in atomic coordinates and partial charges of a molecule. Results A new molecular descriptor based on partial charges is proposed. It uses the autocorrelation function and linear binning to encode all atoms of a molecule into two rotation-translation invariant vectors. Combined with a scoring function, the descriptor allows to rank-order a database of compounds versus a query molecule. The proposed implementation is called ACPC (AutoCorrelation of Partial Charges) and released in open source. Extensive retrospective ligand-based virtual screening experiments were performed and other methods were compared with in order to validate the method and associated protocol. Conclusions While it is a simple method, it performed remarkably well in experiments. At an average speed of 1649 molecules per second, it reached an average median area under the curve of 0.81 on 40 different targets; hence validating the proposed protocol and implementation. PMID:24887178

IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

PubMed

Li, Guo-Bo; Yu, Zhu-Jun; Liu, Sha; Huang, Lu-Yi; Yang, Ling-Ling; Lohans, Christopher T; Yang, Sheng-Yong

2017-07-24

Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH4R Antagonism

PubMed Central

Basile, Livia; Milardi, Danilo; Zeidan, Mouhammed; Raiyn, Jamal; Guccione, Salvatore; Rayan, Anwar

2014-01-01

The human histamine H4 receptor (hH4R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH4R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH4R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH4R bioactivity. An application of the prediction model on external test set composed of more than 160 hH4R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H4R antagonists' candidates. Next, a series of 3D models of hH4R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH4R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner. PMID:25330207

Sequential application of ligand and structure based modeling approaches to index chemicals for their hH4R antagonism.

PubMed

Pappalardo, Matteo; Shachaf, Nir; Basile, Livia; Milardi, Danilo; Zeidan, Mouhammed; Raiyn, Jamal; Guccione, Salvatore; Rayan, Anwar

2014-01-01

The human histamine H4 receptor (hH4R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH4R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH4R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH4R bioactivity. An application of the prediction model on external test set composed of more than 160 hH4R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼ 4000 chemicals highly indexed as H4R antagonists' candidates. Next, a series of 3D models of hH4R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH4R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.

Structure-Based Predictions of Activity Cliffs

PubMed Central

Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

2015-01-01

In drug discovery, it is generally accepted that neighboring molecules in a given descriptors' space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as ‘activity cliffs’. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming co-crystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution

NASA Astrophysics Data System (ADS)

Greenwood, Jeremy R.; Calkins, David; Sullivan, Arron P.; Shelley, John C.

2010-06-01

Generating the appropriate protonation states of drug-like molecules in solution is important for success in both ligand- and structure-based virtual screening. Screening collections of millions of compounds requires a method for determining tautomers and their energies that is sufficiently rapid, accurate, and comprehensive. To maximise enrichment, the lowest energy tautomers must be determined from heterogeneous input, without over-enumerating unfavourable states. While computationally expensive, the density functional theory (DFT) method M06-2X/aug-cc-pVTZ(-f) [PB-SCRF] provides accurate energies for enumerated model tautomeric systems. The empirical Hammett-Taft methodology can very rapidly extrapolate substituent effects from model systems to drug-like molecules via the relationship between pKT and pKa. Combining the two complementary approaches transforms the tautomer problem from a scientific challenge to one of engineering scale-up, and avoids issues that arise due to the very limited number of measured pKT values, especially for the complicated heterocycles often favoured by medicinal chemists for their novelty and versatility. Several hundreds of pre-calculated tautomer energies and substituent pKa effects are tabulated in databases for use in structural adjustment by the program Epik, which treats tautomers as a subset of the larger problem of the protonation states in aqueous ensembles and their energy penalties. Accuracy and coverage is continually improved and expanded by parameterizing new systems of interest using DFT and experimental data. Recommendations are made for how to best incorporate tautomers in molecular design and virtual screening workflows.

Computation-based virtual screening for designing novel antimalarial drugs by targeting falcipain-III: a structure-based drug designing approach.

PubMed

Kesharwani, Rajesh Kumar; Singh, Durg Vijay; Misra, Krishna

2013-01-01

Cysteine proteases (falcipains), a papain-family of enzymes of Plasmodium falciparum, are responsible for haemoglobin degradation and thus necessary for its survival during asexual life cycle phase inside the human red blood cells while remaining non-functional for the human body. Therefore, these can act as potential targets for designing antimalarial drugs. The P. falciparum cysteine proteases, falcipain-II and falcipain- III are the enzymes which initiate the haemoglobin degradation, therefore, have been selected as targets. In the present study, we have designed new leupeptin analogues and subjected to virtual screening using Glide at the active site cavity of falcipain-II and falcipain-III to select the best docked analogues on the basis of Glide score and also compare with the result of AutoDock. The proposed analogues can be synthesized and tested in vivo as future potent antimalarial drugs. Protein falcipain-II and falcipain-III together with bounds inhibitors epoxysuccinate E64 (E64) and leupeptin respectively were retrieved from protein data bank (PDB) and latter leupeptin was used as lead molecule to design new analogues by using Ligbuilder software and refined the molecules on the basis of Lipinski rule of five and fitness score parameters. All the designed leupeptin analogues were screened via docking simulation at the active site cavity of falcipain-II and falcipain-III by using Glide software and AutoDock. The 104 new leupeptin-based antimalarial ligands were designed using structure-based drug designing approach with the help of Ligbuilder and subjected for virtual screening via docking simulation method against falcipain-II and falcipain-III receptor proteins. The Glide docking results suggest that the ligands namely result_037 shows good binding and other two, result_044 and result_042 show nearly similar binding than naturally occurring PDB bound ligand E64 against falcipain-II and in case of falcipain-III, 15 designed leupeptin analogues having better binding affinity compared to the PDB bound inhibitor of falcipain-III. The docking simulation results of falcipain-III with designed leupeptin analogues using Glide compared with AutoDock and find 80% similarity as better binder than leupeptin. These results further highlight new leupeptin analogues as promising future inhibitors for chemotherapeutic prevention of malaria. The result of Glide for falcipain-III has been compared with the result of AutoDock and finds very less differences in their order of binding affinity. Although there are no extra hydrogen bonds, however, equal number of hydrogen bonds with variable strength as compared to leupeptin along with the enhanced hydrophobic and electrostatic interactions in case of analogues supports our study that it holds the ligand molecules strongly within the receptor. The comparative e-pharmacophoric study also suggests and supports our predictions regarding the minimum features required in ligand molecule to behave as falcipain- III inhibitors and is also helpful in screening the large database as future antimalarial inhibitors.

Efficient hit-finding approaches for histone methyltransferases: the key parameters.

PubMed

Ahrens, Thomas; Bergner, Andreas; Sheppard, David; Hafenbradl, Doris

2012-01-01

For many novel epigenetics targets the chemical ligand space and structural information were limited until recently and are still largely unknown for some targets. Hit-finding campaigns are therefore dependent on large and chemically diverse libraries. In the specific case of the histone methyltransferase G9a, the authors have been able to apply an efficient process of intelligent selection of compounds for primary screening, rather than screening the full diverse deck of 900 000 compounds to identify hit compounds. A number of different virtual screening methods have been applied for the compound selection, and the results have been analyzed in the context of their individual success rates. For the primary screening of 2112 compounds, a FlashPlate assay format and full-length histone H3.1 substrate were employed. Validation of hit compounds was performed using the orthogonal fluorescence lifetime technology. Rated by purity and IC(50) value, 18 compounds (0.9% of compound screening deck) were finally considered validated primary G9a hits. The hit-finding approach has led to novel chemotypes being identified, which can facilitate hit-to-lead projects. This study demonstrates the power of virtual screening technologies for novel, therapeutically relevant epigenetics protein targets.

Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking

PubMed Central

Wei, Yu; Li, Jinlong; Qing, Jie; Huang, Mingjie; Wu, Ming; Gao, Fenghua; Li, Dongmei; Hong, Zhangyong; Kong, Lingbao; Huang, Weiqiang; Lin, Jianping

2016-01-01

The NS5B polymerase is one of the most attractive targets for developing new drugs to block Hepatitis C virus (HCV) infection. We describe the discovery of novel potent HCV NS5B polymerase inhibitors by employing a virtual screening (VS) approach, which is based on random forest (RB-VS), e-pharmacophore (PB-VS), and docking (DB-VS) methods. In the RB-VS stage, after feature selection, a model with 16 descriptors was used. In the PB-VS stage, six energy-based pharmacophore (e-pharmacophore) models from different crystal structures of the NS5B polymerase with ligands binding at the palm I, thumb I and thumb II regions were used. In the DB-VS stage, the Glide SP and XP docking protocols with default parameters were employed. In the virtual screening approach, the RB-VS, PB-VS and DB-VS methods were applied in increasing order of complexity to screen the InterBioScreen database. From the final hits, we selected 5 compounds for further anti-HCV activity and cellular cytotoxicity assay. All 5 compounds were found to inhibit NS5B polymerase with IC50 values of 2.01–23.84 μM and displayed anti-HCV activities with EC50 values ranging from 1.61 to 21.88 μM, and all compounds displayed no cellular cytotoxicity (CC50 > 100 μM) except compound N2, which displayed weak cytotoxicity with a CC50 value of 51.3 μM. The hit compound N2 had the best antiviral activity against HCV, with a selective index of 32.1. The 5 hit compounds with new scaffolds could potentially serve as NS5B polymerase inhibitors through further optimization and development. PMID:26845440

Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries.

PubMed

Li, Liwei; Wang, Bo; Meroueh, Samy O

2011-09-26

The community structure-activity resource (CSAR) data sets are used to develop and test a support vector machine-based scoring function in regression mode (SVR). Two scoring functions (SVR-KB and SVR-EP) are derived with the objective of reproducing the trend of the experimental binding affinities provided within the two CSAR data sets. The features used to train SVR-KB are knowledge-based pairwise potentials, while SVR-EP is based on physicochemical properties. SVR-KB and SVR-EP were compared to seven other widely used scoring functions, including Glide, X-score, GoldScore, ChemScore, Vina, Dock, and PMF. Results showed that SVR-KB trained with features obtained from three-dimensional complexes of the PDBbind data set outperformed all other scoring functions, including best performing X-score, by nearly 0.1 using three correlation coefficients, namely Pearson, Spearman, and Kendall. It was interesting that higher performance in rank ordering did not translate into greater enrichment in virtual screening assessed using the 40 targets of the Directory of Useful Decoys (DUD). To remedy this situation, a variant of SVR-KB (SVR-KBD) was developed by following a target-specific tailoring strategy that we had previously employed to derive SVM-SP. SVR-KBD showed a much higher enrichment, outperforming all other scoring functions tested, and was comparable in performance to our previously derived scoring function SVM-SP.

3-Substituted Indole Inhibitors Against Francisella tularensis FabI Identified by Structure-Based Virtual Screening

DTIC Science & Technology

2013-07-01

FabI, but share low sequence identity and are poorly inhibited by triclosan.25,26 S. pneumoniae and P. aeruginosa contain FabK,24 and Vibrio cholerae,27...with 0.2 mM IPTG. The cells were harvested after an overnight induction period at 17 °C. The cells were lysed and sonicated and loaded onto a nickel...of enoyl- (acyl-carrier protein) reductase, FabV, from Vibrio fischeri. Acta Crystallogr., Sect. F: Struct. Biol. Cryst. Commun. 2012, 68, 78−80. (27

Virtual screening for novel Staphylococcus Aureus NorA efflux pump inhibitors from natural products.

PubMed

Thai, Khac-Minh; Ngo, Trieu-Du; Phan, Thien-Vy; Tran, Thanh-Dao; Nguyen, Ngoc-Vinh; Nguyen, Thien-Hai; Le, Minh-Tri

2015-01-01

NorA is a member of the Major Facilitator Superfamily (MFS) drug efflux pumps that have been shown to mediate antibiotic resistance in Staphylococcus aureus (SA). In this study, QSAR analysis, virtual screening and molecular docking were implemented in an effort to discover novel SA NorA efflux pump inhibitors. Originally, a set of 47 structurally diverse compounds compiled from the literature was used to develop linear QSAR models and another set of 15 different compounds were chosen for extra validation. The final model which was estimated by statistical values for the full data set (n = 45, Q(2) = 0.80, RMSE = 0.20) and for the external test set (n = 15, R(2) = 0.60, |res|max = 0.75, |res|min = 0.02) was applied on the collection of 182 flavonoides and the traditional Chinese medicine (TCM) database to screen for novel NorA inhibitors. Finally, 33 lead compounds that met the Lipinski's rules of five/three and had good predicted pIC50 values from in silico screening process were employed to analyze the binding ability by docking studies on NorA homology model in place of its unavailable crystal structures at two active sites, the central channel and the Walker B.

Structure-Guided Discovery of Novel, Potent, and Orally Bioavailable Inhibitors of Lipoprotein-Associated Phospholipase A2.

PubMed

Liu, Qiufeng; Huang, Fubao; Yuan, Xiaojing; Wang, Kai; Zou, Yi; Shen, Jianhua; Xu, Yechun

2017-12-28

Lipoprotein-associated phospholipase A2 (Lp-PLA2) is a promising therapeutic target for atherosclerosis, Alzheimer's disease, and diabetic macular edema. Here we report the identification of novel sulfonamide scaffold Lp-PLA2 inhibitors derived from a relatively weak fragment. Similarity searching on this fragment followed by molecular docking leads to the discovery of a micromolar inhibitor with a 300-fold potency improvement. Subsequently, by the application of a structure-guided design strategy, a successful hit-to-lead optimization was achieved and a number of Lp-PLA2 inhibitors with single-digit nanomolar potency were obtained. After preliminary evaluation of the properties of drug-likeness in vitro and in vivo, compound 37 stands out from this congeneric series of inhibitors for good inhibitory activity and favorable oral bioavailability in male Sprague-Dawley rats, providing a quality candidate for further development. The present study thus clearly demonstrates the power and advantage of integrally employing fragment screening, crystal structures determination, virtual screening, and medicinal chemistry in an efficient lead discovery project, providing a good example for structure-based drug design.

Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.

