Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation

NASA Astrophysics Data System (ADS)

Paluch, Andrew S.; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L.

2015-01-01

We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes.

Computer-aided molecular modeling techniques for predicting the stability of drug cyclodextrin inclusion complexes in aqueous solutions

NASA Astrophysics Data System (ADS)

Faucci, Maria Teresa; Melani, Fabrizio; Mura, Paola

2002-06-01

Molecular modeling was used to investigate factors influencing complex formation between cyclodextrins and guest molecules and predict their stability through a theoretical model based on the search for a correlation between experimental stability constants ( Ks) and some theoretical parameters describing complexation (docking energy, host-guest contact surfaces, intermolecular interaction fields) calculated from complex structures at a minimum conformational energy, obtained through stochastic methods based on molecular dynamic simulations. Naproxen, ibuprofen, ketoprofen and ibuproxam were used as model drug molecules. Multiple Regression Analysis allowed identification of the significant factors for the complex stability. A mathematical model ( r=0.897) related log Ks with complex docking energy and lipophilic molecular fields of cyclodextrin and drug.

Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation

PubMed Central

Paluch, Andrew S.; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L.

2015-01-01

We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes. PMID:25637996

A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV).

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling; Ge, Hui-Lin

2008-02-01

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient (r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient (q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133-145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87-97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups >C= and -CH=. 58 PCBs were divided into an "odd set" and "even set" in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for "odd set", and ME for "even set", can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors.

PubMed

Ivanciuc, O; Ivanciuc, T; Klein, D J; Seitz, W A; Balaban, A T

2001-02-01

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

Molecular model for the diffusion of associating telechelic polymer networks

NASA Astrophysics Data System (ADS)

Ramirez, Jorge; Dursch, Thomas; Olsen, Bradley

Understanding the mechanisms of motion and stress relaxation of associating polymers at the molecular level is critical for advanced technological applications such as enhanced oil-recovery, self-healing materials or drug delivery. In associating polymers, the strength and rates of association/dissociation of the reversible physical crosslinks govern the dynamics of the network and therefore all the macroscopic properties, like self-diffusion and rheology. Recently, by means of forced Rayleigh scattering experiments, we have proved that associating polymers of different architectures show super-diffusive behavior when the free motion of single molecular species is slowed down by association/dissociation kinetics. Here we discuss a new molecular picture for unentangled associating telechelic polymers that considers concentration, molecular weight, number of arms of the molecules and equilibrium and rate constants of association/dissociation. The model predicts super-diffusive behavior under the right combination of values of the parameters. We discuss some of the predictions of the model using scaling arguments, show detailed results from Brownian dynamics simulations of the FRS experiments, and attempt to compare the predictions of the model to experimental data.

Prediction of quantum interference in molecular junctions using a parabolic diagram: Understanding the origin of Fano and anti- resonances

NASA Astrophysics Data System (ADS)

Nozaki, Daijiro; Avdoshenko, Stanislav M.; Sevinçli, Hâldun; Gutierrez, Rafael; Cuniberti, Gianaurelio

2013-03-01

Recently the interest in quantum interference (QI) phenomena in molecular devices (molecular junctions) has been growing due to the unique features observed in the transmission spectra. In order to design single molecular devices exploiting QI effects as desired, it is necessary to provide simple rules for predicting the appearance of QI effects such as anti-resonances or Fano line shapes and for controlling them. In this study, we derive a transmission function of a generic molecular junction with a side group (T-shaped molecular junction) using a minimal toy model. We developed a simple method to predict the appearance of quantum interference, Fano resonances or anti- resonances, and its position in the conductance spectrum by introducing a simple graphical representation (parabolic model). Using it we can easily visualize the relation between the key electronic parameters and the positions of normal resonant peaks and anti-resonant peaks induced by quantum interference in the conductance spectrum. We also demonstrate Fano and anti-resonance in T-shaped molecular junctions using a simple tight-binding model. This parabolic model enables one to infer on-site energies of T-shaped molecules and the coupling between side group and main conduction channel from transmission spectra.

Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

NASA Astrophysics Data System (ADS)

Poovathingal, Savio

An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of reaction products because the reaction probabilities were in the diffusion dominant regime. The molecular beam data at different surface temperatures was then used to build a finite rate model. Each reaction mechanism and all rate parameters of the new model were determined individually based on the molecular beam data. Despite the experiments being performed at near vacuum conditions, the finite rate model developed using the data could be used at pressures and temperatures relevant to hypersonic conditions. The new model was implemented in a computational fluid dynamics (CFD) solver and flow over a hypersonic vehicle was simulated. The new model predicted similar overall mass loss rates compared to existing models, however, the individual species production rates were completely different. The most notable difference was that the new model (based on molecular beam data) predicts CO as the oxidation reaction product with virtually no CO2 production, whereas existing models predict the exact opposite trend. CO being the dominant oxidation product is consistent with recent high enthalpy wind tunnel experiments. The discovery that measurements taken in molecular beam facilities are able to determine individual reaction mechanisms, including dependence on surface coverage, opens up an entirely new way of constructing ablation models.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Choi, Hyun Jin; Kim, Tae-Joong; Lee, Jeong-Won; Kim, Byoung-Gie; Bae, Duk-Soo; Choi, Chel Hun

2018-04-01

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients. We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database. We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival. Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Prediction of Sliding Friction Coefficient Based on a Novel Hybrid Molecular-Mechanical Model.

PubMed

Zhang, Xiaogang; Zhang, Yali; Wang, Jianmei; Sheng, Chenxing; Li, Zhixiong

2018-08-01

Sliding friction is a complex phenomenon which arises from the mechanical and molecular interactions of asperities when examined in a microscale. To reveal and further understand the effects of micro scaled mechanical and molecular components of friction coefficient on overall frictional behavior, a hybrid molecular-mechanical model is developed to investigate the effects of main factors, including different loads and surface roughness values, on the sliding friction coefficient in a boundary lubrication condition. Numerical modelling was conducted using a deterministic contact model and based on the molecular-mechanical theory of friction. In the contact model, with given external loads and surface topographies, the pressure distribution, real contact area, and elastic/plastic deformation of each single asperity contact were calculated. Then asperity friction coefficient was predicted by the sum of mechanical and molecular components of friction coefficient. The mechanical component was mainly determined by the contact width and elastic/plastic deformation, and the molecular component was estimated as a function of the contact area and interfacial shear stress. Numerical results were compared with experimental results and a good agreement was obtained. The model was then used to predict friction coefficients in different operating and surface conditions. Numerical results explain why applied load has a minimum effect on the friction coefficients. They also provide insight into the effect of surface roughness on the mechanical and molecular components of friction coefficients. It is revealed that the mechanical component dominates the friction coefficient when the surface roughness is large (Rq > 0.2 μm), while the friction coefficient is mainly determined by the molecular component when the surface is relatively smooth (Rq < 0.2 μm). Furthermore, optimal roughness values for minimizing the friction coefficient are recommended.

Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations.

PubMed

Gupta, Jasmine; Nunes, Cletus; Vyas, Shyam; Jonnalagadda, Sriramakamal

2011-03-10

The objectives of this study were (i) to develop a computational model based on molecular dynamics technique to predict the miscibility of indomethacin in carriers (polyethylene oxide, glucose, and sucrose) and (ii) to experimentally verify the in silico predictions by characterizing the drug-carrier mixtures using thermoanalytical techniques. Molecular dynamics (MD) simulations were performed using the COMPASS force field, and the cohesive energy density and the solubility parameters were determined for the model compounds. The magnitude of difference in the solubility parameters of drug and carrier is indicative of their miscibility. The MD simulations predicted indomethacin to be miscible with polyethylene oxide and to be borderline miscible with sucrose and immiscible with glucose. The solubility parameter values obtained using the MD simulations values were in reasonable agreement with those calculated using group contribution methods. Differential scanning calorimetry showed melting point depression of polyethylene oxide with increasing levels of indomethacin accompanied by peak broadening, confirming miscibility. In contrast, thermal analysis of blends of indomethacin with sucrose and glucose verified general immiscibility. The findings demonstrate that molecular modeling is a powerful technique for determining the solubility parameters and predicting miscibility of pharmaceutical compounds. © 2011 American Chemical Society

Understanding valence-shell electron-pair repulsion (VSEPR) theory using origami molecular models

NASA Astrophysics Data System (ADS)

Endah Saraswati, Teguh; Saputro, Sulistyo; Ramli, Murni; Praseptiangga, Danar; Khasanah, Nurul; Marwati, Sri

2017-01-01

Valence-shell electron-pair repulsion (VSEPR) theory is conventionally used to predict molecular geometry. However, it is difficult to explore the full implications of this theory by simply drawing chemical structures. Here, we introduce origami modelling as a more accessible approach for exploration of the VSEPR theory. Our technique is simple, readily accessible and inexpensive compared with other sophisticated methods such as computer simulation or commercial three-dimensional modelling kits. This method can be implemented in chemistry education at both the high school and university levels. We discuss the example of a simple molecular structure prediction for ammonia (NH3). Using the origami model, both molecular shape and the scientific justification can be visualized easily. This ‘hands-on’ approach to building molecules will help promote understanding of VSEPR theory.

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.

PubMed

Hieke, Stefanie; Benner, Axel; Schlenl, Richard F; Schumacher, Martin; Bullinger, Lars; Binder, Harald

2016-08-30

High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might work well. We consider imputation of linear predictor values to be a feasible and sensible approach for dealing with partial overlap in complementary integrative analysis of molecular measurements at different levels. More generally, these results indicate that a complementary strategy for integrating different molecular levels can result in more stable risk prediction signatures, potentially providing a more reliable insight into the underlying biology.

Quantifying the Effect of Polymer Blending through Molecular Modelling of Cyanurate Polymers

PubMed Central

Crawford, Alasdair O.; Hamerton, Ian; Cavalli, Gabriel; Howlin, Brendan J.

2012-01-01

Modification of polymer properties by blending is a common practice in the polymer industry. We report here a study of blends of cyanurate polymers by molecular modelling that shows that the final experimentally determined properties can be predicted from first principles modelling to a good degree of accuracy. There is always a compromise between simulation length, accuracy and speed of prediction. A comparison of simulation times shows that 125ps of molecular dynamics simulation at each temperature provides the optimum compromise for models of this size with current technology. This study opens up the possibility of computer aided design of polymer blends with desired physical and mechanical properties. PMID:22970230

Bond-valence methods for pKa prediction. II. Bond-valence, electrostatic, molecular geometry, and solvation effects

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

Bickmore, Barry R.; Rosso, Kevin M.; Tadanier, Christopher J.

2006-08-15

In a previous contribution, we outlined a method for predicting (hydr)oxy-acid and oxide surface acidity constants based on three main factors: bond valence, Me?O bond ionicity, and molecular shape. Here electrostatics calculations and ab initio molecular dynamics simulations are used to qualitatively show that Me?O bond ionicity controls the extent to which the electrostatic work of proton removal departs from ideality, bond valence controls the extent of solvation of individual functional groups, and bond valence and molecular shape controls local dielectric response. These results are consistent with our model of acidity, but completely at odds with other methods of predictingmore » acidity constants for use in multisite complexation models. In particular, our ab initio molecular dynamics simulations of solvated monomers clearly indicate that hydrogen bonding between (hydr)oxo-groups and water molecules adjusts to obey the valence sum rule, rather than maintaining a fixed valence based on the coordination of the oxygen atom as predicted by the standard MUSIC model.« less

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

Barbante, Paolo; Frezzotti, Aldo; Gibelli, Livio

The unsteady evaporation of a thin planar liquid film is studied by molecular dynamics simulations of Lennard-Jones fluid. The obtained results are compared with the predictions of a diffuse interface model in which capillary Korteweg contributions are added to hydrodynamic equations, in order to obtain a unified description of the liquid bulk, liquid-vapor interface and vapor region. Particular care has been taken in constructing a diffuse interface model matching the thermodynamic and transport properties of the Lennard-Jones fluid. The comparison of diffuse interface model and molecular dynamics results shows that, although good agreement is obtained in equilibrium conditions, remarkable deviationsmore » of diffuse interface model predictions from the reference molecular dynamics results are observed in the simulation of liquid film evaporation. It is also observed that molecular dynamics results are in good agreement with preliminary results obtained from a composite model which describes the liquid film by a standard hydrodynamic model and the vapor by the Boltzmann equation. The two mathematical model models are connected by kinetic boundary conditions assuming unit evaporation coefficient.« less

Predicting neuroblastoma using developmental signals and a logic-based model.