PubMed

Brylinski, Michal

2013-11-25

A common strategy for virtual screening considers a systematic docking of a large library of organic compounds into the target sites in protein receptors with promising leads selected based on favorable intermolecular interactions. Despite a continuous progress in the modeling of protein-ligand interactions for pharmaceutical design, important challenges still remain, thus the development of novel techniques is required. In this communication, we describe eSimDock, a new approach to ligand docking and binding affinity prediction. eSimDock employs nonlinear machine learning-based scoring functions to improve the accuracy of ligand ranking and similarity-based binding pose prediction, and to increase the tolerance to structural imperfections in the target structures. In large-scale benchmarking using the Astex/CCDC data set, we show that 53.9% (67.9%) of the predicted ligand poses have RMSD of <2 Å (<3 Å). Moreover, using binding sites predicted by recently developed eFindSite, eSimDock models ligand binding poses with an RMSD of 4 Å for 50.0-39.7% of the complexes at the protein homology level limited to 80-40%. Simulations against non-native receptor structures, whose mean backbone rearrangements vary from 0.5 to 5.0 Å Cα-RMSD, show that the ratio of docking accuracy and the estimated upper bound is at a constant level of ∼0.65. Pearson correlation coefficient between experimental and predicted by eSimDock Ki values for a large data set of the crystal structures of protein-ligand complexes from BindingDB is 0.58, which decreases only to 0.46 when target structures distorted to 3.0 Å Cα-RMSD are used. Finally, two case studies demonstrate that eSimDock can be customized to specific applications as well. These encouraging results show that the performance of eSimDock is largely unaffected by the deformations of ligand binding regions, thus it represents a practical strategy for across-proteome virtual screening using protein models. eSimDock is freely available to the academic community as a Web server at http://www.brylinski.org/esimdock .

Web-based Three-dimensional Virtual Body Structures: W3D-VBS

PubMed Central

Temkin, Bharti; Acosta, Eric; Hatfield, Paul; Onal, Erhan; Tong, Alex

2002-01-01

Major efforts are being made to improve the teaching of human anatomy to foster cognition of visuospatial relationships. The Visible Human Project of the National Library of Medicine makes it possible to create virtual reality-based applications for teaching anatomy. Integration of traditional cadaver and illustration-based methods with Internet-based simulations brings us closer to this goal. Web-based three-dimensional Virtual Body Structures (W3D-VBS) is a next-generation immersive anatomical training system for teaching human anatomy over the Internet. It uses Visible Human data to dynamically explore, select, extract, visualize, manipulate, and stereoscopically palpate realistic virtual body structures with a haptic device. Tracking user’s progress through evaluation tools helps customize lesson plans. A self-guided “virtual tour” of the whole body allows investigation of labeled virtual dissections repetitively, at any time and place a user requires it. PMID:12223495

Web-based three-dimensional Virtual Body Structures: W3D-VBS.

PubMed

Temkin, Bharti; Acosta, Eric; Hatfield, Paul; Onal, Erhan; Tong, Alex

2002-01-01

Major efforts are being made to improve the teaching of human anatomy to foster cognition of visuospatial relationships. The Visible Human Project of the National Library of Medicine makes it possible to create virtual reality-based applications for teaching anatomy. Integration of traditional cadaver and illustration-based methods with Internet-based simulations brings us closer to this goal. Web-based three-dimensional Virtual Body Structures (W3D-VBS) is a next-generation immersive anatomical training system for teaching human anatomy over the Internet. It uses Visible Human data to dynamically explore, select, extract, visualize, manipulate, and stereoscopically palpate realistic virtual body structures with a haptic device. Tracking user's progress through evaluation tools helps customize lesson plans. A self-guided "virtual tour" of the whole body allows investigation of labeled virtual dissections repetitively, at any time and place a user requires it.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Virtual screening methods as tools for drug lead discovery from large chemical libraries.

PubMed

Ma, X H; Zhu, F; Liu, X; Shi, Z; Zhang, J X; Yang, S Y; Wei, Y Q; Chen, Y Z

2012-01-01

Virtual screening methods have been developed and explored as useful tools for searching drug lead compounds from chemical libraries, including large libraries that have become publically available. In this review, we discussed the new developments in exploring virtual screening methods for enhanced performance in searching large chemical libraries, their applications in screening libraries of ~ 1 million or more compounds in the last five years, the difficulties in their applications, and the strategies for further improving these methods.

A knowledge-based approach for identification of drugs against vivapain-2 protein of Plasmodium vivax through pharmacophore-based virtual screening with comparative modelling.

PubMed

Yadav, Manoj Kumar; Singh, Amisha; Swati, D

2014-08-01

Malaria is one of the most infectious diseases in the world. Plasmodium vivax, the pathogen causing endemic malaria in humans worldwide, is responsible for extensive disease morbidity. Due to the emergence of resistance to common anti-malarial drugs, there is a continuous need to develop a new class of drugs for this pathogen. P. vivax cysteine protease, also known as vivapain-2, plays an important role in haemoglobin hydrolysis and is considered essential for the survival of the parasite. The three-dimensional (3D) structure of vivapain-2 is not predicted experimentally, so its structure is modelled by using comparative modelling approach and further validated by Qualitative Model Energy Analysis (QMEAN) and RAMPAGE tools. The potential binding site of selected vivapain-2 structure has been detected by grid-based function prediction method. Drug targets and their respective drugs similar to vivapain-2 have been identified using three publicly available databases: STITCH 3.1, DrugBank and Therapeutic Target Database (TTD). The second approach of this work focuses on docking study of selected drug E-64 against vivapain-2 protein. Docking reveals crucial information about key residues (Asn281, Cys283, Val396 and Asp398) that are responsible for holding the ligand in the active site. The similarity-search criterion is used for the preparation of our in-house database of drugs, obtained from filtering the drugs from the DrugBank database. A five-point 3D pharmacophore model is generated for the docked complex of vivapain-2 with E-64. This study of 3D pharmacophore-based virtual screening results in identifying three new drugs, amongst which one is approved and the other two are experimentally proved. The ADMET properties of these drugs are found to be in the desired range. These drugs with novel scaffolds may act as potent drugs for treating malaria caused by P. vivax.

Structural Studies on Intact Clostridium botulinum Neurotoxins Complexed with Inhibitors Leading to Drug Design

DTIC Science & Technology

2009-02-01

compounds via virtual screening. These compounds include small molecules – transition state analogues and benzimidazoles . Since there is a commonality in...Crystal structure of BoNT/E has been determined helping us to understand the faster action of BoNT/E compared to BoNT/A. • A subset of benzimidazole

Comparison of a homology model and the crystallographic structure of human 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) in a structure-based identification of inhibitors

NASA Astrophysics Data System (ADS)

Miguet, Laurence; Zhang, Ziding; Barbier, Maryse; Grigorov, Martin G.

2006-02-01

Human 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) catalyzes the interconversion of cortisone into active cortisol. 11βHSD1 inhibition is a tempting target for the treatment of a host of human disorders that might benefit from blockade of glucocorticoid action, such as obesity, metabolic syndrome, and diabetes type 2. Here, we report an in silico screening study aimed at identifying new selective inhibitors of human 11βHSD1 enzyme. In the first step, homology modeling was employed to build the 3D structure of 11βHSD1. Further, molecular docking was used to validate the predicted model by showing that it was able to discriminate between known 11βHSD1 inhibitors or substrates and non-inhibitors. The homology model was found to reproduce closely the crystal structure that became publicly available in the final stages of this work. Finally, we carried out structure-based virtual screening experiments on both the homology model and the crystallographic structure with a database of 114'000 natural molecules. Among these, 15 molecules were consistently selected as inhibitors based on both the model and crystal structures of the enzyme, implying a good quality for the homology model. Among these putative 11βHSD1 inhibitors, two were flavonone derivatives that have already been shown to be potent inhibitors of the enzyme.

Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking.

PubMed

Ren, Ji-Xia; Li, Lin-Li; Zheng, Ren-Lin; Xie, Huan-Zhang; Cao, Zhi-Xing; Feng, Shan; Pan, You-Li; Chen, Xin; Wei, Yu-Quan; Yang, Sheng-Yong

2011-06-27

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.

Ligand-Based Pharmacophore Modeling and Virtual Screening for the Discovery of Novel 17β-Hydroxysteroid Dehydrogenase 2 Inhibitors

PubMed Central

2014-01-01

17β-Hydroxysteroid dehydrogenase 2 (17β-HSD2) catalyzes the inactivation of estradiol into estrone. This enzyme is expressed only in a few tissues, and therefore its inhibition is considered as a treatment option for osteoporosis to ameliorate estrogen deficiency. In this study, ligand-based pharmacophore models for 17β-HSD2 inhibitors were constructed and employed for virtual screening. From the virtual screening hits, 29 substances were evaluated in vitro for 17β-HSD2 inhibition. Seven compounds inhibited 17β-HSD2 with low micromolar IC50 values. To investigate structure–activity relationships (SAR), 30 more derivatives of the original hits were tested. The three most potent hits, 12, 22, and 15, had IC50 values of 240 nM, 1 μM, and 1.5 μM, respectively. All but 1 of the 13 identified inhibitors were selective over 17β-HSD1, the enzyme catalyzing conversion of estrone into estradiol. Three of the new, small, synthetic 17β-HSD2 inhibitors showed acceptable selectivity over other related HSDs, and six of them did not affect other HSDs. PMID:24960438

New leads for selective GSK-3 inhibition: pharmacophore mapping and virtual screening studies.

PubMed

Patel, Dhilon S; Bharatam, Prasad V

2006-01-01

Glycogen Synthase Kinase-3 is a regulatory serine/threonine kinase, which is being targeted for the treatment of a number of human diseases including type-2 diabetes mellitus, neurodegenerative diseases, cancer and chronic inflammation. Selective GSK-3 inhibition is an important requirement owing to the possibility of side effects arising from other kinases. A pharmacophore mapping strategy is employed in this work to identify new leads for selective GSK-3 inhibition. Ligands known to show selective GSK-3 inhibition were employed in generating a pharmacophore map using distance comparison method (DISCO). The derived pharmacophore map was validated using (i) important interactions involved in selective GSK-3 inhibitions, and (ii) an in-house database containing different classes of GSK-3 selective, non-selective and inactive molecules. New Lead identification was carried out by performing virtual screening using validated pharmacophoric query and three chemical databases namely NCI, Maybridge and Leadquest. Further data reduction was carried out by employing virtual filters based on (i) Lipinski's rule of 5 (ii) van der Waals bumps and (iii) restricting the number of rotatable bonds to seven. Final screening was carried out using FlexX based molecular docking study.

New leads for selective GSK-3 inhibition: pharmacophore mapping and virtual screening studies

NASA Astrophysics Data System (ADS)

Patel, Dhilon S.; Bharatam, Prasad V.

2006-01-01

Glycogen Synthase Kinase-3 is a regulatory serine/threonine kinase, which is being targeted for the treatment of a number of human diseases including type-2 diabetes mellitus, neurodegenerative diseases, cancer and chronic inflammation. Selective GSK-3 inhibition is an important requirement owing to the possibility of side effects arising from other kinases. A pharmacophore mapping strategy is employed in this work to identify new leads for selective GSK-3 inhibition. Ligands known to show selective GSK-3 inhibition were employed in generating a pharmacophore map using distance comparison method (DISCO). The derived pharmacophore map was validated using (i) important interactions involved in selective GSK-3 inhibitions, and (ii) an in-house database containing different classes of GSK-3 selective, non-selective and inactive molecules. New Lead identification was carried out by performing virtual screening using validated pharmacophoric query and three chemical databases namely NCI, Maybridge and Leadquest. Further data reduction was carried out by employing virtual filters based on (i) Lipinski's rule of 5 (ii) van der Waals bumps and (iii) restricting the number of rotatable bonds to seven. Final screening was carried out using FlexX based molecular docking study.

Design, synthesis, structural characterization by IR, (1) H, (13) C, (15) N, 2D-NMR, X-ray diffraction and evaluation of a new class of phenylaminoacetic acid benzylidene hydrazines as pfENR inhibitors.

PubMed

Samal, Ramanuj P; Khedkar, Vijay M; Pissurlenkar, Raghuvir R S; Bwalya, Angela Gono; Tasdemir, Deniz; Joshi, Ramesh A; Rajamohanan, P R; Puranik, Vedavati G; Coutinho, Evans C

2013-06-01

Recent studies have revealed that plasmodial enoyl-ACP reductase (pfENR, FabI), one of the crucial enzymes in the plasmodial type II fatty acid synthesis II (FAS II) pathway, is a promising target for liver stage malaria infections. Hence, pfENR inhibitors have the potential to be used as causal malarial prophylactic agents. In this study, we report the design, synthesis, structural characterization and evaluation of a new class of pfENR inhibitors. The search for inhibitors began with a virtual screen of the iResearch database by molecular docking. Hits obtained from the virtual screen were ranked according to their Glide score. One hit was selected as a lead and modified to improve its binding to pfENR; from this, a series of phenylamino acetic acid benzylidene hydrazides were designed and synthesized. These molecules were thoroughly characterized by IR, (1) H, (13) C, (15) N, 2D-NMR (COSY, NOESY, (1) H-(13) C, (1) H-(15) N HSQC and HMBC), and X-ray diffraction. NMR studies revealed the existence of conformational/configurational isomers around the amide and imine functionalities. The major species in DMSO solution is the E, E form, which is in dynamic equilibrium with the Z, E isomer. In the solid state, the molecule has a completely extended conformation and forms helical structures that are stabilized by strong hydrogen bond interactions, forming a helical structure stabilized by N-H…O interactions, a feature unique to this class of compounds. Furthermore, detailed investigation of the NMR spectra indicated the presence of a minor impurity in most compounds. The structure of this impurity was deduced as an imidazoline-4-one derivative based on (1) H-(13) C and (1) H-(15) H HMBC spectra and was confirmed from the NOESY spectra. The molecules were screened for in vitro activity against recombinant pfENR enzyme by a spectrophotometric assay. Four molecules, viz. 17, 7, 10, and 12 were found to be active at 7, 8, 10, and 12 μm concentration, respectively, showing promising pfENR inhibitory potential. A classification model was derived based on a binary QSAR approach termed recursive partitioning (RP) to highlight structural characteristics that could be tuned to improve activity. © 2013 John Wiley & Sons A/S.

A virtual screening method for inhibitory peptides of Angiotensin I-converting enzyme.

PubMed

Wu, Hongxi; Liu, Yalan; Guo, Mingrong; Xie, Jingli; Jiang, XiaMin

2014-09-01

Natural small peptides from foods have been proven to be efficient inhibitors of Angiotensin I-converting enzyme (ACE) for the regulation of blood pressure. The traditional ACE inhibitory peptides screening method is both time consuming and money costing, to the contrary, virtual screening method by computation can break these limitations. We establish a virtual screening method to obtain ACE inhibitory peptides with the help of Libdock module of Discovery Studio 3.5 software. A significant relationship between Libdock score and experimental IC(50) was found, Libdock score = 10.063 log(1/IC(50)) + 68.08 (R(2) = 0.62). The credibility of the relationship was confirmed by testing the coincidence of the estimated log(1/IC(50)) and measured log(1/IC(50)) (IC(50) is 50% inhibitory concentration toward ACE, in μmol/L) of 5 synthetic ACE inhibitory peptides, which was virtual hydrolyzed and screened from a kind of seafood, Phascolosoma esculenta. Accordingly, Libdock method is a valid IC(50) estimation tool and virtual screening method for small ACE inhibitory peptides. © 2014 Institute of Food Technologists®

A more detailed picture of the interactions between virtual screening-derived hits and the DNA G-quadruplex: NMR, molecular modelling and ITC studies.

PubMed

Trotta, Roberta; De Tito, Stefano; Lauri, Ilaria; La Pietra, Valeria; Marinelli, Luciana; Cosconati, Sandro; Martino, Luigi; Conte, Maria R; Mayol, Luciano; Novellino, Ettore; Randazzo, Antonio

2011-08-01

The growing amount of literature about G-quadruplex DNA clearly demonstrates that such a structure is no longer viewed as just a biophysical strangeness but it is instead being considered as an important target for the treatment of various human disorders such as cancers or venous thrombosis. In this scenario, with the aim of finding brand new molecular scaffolds able to interact with the groove of the DNA quadruplex [d(TGGGGT)](4), we recently performed a successful structure-based virtual screening (VS) campaign. As a result, six molecules were found to be somehow groove binders. Herein, we report the results of novel NMR titration experiments of these VS-derived ligands with modified quadruplexes, namely [d(TGG(Br)GGT)](4) and [d(TGGGG(Br)T)](4). The novel NMR spectroscopy experiments combined with molecular modelling studies, allow for a more detailed picture of the interaction between each binder and the quadruplex DNA. Noteworthy, isothermal titration calorimetry (ITC) measurements on the above-mentioned compounds revealed that 2, 4, and 6 besides their relatively small dimensions bind the DNA quadruplex [d(TGGGGT)](4) with higher affinity than distamycin A, to the best of our knowledge, the most potent groove binder identified thus far. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Functional Validation of Virtual Screening for Novel Agents with General Anesthetic Action at Ligand-Gated Ion Channels

PubMed Central

Heusser, Stephanie A.; Howard, Rebecca J.; Borghese, Cecilia M.; Cullins, Madeline A.; Broemstrup, Torben; Lee, Ui S.; Lindahl, Erik; Carlsson, Jens

2013-01-01

GABAA receptors play a crucial role in the actions of general anesthetics. The recently published crystal structure of the general anesthetic propofol bound to Gloeobacter violaceus ligand-gated ion channel (GLIC), a bacterial homolog of GABAA receptors, provided an opportunity to explore structure-based ligand discovery for pentameric ligand-gated ion channels (pLGICs). We used molecular docking of 153,000 commercially available compounds to identify molecules that interact with the propofol binding site in GLIC. In total, 29 compounds were selected for functional testing on recombinant GLIC, and 16 of these compounds modulated GLIC function. Active compounds were also tested on recombinant GABAA receptors, and point mutations around the presumed binding pocket were introduced into GLIC and GABAA receptors to test for binding specificity. The potency of active compounds was only weakly correlated with properties such as lipophilicity or molecular weight. One compound was found to mimic the actions of propofol on GLIC and GABAA, and to be sensitive to mutations that reduce the action of propofol in both receptors. Mutant receptors also provided insight about the position of the binding sites and the relevance of the receptor’s conformation for anesthetic actions. Overall, the findings support the feasibility of the use of virtual screening to discover allosteric modulators of pLGICs, and suggest that GLIC is a valid model system to identify novel GABAA receptor ligands. PMID:23950219

Discovery of Novel New Delhi Metallo-β-Lactamases-1 Inhibitors by Multistep Virtual Screening

PubMed Central

Wang, Xuequan; Lu, Meiling; Shi, Yang; Ou, Yu; Cheng, Xiaodong

2015-01-01

The emergence of NDM-1 containing multi-antibiotic resistant "Superbugs" necessitates the needs of developing of novel NDM-1inhibitors. In this study, we report the discovery of novel NDM-1 inhibitors by multi-step virtual screening. From a 2,800,000 virtual drug-like compound library selected from the ZINC database, we generated a focused NDM-1 inhibitor library containing 298 compounds of which 44 chemical compounds were purchased and evaluated experimentally for their ability to inhibit NDM-1 in vitro. Three novel NDM-1 inhibitors with micromolar IC50 values were validated. The most potent inhibitor, VNI-41, inhibited NDM-1 with an IC50 of 29.6 ± 1.3 μM. Molecular dynamic simulation revealed that VNI-41 interacted extensively with the active site. In particular, the sulfonamide group of VNI-41 interacts directly with the metal ion Zn1 that is critical for the catalysis. These results demonstrate the feasibility of applying virtual screening methodologies in identifying novel inhibitors for NDM-1, a metallo-β-lactamase with a malleable active site and provide a mechanism base for rational design of NDM-1 inhibitors using sulfonamide as a functional scaffold. PMID:25734558

A sensor network based virtual beam-like structure method for fault diagnosis and monitoring of complex structures with Improved Bacterial Optimization

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-02-01

This paper proposes a novel method for the fault diagnosis of complex structures based on an optimized virtual beam-like structure approach. A complex structure can be regarded as a combination of numerous virtual beam-like structures considering the vibration transmission path from vibration sources to each sensor. The structural 'virtual beam' consists of a sensor chain automatically obtained by an Improved Bacterial Optimization Algorithm (IBOA). The biologically inspired optimization method (i.e. IBOA) is proposed for solving the discrete optimization problem associated with the selection of the optimal virtual beam for fault diagnosis. This novel virtual beam-like-structure approach needs less or little prior knowledge. Neither does it require stationary response data, nor is it confined to a specific structure design. It is easy to implement within a sensor network attached to the monitored structure. The proposed fault diagnosis method has been tested on the detection of loosening screws located at varying positions in a real satellite-like model. Compared with empirical methods, the proposed virtual beam-like structure method has proved to be very effective and more reliable for fault localization.

Chemical correction of pre-mRNA splicing defects associated with sequestration of muscleblind-like 1 protein by expanded r(CAG)-containing transcripts.

PubMed

Kumar, Amit; Parkesh, Raman; Sznajder, Lukasz J; Childs-Disney, Jessica L; Sobczak, Krzysztof; Disney, Matthew D

2012-03-16

Recently, it was reported that expanded r(CAG) triplet repeats (r(CAG)(exp)) associated with untreatable neurological diseases cause pre-mRNA mis-splicing likely due to sequestration of muscleblind-like 1 (MBNL1) splicing factor. Bioactive small molecules that bind the 5'CAG/3'GAC motif found in r(CAG)(exp) hairpin structure were identified by using RNA binding studies and virtual screening/chemical similarity searching. Specifically, a benzylguanidine-containing small molecule was found to improve pre-mRNA alternative splicing of MBNL1-sensitive exons in cells expressing the toxic r(CAG)(exp). The compound was identified by first studying the binding of RNA 1 × 1 nucleotide internal loops to small molecules known to have affinity for nucleic acids. Those studies identified 4',6-diamidino-2-phenylindole (DAPI) as a specific binder to RNAs with the 5'CAG/3'GAC motif. DAPI was then used as a query molecule in a shape- and chemistry alignment-based virtual screen to identify compounds with improved properties, which identified 4-guanidinophenyl 4-guanidinobenzoate, a small molecule that improves pre-mRNA splicing defects associated with the r(CAG)(exp)-MBNL1 complex. This compound may facilitate the development of therapeutics to treat diseases caused by r(CAG)(exp) and could serve as a useful chemical tool to dissect the mechanisms of r(CAG)(exp) toxicity. The approach used in these studies, defining the small RNA motifs that bind small molecules with known affinity for nucleic acids and then using virtual screening to optimize them for bioactivity, may be generally applicable for designing small molecules that target other RNAs in the human genomic sequence.

Chemical correction of pre-mRNA splicing defects associated with sequestration of muscleblind-like 1 protein by expanded r(CAG) transcripts

PubMed Central

Kumar, Amit; Parkesh, Raman; Sznajder, Lukasz J.; Childs-Disney, Jessica; Sobczak, Krzysztof; Disney, Matthew D.

2012-01-01

Recently, it was reported that expanded r(CAG) triplet repeats (r(CAG)exp) associated with untreatable neurological diseases cause pre-mRNA mis-splicing likely due to sequestration of muscleblind-like 1 (MBNL1) splicing factor. Bioactive small molecules that bind the 5’CAG/3’GAC motif found in r(CAG)exp hairpin structure were identified by using RNA binding studies and virtual screening/chemical similarity searching. Specifically, a benzylguanidine-containing small molecule was found to improve pre-mRNA alternative splicing of MBNL1-sensitive exons in cells expressing the toxic r(CAG)exp. The compound was identified by first studying the binding of RNA 1×1 nucleotide internal loops to small molecules known to have affinity for nucleic acids. Those studies identified 4',6-diamidino-2-phenylindole (DAPI) as a specific binder to RNAs with the 5’CAG/3’GAC motif. DAPI was then used as a query molecule in a shape- and chemistry alignment-based virtual screen to identify compounds with improved properties, which identified 4-guanidinophenyl 4-guanidinobenzoate as small molecule capable of improving pre-mRNA splicing defects associated with the r(CAG)exp-MBNL1 complex. This compound may facilitate the development of therapeutics to treat diseases caused by r(CAG)exp and could serve as a useful chemical tool to dissect the mechanisms of r(CAG)exp toxicity. The approach used in these studies, defining the small RNA motifs that bind known nucleic acid binders and then using virtual screening to optimize them for bioactivity, may be generally applicable for designing small molecules that target other RNAs in human genomic sequence. PMID:22252896

Detection of the antiviral activity of epicatechin isolated from Salacia crassifolia (Celastraceae) against Mayaro virus based on protein C homology modelling and virtual screening.

PubMed

Ferreira, P G; Ferraz, A C; Figueiredo, J E; Lima, C F; Rodrigues, V G; Taranto, A G; Ferreira, J M S; Brandão, G C; Vieira-Filho, S A; Duarte, L P; de Brito Magalhães, C L; de Magalhães, J C

2018-06-01

Mayaro fever, caused by Mayaro virus (MAYV) is a sub-lethal disease with symptoms that are easily confused with those of dengue fever, except for polyarthralgia, which may culminate in physical incapacitation. Recently, outbreaks of MAYV have been documented in metropolitan areas, and to date, there is no therapy or vaccine available. Moreover, there is no information regarding the three-dimensional structure of the viral proteins of MAYV, which is important in the search for antivirals. In this work, we constructed a three-dimensional model of protein C of MAYV by homology modelling, and this was employed in a manner similar to that of receptors in virtual screening studies to evaluate 590 molecules as prospective antiviral agents. In vitro bioassays were utilized to confirm the potential antiviral activity of the flavonoid epicatechin isolated from Salacia crassifolia (Celastraceae). The virtual screening showed that six flavonoids were promising ligands for protein C. The bioassays showed potent antiviral action of epicatechin, which protected the cells from almost all of the effects of viral infection. An effective concentration (EC 50 ) of 0.247 μmol/mL was observed with a selectivity index (SI) of 7. The cytotoxicity assay showed that epicatechin has low toxicity, with a 50% cytotoxic concentration (CC 50 ) greater than 1.723 µmol/mL. Epicatechin was found to be twice as potent as the reference antiviral ribavirin. Furthermore, a replication kinetics assay showed a strong inhibitory effect of epicatechin on MAYV growth, with a reduction of at least four logs in virus production. Our results indicate that epicatechin is a promising candidate for further testing as an antiviral agent against Mayaro virus and other alphaviruses.

Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4.

PubMed

Xing, Jing; Lu, Wenchao; Liu, Rongfeng; Wang, Yulan; Xie, Yiqian; Zhang, Hao; Shi, Zhe; Jiang, Hao; Liu, Yu-Chih; Chen, Kaixian; Jiang, Hualiang; Luo, Cheng; Zheng, Mingyue

2017-07-24

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.

Local concurrent error detection and correction in data structures using virtual backpointers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, C.C.J.; Chen, P.P.; Fuchs, W.K.

1989-11-01

A new technique, based on virtual backpointers, is presented in this paper for local concurrent error detection and correction in linked data structures. Two new data structures utilizing virtual backpointers, the Virtual Double-Linked List and the B-Tree and Virtual Backpointers, are described. For these structures, double errors within a fixed-size checking window can be detected in constant time and single errors detected during forward moves can be corrected in constant time.

The BRIGHTEN program: implementation and evaluation of a program to bridge resources of an interdisciplinary geriatric health team via electronic networking.