PubMed

Kasemeier-Kulesa, Jennifer C; Schnell, Santiago; Woolley, Thomas; Spengler, Jennifer A; Morrison, Jason A; McKinney, Mary C; Pushel, Irina; Wolfe, Lauren A; Kulesa, Paul M

2018-07-01

Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression. Copyright © 2018. Published by Elsevier B.V.

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.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations.

PubMed

Hou, Tingjun; Wang, Junmei; Li, Youyong; Wang, Wei

2011-01-24

The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2, or 4) on the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1) MD simulation length has an obvious impact on the predictions, and longer MD simulation is not always necessary to achieve better predictions. (2) The predictions are quite sensitive to the solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface. (3) Conformational entropy often show large fluctuations in MD trajectories, and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.

Exploring the relative reactivities of the hydroxyl groups of monosaccharides by molecular modeling and molecular mechanics

NASA Astrophysics Data System (ADS)

Box, V. G. S.; Evans-Lora, T.

2000-01-01

The molecular modeling program STR3DI.EXE, and its molecular mechanics module, QVBMM, were used to simulate, and evaluate, the stereo-electronic effects in the mono-alkoxides of the 4,6- O-ethylideneglycopyranosides of allose, mannose, galactose and glucose. This study has confirmed the ability of these molecular modeling tools to predict the regiochemistry and reactivity of these sugar derivatives, and holds considerable implications for unraveling the chemistry of the rare monosaccharides.

Sulphur hexaflouride: low energy (e,2e) experiments and molecular three-body distorted wave theory

NASA Astrophysics Data System (ADS)

Nixon, Kate L.; Murray, Andrew J.; Chaluvadi, H.; Ning, C. G.; Colgan, James; Madison, Don H.

2016-10-01

Experimental and theoretical triple differential ionisation cross-sections (TDCSs) are presented for the highest occupied molecular orbital of sulphur hexafluoride. These measurements were performed in the low energy regime, with outgoing electron energies ranging from 5 to 40 eV in a coplanar geometry, and with energies of 10 and 20 eV in a perpendicular geometry. Complementary theoretical predictions of the TDCS were calculated using the molecular three-body distorted wave formalism. Calculations were performed using a proper average over molecular orientations as well as the orientation-averaged molecular orbital approximation. This more sophisticated model was found to be in closer agreement with the experimental data, however neither model accurately predicts the TDCS over all geometries and energies.

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

NASA Astrophysics Data System (ADS)

Huang, Bing; von Lilienfeld, O. Anatole

2016-10-01

The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Inspired by the postulates of quantum mechanics, we introduce a hierarchy of representations which meet uniqueness and target similarity criteria. To systematically control target similarity, we simply rely on interatomic many body expansions, as implemented in universal force-fields, including Bonding, Angular (BA), and higher order terms. Addition of higher order contributions systematically increases similarity to the true potential energy and predictive accuracy of the resulting ML models. We report numerical evidence for the performance of BAML models trained on molecular properties pre-calculated at electron-correlated and density functional theory level of theory for thousands of small organic molecules. Properties studied include enthalpies and free energies of atomization, heat capacity, zero-point vibrational energies, dipole-moment, polarizability, HOMO/LUMO energies and gap, ionization potential, electron affinity, and electronic excitations. After training, BAML predicts energies or electronic properties of out-of-sample molecules with unprecedented accuracy and speed.

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

Potent New Small-Molecule Inhibitor of Botulinum Neurotoxin Serotype A Endopeptidase Developed by Synthesis-Based Computer-Aided Molecular Design

DTIC Science & Technology

2009-11-01

dynamics of the complex predicted by multiple molecular dynamics simulations , and discuss further structural optimization to achieve better in vivo efficacy...complex with BoNTAe and the dynamics of the complex predicted by multiple molecular dynamics simulations (MMDSs). On the basis of the 3D model, we discuss...is unlimited whereas AHP exhibited 54% inhibition under the same conditions (Table 1). Computer Simulation Twenty different molecular dynamics

Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine.

PubMed

Kamthania, Mohit; Sharma, D K

2015-12-01

Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.

Molecular electronegativity distance vector model for the prediction of bioconcentration factors in fish.

PubMed

Liu, Shu-Shen; Qin, Li-Tang; Liu, Hai-Ling; Yin, Da-Qiang

2008-02-01

Molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was used to describe the structures of 122 nonionic organic compounds (NOCs) and a quantitative relationship between the MEDV descriptors and the bioconcentration factors (BCF) of NOCs in fish was developed using the variable selection and modeling based on prediction (VSMP). It was found that some main structural factors influencing the BCFs of NOCs are the substructures expressed by four atomic types of nos. 2, 3, 5, and 13, i.e., atom groups -CH(2)- or =CH-, -CH< or =C<, -NH(2), and -Cl or -Br where the former two groups exist in the molecular skeleton of NOC and the latter three groups are related closely to the substituting groups on a benzene ring. The best 5-variable model, with the correlation coefficient (r(2)) of 0.9500 and the leave-one-out cross-validation correlation coefficient (q(2)) of 0.9428, was built by multiple linear regressions, which shows a good estimation ability and stability. A predictive power for the external samples was tested by the model from the training set of 80 NOCs and the predictive correlation coefficient (u(2)) for the 42 external samples in the test set was 0.9028.

Molecular structure and gas chromatographic retention behavior of the components of Ylang-Ylang oil.

PubMed

Olivero, J; Gracia, T; Payares, P; Vivas, R; Díaz, D; Daza, E; Geerlings, P

1997-05-01

Using quantitative structure-retention relationships (QSRR) methodologies the Kovats gas chromatographic retention indices for both apolar (DB-1) and polar (DB-Wax) columns for 48 compounds from Ylang-Ylang essential oil were empirically predicted from calculated and experimental data on molecular structure. Topological, geometric, and electronic descriptors were obtained for model generation. Relationships between descriptors and the retention data reported were established by linear multiple regression, giving equations that can be used to predict the Kovats indices for compounds present in essential oils, both in DB-1 and DB-Wax columns. Factor analysis was performed to interpret the meaning of the descriptors included in the models. The prediction model for the DB-1 column includes descriptors such as Randic's first-order connectivity index (1X), the molecular surface (MSA), the sum of the atomic charge on all the hydrogens (QH), Randic's third-order connectivity index (3X) and the molecular electronegativity (chi). The prediction model for the DB-Wax column includes the first three descriptors mentioned for the DB-1 column (1X, MSA and QH) and the most negative charge (MNC), the global softness (S), and the difference between Randic's and Kier and Hall's third-order connectivity indexes (3X-3XV).

A fragmentation and reassembly method for ab initio phasing.

PubMed

Shrestha, Rojan; Zhang, Kam Y J

2015-02-01

Ab initio phasing with de novo models has become a viable approach for structural solution from protein crystallographic diffraction data. This approach takes advantage of the known protein sequence information, predicts de novo models and uses them for structure determination by molecular replacement. However, even the current state-of-the-art de novo modelling method has a limit as to the accuracy of the model predicted, which is sometimes insufficient to be used as a template for successful molecular replacement. A fragment-assembly phasing method has been developed that starts from an ensemble of low-accuracy de novo models, disassembles them into fragments, places them independently in the crystallographic unit cell by molecular replacement and then reassembles them into a whole structure that can provide sufficient phase information to enable complete structure determination by automated model building. Tests on ten protein targets showed that the method could solve structures for eight of these targets, although the predicted de novo models cannot be used as templates for successful molecular replacement since the best model for each target is on average more than 4.0 Å away from the native structure. The method has extended the applicability of the ab initio phasing by de novo models approach. The method can be used to solve structures when the best de novo models are still of low accuracy.

Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures.

PubMed

Selby-Pham, Sophie N B; Howell, Kate S; Dunshea, Frank R; Ludbey, Joel; Lutz, Adrian; Bennett, Louise

2018-04-15

A diet rich in phytochemicals confers benefits for health by reducing the risk of chronic diseases via regulation of oxidative stress and inflammation (OSI). For optimal protective bio-efficacy, the time required for phytochemicals and their metabolites to reach maximal plasma concentrations (T max ) should be synchronised with the time of increased OSI. A statistical model has been reported to predict T max of individual phytochemicals based on molecular mass and lipophilicity. We report the application of the model for predicting the absorption profile of an uncharacterised phytochemical mixture, herein referred to as the 'functional fingerprint'. First, chemical profiles of phytochemical extracts were acquired using liquid chromatography mass spectrometry (LC-MS), then the molecular features for respective components were used to predict their plasma absorption maximum, based on molecular mass and lipophilicity. This method of 'functional fingerprinting' of plant extracts represents a novel tool for understanding and optimising the health efficacy of plant extracts. Copyright © 2017 Elsevier Ltd. All rights reserved.

PREDICTION OF MOLECULAR PROPERTIES WITH MID-INFRARED SPECTRA AND INTERFEROGRAMS

EPA Science Inventory

We have built infrared spectroscopy-based partial least squares (PLS) models for molecular polarizabilities using a 97 member training set and a 59 member independent prediction set. These 156 compounds span a very wide range of chemical structure. Our goal was to use this well...

Hierarchical modeling of molecular energies using a deep neural network

NASA Astrophysics Data System (ADS)

Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Molecular modeling of the microstructure evolution during carbon fiber processing

NASA Astrophysics Data System (ADS)

Desai, Saaketh; Li, Chunyu; Shen, Tongtong; Strachan, Alejandro

2017-12-01

The rational design of carbon fibers with desired properties requires quantitative relationships between the processing conditions, microstructure, and resulting properties. We developed a molecular model that combines kinetic Monte Carlo and molecular dynamics techniques to predict the microstructure evolution during the processes of carbonization and graphitization of polyacrylonitrile (PAN)-based carbon fibers. The model accurately predicts the cross-sectional microstructure of the fibers with the molecular structure of the stabilized PAN fibers and physics-based chemical reaction rates as the only inputs. The resulting structures exhibit key features observed in electron microcopy studies such as curved graphitic sheets and hairpin structures. In addition, computed X-ray diffraction patterns are in good agreement with experiments. We predict the transverse moduli of the resulting fibers between 1 GPa and 5 GPa, in good agreement with experimental results for high modulus fibers and slightly lower than those of high-strength fibers. The transverse modulus is governed by sliding between graphitic sheets, and the relatively low value for the predicted microstructures can be attributed to their perfect longitudinal texture. Finally, the simulations provide insight into the relationships between chemical kinetics and the final microstructure; we observe that high reaction rates result in porous structures with lower moduli.

Developing model asphalt systems using molecular simulation : final model.

DOT National Transportation Integrated Search

2009-09-01

Computer based molecular simulations have been used towards developing simple mixture compositions whose : physical properties resemble those of real asphalts. First, Monte Carlo simulations with the OPLS all-atom force : field were used to predict t...

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

DemQSAR: predicting human volume of distribution and clearance of drugs

NASA Astrophysics Data System (ADS)

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/.

DemQSAR: predicting human volume of distribution and clearance of drugs.

PubMed

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/ .

Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors.

PubMed

Dai, Yujie; Wang, Qiang; Zhang, Xiuli; Jia, Shiru; Zheng, Heng; Feng, Dacheng; Yu, Peng

2010-12-01

In order to develop more potent, selective and less toxic steroidal aromatase (AR) inhibitors, molecular docking, 2D and 3D hybrid quantitative structure-activity relationship (QSAR) study have been conducted using topological, molecular shape, spatial, structural and thermodynamic descriptors on 32 steroidal compounds. The molecular docking study shows that one or more hydrogen bonds with MET374 are one of the essential requirements for the optimum binding of ligands. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3_C, Jurs_WNSA_1, Jurs_WPSA_1 and decreasing CDOCKER interaction energy (ECD), IAC_Total and Shadow_XZfrac. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles.

PubMed

Toropova, Alla P; Toropov, Andrey A

2017-06-05

Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by n validation =29, r 2 validation =0.8596, RMSE validation =0.489. Mechanistic interpretation and domain of applicability for these models are defined. Copyright © 2017 Elsevier B.V. All rights reserved.

Study design requirements for RNA sequencing-based breast cancer diagnostics.

PubMed

Mer, Arvind Singh; Klevebring, Daniel; Grönberg, Henrik; Rantalainen, Mattias

2016-02-01

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

Prediction of anticancer activity of diterpenes isolated from the paraiban flora through a PLS model and molecular surfaces.

PubMed

Scotti, Luciana; Scotti, Marcus T; Ishiki, Hamilton; Junior, Francisco J B M; dos, Santos Paula F; Tavares, Josean F; da Silva, Marcelo S

2014-05-01

The aim of this work was to predict the anticancer potential of 3 atisane, and 3 trachylobane diterpene compounds extracted from the roots of Xylopia langsdorffiana. The prediction of anticancer activity as expressed against PC-3 tumor cells was made using a PLS model built with 26 diterpenes in the training set. Significant statistical measures were obtained. The six investigated diterpenes were applied to the model and their activities against PC-3 cells were calculated. All the diterpenes were active, with atisane diterpenes showing the higher pICso values. In human prostate carcinoma PC-3 cells, the apoptosis mechanism is related to an inhibition of IKK/NF-KB. Antioxidant potential implies a greater electronic molecular atmosphere (increased donor electron capacity), which can reduce radical reactivity, and facilitate post donation charge accommodation. Molecular surfaces indicated a much greater electronic cloud over atisane diterpenes.

Quantitative prediction of solvation free energy in octanol of organic compounds.

PubMed

Delgado, Eduardo J; Jaña, Gonzalo A

2009-03-01

The free energy of solvation, DeltaGS0, in octanol of organic compounds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a DeltaGS0 range from about -50 to 0 kJ.mol(-1). The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ.mol(-1), just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set.

Quantitative Prediction of Solvation Free Energy in Octanol of Organic Compounds

PubMed Central

Delgado, Eduardo J.; Jaña, Gonzalo A.

2009-01-01

The free energy of solvation, ΔGS0, in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a ΔGS0 range from about −50 to 0 kJ·mol−1. The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ·mol−1, just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set. PMID:19399236

Protein Folding Simulations Combining Self-Guided Langevin Dynamics and Temperature-Based Replica Exchange

DTIC Science & Technology

2010-01-01

formulations of molecular dynamics (MD) and Langevin dynamics (LD) simulations for the prediction of thermodynamic folding observables of the Trp-cage...ad hoc force term in the SGLD model. Introduction Molecular dynamics (MD) simulations of small proteins provide insight into the mechanisms and... molecular dynamics (MD) and Langevin dynamics (LD) simulations for the prediction of thermodynamic folding observables of the Trp-cage mini-protein. All

Computational Molecular Modeling for Evaluating the Toxicity of Environmental Chemicals: Prioritizing Bioassay Requirements

EPA Science Inventory

This commentary provides an overview of the challenges that arise from applying molecular modeling tools developed and commonly used for pharmaceutical discovery to the problem of predicting the potential toxicities of environmental chemicals.

An alternative covariance estimator to investigate genetic heterogeneity in populations

USDA-ARS?s Scientific Manuscript database

Genomic predictions and GWAS have used mixed models for identification of associations and trait predictions. In both cases, the covariance between individuals for performance is estimated using molecular markers. Mixed model properties indicate that the use of the data for prediction is optimal if ...

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

The Use of Molecular Modeling as "Pseudoexperimental" Data for Teaching VSEPR as a Hands-On General Chemistry Activity

ERIC Educational Resources Information Center

Martin, Christopher B.; Vandehoef, Crissie; Cook, Allison

2015-01-01

A hands-on activity appropriate for first-semester general chemistry students is presented that combines traditional VSEPR methods of predicting molecular geometries with introductory use of molecular modeling. Students analyze a series of previously calculated output files consisting of several molecules each in various geometries. Each structure…

Molecular surface area based predictive models for the adsorption and diffusion of disperse dyes in polylactic acid matrix.

PubMed

Xu, Suxin; Chen, Jiangang; Wang, Bijia; Yang, Yiqi

2015-11-15

Two predictive models were presented for the adsorption affinities and diffusion coefficients of disperse dyes in polylactic acid matrix. Quantitative structure-sorption behavior relationship would not only provide insights into sorption process, but also enable rational engineering for desired properties. The thermodynamic and kinetic parameters for three disperse dyes were measured. The predictive model for adsorption affinity was based on two linear relationships derived by interpreting the experimental measurements with molecular structural parameters and compensation effect: ΔH° vs. dye size and ΔS° vs. ΔH°. Similarly, the predictive model for diffusion coefficient was based on two derived linear relationships: activation energy of diffusion vs. dye size and logarithm of pre-exponential factor vs. activation energy of diffusion. The only required parameters for both models are temperature and solvent accessible surface area of the dye molecule. These two predictive models were validated by testing the adsorption and diffusion properties of new disperse dyes. The models offer fairly good predictive ability. The linkage between structural parameter of disperse dyes and sorption behaviors might be generalized and extended to other similar polymer-penetrant systems. Copyright © 2015 Elsevier Inc. All rights reserved.

Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function.

PubMed

Perco, Paul; Heinzel, Andreas; Leierer, Johannes; Schneeberger, Stefan; Bösmüller, Claudia; Oberhuber, Rupert; Wagner, Silvia; Engler, Franziska; Mayer, Gert

2018-05-03

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

Multiscale Modeling of PEEK Using Reactive Molecular Dynamics Modeling and Micromechanics

NASA Technical Reports Server (NTRS)

Pisani, William A.; Radue, Matthew; Chinkanjanarot, Sorayot; Bednarcyk, Brett A.; Pineda, Evan J.; King, Julia A.; Odegard, Gregory M.

2018-01-01

Polyether ether ketone (PEEK) is a high-performance, semi-crystalline thermoplastic that is used in a wide range of engineering applications, including some structural components of aircraft. The design of new PEEK-based materials requires a precise understanding of the multiscale structure and behavior of semi-crystalline PEEK. Molecular Dynamics (MD) modeling can efficiently predict bulk-level properties of single phase polymers, and micromechanics can be used to homogenize those phases based on the overall polymer microstructure. In this study, MD modeling was used to predict the mechanical properties of the amorphous and crystalline phases of PEEK. The hierarchical microstructure of PEEK, which combines the aforementioned phases, was modeled using a multiscale modeling approach facilitated by NASA's MSGMC. The bulk mechanical properties of semi-crystalline PEEK predicted using MD modeling and MSGMC agree well with vendor data, thus validating the multiscale modeling approach.

Assessing the accuracy of improved force-matched water models derived from Ab initio molecular dynamics simulations.

PubMed

Köster, Andreas; Spura, Thomas; Rutkai, Gábor; Kessler, Jan; Wiebeler, Hendrik; Vrabec, Jadran; Kühne, Thomas D

2016-07-15

The accuracy of water models derived from ab initio molecular dynamics simulations by means on an improved force-matching scheme is assessed for various thermodynamic, transport, and structural properties. It is found that although the resulting force-matched water models are typically less accurate than fully empirical force fields in predicting thermodynamic properties, they are nevertheless much more accurate than generally appreciated in reproducing the structure of liquid water and in fact superseding most of the commonly used empirical water models. This development demonstrates the feasibility to routinely parametrize computationally efficient yet predictive potential energy functions based on accurate ab initio molecular dynamics simulations for a large variety of different systems. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Experimental and Numerical Analysis of Narrowband Coherent Rayleigh-Brillouin Scattering in Atomic and Molecular Species (Pre Print)

DTIC Science & Technology

2012-02-01

use of polar gas species. While current simplified models have adequately predicted CRS and CRBS line shapes for a wide variety of cases, multiple ...published simplified models are presented for argon, molecular nitrogen, and methane at 300 & 500 K and 1 atm. The simplified models require uncertain gas... models are presented for argon, molecular nitrogen, and methane at 300 & 500 K and 1 atm. The simplified models require uncertain gas properties

Supercomputer applications in molecular modeling.

PubMed

Gund, T M

1988-01-01

An overview of the functions performed by molecular modeling is given. Molecular modeling techniques benefiting from supercomputing are described, namely, conformation, search, deriving bioactive conformations, pharmacophoric pattern searching, receptor mapping, and electrostatic properties. The use of supercomputers for problems that are computationally intensive, such as protein structure prediction, protein dynamics and reactivity, protein conformations, and energetics of binding is also examined. The current status of supercomputing and supercomputer resources are discussed.

System and methods for predicting transmembrane domains in membrane proteins and mining the genome for recognizing G-protein coupled receptors

DOEpatents

Trabanino, Rene J; Vaidehi, Nagarajan; Hall, Spencer E; Goddard, William A; Floriano, Wely

2013-02-05

The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the presence of transmembrane regions in proteins, such as G-Protein Coupled Receptors (GPCR), and protein structural models generated according to the protocol. The protocol features a coarse grain sampling method, such as hydrophobicity analysis, to provide a fast and accurate procedure for predicting transmembrane regions. Methods and apparatus of the invention are useful to screen protein or polynucleotide databases for encoded proteins with transmembrane regions, such as GPCRs.

Modeling hole transport in wet and dry DNA.

PubMed

Pavanello, Michele; Adamowicz, Ludwik; Volobuyev, Maksym; Mennucci, Benedetta

2010-04-08

We present a DFT/classical molecular dynamics model of DNA charge conductivity. The model involves a temperature-driven, hole-hopping charge transfer and includes the time-dependent nonequilibrium interaction of DNA with its molecular environment. We validate our method against a variety of hole transport experiments. The method predicts a significant hole-transfer slowdown of approximately 35% from dry to wet DNA with and without electric field bias. In addition, in agreement with experiments, it also predicts an insulating behavior of (GC)(N) oligomers for 40 < N < 1000, depending on the experimental setup.

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

PubMed Central

Garcia Lopez, Sebastian; Kim, Philip M.

2014-01-01

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.

PubMed

Zhang, Hui; Cao, Zhi-Xing; Li, Meng; Li, Yu-Zhi; Peng, Cheng

2016-11-01

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M W ), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecular Weight and Molecular Weight Distributions in Synthetic Polymers.

ERIC Educational Resources Information Center

Ward, Thomas Carl

1981-01-01

Focuses on molecular weight and molecular weight distributions (MWD) and models for predicting MWD in a pedagogical way. In addition, instrumental methods used to characterize MWD are reviewed with emphasis on physical chemistry of each, including end-group determination, osmometry, light scattering, solution viscosity, fractionation, and…

Modeling and Prediction of Drug Dispersability in Polyvinylpyrrolidone-Vinyl Acetate Copolymer Using a Molecular Descriptor.

PubMed

DeBoyace, Kevin; Buckner, Ira S; Gong, Yuchuan; Ju, Tzu-Chi Rob; Wildfong, Peter L D

2018-01-01

The expansion of a novel in silico model for the prediction of the dispersability of 18 model compounds with polyvinylpyrrolidone-vinyl acetate copolymer is described. The molecular descriptor R3m (atomic mass weighted 3rd-order autocorrelation index) is shown to be predictive of the formation of amorphous solid dispersions at 2 drug loadings (15% and 75% w/w) using 2 preparation methods (melt quenching and solvent evaporation using a rotary evaporator). Cosolidified samples were characterized using a suite of analytical techniques, which included differential scanning calorimetry, powder X-ray diffraction, pair distribution function analysis, polarized light microscopy, and hot stage microscopy. Logistic regression was applied, where appropriate, to model the success and failure of compound dispersability in polyvinylpyrrolidone-vinyl acetate copolymer. R3m had combined prediction accuracy greater than 90% for tested samples. The usefulness of this descriptor appears to be associated with the presence of heavy atoms in the molecular structure of the active pharmaceutical ingredient, and their location with respect to the geometric center of the molecule. Given the higher electronegativity and atomic volume of these types of atoms, it is hypothesized that they may impact the molecular mobility of the active pharmaceutical ingredient, or increase the likelihood of forming nonbonding interactions with the carrier polymer. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Predictive Models of Liver Cancer

EPA Science Inventory

Predictive models of chemical-induced liver cancer face the challenge of bridging causative molecular mechanisms to adverse clinical outcomes. The latent sequence of intervening events from chemical insult to toxicity are poorly understood because they span multiple levels of bio...

A network of molecular switches controls the activation of the two-component response regulator NtrC

NASA Astrophysics Data System (ADS)

Vanatta, Dan K.; Shukla, Diwakar; Lawrenz, Morgan; Pande, Vijay S.

2015-06-01

Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism. Moreover, these results suggest a novel mechanistic paradigm for conformational switching.

Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

PubMed

Pérez-Garrido, Alfonso; Morales Helguera, Aliuska; Abellán Guillén, Adela; Cordeiro, M Natália D S; Garrido Escudero, Amalio

2009-01-15

This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

Digital Learning Material for Student-Directed Model Building in Molecular Biology

ERIC Educational Resources Information Center

Aegerter-Wilmsen, Tinri; Coppens, Marjolijn; Janssen, Fred; Hartog, Rob; Bisseling, Ton

2005-01-01

The building of models to explain data and make predictions constitutes an important goal in molecular biology research. To give students the opportunity to practice such model building, two digital cases had previously been developed in which students are guided to build a model step by step. In this article, the development and initial…

Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

PubMed

Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

2014-10-14

Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

PubMed

Pronobis, Wiktor; Tkatchenko, Alexandre; Müller, Klaus-Robert

2018-06-12

Machine learning (ML) based prediction of molecular properties across chemical compound space is an important and alternative approach to efficiently estimate the solutions of highly complex many-electron problems in chemistry and physics. Statistical methods represent molecules as descriptors that should encode molecular symmetries and interactions between atoms. Many such descriptors have been proposed; all of them have advantages and limitations. Here, we propose a set of general two-body and three-body interaction descriptors which are invariant to translation, rotation, and atomic indexing. By adapting the successfully used kernel ridge regression methods of machine learning, we evaluate our descriptors on predicting several properties of small organic molecules calculated using density-functional theory. We use two data sets. The GDB-7 set contains 6868 molecules with up to 7 heavy atoms of type CNO. The GDB-9 set is composed of 131722 molecules with up to 9 heavy atoms containing CNO. When trained on 5000 random molecules, our best model achieves an accuracy of 0.8 kcal/mol (on the remaining 1868 molecules of GDB-7) and 1.5 kcal/mol (on the remaining 126722 molecules of GDB-9) respectively. Applying a linear regression model on our novel many-body descriptors performs almost equal to a nonlinear kernelized model. Linear models are readily interpretable: a feature importance ranking measure helps to obtain qualitative and quantitative insights on the importance of two- and three-body molecular interactions for predicting molecular properties computed with quantum-mechanical methods.

Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

PubMed

Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi

2015-01-01

The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Development and validation of risk models and molecular diagnostics to permit personalized management of cancer.

PubMed

Pu, Xia; Ye, Yuanqing; Wu, Xifeng

2014-01-01

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation. © 2013 American Cancer Society.

In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations.

PubMed

Gao, Xiaodong; Han, Liping; Ren, Yujie

2016-05-05

Checkpoint kinase 1 (Chk1) is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q² values (0.531, 0.726), fitted correlation r² coefficients (higher than 0.90), and standard error of prediction (less than 0.250). These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

PubMed

Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

2016-10-01

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Rational design of methicillin resistance staphylococcus aureus inhibitors through 3D-QSAR, molecular docking and molecular dynamics simulations.

PubMed

Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

2018-04-01

Staphylococcus aureus is a gram positive bacterium. It is the leading cause of skin and respiratory infections, osteomyelitis, Ritter's disease, endocarditis, and bacteraemia in the developed world. We employed combined studies of 3D QSAR, molecular docking which are validated by molecular dynamics simulations and in silico ADME prediction have been performed on Isothiazoloquinolones inhibitors against methicillin resistance Staphylococcus aureus. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study was applied using comparative molecular field analysis (CoMFA) with Q 2 of 0.578, R 2 of 0.988, and comparative molecular similarity indices analysis (CoMSIA) with Q 2 of 0.554, R 2 of 0.975. The predictive ability of these model was determined using a test set of molecules that gave acceptable predictive correlation (r 2 Pred) values 0.55 and 0.57 of CoMFA and CoMSIA respectively. Docking, simulations were employed to position the inhibitors into protein active site to find out the most probable binding mode and most reliable conformations. Developed models and Docking methods provide guidance to design molecules with enhanced activity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

NASA Astrophysics Data System (ADS)

Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

2015-03-01

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors

PubMed Central

Schütte, Moritz; Risch, Thomas; Abdavi-Azar, Nilofar; Boehnke, Karsten; Schumacher, Dirk; Keil, Marlen; Yildiriman, Reha; Jandrasits, Christine; Borodina, Tatiana; Amstislavskiy, Vyacheslav; Worth, Catherine L.; Schweiger, Caroline; Liebs, Sandra; Lange, Martin; Warnatz, Hans- Jörg; Butcher, Lee M.; Barrett, James E.; Sultan, Marc; Wierling, Christoph; Golob-Schwarzl, Nicole; Lax, Sigurd; Uranitsch, Stefan; Becker, Michael; Welte, Yvonne; Regan, Joseph Lewis; Silvestrov, Maxine; Kehler, Inge; Fusi, Alberto; Kessler, Thomas; Herwig, Ralf; Landegren, Ulf; Wienke, Dirk; Nilsson, Mats; Velasco, Juan A.; Garin-Chesa, Pilar; Reinhard, Christoph; Beck, Stephan; Schäfer, Reinhold; Regenbrecht, Christian R. A.; Henderson, David; Lange, Bodo; Haybaeck, Johannes; Keilholz, Ulrich; Hoffmann, Jens; Lehrach, Hans; Yaspo, Marie-Laure

2017-01-01

Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I–IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab. PMID:28186126

3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles

NASA Astrophysics Data System (ADS)

Ghaleb, Adib; Aouidate, Adnane; Ghamali, Mounir; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

2017-10-01

3D-QSAR (comparative molecular field analysis (CoMFA)) and comparative molecular similarity indices analysis (CoMSIA) were performed on novel 2,5 disubstituted 1,3,4-oxadiazoles analogues as anti-fungal agents. The CoMFA and CoMSIA models using 13 compounds in the training set gives Q2 values of 0.52 and 0.51 respectively, while R2 values of 0.92. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to determine a three-dimensional quantitative structure-activity relationship. Based on this study a set of new molecules with high predicted activities were designed. Surflex-docking confirmed the stability of predicted molecules in the receptor.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969

Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

PubMed

Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

2012-08-01

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

In silico modelling of thiazolidine derivatives with antioxidant potency: Models quantify the degree of contribution of molecular fragments towards the free radical scavenging ability

NASA Astrophysics Data System (ADS)

De, Biplab; Adhikari, Indrani; Nandy, Ashis; Saha, Achintya; Goswami, Binoy Behari

2017-06-01

Design and development of antioxidant supplements constitute an essential aspect of research in order to derive molecules that would help to combat the free radical invasion to the human body and curb oxidative stress related diseases. The present work deals with the development of in silico models for a series of thiazolidine derivatives having antioxidant potential. The objective of the work is to obtain models that would help to design new thazolidine derivatives based on substituent modification and thereby predict their activity profile. The QSAR model thus developed helps in quantification of the extent of contribution of the various molecular fragments towards the activity of the molecules, while the 3D pharmacophore model provides a brief idea of the essential molecular features that help the molecules to interact with the neighbouring free radicals. Both the models have been extensively validated which ensures their predictive ability as well the potential to search molecular databases for selection of thiazolidine derivatives with potent antioxidant activity. The models can thus be utilised effectively for database searching with the aim to isolate active antioxidants belonging to the thiazolidine group.

Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS.

PubMed

Cresswell, Alexander J; Wheatley, Richard J; Wilkinson, Richard D; Graham, Richard S

2016-10-20

Impurities from the CCS chain can greatly influence the physical properties of CO 2 . This has important design, safety and cost implications for the compression, transport and storage of CO 2 . There is an urgent need to understand and predict the properties of impure CO 2 to assist with CCS implementation. However, CCS presents demanding modelling requirements. A suitable model must both accurately and robustly predict CO 2 phase behaviour over a wide range of temperatures and pressures, and maintain that predictive power for CO 2 mixtures with numerous, mutually interacting chemical species. A promising technique to address this task is molecular simulation. It offers a molecular approach, with foundations in firmly established physical principles, along with the potential to predict the wide range of physical properties required for CCS. The quality of predictions from molecular simulation depends on accurate force-fields to describe the interactions between CO 2 and other molecules. Unfortunately, there is currently no universally applicable method to obtain force-fields suitable for molecular simulation. In this paper we present two methods of obtaining force-fields: the first being semi-empirical and the second using ab initio quantum-chemical calculations. In the first approach we optimise the impurity force-field against measurements of the phase and pressure-volume behaviour of CO 2 binary mixtures with N 2 , O 2 , Ar and H 2 . A gradient-free optimiser allows us to use the simulation itself as the underlying model. This leads to accurate and robust predictions under conditions relevant to CCS. In the second approach we use quantum-chemical calculations to produce ab initio evaluations of the interactions between CO 2 and relevant impurities, taking N 2 as an exemplar. We use a modest number of these calculations to train a machine-learning algorithm, known as a Gaussian process, to describe these data. The resulting model is then able to accurately predict a much broader set of ab initio force-field calculations at comparatively low numerical cost. Although our method is not yet ready to be implemented in a molecular simulation, we outline the necessary steps here. Such simulations have the potential to deliver first-principles simulation of the thermodynamic properties of impure CO 2 , without fitting to experimental data.

AHPCRC (Army High Performance Computing Rsearch Center) Bulletin. Volume 1, Issue 4

DTIC Science & Technology

2011-01-01

Computational and Mathematical Engineering, Stanford University esgs@stanford.edu (650) 723-3764 Molecular Dynamics Models of Antimicrobial ...simulations using low-fidelity Reynolds-av- eraged models illustrate the limited predictive capabili- ties of these schemes. The predictions for scalar and...driving force. The AHPCRC group has used their models to predict nonuniform concentra- tion profiles across small channels as a result of variations

The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

PubMed

Li, Jiazhong; Gramatica, Paola

2010-11-01

Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common compounds). In order to verify the real assignments for these inconsistent compounds, we predicted these samples, as a blind external set, by our regression models and compared the results with the two databases. The results indicated that most of the predictions were consistent with METI. Furthermore, we built a kNN classification model using the 104 consistent compounds to predict those inconsistent ones, and most of the predictions were also in agreement with METI database.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Prediction of frozen food properties during freezing using product composition.

PubMed

Boonsupthip, W; Heldman, D R

2007-06-01

Frozen water fraction (FWF), as a function of temperature, is an important parameter for use in the design of food freezing processes. An FWF-prediction model, based on concentrations and molecular weights of specific product components, has been developed. Published food composition data were used to determine the identity and composition of key components. The model proposed in this investigation had been verified using published experimental FWF data and initial freezing temperature data, and by comparison to outputs from previously published models. It was found that specific food components with significant influence on freezing temperature depression of food products included low molecular weight water-soluble compounds with molality of 50 micromol per 100 g food or higher. Based on an analysis of 200 high-moisture food products, nearly 45% of the experimental initial freezing temperature data were within an absolute difference (AD) of +/- 0.15 degrees C and standard error (SE) of +/- 0.65 degrees C when compared to values predicted by the proposed model. The predicted relationship between temperature and FWF for all analyzed food products provided close agreements with experimental data (+/- 0.06 SE). The proposed model provided similar prediction capability for high- and intermediate-moisture food products. In addition, the proposed model provided statistically better prediction of initial freezing temperature and FWF than previous published models.

Prediction of triple-charm molecular pentaquarks

NASA Astrophysics Data System (ADS)

Chen, Rui; Hosaka, Atsushi; Liu, Xiang

2017-12-01

In a one-boson-exchange model, we study molecular states of double-charm baryon [Ξc c(3621 )] and a charmed meson (D and D*). Our model indicates that there exist two possible triple-charm molecular pentaquarks, a Ξc cD state with I (JP)=0 (1 /2-), and a Ξc cD* state with I (JP)=0 (3 /2-), and we do not find bound solutions for isotriplet states. In addition, we also extend our formula to explore Ξc cB¯(*), Ξc cD¯(*), and Ξc cB(*) systems and find more possible heavy flavor molecular pentaquarks, a Ξc cB ¯ state with I (JP)=0 (1 /2-), a Ξc cB¯* state with I (JP)=0 (3 /2-), and Ξc cD¯*/Ξc cB* states with I (JP)=0 (1 /2-). Experimental research for these predicted triple-charm molecular pentaquarks is encouraged.

Molecular dynamics simulation of potentiometric sensor response: the effect of biomolecules, surface morphology and surface charge.