PubMed

Emery, Erin E; Lapidos, Stan; Eisenstein, Amy R; Ivan, Iulia I; Golden, Robyn L

2012-12-01

To demonstrate the feasibility of the BRIGHTEN Program (Bridging Resources of an Interdisciplinary Geriatric Health Team via Electronic Networking), an interdisciplinary team intervention for assessing and treating older adults for depression in outpatient primary and specialty medical clinics. The BRIGHTEN team collaborates "virtually" to review patient assessment results, develop a treatment plan, and refer to appropriate team members for follow-up care. Older adults in 9 academic medical center clinics and 2 community-based clinics completed screening forms for symptoms of depression and anxiety. Those with positive screens engaged in comprehensive assessment with the BRIGHTEN Program Coordinator; the BRIGHTEN virtual team provided treatment recommendations based on the results of assessment. A collaborative treatment plan was developed with each participant, who was then connected to appropriate services. Two thousand four hundred twenty-two older adults were screened in participating clinics over a 40-month period. Eight hundred fifty-nine older adults screened positive, and 150 elected to enroll in BRIGHTEN. From baseline to 6 months, significant improvements were found in depression symptoms (Geriatric Depression Scale, p < .01) and general mental health (SF-12 Mental Component, p < .01). The BRIGHTEN Program demonstrated that an interdisciplinary virtual team linked with outpatient medical clinics can be an effective, nonthreatening, and seamless approach to enable older adults to access treatment for depression.

Structure-based virtual screening toward the discovery of novel inhibitors for impeding the protein-protein interaction between HIV-1 integrase and human lens epithelium-derived growth factor (LEDGF/p75).

PubMed

Panwar, Umesh; Singh, Sanjeev Kumar

2017-10-23

HIV-1 integrase is a unique promising component of the viral replication cycle, catalyzing the integration of reverse transcribed viral cDNA into the host cell genome. Generally, IN activity requires both viral as well as a cellular co-factor in the processing replication cycle. Among them, the human lens epithelium-derived growth factor (LEDGF/p75) represented as promising cellular co-factor which supports the viral replication by tethering IN to the chromatin. Due to its major importance in the early steps of HIV replication, the interaction between IN and LEDGF/p75 has become a pleasing target for anti-HIV drug discovery. The present study involves the finding of novel inhibitor based on the information of dimeric CCD of IN in complex with known inhibitor, which were carried out by applying a structure-based virtual screening concept with molecular docking. Additionally, Free binding energy, ADME properties, PAINS analysis, Density Functional Theory, and Enrichment Calculations were performed on selected compounds for getting a best lead molecule. On the basis of these analyses, the current study proposes top 3 compounds: Enamine-Z742267384, Maybridge-HTS02400, and Specs-AE-848/37125099 with acceptable pharmacological properties and enhanced binding affinity to inhibit the interaction between IN and LEDGF/p75. Furthermore, Simulation studies were carried out on these molecules to expose their dynamics behavior and stability. We expect that the findings obtained here could be future therapeutic agents and may provide an outline for the experimental studies to stimulate the innovative strategy for research community.

Anticonvulsant activity of artificial sweeteners: a structural link between sweet-taste receptor T1R3 and brain glutamate receptors.

PubMed

Talevi, Alan; Enrique, Andrea V; Bruno-Blanch, Luis E

2012-06-15

A virtual screening campaign based on application of a topological discriminant function capable of identifying novel anticonvulsant agents indicated several widely-used artificial sweeteners as potential anticonvulsant candidates. Acesulfame potassium, cyclamate and saccharin were tested in the Maximal Electroshock Seizure model (mice, ip), showing moderate anticonvulsant activity. We hypothesized a probable structural link between the receptor responsible of sweet taste and anticonvulsant molecular targets. Bioinformatic tools confirmed a highly significant sequence-similarity between taste-related protein T1R3 and several metabotropic glutamate receptors from different species, including glutamate receptors upregulated in epileptogenesis and certain types of epilepsy. Copyright © 2012 Elsevier Ltd. All rights reserved.

DOVIS: an implementation for high-throughput virtual screening using AutoDock.

PubMed

Zhang, Shuxing; Kumar, Kamal; Jiang, Xiaohui; Wallqvist, Anders; Reifman, Jaques

2008-02-27

Molecular-docking-based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated. In addition to the selection of a sound docking strategy with appropriate scoring functions, another technical challenge is to in silico screen millions of compounds in a reasonable time. To meet this challenge, it is necessary to use high performance computing (HPC) platforms and techniques. However, the development of an integrated HPC system that makes efficient use of its elements is not trivial. We have developed an application termed DOVIS that uses AutoDock (version 3) as the docking engine and runs in parallel on a Linux cluster. DOVIS can efficiently dock large numbers (millions) of small molecules (ligands) to a receptor, screening 500 to 1,000 compounds per processor per day. Furthermore, in DOVIS, the docking session is fully integrated and automated in that the inputs are specified via a graphical user interface, the calculations are fully integrated with a Linux cluster queuing system for parallel processing, and the results can be visualized and queried. DOVIS removes most of the complexities and organizational problems associated with large-scale high-throughput virtual screening, and provides a convenient and efficient solution for AutoDock users to use this software in a Linux cluster platform.

Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands

PubMed Central

2015-01-01

The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure–activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs. PMID:24410373

Pharmacophore Modeling and in Silico/in Vitro Screening for Human Cytochrome P450 11B1 and Cytochrome P450 11B2 Inhibitors.

PubMed

Akram, Muhammad; Waratchareeyakul, Watcharee; Haupenthal, Joerg; Hartmann, Rolf W; Schuster, Daniela

2017-01-01

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing's syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8 - 10 ), one selective CYP11B1 inhibitor (Compound 11 , IC 50 = 2.5 μM), and one selective CYP11B2 inhibitor (compound 12 , IC 50 = 1.1 μM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

Pharmacophore modeling and in silico / in vitro screening for human cytochrome P450 11B1 & cytochrome P450 11B2 inhibitors

NASA Astrophysics Data System (ADS)

Akram, Muhammad; Waratchareeyakul, Watcharee; Haupenthal, Joerg; Hartmann, Rolf W.; Schuster, Daniela

2017-12-01

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing’s syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8-10), one selective CYP11B1 inhibitor (Compound 11, IC50 = 2.5 µM), and one selective CYP11B2 inhibitor (compound 12, IC50 = 1.1 µM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model.

PubMed

Kim, J; Lee, C; Chong, Y

2009-01-01

Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q(2) = 0.763, r(2) = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.

Identification of novel antitubulin agents by using a virtual screening approach based on a 7-point pharmacophore model of the tubulin colchi-site.

PubMed

Massarotti, Alberto; Theeramunkong, Sewan; Mesenzani, Ornella; Caldarelli, Antonio; Genazzani, Armando A; Tron, Gian Cesare

2011-12-01

Tubulin inhibition represents an established target in the field of anticancer research, and over the last 20 years, an intensive search for new antimicrotubule agents has occurred. Indeed, in silico models have been presented that might aid the discovery of novel agents. Among these, a 7-point pharmacophore model has been recently proposed. As a formal proof of this model, we carried out a ligand-based virtual screening on the colchicine-binding site. In vitro testing demonstrated that two compounds displayed a cytotoxic profile on neuroblastoma cancer cells (SH-SY5H) and one had an antitubulinic profile. © 2011 John Wiley & Sons A/S.

Discovery of novel bacterial RNA polymerase inhibitors: pharmacophore-based virtual screening and hit optimization.

PubMed

Hinsberger, Stefan; Hüsecken, Kristina; Groh, Matthias; Negri, Matthias; Haupenthal, Jörg; Hartmann, Rolf W

2013-11-14

The bacterial RNA polymerase (RNAP) is a validated target for broad spectrum antibiotics. However, the efficiency of drugs is reduced by resistance. To discover novel RNAP inhibitors, a pharmacophore based on the alignment of described inhibitors was used for virtual screening. In an optimization process of hit compounds, novel derivatives with improved in vitro potency were discovered. Investigations concerning the molecular mechanism of RNAP inhibition reveal that they prevent the protein-protein interaction (PPI) between σ(70) and the RNAP core enzyme. Besides of reducing RNA formation, the inhibitors were shown to interfere with bacterial lipid biosynthesis. The compounds were active against Gram-positive pathogens and revealed significantly lower resistance frequencies compared to clinically used rifampicin.

Performance of a docking/molecular dynamics protocol for virtual screening of nutlin-class inhibitors of Mdmx.

PubMed

Bharatham, Nagakumar; Finch, Kristin E; Min, Jaeki; Mayasundari, Anand; Dyer, Michael A; Guy, R Kiplin; Bashford, Donald

2017-06-01

A virtual screening protocol involving docking and molecular dynamics has been tested against the results of fluorescence polarization assays testing the potency of a series of compounds of the nutlin class for inhibition of the interaction between p53 and Mdmx, an interaction identified as a driver of certain cancers. The protocol uses a standard docking method (AutoDock) with a cutoff based on the AutoDock score (ADscore), followed by molecular dynamics simulation with a cutoff based on root-mean-square-deviation (RMSD) from the docked pose. An analysis of the experimental and computational results shows modest performance of ADscore alone, but dramatically improved performance when RMSD is also used. Published by Elsevier Inc.

CamMedNP: building the Cameroonian 3D structural natural products database for virtual screening.

PubMed

Ntie-Kang, Fidele; Mbah, James A; Mbaze, Luc Meva'a; Lifongo, Lydia L; Scharfe, Michael; Hanna, Joelle Ngo; Cho-Ngwa, Fidelis; Onguéné, Pascal Amoa; Owono Owono, Luc C; Megnassan, Eugene; Sippl, Wolfgang; Efange, Simon M N

2013-04-16

Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. We present CamMedNP - a new database beginning with more than 2,500 compounds of natural origin, along with some of their derivatives which were obtained through hemisynthesis. These are pure compounds which have been previously isolated and characterized using modern spectroscopic methods and published by several research teams spread across Cameroon. In the present study, 224 distinct medicinal plant species belonging to 55 plant families from the Cameroonian flora have been considered. About 80 % of these have been previously published and/or referenced in internationally recognized journals. For each compound, the optimized 3D structure, drug-like properties, plant source, collection site and currently known biological activities are given, as well as literature references. We have evaluated the "drug-likeness" of this database using Lipinski's "Rule of Five". A diversity analysis has been carried out in comparison with the ChemBridge diverse database. CamMedNP could be highly useful for database screening and natural product lead generation programs.

Building Virtuality into University-Based Human Resources Policy in China's Universities

ERIC Educational Resources Information Center

Guoliang, Zhang

2005-01-01

On the basis of discussing the notion of virtual human resources and its structure, this paper analyzes the necessity of building up virtual university teaching staff and proposes a model for the structural makeup of virtual university teaching staff.

In silico strategies for the selection of chelating compounds with potential application in metal-promoted neurodegenerative diseases

NASA Astrophysics Data System (ADS)

Rodríguez-Rodríguez, Cristina; Rimola, Albert; Alí-Torres, Jorge; Sodupe, Mariona; González-Duarte, Pilar

2011-01-01

The development of new strategies to find commercial molecules with promising biochemical features is a main target in the field of biomedicine chemistry. In this work we present an in silico-based protocol that allows identifying commercial compounds with suitable metal coordinating and pharmacokinetic properties to act as metal-ion chelators in metal-promoted neurodegenerative diseases (MpND). Selection of the chelating ligands is done by combining quantum chemical calculations with the search of commercial compounds on different databases via virtual screening. Starting from different designed molecular frameworks, which mainly constitute the binding site, the virtual screening on databases facilitates the identification of different commercial molecules that enclose such scaffolds and, by imposing a set of chemical and pharmacokinetic filters, obey some drug-like requirements mandatory to deal with MpND. The quantum mechanical calculations are useful to gauge the chelating properties of the selected candidate molecules by determining the structure of metal complexes and evaluating their stability constants. With the proposed strategy, commercial compounds containing N and S donor atoms in the binding sites and capable to cross the BBB have been identified and their chelating properties analyzed.

Two-track virtual screening approach to identify both competitive and allosteric inhibitors of human small C-terminal domain phosphatase 1

NASA Astrophysics Data System (ADS)

Park, Hwangseo; Lee, Hye Seon; Ku, Bonsu; Lee, Sang-Rae; Kim, Seung Jun

2017-08-01

Despite a wealth of persuasive evidence for the involvement of human small C-terminal domain phosphatase 1 (Scp1) in the impairment of neuronal differentiation and in Huntington's disease, small-molecule inhibitors of Scp1 have been rarely reported so far. This study aims to the discovery of both competitive and allosteric Scp1 inhibitors through the two-track virtual screening procedure. By virtue of the improvement of the scoring function by implementing a new molecular solvation energy term and by reoptimizing the atomic charges for the active-site Mg2+ ion cluster, we have been able to identify three allosteric and five competitive Scp1 inhibitors with low-micromolar inhibitory activity. Consistent with the results of kinetic studies on the inhibitory mechanisms, the allosteric inhibitors appear to be accommodated in the peripheral binding pocket through the hydrophobic interactions with the nonpolar residues whereas the competitive ones bind tightly in the active site with a direct coordination to the central Mg2+ ion. Some structural modifications to improve the biochemical potency of the newly identified inhibitors are proposed based on the binding modes estimated with docking simulations.

Identification of potential hit compounds for Dengue virus NS2B/NS3 protease inhibitors by combining virtual screening and binding free energy calculations.

PubMed

Wichapong, K; Nueangaudom, A; Pianwanit, S; Sippl, W; Kokpol, S

2013-09-01

Dengue virus (DV) infections are a serious public health problem and there is currently no vaccine or drug treatment. NS2B/NS3 protease, an essential enzyme for viral replication, is one of the promising targets in the search for drugs against DV. In this research work, virtual screening (VS) was carried out on four multi-conformational databases using several criteria. Firstly, molecular dynamics simulations of the NS2B/NS3 protease and four known inhibitors, which reveal an importance of both electrostatic and van der Waals interactions in stabilizing the ligand-enzyme interaction, were used to generate three different pharmacophore models (a structure-based, a static and a dynamic). Subsequently, these three models were employed for pharmacophore search in the VS. Secondly, compounds passing the first criterion were further reduced using the Lipinski's rule of five to keep only compounds with drug-like properties. Thirdly, molecular docking calculations were performed to remove compounds with unsuitable ligand-enzyme interactions. Finally, binding free energy of each compound was calculated. Compounds having better energy than the known inhibitors were selected and thus 20 potential hits were obtained.