PubMed

Lowe, B M; Skylaris, C-K; Green, N G; Shibuta, Y; Sakata, T

2018-05-10

The silica-water interface is critical to many modern technologies in chemical engineering and biosensing. One technology used commonly in biosensors, the potentiometric sensor, operates by measuring the changes in electric potential due to changes in the interfacial electric field. Predictive modelling of this response caused by surface binding of biomolecules remains highly challenging. In this work, through the most extensive molecular dynamics simulation of the silica-water interfacial potential and electric field to date, we report a novel prediction and explanation of the effects of nano-morphology on sensor response. Amorphous silica demonstrated a larger potentiometric response than an equivalent crystalline silica model due to increased sodium adsorption, in agreement with experiments showing improved sensor response with nano-texturing. We provide proof-of-concept that molecular dynamics can be used as a complementary tool for potentiometric biosensor response prediction. Effects that are conventionally neglected, such as surface morphology, water polarisation, biomolecule dynamics and finite-size effects, are explicitly modelled.

QSPR models for various physical properties of carbohydrates based on molecular mechanics and quantum chemical calculations.

PubMed

Dyekjaer, Jane Dannow; Jónsdóttir, Svava Osk

2004-01-22

Quantitative Structure-Property Relationships (QSPR) have been developed for a series of monosaccharides, including the physical properties of partial molar heat capacity, heat of solution, melting point, heat of fusion, glass-transition temperature, and solid state density. The models were based on molecular descriptors obtained from molecular mechanics and quantum chemical calculations, combined with other types of descriptors. Saccharides exhibit a large degree of conformational flexibility, therefore a methodology for selecting the energetically most favorable conformers has been developed, and was used for the development of the QSPR models. In most cases good correlations were obtained for monosaccharides. For five of the properties predictions were made for disaccharides, and the predicted values for the partial molar heat capacities were in excellent agreement with experimental values.

Molecular Modeling of Thermodynamic and Transport Properties for CO 2 and Aqueous Brines

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

Jiang, Hao; Economou, Ioannis G.; Panagiotopoulos, Athanassios Z.

Molecular simulation techniques using classical force-fields occupy the space between ab initio quantum mechanical methods and phenomenological correlations. In particular, Monte Carlo and molecular dynamics algorithms can be used to provide quantitative predictions of thermodynamic and transport properties of fluids relevant for geologic carbon sequestration at conditions for which experimental data are uncertain or not available. These methods can cover time and length scales far exceeding those of quantum chemical methods, while maintaining transferability and predictive power lacking from phenomenological correlations. The accuracy of predictions depends sensitively on the quality of the molecular models used. Many existing fixed-point-charge models formore » water and aqueous mixtures fail to represent accurately these fluid properties, especially when descriptions covering broad ranges of thermodynamic conditions are needed. Recent work on development of accurate models for water, CO 2, and dissolved salts, as well as their mixtures, is summarized in this Account. Polarizable models that can respond to the different dielectric environments in aqueous versus nonaqueous phases are necessary for predictions of properties over extended ranges of temperatures and pressures. Phase compositions and densities, activity coefficients of the dissolved salts, interfacial tensions, viscosities and diffusivities can be obtained in near-quantitative agreement to available experimental data, using relatively modest computational resources. In some cases, for example, for the composition of the CO 2-rich phase in coexistence with an aqueous phase, recent results from molecular simulations have helped discriminate among conflicting experimental data sets. The sensitivity of properties on the quality of the intermolecular interaction model varies significantly. Properties such as the phase compositions or electrolyte activity coefficients are much more sensitive than phase densities, viscosities, or component diffusivities. Strong confinement effects on physical properties in nanoscale media can also be directly obtained from molecular simulations. Future work on molecular modeling for CO 2 and aqueous brines is likely to be focused on more systematic generation of interaction models by utilizing quantum chemical as well as direct experimental measurements. New ion models need to be developed for use with the current generation of polarizable water models, including ion–ion interactions that will allow for accurate description of dense, mixed brines. Methods will need to be devised that go beyond the use of effective potentials for incorporation of quantum effects known to be important for water, and reactive force fields developed that can handle bond creation and breaking in systems with carbonate and silicate minerals. Lastly, another area of potential future work is the integration of molecular simulation methods in multiscale models for the chemical reactions leading to mineral dissolution and flow within the porous media in underground formations.« less

Molecular Modeling of Thermodynamic and Transport Properties for CO2 and Aqueous Brines.

PubMed

Jiang, Hao; Economou, Ioannis G; Panagiotopoulos, Athanassios Z

2017-04-18

Molecular simulation techniques using classical force-fields occupy the space between ab initio quantum mechanical methods and phenomenological correlations. In particular, Monte Carlo and molecular dynamics algorithms can be used to provide quantitative predictions of thermodynamic and transport properties of fluids relevant for geologic carbon sequestration at conditions for which experimental data are uncertain or not available. These methods can cover time and length scales far exceeding those of quantum chemical methods, while maintaining transferability and predictive power lacking from phenomenological correlations. The accuracy of predictions depends sensitively on the quality of the molecular models used. Many existing fixed-point-charge models for water and aqueous mixtures fail to represent accurately these fluid properties, especially when descriptions covering broad ranges of thermodynamic conditions are needed. Recent work on development of accurate models for water, CO 2 , and dissolved salts, as well as their mixtures, is summarized in this Account. Polarizable models that can respond to the different dielectric environments in aqueous versus nonaqueous phases are necessary for predictions of properties over extended ranges of temperatures and pressures. Phase compositions and densities, activity coefficients of the dissolved salts, interfacial tensions, viscosities and diffusivities can be obtained in near-quantitative agreement to available experimental data, using relatively modest computational resources. In some cases, for example, for the composition of the CO 2 -rich phase in coexistence with an aqueous phase, recent results from molecular simulations have helped discriminate among conflicting experimental data sets. The sensitivity of properties on the quality of the intermolecular interaction model varies significantly. Properties such as the phase compositions or electrolyte activity coefficients are much more sensitive than phase densities, viscosities, or component diffusivities. Strong confinement effects on physical properties in nanoscale media can also be directly obtained from molecular simulations. Future work on molecular modeling for CO 2 and aqueous brines is likely to be focused on more systematic generation of interaction models by utilizing quantum chemical as well as direct experimental measurements. New ion models need to be developed for use with the current generation of polarizable water models, including ion-ion interactions that will allow for accurate description of dense, mixed brines. Methods will need to be devised that go beyond the use of effective potentials for incorporation of quantum effects known to be important for water, and reactive force fields developed that can handle bond creation and breaking in systems with carbonate and silicate minerals. Another area of potential future work is the integration of molecular simulation methods in multiscale models for the chemical reactions leading to mineral dissolution and flow within the porous media in underground formations.

Molecular Modeling of Thermodynamic and Transport Properties for CO 2 and Aqueous Brines

DOE PAGES

Jiang, Hao; Economou, Ioannis G.; Panagiotopoulos, Athanassios Z.

2017-02-24

Molecular simulation techniques using classical force-fields occupy the space between ab initio quantum mechanical methods and phenomenological correlations. In particular, Monte Carlo and molecular dynamics algorithms can be used to provide quantitative predictions of thermodynamic and transport properties of fluids relevant for geologic carbon sequestration at conditions for which experimental data are uncertain or not available. These methods can cover time and length scales far exceeding those of quantum chemical methods, while maintaining transferability and predictive power lacking from phenomenological correlations. The accuracy of predictions depends sensitively on the quality of the molecular models used. Many existing fixed-point-charge models formore » water and aqueous mixtures fail to represent accurately these fluid properties, especially when descriptions covering broad ranges of thermodynamic conditions are needed. Recent work on development of accurate models for water, CO 2, and dissolved salts, as well as their mixtures, is summarized in this Account. Polarizable models that can respond to the different dielectric environments in aqueous versus nonaqueous phases are necessary for predictions of properties over extended ranges of temperatures and pressures. Phase compositions and densities, activity coefficients of the dissolved salts, interfacial tensions, viscosities and diffusivities can be obtained in near-quantitative agreement to available experimental data, using relatively modest computational resources. In some cases, for example, for the composition of the CO 2-rich phase in coexistence with an aqueous phase, recent results from molecular simulations have helped discriminate among conflicting experimental data sets. The sensitivity of properties on the quality of the intermolecular interaction model varies significantly. Properties such as the phase compositions or electrolyte activity coefficients are much more sensitive than phase densities, viscosities, or component diffusivities. Strong confinement effects on physical properties in nanoscale media can also be directly obtained from molecular simulations. Future work on molecular modeling for CO 2 and aqueous brines is likely to be focused on more systematic generation of interaction models by utilizing quantum chemical as well as direct experimental measurements. New ion models need to be developed for use with the current generation of polarizable water models, including ion–ion interactions that will allow for accurate description of dense, mixed brines. Methods will need to be devised that go beyond the use of effective potentials for incorporation of quantum effects known to be important for water, and reactive force fields developed that can handle bond creation and breaking in systems with carbonate and silicate minerals. Lastly, another area of potential future work is the integration of molecular simulation methods in multiscale models for the chemical reactions leading to mineral dissolution and flow within the porous media in underground formations.« less

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

PubMed Central

Thomas, Reuben; Thomas, Russell S.; Auerbach, Scott S.; Portier, Christopher J.

2013-01-01

Background Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. Objectives To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Methods Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Results Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Conclusions Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species. PMID:23737943

Biological networks for predicting chemical hepatocarcinogenicity using gene expression data from treated mice and relevance across human and rat species.

PubMed

Thomas, Reuben; Thomas, Russell S; Auerbach, Scott S; Portier, Christopher J

2013-01-01

Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species.

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor.

PubMed

Chitta, Aparna; Jatavath, Mohan Babu; Fatima, Sabiha; Manga, Vijjulatha

2012-02-01

To study the pharmacophore properties of quinazolinone derivatives as 5HT(7) inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT(7) inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q(2) (cross validated correlation coefficient) of 0.642, 0.602 and r(2) (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT(7) antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r(2) obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.

The solid-state terahertz spectrum of MDMA (Ecstasy) - A unique test for molecular modeling assignments

NASA Astrophysics Data System (ADS)

Allis, Damian G.; Hakey, Patrick M.; Korter, Timothy M.

2008-10-01

The terahertz (THz, far-infrared) spectrum of 3,4-methylene-dioxymethamphetamine hydrochloride (Ecstasy) is simulated using solid-state density functional theory. While a previously reported isolated-molecule calculation is noteworthy for the precision of its solid-state THz reproduction, the solid-state calculation predicts that the isolated-molecule modes account for only half of the spectral features in the THz region, with the remaining structure arising from lattice vibrations that cannot be predicted without solid-state molecular modeling. The molecular origins of the internal mode contributions to the solid-state THz spectrum, as well as the proper consideration of the protonation state of the molecule, are also considered.

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

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

Cipcigan, Flaviu S., E-mail: flaviu.cipcigan@ed.ac.uk; National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW; Sokhan, Vlad P.

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082–1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker inmore » the 1980s [Phys. Rev. Lett. 57 (1986) 230–233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeler through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO-MD. - Highlights: • Electronic coarse graining unites many-body dispersion and polarisation beyond the dipole limit. • It consists of replacing the electrons of a molecule using a quantum harmonic oscillator, called a Quantum Drude Oscillator. • We present the first general implementation of Quantum Drude Oscillators in the molecular dynamics package QDO-MD. • We highlight the successful construction of a new, transferable molecular model of water: QDO-water. - Graphical abstract:.« less

Molecular design of new aggrecanases-2 inhibitors.

PubMed

Shan, Zhi Jie; Zhai, Hong Lin; Huang, Xiao Yan; Li, Li Na; Zhang, Xiao Yun

2013-10-01

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications. Copyright © 2013 Elsevier Ltd. All rights reserved.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling

2010-02-01

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

NASA Astrophysics Data System (ADS)

Cipcigan, Flaviu S.; Sokhan, Vlad P.; Crain, Jason; Martyna, Glenn J.

2016-12-01

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082-1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230-233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeller through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO_MD.

Modeling of molecular diffusion and thermal conduction with multi-particle interaction in compressible turbulence

NASA Astrophysics Data System (ADS)

Tai, Y.; Watanabe, T.; Nagata, K.

2018-03-01

A mixing volume model (MVM) originally proposed for molecular diffusion in incompressible flows is extended as a model for molecular diffusion and thermal conduction in compressible turbulence. The model, established for implementation in Lagrangian simulations, is based on the interactions among spatially distributed notional particles within a finite volume. The MVM is tested with the direct numerical simulation of compressible planar jets with the jet Mach number ranging from 0.6 to 2.6. The MVM well predicts molecular diffusion and thermal conduction for a wide range of the size of mixing volume and the number of mixing particles. In the transitional region of the jet, where the scalar field exhibits a sharp jump at the edge of the shear layer, a smaller mixing volume is required for an accurate prediction of mean effects of molecular diffusion. The mixing time scale in the model is defined as the time scale of diffusive effects at a length scale of the mixing volume. The mixing time scale is well correlated for passive scalar and temperature. Probability density functions of the mixing time scale are similar for molecular diffusion and thermal conduction when the mixing volume is larger than a dissipative scale because the mixing time scale at small scales is easily affected by different distributions of intermittent small-scale structures between passive scalar and temperature. The MVM with an assumption of equal mixing time scales for molecular diffusion and thermal conduction is useful in the modeling of the thermal conduction when the modeling of the dissipation rate of temperature fluctuations is difficult.

Molecular Dynamic Simulation and Inhibitor Prediction of Cysteine Synthase Structured Model as a Potential Drug Target for Trichomoniasis

PubMed Central

Singh, Satendra; Singh, Atul Kumar; Gautam, Budhayash

2013-01-01

In our presented research, we made an attempt to predict the 3D model for cysteine synthase (A2GMG5_TRIVA) using homology-modeling approaches. To investigate deeper into the predicted structure, we further performed a molecular dynamics simulation for 10 ns and calculated several supporting analysis for structural properties such as RMSF, radius of gyration, and the total energy calculation to support the predicted structured model of cysteine synthase. The present findings led us to conclude that the proposed model is stereochemically stable. The overall PROCHECK G factor for the homology-modeled structure was −0.04. On the basis of the virtual screening for cysteine synthase against the NCI subset II molecule, we present the molecule 1-N, 4-N-bis [3-(1H-benzimidazol-2-yl) phenyl] benzene-1,4-dicarboxamide (ZINC01690699) having the minimum energy score (−13.0 Kcal/Mol) and a log P value of 6 as a potential inhibitory molecule used to inhibit the growth of T. vaginalis infection. PMID:24073401

A molecular thermodynamic model for the stability of hepatitis B capsids

NASA Astrophysics Data System (ADS)

Kim, Jehoon; Wu, Jianzhong

2014-06-01

Self-assembly of capsid proteins and genome encapsidation are two critical steps in the life cycle of most plant and animal viruses. A theoretical description of such processes from a physiochemical perspective may help better understand viral replication and morphogenesis thus provide fresh insights into the experimental studies of antiviral strategies. In this work, we propose a molecular thermodynamic model for predicting the stability of Hepatitis B virus (HBV) capsids either with or without loading nucleic materials. With the key components represented by coarse-grained thermodynamic models, the theoretical predictions are in excellent agreement with experimental data for the formation free energies of empty T4 capsids over a broad range of temperature and ion concentrations. The theoretical model predicts T3/T4 dimorphism also in good agreement with the capsid formation at in vivo and in vitro conditions. In addition, we have studied the stability of the viral particles in response to physiological cellular conditions with the explicit consideration of the hydrophobic association of capsid subunits, electrostatic interactions, molecular excluded volume effects, entropy of mixing, and conformational changes of the biomolecular species. The course-grained model captures the essential features of the HBV nucleocapsid stability revealed by recent experiments.

Analysis of Physical and Numerical Factors for Prediction of UV Radiation from High Altitude Two-Phase Plumes

DTIC Science & Technology

2008-05-30

varies from continuum inside the nozzle, to transitional in the near field, to free molecular in the far field of the plume. The scales of interest vary...unity based on the rocket length. This results in the formation of a viscous shock layer characterized by a bimodal molecular velocity distribution. The...transfer model. Previous analysis21 have shown that the heat transfer model implemented in CFD++ is reproduced closely by the free molecular model

Ratios of molecular hydrogen line intensities in shocked gas - Evidence for cooling zones

NASA Technical Reports Server (NTRS)

Brand, P. W. J. L.; Moorhouse, A.; Bird, M.; Burton, M. G.; Geballe, T. R.

1988-01-01

Column densities of molecular hydrogen have been calculated from 19 infrared vibration-rotation and pure rotational line intensities measured at peak 1 of the Orion molecular outflow. The run of column density with energy level is similar to a simple coolng zone model of the line-emitting region, but is not well fitted by predictions of C-shock models current in the literature.

Molecular Modeling of Chem-Bio (CB) Contaminant Sorption/Desorption and Reactions in Chlorinated Water Systems

DTIC Science & Technology

2012-05-01

The Smoluchowski model allows us to predict both the flux of DMMP molecules onto the channel membrane in the initial phase of the simulations, as... predicts both the transient and steady-state behavior of the MD simulations. However, the model breaks down for the silica sur- faces, because the...within the range predicted by the “one versus two contact point” conjecture outlined above. Subsequent chemical modeling obtained by Ginsberg (ERDC

Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules

PubMed Central

Park, Hahnbeom; Bradley, Philip; Greisen, Per; Liu, Yuan; Mulligan, Vikram Khipple; Kim, David E.; Baker, David; DiMaio, Frank

2017-01-01

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking, have been parameterized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties. PMID:27766851

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

PubMed

Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

2016-06-01

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

PubMed Central

Todd, Robert G.; van der Zee, Lucas

2016-01-01

Abstract The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network. PMID:27993914

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity.

PubMed

Agatonovic-Kustrin, Snezana; Davies, Paul; Turner, Joseph V

2009-05-01

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Estimating Grass-Soil Bioconcentration of Munitions Compounds from Molecular Structure.

PubMed

Torralba Sanchez, Tifany L; Liang, Yuzhen; Di Toro, Dominic M

2017-10-03

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle. The model is validated by predicting concentrations measured in the whole plant during independent uptake experiments with a root-mean-square error (log predicted plant concentration-log observed plant concentration) of 0.429. This highlights the dominant role of partitioning between the exposure medium and the plant cuticle in the bioconcentration of these compounds. The pp-LFERs can be used to assess the environmental risk of munitions compounds and munition-like compounds using only their molecular structure as input.

Quantitative structure-property relationships for octanol-water partition coefficients of polybrominated diphenyl ethers.

PubMed

Li, Linnan; Xie, Shaodong; Cai, Hao; Bai, Xuetao; Xue, Zhao

2008-08-01

Theoretical molecular descriptors were tested against logK(OW) values for polybrominated diphenyl ethers (PBDEs) using the Partial Least-Squares Regression method which can be used to analyze data with many variables and few observations. A quantitative structure-property relationship (QSPR) model was successfully developed with a high cross-validated value (Q(cum)(2)) of 0.961, indicating a good predictive ability and stability of the model. The predictive power of the QSPR model was further cross-validated. The values of logK(OW) for PBDEs are mainly governed by molecular surface area, energy of the lowest unoccupied molecular orbital and the net atomic charges on the oxygen atom. All these descriptors have been discussed to interpret the partitioning mechanism of PBDE chemicals. The bulk property of the molecules represented by molecular surface area is the leading factor, and K(OW) values increase with the increase of molecular surface area. Higher energy of the lowest unoccupied molecular orbital and higher net atomic charge on the oxygen atom of PBDEs result in smaller K(OW). The energy of the lowest unoccupied molecular orbital and the net atomic charge on PBDEs oxygen also play important roles in affecting the partition of PBDEs between octanol and water by influencing the interactions between PBDEs and solvent molecules.

Structural similarity based kriging for quantitative structure activity and property relationship modeling.

PubMed

Teixeira, Ana L; Falcao, Andre O

2014-07-28

Structurally similar molecules tend to have similar properties, i.e. closer molecules in the molecular space are more likely to yield similar property values while distant molecules are more likely to yield different values. Based on this principle, we propose the use of a new method that takes into account the high dimensionality of the molecular space, predicting chemical, physical, or biological properties based on the most similar compounds with measured properties. This methodology uses ordinary kriging coupled with three different molecular similarity approaches (based on molecular descriptors, fingerprints, and atom matching) which creates an interpolation map over the molecular space that is capable of predicting properties/activities for diverse chemical data sets. The proposed method was tested in two data sets of diverse chemical compounds collected from the literature and preprocessed. One of the data sets contained dihydrofolate reductase inhibition activity data, and the second molecules for which aqueous solubility was known. The overall predictive results using kriging for both data sets comply with the results obtained in the literature using typical QSPR/QSAR approaches. However, the procedure did not involve any type of descriptor selection or even minimal information about each problem, suggesting that this approach is directly applicable to a large spectrum of problems in QSAR/QSPR. Furthermore, the predictive results improve significantly with the similarity threshold between the training and testing compounds, allowing the definition of a confidence threshold of similarity and error estimation for each case inferred. The use of kriging for interpolation over the molecular metric space is independent of the training data set size, and no reparametrizations are necessary when more compounds are added or removed from the set, and increasing the size of the database will consequentially improve the quality of the estimations. Finally it is shown that this model can be used for checking the consistency of measured data and for guiding an extension of the training set by determining the regions of the molecular space for which new experimental measurements could be used to maximize the model's predictive performance.

Automated chemical kinetic modeling via hybrid reactive molecular dynamics and quantum chemistry simulations.

PubMed

Döntgen, Malte; Schmalz, Felix; Kopp, Wassja A; Kröger, Leif C; Leonhard, Kai

2018-06-13

An automated scheme for obtaining chemical kinetic models from scratch using reactive molecular dynamics and quantum chemistry simulations is presented. This methodology combines the phase space sampling of reactive molecular dynamics with the thermochemistry and kinetics prediction capabilities of quantum mechanics. This scheme provides the NASA polynomial and modified Arrhenius equation parameters for all species and reactions that are observed during the simulation and supplies them in the ChemKin format. The ab initio level of theory for predictions is easily exchangeable and the presently used G3MP2 level of theory is found to reliably reproduce hydrogen and methane oxidation thermochemistry and kinetics data. Chemical kinetic models obtained with this approach are ready-to-use for, e.g., ignition delay time simulations, as shown for hydrogen combustion. The presented extension of the ChemTraYzer approach can be used as a basis for methodologically advancing chemical kinetic modeling schemes and as a black-box approach to generate chemical kinetic models.

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

PubMed

Crossa, José; Campos, Gustavo de Los; Pérez, Paulino; Gianola, Daniel; Burgueño, Juan; Araus, José Luis; Makumbi, Dan; Singh, Ravi P; Dreisigacker, Susanne; Yan, Jianbing; Arief, Vivi; Banziger, Marianne; Braun, Hans-Joachim

2010-10-01

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

Computational prediction of formulation strategies for beyond-rule-of-5 compounds.

PubMed

Bergström, Christel A S; Charman, William N; Porter, Christopher J H

2016-06-01

The physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, since these can provide an early signal that enabling formulation strategies will be required. Based on the analysis we conclude that computational biopharmaceutical profiling can be used to identify where non-conventional gateways, such as prediction of 'formulate-ability' during lead optimisation and early development stages, are important and may ultimately increase the number of orally tractable contemporary targets. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

EPA Science Inventory

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently pu...

Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers.

PubMed

Cerruela García, G; García-Pedrajas, N; Luque Ruiz, I; Gómez-Nieto, M Á

2018-03-01

This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.

Computational Modeling and Treatment Identification in the Myelodysplastic Syndromes.

PubMed

Drusbosky, Leylah M; Cogle, Christopher R

2017-10-01

This review discusses the need for computational modeling in myelodysplastic syndromes (MDS) and early test results. As our evolving understanding of MDS reveals a molecularly complicated disease, the need for sophisticated computer analytics is required to keep track of the number and complex interplay among the molecular abnormalities. Computational modeling and digital drug simulations using whole exome sequencing data input have produced early results showing high accuracy in predicting treatment response to standard of care drugs. Furthermore, the computational MDS models serve as clinically relevant MDS cell lines for pre-clinical assays of investigational agents. MDS is an ideal disease for computational modeling and digital drug simulations. Current research is focused on establishing the prediction value of computational modeling. Future research will test the clinical advantage of computer-informed therapy in MDS.

QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

NASA Astrophysics Data System (ADS)

Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

2015-10-01

Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients (rpred2) of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

Modeling and validation of autoinducer-mediated bacterial gene expression in microfluidic environments

PubMed Central

Austin, Caitlin M.; Stoy, William; Su, Peter; Harber, Marie C.; Bardill, J. Patrick; Hammer, Brian K.; Forest, Craig R.

2014-01-01

Biosensors exploiting communication within genetically engineered bacteria are becoming increasingly important for monitoring environmental changes. Currently, there are a variety of mathematical models for understanding and predicting how genetically engineered bacteria respond to molecular stimuli in these environments, but as sensors have miniaturized towards microfluidics and are subjected to complex time-varying inputs, the shortcomings of these models have become apparent. The effects of microfluidic environments such as low oxygen concentration, increased biofilm encapsulation, diffusion limited molecular distribution, and higher population densities strongly affect rate constants for gene expression not accounted for in previous models. We report a mathematical model that accurately predicts the biological response of the autoinducer N-acyl homoserine lactone-mediated green fluorescent protein expression in reporter bacteria in microfluidic environments by accommodating these rate constants. This generalized mass action model considers a chain of biomolecular events from input autoinducer chemical to fluorescent protein expression through a series of six chemical species. We have validated this model against experimental data from our own apparatus as well as prior published experimental results. Results indicate accurate prediction of dynamics (e.g., 14% peak time error from a pulse input) and with reduced mean-squared error with pulse or step inputs for a range of concentrations (10 μM–30 μM). This model can help advance the design of genetically engineered bacteria sensors and molecular communication devices. PMID:25379076

Predicting Molecular Crystal Properties from First Principles: Finite-Temperature Thermochemistry to NMR Crystallography.

PubMed

Beran, Gregory J O; Hartman, Joshua D; Heit, Yonaton N

2016-11-15

Molecular crystals occur widely in pharmaceuticals, foods, explosives, organic semiconductors, and many other applications. Thanks to substantial progress in electronic structure modeling of molecular crystals, attention is now shifting from basic crystal structure prediction and lattice energy modeling toward the accurate prediction of experimentally observable properties at finite temperatures and pressures. This Account discusses how fragment-based electronic structure methods can be used to model a variety of experimentally relevant molecular crystal properties. First, it describes the coupling of fragment electronic structure models with quasi-harmonic techniques for modeling the thermal expansion of molecular crystals, and what effects this expansion has on thermochemical and mechanical properties. Excellent agreement with experiment is demonstrated for the molar volume, sublimation enthalpy, entropy, and free energy, and the bulk modulus of phase I carbon dioxide when large basis second-order Møller-Plesset perturbation theory (MP2) or coupled cluster theories (CCSD(T)) are used. In addition, physical insight is offered into how neglect of thermal expansion affects these properties. Zero-point vibrational motion leads to an appreciable expansion in the molar volume; in carbon dioxide, it accounts for around 30% of the overall volume expansion between the electronic structure energy minimum and the molar volume at the sublimation point. In addition, because thermal expansion typically weakens the intermolecular interactions, neglecting thermal expansion artificially stabilizes the solid and causes the sublimation enthalpy to be too large at higher temperatures. Thermal expansion also frequently weakens the lower-frequency lattice phonon modes; neglecting thermal expansion causes the entropy of sublimation to be overestimated. Interestingly, the sublimation free energy is less significantly affected by neglecting thermal expansion because the systematic errors in the enthalpy and entropy cancel somewhat. Second, because solid state nuclear magnetic resonance (NMR) plays an increasingly important role in molecular crystal studies, this Account discusses how fragment methods can be used to achieve higher-accuracy chemical shifts in molecular crystals. Whereas widely used plane wave density functional theory models are largely restricted to generalized gradient approximation (GGA) functionals like PBE in practice, fragment methods allow the routine use of hybrid density functionals with only modest increases in computational cost. In extensive molecular crystal benchmarks, hybrid functionals like PBE0 predict chemical shifts with 20-30% higher accuracy than GGAs, particularly for 1 H, 13 C, and 15 N nuclei. Due to their higher sensitivity to polarization effects, 17 O chemical shifts prove slightly harder to predict with fragment methods. Nevertheless, the fragment model results are still competitive with those from GIPAW. The improved accuracy achievable with fragment approaches and hybrid density functionals increases discrimination between different potential assignments of individual shifts or crystal structures, which is critical in NMR crystallography applications. This higher accuracy and greater discrimination are highlighted in application to the solid state NMR of different acetaminophen and testosterone crystal forms.

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

PubMed

Park, Soo Hyun; Talebi, Mohammad; Amos, Ruth I J; Tyteca, Eva; Haddad, Paul R; Szucs, Roman; Pohl, Christopher A; Dolan, John W

2017-11-10

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Q ext(F2) 2 >0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

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

McLaughlin, E.; Gupta, S.

This project mainly involves a molecular dynamics and Monte Carlo study of the effect of molecular shape on thermophysical properties of bulk fluids with an emphasis on the aromatic hydrocarbon liquids. In this regard we have studied the modeling, simulation methodologies, and predictive and correlating methods for thermodynamic properties of fluids of nonspherical molecules. In connection with modeling we have studied the use of anisotropic site-site potentials, through a modification of the Gay-Berne Gaussian overlap potential, to successfully model the aromatic rings after adding the necessary electrostatic moments. We have also shown these interaction sites should be located at themore » geometric centers of the chemical groups. In connection with predictive methods, we have shown two perturbation type theories to work well for fluids modeled using one-center anisotropic potentials and the possibility exists for extending these to anisotropic site-site models. In connection with correlation methods, we have studied, through simulations, the effect of molecular shape on the attraction term in the generalized van der Waals equation of state for fluids of nonspherical molecules and proposed a possible form which is to be studied further. We have successfully studied the vector and parallel processing aspects of molecular simulations for fluids of nonspherical molecules.« less

Structure–property reduced order model for viscosity prediction in single-component CO 2 -binding organic liquids

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

Cantu, David C.; Malhotra, Deepika; Koech, Phillip K.

2016-01-01

CO2 capture from power generation with aqueous solvents remains energy intensive due to the high water content of the current technology, or the high viscosity of non-aqueous alternatives. Quantitative reduced models, connecting molecular structure to bulk properties, are key for developing structure-property relationships that enable molecular design. In this work, we describe such a model that quantitatively predicts viscosities of CO2 binding organic liquids (CO2BOLs) based solely on molecular structure and the amount of bound CO2. The functional form of the model correlates the viscosity with the CO2 loading and an electrostatic term describing the charge distribution between the CO2-bearingmore » functional group and the proton-receiving amine. Molecular simulations identify the proton shuttle between these groups within the same molecule to be the critical indicator of low viscosity. The model, developed to allow for quick screening of solvent libraries, paves the way towards the rational design of low viscosity non-aqueous solvent systems for post-combustion CO2 capture. Following these theoretical recommendations, synthetic efforts of promising candidates and viscosity measurement provide experimental validation and verification.« less

Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector.

PubMed

Liu, Shu-Shen; Liu, Yan; Yin, Da-Qian; Wang, Xiao-Dong; Wang, Lian-Sheng

2006-02-01

Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

Modeling the effect of 3 missense AGXT mutations on dimerization of the AGT enzyme in primary hyperoxaluria type 1.

PubMed

Robbiano, Angela; Frecer, Vladimir; Miertus, Jan; Zadro, Cristina; Ulivi, Sheila; Bevilacqua, Elena; Mandrile, Giorgia; De Marchi, Mario; Miertus, Stanislav; Amoroso, Antonio

2010-01-01

Mutations of the AGXT gene encoding the alanine:glyoxylate aminotransferase liver enzyme (AGT) cause primary hyperoxaluria type 1 (PH1). Here we report a molecular modeling study of selected missense AGXT mutations: the common Gly170Arg and the recently described Gly47Arg and Ser81Leu variants, predicted to be pathogenic using standard criteria. Taking advantage of the refined 3D structure of AGT, we computed the dimerization energy of the wild-type and mutated proteins. Molecular modeling predicted that Gly47Arg affects dimerization with a similar effect to that shown previously for Gly170Arg through classical biochemical approaches. In contrast, no effect on dimerization was predicted for Ser81Leu. Therefore, this probably demonstrates pathogenic properties via a different mechanism, similar to that described for the adjacent Gly82Glu mutation that affects pyridoxine binding. This study shows that the molecular modeling approach can contribute to evaluating the pathogenicity of some missense variants that affect dimerization. However, in silico studies--aimed to assess the relationship between structural change and biological effects--require the integrated use of more than 1 tool.

Combining molecular docking and QSAR studies for modeling the anti-tyrosinase activity of aromatic heterocycle thiosemicarbazone analogues

NASA Astrophysics Data System (ADS)

Dong, Huanhuan; Liu, Jing; Liu, Xiaoru; Yu, Yanying; Cao, Shuwen

2018-01-01

A collection of thirty-six aromatic heterocycle thiosemicarbazone analogues presented a broad span of anti-tyrosinase activities were designed and obtained. A robust and reliable two-dimensional quantitative structure-activity relationship model, as evidenced by the high q2 and r2 values (0.848 and 0.893, respectively), was gained based on the analogues to predict the quantitative chemical-biological relationship and the new modifier direction. Inhibitory activities of the compounds were found to greatly depend on molecular shape and orbital energy. Substituents brought out large ovality and high highest-occupied molecular orbital energy values helped to improve the activity of these analogues. The molecular docking results provided visual evidence for QSAR analysis and inhibition mechanism. Based on these, two novel tyrosinase inhibitors O04 and O05 with predicted IC50 of 0.5384 and 0.8752 nM were designed and suggested for further research.

Learning To Fold Proteins Using Energy Landscape Theory

PubMed Central

Schafer, N.P.; Kim, B.L.; Zheng, W.; Wolynes, P.G.

2014-01-01

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation. PMID:25308991

Multi-scale modelling of supercapacitors: From molecular simulations to a transmission line model

NASA Astrophysics Data System (ADS)

Pean, C.; Rotenberg, B.; Simon, P.; Salanne, M.

2016-09-01

We perform molecular dynamics simulations of a typical nanoporous-carbon based supercapacitor. The organic electrolyte consists in 1-ethyl-3-methylimidazolium and hexafluorophosphate ions dissolved in acetonitrile. We simulate systems at equilibrium, for various applied voltages. This allows us to determine the relevant thermodynamic (capacitance) and transport (in-pore resistivities) properties. These quantities are then injected in a transmission line model for testing its ability to predict the charging properties of the device. The results from this macroscopic model are in good agreement with non-equilibrium molecular dynamics simulations, which validates its use for interpreting electrochemical impedance experiments.

QSAR, molecular docking studies of thiophene and imidazopyridine derivatives as polo-like kinase 1 inhibitors

NASA Astrophysics Data System (ADS)

Cao, Shandong

2012-08-01

The purpose of the present study was to develop in silico models allowing for a reliable prediction of polo-like kinase inhibitors based on a large diverse dataset of 136 compounds. As an effective method, quantitative structure activity relationship (QSAR) was applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The proposed QSAR models showed reasonable predictivity of thiophene analogs (Rcv2=0.533, Rpred2=0.845) and included four molecular descriptors, namely IC3, RDF075m, Mor02m and R4e+. The optimal model for imidazopyridine derivatives (Rcv2=0.776, Rpred2=0.876) was shown to perform good in prediction accuracy, using GATS2m and BEHe1 descriptors. Analysis of the contour maps helped to identify structural requirements for the inhibitors and served as a basis for the design of the next generation of the inhibitor analogues. Docking studies were also employed to position the inhibitors into the polo-like kinase active site to determine the most probable binding mode. These studies may help to understand the factors influencing the binding affinity of chemicals and to develop alternative methods for prescreening and designing of polo-like kinase inhibitors.

Development of Short-term Molecular Thresholds to Predict Long-term Mouse Liver Tumor Outcomes: Phthalate Case StudyTo be

EPA Science Inventory

Molecular Thresholds for Early Key Events in Liver Tumorgensis: PhthalateCase StudyTriangleShort-term changes in molecular profiles are a central component of strategies to model health effects of environmental chemicals such as phthalates, for which there is widespread human exp...

Cross-Platform Toxicogenomics for the Prediction of Non-Genotoxic Hepatocarcinogenesis in Rat

PubMed Central

Metzger, Ute; Templin, Markus F.; Plummer, Simon; Ellinger-Ziegelbauer, Heidrun; Zell, Andreas

2014-01-01

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens. PMID:24830643

An ensemble predictive modeling framework for breast cancer classification.