Stereoscopic vascular models of the head and neck: A computed tomography angiography visualization.

PubMed

Cui, Dongmei; Lynch, James C; Smith, Andrew D; Wilson, Timothy D; Lehman, Michael N

2016-01-01

Computer-assisted 3D models are used in some medical and allied health science schools; however, they are often limited to online use and 2D flat screen-based imaging. Few schools take advantage of 3D stereoscopic learning tools in anatomy education and clinically relevant anatomical variations when teaching anatomy. A new approach to teaching anatomy includes use of computed tomography angiography (CTA) images of the head and neck to create clinically relevant 3D stereoscopic virtual models. These high resolution images of the arteries can be used in unique and innovative ways to create 3D virtual models of the vasculature as a tool for teaching anatomy. Blood vessel 3D models are presented stereoscopically in a virtual reality environment, can be rotated 360° in all axes, and magnified according to need. In addition, flexible views of internal structures are possible. Images are displayed in a stereoscopic mode, and students view images in a small theater-like classroom while wearing polarized 3D glasses. Reconstructed 3D models enable students to visualize vascular structures with clinically relevant anatomical variations in the head and neck and appreciate spatial relationships among the blood vessels, the skull and the skin. © 2015 American Association of Anatomists.

Prevention of IcaA regulated poly N-acetyl glucosamine formation in Staphylococcus aureus biofilm through new-drug like inhibitors: In silico approach and MD simulation study.

PubMed

Gupta, Ayushi; Mishra, Swechha; Singh, Sangeeta; Mishra, Sonali

2017-09-01

The effectiveness of various ligands against the protein structure of IcaA of the IcaABCD gene locus of Staphylococcus aureus were examined using the approach of structure based drug designing in reference with the protein's efficiency to form biofilms. Four compounds CID42738592, CID90468752, CID24277882, and CID6435208 were secluded from a database of 31,242 inhibitory ligands on the justification of the evaluated values falling under the four - tier structure based virtual screening. Under this principle value of least binding energy, human oral absorption and ADME properties were taken into consideration. Using the Glide module of Schrödinger, the above mentioned ligands showed an effective action against the protein IcaA which showed reduced activity as a glucosaminyl transferase. The complex of protein and ligand with best docking score was chosen for simulation studies. Structure based drug designing for the protein IcaA has given us potential leads as anti - biofilm agents. These screened out ligands might enable the development of new therapeutic strategies aimed at disrupting Staphylococcus aureus biofilms. The complex was showing stability towards the end of time for which it has been put for simulation. Thus molecule could be considered for making of biofilms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigation of MM-PBSA rescoring of docking poses.

PubMed

Thompson, David C; Humblet, Christine; Joseph-McCarthy, Diane

2008-05-01

Target-based virtual screening is increasingly used to generate leads for targets for which high quality three-dimensional (3D) structures are available. To allow large molecular databases to be screened rapidly, a tiered scoring scheme is often employed whereby a simple scoring function is used as a fast filter of the entire database and a more rigorous and time-consuming scoring function is used to rescore the top hits to produce the final list of ranked compounds. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approaches are currently thought to be quite effective at incorporating implicit solvation into the estimation of ligand binding free energies. In this paper, the ability of a high-throughput MM-PBSA rescoring function to discriminate between correct and incorrect docking poses is investigated in detail. Various initial scoring functions are used to generate docked poses for a subset of the CCDC/Astex test set and to dock one set of actives/inactives from the DUD data set. The effectiveness of each of these initial scoring functions is discussed. Overall, the ability of the MM-PBSA rescoring function to (i) regenerate the set of X-ray complexes when docking the bound conformation of the ligand, (ii) regenerate the X-ray complexes when docking conformationally expanded databases for each ligand which include "conformation decoys" of the ligand, and (iii) enrich known actives in a virtual screen for the mineralocorticoid receptor in the presence of "ligand decoys" is assessed. While a pharmacophore-based molecular docking approach, PhDock, is used to carry out the docking, the results are expected to be general to use with any docking method.

Importance of the pharmacological profile of the bound ligand in enrichment on nuclear receptors: toward the use of experimentally validated decoy ligands.

PubMed

Lagarde, Nathalie; Zagury, Jean-François; Montes, Matthieu

2014-10-27

The evaluation of virtual ligand screening methods is of major importance to ensure their reliability. Taking into account the agonist/antagonist pharmacological profile should improve the quality of the benchmarking data sets since ligand binding can induce conformational changes in the nuclear receptor structure and such changes may vary according to the agonist/antagonist ligand profile. We indeed found that splitting the agonist and antagonist ligands into two separate data sets for a given nuclear receptor target significantly enhances the quality of the evaluation. The pharmacological profile of the ligand bound in the binding site of the target structure was also found to be an additional critical parameter. We also illustrate that active compound data sets for a given pharmacological activity can be used as a set of experimentally validated decoy ligands for another pharmacological activity to ensure a reliable and challenging evaluation of virtual screening methods.

Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer's disease.

PubMed

Patel, Chirag N; Georrge, John J; Modi, Krunal M; Narechania, Moksha B; Patel, Daxesh P; Gonzalez, Frank J; Pandya, Himanshu A

2017-12-27

Alzheimer's disease (AD) is one of the most significant neurodegenerative disorders and its symptoms mostly appear in aged people. Catechol-o-methyltransferase (COMT) is one of the known target enzymes responsible for AD. With the use of 23 known inhibitors of COMT, a query has been generated and validated by screening against the database of 1500 decoys to obtain the GH score and enrichment value. The crucial features of the known inhibitors were evaluated by the online ZINC Pharmer to identify new leads from a ZINC database. Five hundred hits were retrieved from ZINC Pharmer and by ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering by using FAF-Drug-3 and 36 molecules were considered for molecular docking. From the COMT inhibitors, opicapone, fenoldopam, and quercetin were selected, while ZINC63625100_413 ZINC39411941_412, ZINC63234426_254, ZINC63637968_451, and ZINC64019452_303 were chosen for the molecular dynamics simulation analysis having high binding affinity and structural recognition. This study identified the potential COMT inhibitors through pharmacophore-based inhibitor screening leading to a more complete understanding of molecular-level interactions.

Structure-based discovery of pyrazolobenzothiazine derivatives as inhibitors of hepatitis C virus replication

PubMed Central

Barreca, Maria Letizia; Manfroni, Giuseppe; Leyssen, Pieter; Winquist, Johan; Kaushik-Basu, Neerja; Paeshuyse, Jan; Krishnan, Ramalingam; Iraci, Nunzio; Sabatini, Stefano; Tabarrini, Oriana; Basu, Amartya; Danielson, U. Helena; Neyts, Johan; Cecchetti, Violetta

2013-01-01

The NS5B RNA-dependent RNA polymerase is an attractive target for the development of novel and selective inhibitors of hepatitis C virus replication. In order to identify novel structural hits as anti-HCV agents, we performed structure-based virtual screening of our in-house library followed by rational drug design, organic synthesis and biological testing. These studies led to the identification of pyrazolobenzothiazine scaffold as a suitable template for obtaining novel anti-HCV agents targeting the NS5B polymerase. The best compound of this series was the meta-fluoro-N-1-phenyl pyrazolobenzothiazine derivative 4a, which exhibited an EC50= 3.6 µM, EC90= 25.6 µM and CC50 > 180 µM in the Huh 9–13 replicon system, thus providing a good starting point for further hit evolution. PMID:23409936

Molecular dynamics, flexible docking, virtual screening, ADMET predictions, and molecular interaction field studies to design novel potential MAO-B inhibitors.

PubMed

Braun, Glaucia H; Jorge, Daniel M M; Ramos, Henrique P; Alves, Raquel M; da Silva, Vinicius B; Giuliatti, Silvana; Sampaio, Suley Vilela; Taft, Carlton A; Silva, Carlos H T P

2008-02-01

Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson's disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.

Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products.

PubMed

Gao, Qi; Wang, Yijun; Hou, Jiaying; Yao, Qizheng; Zhang, Ji

2017-07-01

Matrix metalloproteinase-9 (MMP-9) is an attractive target for cancer therapy. In this study, the pharmacophore model of MMP-9 inhibitors is built based on the experimental binding structures of multiple receptor-ligand complexes. It is found that the pharmacophore model consists of six chemical features, including two hydrogen bond acceptors, one hydrogen bond donor, one ring aromatic regions, and two hydrophobic (HY) features. Among them, the two HY features are especially important because they can enter the S1' pocket of MMP-9 which determines the selectivity of MMP-9 inhibitors. The reliability of pharmacophore model is validated based on the two different decoy sets and relevant experimental data. The virtual screening, combining pharmacophore model with molecular docking, is performed to identify the selective MMP-9 inhibitors from a database of natural products. The four novel MMP-9 inhibitors of natural products, NP-000686, NP-001752, NP-014331, and NP-015905, are found; one of them, NP-000686, is used to perform the experiment of in vitro bioassay inhibiting MMP-9, and the IC 50 value was estimated to be only 13.4 µM, showing the strongly inhibitory activity of NP-000686 against MMP-9, which suggests that our screening results should be reliable. The binding modes of screened inhibitors with MMP-9 active sites were discussed. In addition, the ADMET properties and physicochemical properties of screened four compounds were assessed. The found MMP-9 inhibitors of natural products could serve as the lead compounds for designing the new MMP-9 inhibitors by carrying out structural modifications in the future.

Virtual reality skills training for health care professionals in alcohol screening and brief intervention.

PubMed

Fleming, Michael; Olsen, Dale; Stathes, Hilary; Boteler, Laura; Grossberg, Paul; Pfeifer, Judie; Schiro, Stephanie; Banning, Jane; Skochelak, Susan

2009-01-01

Educating physicians and other health care professionals about the identification and treatment of patients who drink more than recommended limits is an ongoing challenge. An educational randomized controlled trial was conducted to test the ability of a stand-alone training simulation to improve the clinical skills of health care professionals in alcohol screening and intervention. The "virtual reality simulation" combined video, voice recognition, and nonbranching logic to create an interactive environment that allowed trainees to encounter complex social cues and realistic interpersonal exchanges. The simulation included 707 questions and statements and 1207 simulated patient responses. A sample of 102 health care professionals (10 physicians; 30 physician assistants or nurse practitioners; 36 medical students; 26 pharmacy, physican assistant, or nurse practitioner students) were randomly assigned to a no training group (n = 51) or a computer-based virtual reality intervention (n = 51). Professionals in both groups had similar pretest standardized patient alcohol screening skill scores: 53.2 (experimental) vs 54.4 (controls), 52.2 vs 53.7 alcohol brief intervention skills, and 42.9 vs 43.5 alcohol referral skills. After repeated practice with the simulation there were significant increases in the scores of the experimental group at 6 months after randomization compared with the control group for the screening (67.7 vs 58.1; P < .001) and brief intervention (58.3 vs 51.6; P < .04) scenarios. The technology tested in this trial is the first virtual reality simulation to demonstrate an increase in the alcohol screening and brief intervention skills of health care professionals.

Virtual Reality Skills Training for Health Care Professionals in Alcohol Screening and Brief Intervention

PubMed Central

Fleming, Michael; Olsen, Dale; Stathes, Hilary; Boteler, Laura; Grossberg, Paul; Pfeifer, Judie; Schiro, Stephanie; Banning, Jane; Skochelak, Susan

2009-01-01

Background Educating physicians and other health care professionals to identify and treat patients who drink above recommended limits is an ongoing challenge. Methods An educational Randomized Control Trial (RCT) was conducted to test the ability of a stand alone training simulation to improve the clinical skills of health care professionals in alcohol screening and intervention. The “virtual reality simulation” combines video, voice recognition and non branching logic to create an interactive environment that allows trainees to encounter complex social cues and realistic interpersonal exchanges. The simulation includes 707 questions and statements and 1207 simulated patient responses. Results A sample of 102 health care professionals (10 physicians; 30 physician assistants [PAs] or nurse practitioners [NPs]; 36 medical students; 26 pharmacy, PA or NP students) were randomly assigned to no training (n=51) or a computer based virtual reality intervention (n=51). Subjects in both groups had similar pre-test standardized patient alcohol screening skill scores – 53.2 (experimental) vs. 54.4 (controls), 52.2 vs. 53.7 alcohol brief intervention skills, and 42.9 vs. 43.5 alcohol referral skills. Following repeated practice with the simulation there were significant increases in the scores of the experimental group at 6 months post-randomization compared to the control group for the screening (67.7 vs. 58.1, p<.001) and brief intervention (58.3 vs. 51.6, p<.04) scenarios. Conclusions The technology tested in this trial is the first virtual reality simulation to demonstrate an increase in the alcohol screening and brief intervention skills of health care professionals. PMID:19587253

A Rapid Python-Based Methodology for Target-Focused Combinatorial Library Design.

PubMed

Li, Shiliang; Song, Yuwei; Liu, Xiaofeng; Li, Honglin

2016-01-01

The chemical space is so vast that only a small portion of it has been examined. As a complementary approach to systematically probe the chemical space, virtual combinatorial library design has extended enormous impacts on generating novel and diverse structures for drug discovery. Despite the favorable contributions, high attrition rates in drug development that mainly resulted from lack of efficacy and side effects make it increasingly challenging to discover good chemical starting points. In most cases, focused libraries, which are restricted to particular regions of the chemical space, are deftly exploited to maximize hit rate and improve efficiency at the beginning of the drug discovery and drug development pipeline. This paper presented a valid methodology for fast target-focused combinatorial library design in both reaction-based and production-based ways with the library creating rates of approximately 70,000 molecules per second. Simple, quick and convenient operating procedures are the specific features of the method. SHAFTS, a hybrid 3D similarity calculation software, was embedded to help refine the size of the libraries and improve hit rates. Two target-focused (p38-focused and COX2-focused) libraries were constructed efficiently in this study. This rapid library enumeration method is portable and applicable to any other targets for good chemical starting points identification collaborated with either structure-based or ligand-based virtual screening.

Design of a Generic Questionnaire for Reflective Evaluation of a Virtual Reality-Based Intervention Using Virtual Dolphins for Children with Autism

ERIC Educational Resources Information Center

Chia, Noel Kok Hwee; Li, Jenyi

2012-01-01

There is an alarming increase in more Singaporean children diagnosed with special needs and it could be attributed to higher awareness and better screening procedure. However, research and development on various intervention strategies for children with special needs is still very lacking. With the introduction of information and communication…

Modelling, simulation and verification of the screening process of a swing-bar sieve based on the DEM

NASA Astrophysics Data System (ADS)

Wang, Yang; Yu, Jianqun; Yu, Yajun

2018-05-01

To solve the problems in the DEM simulations of the screening process of a swing-bar sieve, in this paper we propose the real-virtual boundary method to build the geometrical model of the screen deck on a swing-bar sieve. The motion of the swing-bar sieve is modelled by the planer multi-body kinematics. A coupled model of the discrete element method (DEM) with multi-body kinematics (MBK) is presented to simulate the flowing and passing processes of soybean particles on the screen deck. By the comparison of the simulated results with the experimental results of the screening process of the LA-LK laboratory scale swing-bar sieve, the feasibility and validity of the real-virtual boundary method and the coupled DEM-MBK model we proposed in this paper can be verified. This work provides the basis for the optimization design of the swing-bar sieve with circular apertures and complex motion.

Dynamic undocking and the quasi-bound state as tools for drug discovery

NASA Astrophysics Data System (ADS)

Ruiz-Carmona, Sergio; Schmidtke, Peter; Luque, F. Javier; Baker, Lisa; Matassova, Natalia; Davis, Ben; Roughley, Stephen; Murray, James; Hubbard, Rod; Barril, Xavier

2017-03-01

There is a pressing need for new technologies that improve the efficacy and efficiency of drug discovery. Structure-based methods have contributed towards this goal but they focus on predicting the binding affinity of protein-ligand complexes, which is notoriously difficult. We adopt an alternative approach that evaluates structural, rather than thermodynamic, stability. As bioactive molecules present a static binding mode, we devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a quasi-bound state at which the ligand has just broken the most important native contact with the receptor. This non-equilibrium property is surprisingly effective in virtual screening because true ligands form more-resilient interactions than decoys. Notably, DUck is orthogonal to docking and other 'thermodynamic' methods. We demonstrate the potential of the docking-undocking combination in a fragment screening against the molecular chaperone and oncology target Hsp90, for which we obtain novel chemotypes and a hit rate that approaches 40%.

Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists

PubMed Central

Koes, David; Khoury, Kareem; Huang, Yijun; Wang, Wei; Bista, Michal; Popowicz, Grzegorz M.; Wolf, Siglinde; Holak, Tad A.; Dömling, Alexander; Camacho, Carlos J.

2012-01-01

Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure. PMID:22427896

Rational design of novel TLR4 ligands by in silico screening and their functional and structural characterization in vitro.

PubMed

Honegr, Jan; Malinak, David; Dolezal, Rafael; Soukup, Ondrej; Benkova, Marketa; Hroch, Lukas; Benek, Ondrej; Janockova, Jana; Kuca, Kamil; Prymula, Roman

2018-02-25

The purpose of this study was to identify new small molecules that possess activity on human toll-like receptor 4 associated with the myeloid differentiation protein 2 (hTLR4/MD2). Following current rational drug design principles, we firstly performed a ligand and structure based virtual screening of more than 130 000 compounds to discover until now unknown class of hTLR4/MD2 modulators that could be used as novel type of immunologic adjuvants. The core of the in silico study was molecular docking of flexible ligands in a partially flexible hTLR4/MD2 receptor model using a peta-flops-scale supercomputer. The most promising substances resulting from this study, related to anthracene-succimide hybrids, were synthesized and tested. The best prepared candidate exhibited 80% of Monophosphoryl Lipid A in vitro agonistic activity in cell lines expressing hTLR4/MD2. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Complete genome-wide screening and subtractive genomic approach revealed new virulence factors, potential drug targets against bio-war pathogen Brucella melitensis 16M

PubMed Central

Pradeepkiran, Jangampalli Adi; Sainath, Sri Bhashyam; Kumar, Konidala Kranthi; Bhaskar, Matcha

2015-01-01

Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes) to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50%) to Silicibacter pomeroyi DUF1285 family protein (2RE3). A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the glycerol structural analogs from the PubChem database. We identified five best inhibitors with strong affinities, stable interactions, and also with reliable drug-like properties. Hence, these leads might be used as the most effective inhibitors of modeled protein. The outcome of the present work of virtual screening of putative gene targets might facilitate design of potential drugs for better treatment against brucellosis. PMID:25834405

In Silico Identification of Mimicking Molecules as Defense Inducers Triggering Jasmonic Acid Mediated Immunity against Alternaria Blight Disease in Brassica Species

PubMed Central

Pathak, Rajesh K.; Baunthiyal, Mamta; Shukla, Rohit; Pandey, Dinesh; Taj, Gohar; Kumar, Anil

2017-01-01

Alternaria brassicae and Alternaria brassicicola are two major phytopathogenic fungi which cause Alternaria blight, a recalcitrant disease on Brassica crops throughout the world, which is highly destructive and responsible for significant yield losses. Since no resistant source is available against Alternaria blight, therefore, efforts have been made in the present study to identify defense inducer molecules which can induce jasmonic acid (JA) mediated defense against the disease. It is believed that JA triggered defense response will prevent necrotrophic mode of colonization of Alternaria brassicae fungus. The JA receptor, COI1 is one of the potential targets for triggering JA mediated immunity through interaction with JA signal. In the present study, few mimicking compounds more efficient than naturally occurring JA in terms of interaction with COI1 were identified through virtual screening and molecular dynamics simulation studies. A high quality structural model of COI1 was developed using the protein sequence of Brassica rapa. This was followed by virtual screening of 767 analogs of JA from ZINC database for interaction with COI1. Two analogs viz. ZINC27640214 and ZINC43772052 showed more binding affinity with COI1 as compared to naturally occurring JA. Molecular dynamics simulation of COI1 and COI1-JA complex, as well as best screened interacting structural analogs of JA with COI1 was done for 50 ns to validate the stability of system. It was found that ZINC27640214 possesses efficient, stable, and good cell permeability properties. Based on the obtained results and its physicochemical properties, it is capable of mimicking JA signaling and may be used as defense inducers for triggering JA mediated resistance against Alternaria blight, only after further validation through field trials. PMID:28487711

Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

PubMed

Kirchmair, Johannes; Markt, Patrick; Distinto, Simona; Wolber, Gerhard; Langer, Thierry

2008-01-01

Within the last few years a considerable amount of evaluative studies has been published that investigate the performance of 3D virtual screening approaches. Thereby, in particular assessments of protein-ligand docking are facing remarkable interest in the scientific community. However, comparing virtual screening approaches is a non-trivial task. Several publications, especially in the field of molecular docking, suffer from shortcomings that are likely to affect the significance of the results considerably. These quality issues often arise from poor study design, biasing, by using improper or inexpressive enrichment descriptors, and from errors in interpretation of the data output. In this review we analyze recent literature evaluating 3D virtual screening methods, with focus on molecular docking. We highlight problematic issues and provide guidelines on how to improve the quality of computational studies. Since 3D virtual screening protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of test sets on the outcome of evaluations. Moreover, we investigate the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D virtual screening methods. Furthermore, we review the significance and suitability of RMSD as a measure for the accuracy of protein-ligand docking algorithms and of conformational space sub sampling algorithms.

Virtual screening of a milk peptide database for the identification of food-derived antimicrobial peptides.

PubMed

Liu, Yufang; Eichler, Jutta; Pischetsrieder, Monika

2015-11-01

Milk provides a wide range of bioactive substances, such as antimicrobial peptides and proteins. Our study aimed to identify novel antimicrobial peptides naturally present in milk. The components of an endogenous bovine milk peptide database were virtually screened for charge, amphipathy, and predicted secondary structure. Thus, 23 of 248 screened peptides were identified as candidates for antimicrobial effects. After commercial synthesis, their antimicrobial activities were determined against Escherichia coli NEB5α, E. coli ATCC25922, and Bacillus subtilis ATCC6051. In the tested concentration range (<2 mM), bacteriostatic activity of 14 peptides was detected including nine peptides inhibiting both Gram-positive and Gram-negative bacteria. The most effective fragment was TKLTEEEKNRLNFLKKISQRYQKFΑLPQYLK corresponding to αS2 -casein151-181 , with minimum inhibitory concentration (MIC) of 4.0 μM against B. subtilis ATCC6051, and minimum inhibitory concentrations of 16.2 μM against both E. coli strains. Circular dichroism spectroscopy revealed conformational changes of most active peptides in a membrane-mimic environment, transitioning from an unordered to α-helical structure. Screening of food peptide databases by prediction tools is an efficient method to identify novel antimicrobial food-derived peptides. Milk-derived antimicrobial peptides may have potential use as functional food ingredients and help to understand the molecular mechanisms of anti-infective milk effects. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Computer-aided training sensorimotor cortex functions in humans before the upper limb transplantation using virtual reality and sensory feedback.

PubMed

Kurzynski, Marek; Jaskolska, Anna; Marusiak, Jaroslaw; Wolczowski, Andrzej; Bierut, Przemyslaw; Szumowski, Lukasz; Witkowski, Jerzy; Kisiel-Sajewicz, Katarzyna

2017-08-01

One of the biggest problems of upper limb transplantation is lack of certainty as to whether a patient will be able to control voluntary movements of transplanted hands. Based on findings of the recent research on brain cortex plasticity, a premise can be drawn that mental training supported with visual and sensory feedback can cause structural and functional reorganization of the sensorimotor cortex, which leads to recovery of function associated with the control of movements performed by the upper limbs. In this study, authors - based on the above observations - propose the computer-aided training (CAT) system, which generating visual and sensory stimuli, should enhance the effectiveness of mental training applied to humans before upper limb transplantation. The basis for the concept of computer-aided training system is a virtual hand whose reaching and grasping movements the trained patient can observe on the VR headset screen (visual feedback) and whose contact with virtual objects the patient can feel as a touch (sensory feedback). The computer training system is composed of three main components: (1) the system generating 3D virtual world in which the patient sees the virtual limb from the perspective as if it were his/her own hand; (2) sensory feedback transforming information about the interaction of the virtual hand with the grasped object into mechanical vibration; (3) the therapist's panel for controlling the training course. Results of the case study demonstrate that mental training supported with visual and sensory stimuli generated by the computer system leads to a beneficial change of the brain activity related to motor control of the reaching in the patient with bilateral upper limb congenital transverse deficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

NASA Astrophysics Data System (ADS)

Sulea, Traian; Hogues, Hervé; Purisima, Enrico O.

2012-05-01

We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

Covalent Docking of Large Libraries for the Discovery of Chemical Probes

PubMed Central

London, Nir; Miller, Rand M.; Krishnan, Shyam; Uchida, Kenji; Irwin, John J.; Eidam, Oliv; Gibold, Lucie; Cimermančič, Peter; Bonnet, Richard; Shoichet, Brian K.; Taunton, Jack

2014-01-01

Chemical probes that form a covalent bond with a protein target often show enhanced selectivity, potency, and utility for biological studies. Despite these advantages, protein-reactive compounds are usually avoided in high-throughput screening campaigns. Here we describe a general method (DOCKovalent) for screening large virtual libraries of electrophilic small molecules. We apply this method prospectively to discover reversible covalent fragments that target distinct protein nucleophiles, including the catalytic serine of AmpC β-lactamase and noncatalytic cysteines in RSK2, MSK1, and JAK3 kinases. We identify submicromolar to low-nanomolar hits with high ligand efficiency, cellular activity and selectivity, including the first reported reversible covalent inhibitors of JAK3. Crystal structures of inhibitor complexes with AmpC and RSK2 confirm the docking predictions and guide further optimization. As covalent virtual screening may have broad utility for the rapid discovery of chemical probes, we have made the method freely available through an automated web server (http://covalent.docking.org). PMID:25344815

Covalent docking of large libraries for the discovery of chemical probes.

PubMed

London, Nir; Miller, Rand M; Krishnan, Shyam; Uchida, Kenji; Irwin, John J; Eidam, Oliv; Gibold, Lucie; Cimermančič, Peter; Bonnet, Richard; Shoichet, Brian K; Taunton, Jack

2014-12-01

Chemical probes that form a covalent bond with a protein target often show enhanced selectivity, potency and utility for biological studies. Despite these advantages, protein-reactive compounds are usually avoided in high-throughput screening campaigns. Here we describe a general method (DOCKovalent) for screening large virtual libraries of electrophilic small molecules. We apply this method prospectively to discover reversible covalent fragments that target distinct protein nucleophiles, including the catalytic serine of AmpC β-lactamase and noncatalytic cysteines in RSK2, MSK1 and JAK3 kinases. We identify submicromolar to low-nanomolar hits with high ligand efficiency, cellular activity and selectivity, including what are to our knowledge the first reported reversible covalent inhibitors of JAK3. Crystal structures of inhibitor complexes with AmpC and RSK2 confirm the docking predictions and guide further optimization. As covalent virtual screening may have broad utility for the rapid discovery of chemical probes, we have made the method freely available through an automated web server (http://covalent.docking.org/).

The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay

PubMed Central

Hu, Huabin; Xue, Wenjie; Wang, Xiang Simon; Wu, Song

2018-01-01

Abstract Histone deacetylase 3 (HDAC3) is a potential target for the treatment of human diseases such as cancers, diabetes, chronic inflammation and neurodegenerative diseases. Previously, we proposed a virtual screening (VS) pipeline named “Hypo1_FRED_SAHA-3” for the discovery of HDAC3 inhibitors (HDAC3Is) and had thoroughly validated it by theoretical calculations. In this study, we attempted to explore its practical utility in a large-scale VS campaign. To this end, we used the VS pipeline to hierarchically screen the Specs chemical library. In order to facilitate compound cherry-picking, we then developed a knowledge-based pose filter (PF) by using our in-house quantitative structure activity relationship- (QSAR-) modelling approach and coupled it with FRED and Autodock Vina. Afterward, we purchased and tested 11 diverse compounds for their HDAC3 inhibitory activity in vitro. The bioassay has identified compound 2 (Specs ID: AN-979/41971160) as a HDAC3I (IC50 = 6.1 μM), which proved the efficacy of our workflow. As a medicinal chemistry study, we performed a follow-up substructure search and identified two more hit compounds of the same chemical type, i.e. 2–1 (AQ-390/42122119, IC50 = 1.3 μM) and 2–2 (AN-329/43450111, IC50 = 12.5 μM). Based on the chemical structures and activities, we have demonstrated the essential role of the capping group in maintaining the activity for this class of HDAC3Is. In addition, we tested the hit compounds for their in vitro activities on other HDACs, including HDAC1, HDAC2, HDAC8, HDAC4 and HDAC6. We have identified these compounds are HDAC1/2/3 selective inhibitors, of which compound 2 show the best selectivity profile. Taken together, the present study is an experimental validation and an update to our earlier VS strategy. The identified hits could be used as starting structures for the development of highly potent and selective HDAC3Is. PMID:29464997

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity.

PubMed

Vyas, V K; Qureshi, G; Ghate, M; Patel, H; Dalai, S

2016-06-01

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC50 values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere-Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a QFIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.

Open-source platform to benchmark fingerprints for ligand-based virtual screening

PubMed Central

2013-01-01

Similarity-search methods using molecular fingerprints are an important tool for ligand-based virtual screening. A huge variety of fingerprints exist and their performance, usually assessed in retrospective benchmarking studies using data sets with known actives and known or assumed inactives, depends largely on the validation data sets used and the similarity measure used. Comparing new methods to existing ones in any systematic way is rather difficult due to the lack of standard data sets and evaluation procedures. Here, we present a standard platform for the benchmarking of 2D fingerprints. The open-source platform contains all source code, structural data for the actives and inactives used (drawn from three publicly available collections of data sets), and lists of randomly selected query molecules to be used for statistically valid comparisons of methods. This allows the exact reproduction and comparison of results for future studies. The results for 12 standard fingerprints together with two simple baseline fingerprints assessed by seven evaluation methods are shown together with the correlations between methods. High correlations were found between the 12 fingerprints and a careful statistical analysis showed that only the two baseline fingerprints were different from the others in a statistically significant way. High correlations were also found between six of the seven evaluation methods, indicating that despite their seeming differences, many of these methods are similar to each other. PMID:23721588

Structure-based design and screening of inhibitors for an essential bacterial GTPase, Der.

PubMed

Hwang, Jihwan; Tseitin, Vladimir; Ramnarayan, Kal; Shenderovich, Mark D; Inouye, Masayori

2012-05-01

Der is an essential and widely conserved GTPase that assists assembly of a large ribosomal subunit in bacteria. Der associates specifically with the 50S subunit in a GTP-dependent manner and the cells depleted of Der accumulate the structurally unstable 50S subunit, which dissociates into an aberrant subunit at a lower Mg(2+) concentration. As Der is an essential and ubiquitous protein in bacteria, it may prove to be an ideal cellular target against which new antibiotics can be developed. In the present study, we describe our attempts to identify novel antibiotics specifically targeting Der GTPase. We performed the structure-based design of Der inhibitors using the X-ray crystal structure of Thermotoga maritima Der (TmDer). Virtual screening of commercially available chemical library retrieved 257 small molecules that potentially inhibit Der GTPase activity. These 257 chemicals were tested for their in vitro effects on TmDer GTPase and in vivo antibacterial activities. We identified three structurally diverse compounds, SBI-34462, -34566 and -34612, that are both biologically active against bacterial cells and putative enzymatic inhibitors of Der GTPase homologs. We also presented the possible interactions of each compound with the Der GTP-binding site to understand the mechanism of inhibition. Therefore, our lead compounds inhibiting Der GTPase provide scaffolds for the development of novel antibiotics against antibiotic-resistant pathogenic bacteria.

Stereo 3D vision adapter using commercial DIY goods

NASA Astrophysics Data System (ADS)

Sakamoto, Kunio; Ohara, Takashi

2009-10-01

The conventional display can show only one screen, but it is impossible to enlarge the size of a screen, for example twice. Meanwhile the mirror supplies us with the same image but this mirror image is usually upside down. Assume that the images on an original screen and a virtual screen in the mirror are completely different and both images can be displayed independently. It would be possible to enlarge a screen area twice. This extension method enables the observers to show the virtual image plane and to enlarge a screen area twice. Although the displaying region is doubled, this virtual display could not produce 3D images. In this paper, we present an extension method using a unidirectional diffusing image screen and an improvement for displaying a 3D image using orthogonal polarized image projection.

Modeling of luminance distribution in CAVE-type virtual reality systems

NASA Astrophysics Data System (ADS)

Meironke, Michał; Mazikowski, Adam

2017-08-01

At present, one of the most advanced virtual reality systems are CAVE-type (Cave Automatic Virtual Environment) installations. Such systems are usually consisted of four, five or six projection screens and in case of six screens arranged in form of a cube. Providing the user with a high level of immersion feeling in such systems is largely dependent of optical properties of the system. The modeling of physical phenomena plays nowadays a huge role in the most fields of science and technology. It allows to simulate work of device without a need to make any changes in the physical constructions. In this paper distribution of luminance in CAVE-type virtual reality systems were modelled. Calculations were performed for the model of 6-walled CAVE-type installation, based on Immersive 3D Visualization Laboratory, situated at the Faculty of Electronics, Telecommunications and Informatics at the Gdańsk University of Technology. Tests have been carried out for two different scattering distribution of the screen material in order to check how these characteristicinfluence on the luminance distribution of the whole CAVE. The basis assumption and simplification of modeled CAVE-type installation and results were presented. The brief discussion about the results and usefulness of developed model were also carried out.

Using Hierarchical Virtual Screening To Combat Drug Resistance of the HIV-1 Protease.

PubMed

Li, Nan; Ainsworth, Richard I; Ding, Bo; Hou, Tingjun; Wang, Wei

2015-07-27

Human immunodeficiency virus (HIV) protease inhibitors (PIs) are important components of highly active anti-retroviral therapy (HAART) that block the catalytic site of HIV protease, thus preventing maturation of the HIV virion. However, with two decades of PI prescriptions in clinical practice, drug-resistant HIV mutants have now been found for all of the PI drugs. Therefore, the continuous development of new PI drugs is crucial both to combat the existing drug-resistant HIV strains and to provide treatments for future patients. Here we purpose an HIV PI drug design strategy to select candidate PIs with binding energy distributions dominated by interactions with conserved protease residues in both wild-type and various drug-resistant mutants. On the basis of this strategy, we have constructed a virtual screening pipeline including combinatorial library construction, combinatorial docking, MM/GBSA-based rescoring, and reranking on the basis of the binding energy distribution. We have tested our strategy on lopinavir by modifying its two functional groups. From an initial 751 689 candidate molecules, 18 candidate inhibitors were selected using the pipeline for experimental validation. IC50 measurements and drug resistance predictions successfully identified two ligands with both HIV protease inhibitor activity and an improved drug resistance profile on 2382 HIV mutants. This study provides a proof of concept for the integration of MM/GBSA energy analysis and drug resistance information at the stage of virtual screening and sheds light on future HIV drug design and the use of virtual screening to combat drug resistance.

Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge

PubMed Central

Gallicchio, Emilio; Deng, Nanjie; He, Peng; Wickstrom, Lauren; Perryman, Alexander L.; Santiago, Daniel N.; Forli, Stefano; Olson, Arthur J.; Levy, Ronald M.

2014-01-01

As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization. PMID:24504704

Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

PubMed

Tropsha, Alexander; Golbraikh, Alexander

2007-01-01

Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied as an evaluative approach, i.e., with the focus on developing retrospective and explanatory models of existing data. Model extrapolation was considered if only in hypothetical sense in terms of potential modifications of known biologically active chemicals that could improve compounds' activity. This critical review re-examines the strategy and the output of the modern QSAR modeling approaches. We provide examples and arguments suggesting that current methodologies may afford robust and validated models capable of accurate prediction of compound properties for molecules not included in the training sets. We discuss a data-analytical modeling workflow developed in our laboratory that incorporates modules for combinatorial QSAR model development (i.e., using all possible binary combinations of available descriptor sets and statistical data modeling techniques), rigorous model validation, and virtual screening of available chemical databases to identify novel biologically active compounds. Our approach places particular emphasis on model validation as well as the need to define model applicability domains in the chemistry space. We present examples of studies where the application of rigorously validated QSAR models to virtual screening identified computational hits that were confirmed by subsequent experimental investigations. The emerging focus of QSAR modeling on target property forecasting brings it forward as predictive, as opposed to evaluative, modeling approach.

Pharmacoinformatics exploration of polyphenol oxidases leading to novel inhibitors by virtual screening and molecular dynamic simulation study.

PubMed

Hassan, Mubashir; Abbas, Qamar; Ashraf, Zaman; Moustafa, Ahmed A; Seo, Sung-Yum

2017-06-01

Polyphenol oxidases (PPOs)/tyrosinases are metal-dependent enzymes and known as important targets for melanogenesis. Although considerable attempts have been conducted to control the melanin-associated diseases by using various inhibitors. However, the exploration of the best anti-melanin inhibitor without side effect still remains a challenge in drug discovery. In present study, protein structure prediction, ligand-based pharmacophore modeling, virtual screening, molecular docking and dynamic simulation study were used to screen the strong novel inhibitor to cure melanogenesis. The 3D structures of PPO1 and PPO2 were built through homology modeling, while the 3D crystal structures of PPO3 and PPO4 were retrieved from PDB. Pharmacophore modeling was performed using LigandScout 3.1 software and top five models were selected to screen the libraries (2601 of Aurora and 727, 842 of ZINC). Top 10 hit compounds (C1-10) were short-listed having strong binding affinities for PPO1-4. Drug and synthetic accessibility (SA) scores along with absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment were employed to scrutinize the best lead hit. C4 (name) hit showed the best predicted SA score (5.75), ADMET properties and drug-likeness behavior among the short-listed compounds. Furthermore, docking simulations were performed to check the binding affinity of C1-C10 compounds against target proteins (PPOs). The binding affinity values of complex between C4 and PPOs were higher than those of other complexes (-11.70, -12.1, -9.90 and -11.20kcal/mol with PPO1, PPO2, PPO3, or PPO4, respectively). From comparative docking energy and binding analyses, PPO2 may be considered as better target for melanogenesis than others. The potential binding modes of C4, C8 and C10 against PPO2 were explored using molecular dynamics simulations. The root mean square deviation and fluctuation (RMSD/RMSF) graphs results depict the significance of C4 over the other compounds. Overall, bioactivity and ligand efficiency profiles suggested that the proposed hit may be more effective inhibitors for melanogenesis. Copyright © 2017 Elsevier Ltd. All rights reserved.

From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery.

PubMed

Ferreira, Leonardo G; Andricopulo, Adriano D

2017-01-01

Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Drug search for leishmaniasis: a virtual screening approach by grid computing

NASA Astrophysics Data System (ADS)

Ochoa, Rodrigo; Watowich, Stanley J.; Flórez, Andrés; Mesa, Carol V.; Robledo, Sara M.; Muskus, Carlos

2016-07-01

The trypanosomatid protozoa Leishmania is endemic in 100 countries, with infections causing 2 million new cases of leishmaniasis annually. Disease symptoms can include severe skin and mucosal ulcers, fever, anemia, splenomegaly, and death. Unfortunately, therapeutics approved to treat leishmaniasis are associated with potentially severe side effects, including death. Furthermore, drug-resistant Leishmania parasites have developed in most endemic countries. To address an urgent need for new, safe and inexpensive anti-leishmanial drugs, we utilized the IBM World Community Grid to complete computer-based drug discovery screens (Drug Search for Leishmaniasis) using unique leishmanial proteins and a database of 600,000 drug-like small molecules. Protein structures from different Leishmania species were selected for molecular dynamics (MD) simulations, and a series of conformational "snapshots" were chosen from each MD trajectory to simulate the protein's flexibility. A Relaxed Complex Scheme methodology was used to screen 2000 MD conformations against the small molecule database, producing >1 billion protein-ligand structures. For each protein target, a binding spectrum was calculated to identify compounds predicted to bind with highest average affinity to all protein conformations. Significantly, four different Leishmania protein targets were predicted to strongly bind small molecules, with the strongest binding interactions predicted to occur for dihydroorotate dehydrogenase (LmDHODH; PDB:3MJY). A number of predicted tight-binding LmDHODH inhibitors were tested in vitro and potent selective inhibitors of Leishmania panamensis were identified. These promising small molecules are suitable for further development using iterative structure-based optimization and in vitro/in vivo validation assays.

Drug search for leishmaniasis: a virtual screening approach by grid computing.

PubMed

Ochoa, Rodrigo; Watowich, Stanley J; Flórez, Andrés; Mesa, Carol V; Robledo, Sara M; Muskus, Carlos

2016-07-01

The trypanosomatid protozoa Leishmania is endemic in ~100 countries, with infections causing ~2 million new cases of leishmaniasis annually. Disease symptoms can include severe skin and mucosal ulcers, fever, anemia, splenomegaly, and death. Unfortunately, therapeutics approved to treat leishmaniasis are associated with potentially severe side effects, including death. Furthermore, drug-resistant Leishmania parasites have developed in most endemic countries. To address an urgent need for new, safe and inexpensive anti-leishmanial drugs, we utilized the IBM World Community Grid to complete computer-based drug discovery screens (Drug Search for Leishmaniasis) using unique leishmanial proteins and a database of 600,000 drug-like small molecules. Protein structures from different Leishmania species were selected for molecular dynamics (MD) simulations, and a series of conformational "snapshots" were chosen from each MD trajectory to simulate the protein's flexibility. A Relaxed Complex Scheme methodology was used to screen ~2000 MD conformations against the small molecule database, producing >1 billion protein-ligand structures. For each protein target, a binding spectrum was calculated to identify compounds predicted to bind with highest average affinity to all protein conformations. Significantly, four different Leishmania protein targets were predicted to strongly bind small molecules, with the strongest binding interactions predicted to occur for dihydroorotate dehydrogenase (LmDHODH; PDB:3MJY). A number of predicted tight-binding LmDHODH inhibitors were tested in vitro and potent selective inhibitors of Leishmania panamensis were identified. These promising small molecules are suitable for further development using iterative structure-based optimization and in vitro/in vivo validation assays.

Structure-Based Virtual Ligand Screening on the XRCC4/DNA Ligase IV Interface

NASA Astrophysics Data System (ADS)

Menchon, Grégory; Bombarde, Oriane; Trivedi, Mansi; Négrel, Aurélie; Inard, Cyril; Giudetti, Brigitte; Baltas, Michel; Milon, Alain; Modesti, Mauro; Czaplicki, Georges; Calsou, Patrick

2016-03-01

The association of DNA Ligase IV (Lig4) with XRCC4 is essential for repair of DNA double-strand breaks (DSBs) by Non-homologous end-joining (NHEJ) in humans. DSBs cytotoxicity is largely exploited in anticancer therapy. Thus, NHEJ is an attractive target for strategies aimed at increasing the sensitivity of tumors to clastogenic anticancer treatments. However the high affinity of the XRCC4/Lig4 interaction and the extended protein-protein interface make drug screening on this target particularly challenging. Here, we conducted a pioneering study aimed at interfering with XRCC4/Lig4 assembly. By Molecular Dynamics simulation using the crystal structure of the complex, we first delineated the Lig4 clamp domain as a limited suitable target. Then, we performed in silico screening of ~95,000 filtered molecules on this Lig4 subdomain. Hits were evaluated by Differential Scanning Fluorimetry, Saturation Transfer Difference - NMR spectroscopy and interaction assays with purified recombinant proteins. In this way we identified the first molecule able to prevent Lig4 binding to XRCC4 in vitro. This compound has a unique tripartite interaction with the Lig4 clamp domain that suggests a starting chemotype for rational design of analogous molecules with improved affinity.

Pharmacophore-based virtual screening, molecular docking, molecular dynamics simulation, and biological evaluation for the discovery of novel BRD4 inhibitors.

PubMed

Yan, Guoyi; Hou, Manzhou; Luo, Jiang; Pu, Chunlan; Hou, Xueyan; Lan, Suke; Li, Rui

2018-02-01

Bromodomain is a recognition module in the signal transduction of acetylated histone. BRD4, one of the bromodomain members, is emerging as an attractive therapeutic target for several types of cancer. Therefore, in this study, an attempt has been made to screen compounds from an integrated database containing 5.5 million compounds for BRD4 inhibitors using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulations. As a result, two molecules of twelve hits were found to be active in bioactivity tests. Among the molecules, compound 5 exhibited potent anticancer activity, and the IC 50 values against human cancer cell lines MV4-11, A375, and HeLa were 4.2, 7.1, and 11.6 μm, respectively. After that, colony formation assay, cell cycle, apoptosis analysis, wound-healing migration assay, and Western blotting were carried out to learn the bioactivity of compound 5. © 2017 John Wiley & Sons A/S.

Pharmacophore modeling, virtual screening and molecular docking of ATPase inhibitors of HSP70.

PubMed

Sangeetha, K; Sasikala, R P; Meena, K S

2017-10-01

Heat shock protein 70 is an effective anticancer target as it influences many signaling pathways. Hence the study investigated the important pharmacophore feature required for ATPase inhibitors of HSP70 by generating a ligand based pharmacophore model followed by virtual based screening and subsequent validation by molecular docking in Discovery studio V4.0. The most extrapolative pharmacophore model (hypotheses 8) consisted of four hydrogen bond acceptors. Further validation by external test set prediction identified 200 hits from Mini Maybridge, Drug Diverse, SCPDB compounds and Phytochemicals. Consequently, the screened compounds were refined by rule of five, ADMET and molecular docking to retain the best competitive hits. Finally Phytochemical compounds Muricatetrocin B, Diacetylphiladelphicalactone C, Eleutheroside B and 5-(3-{[1-(benzylsulfonyl)piperidin-4-yl]amino}phenyl)- 4-bromo-3-(carboxymethoxy)thiophene-2-carboxylic acid were obtained as leads to inhibit the ATPase activity of HSP70 in our findings and thus can be proposed for further in vitro and in vivo evaluation. Copyright © 2017 Elsevier Ltd. All rights reserved.

An enantiomer-based virtual screening approach: Discovery of chiral organophosphates as acetyl cholinesterase inhibitors.

PubMed

Zhang, Aiqian; Mu, Yunsong; Wu, Fengchang

2017-04-01

Chiral organophosphates (OPs) have been used widely around the world, very little is known about binding mechanisms with biological macromolecules. An in-depth understanding of the stereo selectivity of human AChE and discovering bioactive enantiomers of OPs can decrease health risks of these chiral chemicals. In the present study, a flexible molecular docking approach was conducted to investigate different binding modes of twelve phosphorus enantiomers. A pharmacophore model was then developed on basis of the bioactive conformations of these compounds. After virtual screening, twenty-four potential bioactive compounds were found, of which three compounds (Ethyl p-nitrophenyl phenylphosphonate (EPN), 1-naphthaleneacetic anhydride and N,4-dimethyl-N-phenyl-benzenesulfonamide) were tested by use of different in vitro assays. S-isomer of EPN was also found to exhibit greater inhibitory activity towards human AChE than the corresponding R-isomer. These findings affirm that stereochemistry plays a crucial role in virtual screening, and provide a new insight into designing safer organ phosphorus pesticides on human health. Copyright © 2017 Elsevier Inc. All rights reserved.

Development of a novel class of B-RafV600E-selective inhibitors through virtual screening and hierarchical hit optimization

PubMed Central

Kong, Xiangqian; Qin, Jie; Li, Zeng; Vultur, Adina; Tong, Linjiang; Feng, Enguang; Rajan, Geena; Liu, Shien; Lu, Junyan; Liang, Zhongjie; Zheng, Mingyue; Zhu, Weiliang; Jiang, Hualiang; Herlyn, Meenhard; Liu, Hong; Marmorstein, Ronen; Luo, Cheng

2012-01-01

Oncogenic mutations in critical nodes of cellular signaling pathways have been associated with tumorigenesis and progression. The B-Raf protein kinase, a key hub in the canonical MAPK signaling cascade, is mutated in a broad range of human cancers and especially in malignant melanoma. The most prevalent B-RafV600E mutant exhibits elevated kinase activity and results in constitutive activation of the MAPK pathway, thus making it a promising drug target for cancer therapy. Herein, we described the development of novel B-RafV600E selective inhibitors via multi-step virtual screening and hierarchical hit optimization. Nine hit compounds with low micromolar IC50 values were identified as B-RafV600E inhibitors through virtual screening. Subsequent scaffold-based analogue searching and medicinal chemistry efforts significantly improved both the inhibitor potency and oncogene selectivity. In particular, compounds 22f and 22q possess nanomolar IC50 values with selectivity for B-RafV600E in vitro and exclusive cytotoxicity against B-RafV600E harboring cancer cells. PMID:22875039

Development of a novel class of B-Raf(V600E)-selective inhibitors through virtual screening and hierarchical hit optimization.

PubMed

Kong, Xiangqian; Qin, Jie; Li, Zeng; Vultur, Adina; Tong, Linjiang; Feng, Enguang; Rajan, Geena; Liu, Shien; Lu, Junyan; Liang, Zhongjie; Zheng, Mingyue; Zhu, Weiliang; Jiang, Hualiang; Herlyn, Meenhard; Liu, Hong; Marmorstein, Ronen; Luo, Cheng

2012-09-28

Oncogenic mutations in critical nodes of cellular signaling pathways have been associated with tumorigenesis and progression. The B-Raf protein kinase, a key hub in the canonical MAPK signaling cascade, is mutated in a broad range of human cancers and especially in malignant melanoma. The most prevalent B-Raf(V600E) mutant exhibits elevated kinase activity and results in constitutive activation of the MAPK pathway, thus making it a promising drug target for cancer therapy. Herein, we describe the development of novel B-Raf(V600E) selective inhibitors via multi-step virtual screening and hierarchical hit optimization. Nine hit compounds with low micromolar IC(50) values were identified as B-Raf(V600E) inhibitors through virtual screening. Subsequent scaffold-based analogue searching and medicinal chemistry efforts significantly improved both the inhibitor potency and oncogene selectivity. In particular, compounds 22f and 22q possess nanomolar IC(50) values with selectivity for B-Raf(V600E)in vitro and exclusive cytotoxicity against B-Raf(V600E) harboring cancer cells.

Tools for building a comprehensive modeling system for virtual screening under real biological conditions: The Computational Titration algorithm.

PubMed

Kellogg, Glen E; Fornabaio, Micaela; Chen, Deliang L; Abraham, Donald J; Spyrakis, Francesca; Cozzini, Pietro; Mozzarelli, Andrea

2006-05-01

Computational tools utilizing a unique empirical modeling system based on the hydrophobic effect and the measurement of logP(o/w) (the partition coefficient for solvent transfer between 1-octanol and water) are described. The associated force field, Hydropathic INTeractions (HINT), contains much rich information about non-covalent interactions in the biological environment because of its basis in an experiment that measures interactions in solution. HINT is shown to be the core of an evolving virtual screening system that is capable of taking into account a number of factors often ignored such as entropy, effects of solvent molecules at the active site, and the ionization states of acidic and basic residues and ligand functional groups. The outline of a comprehensive modeling system for virtual screening that incorporates these features is described. In addition, a detailed description of the Computational Titration algorithm is provided. As an example, three complexes of dihydrofolate reductase (DHFR) are analyzed with our system and these results are compared with the experimental free energies of binding.

Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors.

PubMed

Xue, Xin; Wei, Jin-Lian; Xu, Li-Li; Xi, Mei-Yang; Xu, Xiao-Li; Liu, Fang; Guo, Xiao-Ke; Wang, Lei; Zhang, Xiao-Jin; Zhang, Ming-Ye; Lu, Meng-Chen; Sun, Hao-Peng; You, Qi-Dong

2013-10-28

Protein-protein interactions (PPIs) play a crucial role in cellular function and form the backbone of almost all biochemical processes. In recent years, protein-protein interaction inhibitors (PPIIs) have represented a treasure trove of potential new drug targets. Unfortunately, there are few successful drugs of PPIIs on the market. Structure-based pharmacophore (SBP) combined with docking has been demonstrated as a useful Virtual Screening (VS) strategy in drug development projects. However, the combination of target complexity and poor binding affinity prediction has thwarted the application of this strategy in the discovery of PPIIs. Here we report an effective VS strategy on p53-MDM2 PPI. First, we built a SBP model based on p53-MDM2 complex cocrystal structures. The model was then simplified by using a Receptor-Ligand complex-based pharmacophore model considering the critical binding features between MDM2 and its small molecular inhibitors. Cascade docking was subsequently applied to improve the hit rate. Based on this strategy, we performed VS on NCI and SPECS databases and successfully discovered 6 novel compounds from 15 hits with the best, compound 1 (NSC 5359), K(i) = 180 ± 50 nM. These compounds can serve as lead compounds for further optimization.

Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors.

PubMed

Shih, Kuei-Chung; Shiau, Chung-Wai; Chen, Ting-Shou; Ko, Ching-Huai; Lin, Chih-Lung; Lin, Chun-Yuan; Hwang, Chrong-Shiong; Tang, Chuan-Yi; Chen, Wan-Ru; Huang, Jui-Wen

2011-08-01

Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer's randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski's rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates.

PubMed

Tyzack, Jonathan D; Fernando, Laurent; Ribeiro, Antonio J M; Borkakoti, Neera; Thornton, Janet M

2018-04-03

There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Research on tactical information display technology for interactive virtual cockpit

NASA Astrophysics Data System (ADS)

Sun, Zhongyun; Tian, Tao; Su, Feng

2018-04-01

Based on a fact that traditional tactical information display technology suffers from disadvantages of a large number of data to be transferred and low plotting efficiency in an interactive virtual cockpit, a GID protocol-based simulation has been designed. This method dissolves complex tactical information screens into basic plotting units. The indication of plotting units is controlled via the plotting commands, which solves the incompatibility between the tactical information display in traditional simulation and the desktop-based virtual simulation training system. Having been used in desktop systems for helicopters, fighters, and transporters, this method proves to be scientific and reasonable in design and simple and efficient in usage, which exerts a significant value in establishing aviation equipment technology support training products.

Analysis of biomolecular solvation sites by 3D-RISM theory.

PubMed

Sindhikara, Daniel J; Hirata, Fumio

2013-06-06

We derive, implement, and apply equilibrium solvation site analysis for biomolecules. Our method utilizes 3D-RISM calculations to quickly obtain equilibrium solvent distributions without either necessity of simulation or limits of solvent sampling. Our analysis of these distributions extracts highest likelihood poses of solvent as well as localized entropies, enthalpies, and solvation free energies. We demonstrate our method on a structure of HIV-1 protease where excellent structural and thermodynamic data are available for comparison. Our results, obtained within minutes, show systematic agreement with available experimental data. Further, our results are in good agreement with established simulation-based solvent analysis methods. This method can be used not only for visual analysis of active site solvation but also for virtual screening methods and experimental refinement.

1001 Ways to run AutoDock Vina for virtual screening

NASA Astrophysics Data System (ADS)

Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D.

2016-03-01

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

1001 Ways to run AutoDock Vina for virtual screening.

PubMed

Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D

2016-03-01

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

Colon cancer screening

MedlinePlus

Screening for colon cancer; Colonoscopy - screening; Sigmoidoscopy - screening; Virtual colonoscopy - screening; Fecal immunochemical test; Stool DNA test; sDNA test; Colorectal cancer - screening; Rectal ...