PubMed

Nagarajan, Radhakrishnan; Upreti, Meenakshi

2017-12-01

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Application of JAERI quantum molecular dynamics model for collisions of heavy nuclei

NASA Astrophysics Data System (ADS)

Ogawa, Tatsuhiko; Hashimoto, Shintaro; Sato, Tatsuhiko; Niita, Koji

2016-06-01

The quantum molecular dynamics (QMD) model incorporated into the general-purpose radiation transport code PHITS was revised for accurate prediction of fragment yields in peripheral collisions. For more accurate simulation of peripheral collisions, stability of the nuclei at their ground state was improved and the algorithm to reject invalid events was modified. In-medium correction on nucleon-nucleon cross sections was also considered. To clarify the effect of this improvement on fragmentation of heavy nuclei, the new QMD model coupled with a statistical decay model was used to calculate fragment production cross sections of Ag and Au targets and compared with the data of earlier measurement. It is shown that the revised version can predict cross section more accurately.

Computational Nanotechnology of Molecular Materials, Electronics and Machines

NASA Technical Reports Server (NTRS)

Srivastava, D.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

This viewgraph presentation covers carbon nanotubes, their characteristics, and their potential future applications. The presentation include predictions on the development of nanostructures and their applications, the thermal characteristics of carbon nanotubes, mechano-chemical effects upon carbon nanotubes, molecular electronics, and models for possible future nanostructure devices. The presentation also proposes a neural model for signal processing.

Is pigment patterning in fish skin determined by the Turing mechanism?

PubMed

Watanabe, Masakatsu; Kondo, Shigeru

2015-02-01

More than half a century ago, Alan Turing postulated that pigment patterns may arise from a mechanism that could be mathematically modeled based on the diffusion of two substances that interact with each other. Over the past 15 years, the molecular and genetic tools to verify this prediction have become available. Here, we review experimental studies aimed at identifying the mechanism underlying pigment pattern formation in zebrafish. Extensive molecular genetic studies in this model organism have revealed the interactions between the pigment cells that are responsible for the patterns. The mechanism discovered is substantially different from that predicted by the mathematical model, but it retains the property of 'local activation and long-range inhibition', a necessary condition for Turing pattern formation. Although some of the molecular details of pattern formation remain to be elucidated, current evidence confirms that the underlying mechanism is mathematically equivalent to the Turing mechanism. Copyright © 2014 Elsevier Ltd. All rights reserved.

Conformational free energy modeling of druglike molecules by metadynamics in the WHIM space.

PubMed

Spiwok, Vojtěch; Hlat-Glembová, Katarína; Tvaroška, Igor; Králová, Blanka

2012-03-26

Protein-ligand affinities can be significantly influenced not only by the interaction itself but also by conformational equilibrium of both binding partners, free ligand and free protein. Identification of important conformational families of a ligand and prediction of their thermodynamics is important for efficient ligand design. Here we report conformational free energy modeling of nine small-molecule drugs in explicitly modeled water by metadynamics with a bias potential applied in the space of weighted holistic invariant molecular (WHIM) descriptors. Application of metadynamics enhances conformational sampling compared to unbiased molecular dynamics simulation and allows to predict relative free energies of key conformations. Selected free energy minima and one example of transition state were tested by a series of unbiased molecular dynamics simulation. Comparison of free energy surfaces of free and target-bound Imatinib provides an estimate of free energy penalty of conformational change induced by its binding to the target. © 2012 American Chemical Society

Improved Electrostatic Embedding for Fragment-Based Chemical Shift Calculations in Molecular Crystals.

PubMed

Hartman, Joshua D; Balaji, Ashwin; Beran, Gregory J O

2017-12-12

Fragment-based methods predict nuclear magnetic resonance (NMR) chemical shielding tensors in molecular crystals with high accuracy and computational efficiency. Such methods typically employ electrostatic embedding to mimic the crystalline environment, and the quality of the results can be sensitive to the embedding treatment. To improve the quality of this embedding environment for fragment-based molecular crystal property calculations, we borrow ideas from the embedded ion method to incorporate self-consistently polarized Madelung field effects. The self-consistent reproduction of the Madelung potential (SCRMP) model developed here constructs an array of point charges that incorporates self-consistent lattice polarization and which reproduces the Madelung potential at all atomic sites involved in the quantum mechanical region of the system. The performance of fragment- and cluster-based 1 H, 13 C, 14 N, and 17 O chemical shift predictions using SCRMP and density functionals like PBE and PBE0 are assessed. The improved embedding model results in substantial improvements in the predicted 17 O chemical shifts and modest improvements in the 15 N ones. Finally, the performance of the model is demonstrated by examining the assignment of the two oxygen chemical shifts in the challenging γ-polymorph of glycine. Overall, the SCRMP-embedded NMR chemical shift predictions are on par with or more accurate than those obtained with the widely used gauge-including projector augmented wave (GIPAW) model.

Alignment-independent technique for 3D QSAR analysis

NASA Astrophysics Data System (ADS)

Wilkes, Jon G.; Stoyanova-Slavova, Iva B.; Buzatu, Dan A.

2016-04-01

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test 2 = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test 2 = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test 2 = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Elucidation of the molecular mechanisms underlying adverse reactions associated with a kinase inhibitor using systems toxicology

PubMed Central

Amemiya, Takahiro; Honma, Masashi; Kariya, Yoshiaki; Ghosh, Samik; Kitano, Hiroaki; Kurachi, Yoshihisa; Fujita, Ken-ichi; Sasaki, Yasutsuna; Homma, Yukio; Abernethy, Darrel R; Kume, Haruki; Suzuki, Hiroshi

2015-01-01

Background/Objectives: Targeted kinase inhibitors are an important class of agents in anticancer therapeutics, but their limited tolerability hampers their clinical performance. Identification of the molecular mechanisms underlying the development of adverse reactions will be helpful in establishing a rational method for the management of clinically adverse reactions. Here, we selected sunitinib as a model and demonstrated that the molecular mechanisms underlying the adverse reactions associated with kinase inhibitors can efficiently be identified using a systems toxicological approach. Methods: First, toxicological target candidates were short-listed by comparing the human kinase occupancy profiles of sunitinib and sorafenib, and the molecular mechanisms underlying adverse reactions were predicted by sequential simulations using publicly available mathematical models. Next, to evaluate the probability of these predictions, a clinical observation study was conducted in six patients treated with sunitinib. Finally, mouse experiments were performed for detailed confirmation of the hypothesized molecular mechanisms and to evaluate the efficacy of a proposed countermeasure against adverse reactions to sunitinib. Results: In silico simulations indicated the possibility that sunitinib-mediated off-target inhibition of phosphorylase kinase leads to the generation of oxidative stress in various tissues. Clinical observations of patients and mouse experiments confirmed the validity of this prediction. The simulation further suggested that concomitant use of an antioxidant may prevent sunitinib-mediated adverse reactions, which was confirmed in mouse experiments. Conclusions: A systems toxicological approach successfully predicted the molecular mechanisms underlying clinically adverse reactions associated with sunitinib and was used to plan a rational method for the management of these adverse reactions. PMID:28725458

RACER a Coarse-Grained RNA Model for Capturing Folding Free Energy in Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Cheng, Sara; Bell, David; Ren, Pengyu

RACER is a coarse-grained RNA model that can be used in molecular dynamics simulations to predict native structures and sequence-specific variation of free energy of various RNA structures. RACER is capable of accurate prediction of native structures of duplexes and hairpins (average RMSD of 4.15 angstroms), and RACER can capture sequence-specific variation of free energy in excellent agreement with experimentally measured stabilities (r-squared =0.98). The RACER model implements a new effective non-bonded potential and re-parameterization of hydrogen bond and Debye-Huckel potentials. Insights from the RACER model include the importance of treating pairing and stacking interactions separately in order to distinguish folded an unfolded states and identification of hydrogen-bonding, base stacking, and electrostatic interactions as essential driving forces for RNA folding. Future applications of the RACER model include predicting free energy landscapes of more complex RNA structures and use of RACER for multiscale simulations.

Characteristics of the mixing volume model with the interactions among spatially distributed particles for Lagrangian simulations of turbulent mixing

NASA Astrophysics Data System (ADS)

Watanabe, Tomoaki; Nagata, Koji

2016-11-01

The mixing volume model (MVM), which is a mixing model for molecular diffusion in Lagrangian simulations of turbulent mixing problems, is proposed based on the interactions among spatially distributed particles in a finite volume. The mixing timescale in the MVM is derived by comparison between the model and the subgrid scale scalar variance equation. A-priori test of the MVM is conducted based on the direct numerical simulations of planar jets. The MVM is shown to predict well the mean effects of the molecular diffusion under various conditions. However, a predicted value of the molecular diffusion term is positively correlated to the exact value in the DNS only when the number of the mixing particles is larger than two. Furthermore, the MVM is tested in the hybrid implicit large-eddy-simulation/Lagrangian-particle-simulation (ILES/LPS). The ILES/LPS with the present mixing model predicts well the decay of the scalar variance in planar jets. This work was supported by JSPS KAKENHI Nos. 25289030 and 16K18013. The numerical simulations presented in this manuscript were carried out on the high performance computing system (NEC SX-ACE) in the Japan Agency for Marine-Earth Science and Technology.

Parametric models to compute tryptophan fluorescence wavelengths from classical protein simulations.

PubMed

Lopez, Alvaro J; Martínez, Leandro

2018-02-26

Fluorescence spectroscopy is an important method to study protein conformational dynamics and solvation structures. Tryptophan (Trp) residues are the most important and practical intrinsic probes for protein fluorescence due to the variability of their fluorescence wavelengths: Trp residues emit in wavelengths ranging from 308 to 360 nm depending on the local molecular environment. Fluorescence involves electronic transitions, thus its computational modeling is a challenging task. We show that it is possible to predict the wavelength of emission of a Trp residue from classical molecular dynamics simulations by computing the solvent-accessible surface area or the electrostatic interaction between the indole group and the rest of the system. Linear parametric models are obtained to predict the maximum emission wavelengths with standard errors of the order 5 nm. In a set of 19 proteins with emission wavelengths ranging from 308 to 352 nm, the best model predicts the maximum wavelength of emission with a standard error of 4.89 nm and a quadratic Pearson correlation coefficient of 0.81. These models can be used for the interpretation of fluorescence spectra of proteins with multiple Trp residues, or for which local Trp environmental variability exists and can be probed by classical molecular dynamics simulations. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data.

PubMed

Lado, Bettina; Matus, Ivan; Rodríguez, Alejandra; Inostroza, Luis; Poland, Jesse; Belzile, François; del Pozo, Alejandro; Quincke, Martín; Castro, Marina; von Zitzewitz, Jarislav

2013-12-09

In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Is there hope for multi-site complexation modeling?

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

Bickmore, Barry R.; Rosso, Kevin M.; Mitchell, S. C.

2006-06-06

It has been shown here that the standard formulation of the MUSIC model does not deliver the molecular-scale insight into oxide surface reactions that it promises. The model does not properly divide long-range electrostatic and short-range contributions to acid-base reaction energies, and it does not treat solvation in a physically realistic manner. However, even if the current MUSIC model does not succeed in its ambitions, its ambitions are still reasonable. It was a pioneering attempt in that Hiemstra and coworkers recognized that intrinsic equilibrium constants, where the effects of long-range electrostatic effects have been removed, must be theoretically constrained priormore » to model fitting if there is to be any hope of obtaining molecular-scale insights from SCMs. We have also shown, on the other hand, that it may be premature to dismiss all valence-based models of acidity. Not only can some such models accurately predict intrinsic acidity constants, but they can also now be linked to the results of molecular dynamics simulations of solvated systems. Significant challenges remain for those interested in creating SCMs that are accurate at the molecular scale. It will only be after all model parameters can be predicted from theory, and the models validated against titration data that we will be able to begin to have some confidence that we really are adequately describing the chemical systems in question.« less

eMolTox: prediction of molecular toxicity with confidence.

PubMed

Ji, Changge; Svensson, Fredrik; Zoufir, Azedine; Bender, Andreas

2018-03-07

In this work we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development. The eMolTox Server is freely available for use on the web at http://xundrug.cn/moltox. chicago.ji@gmail.com or ab454@cam.ac.uk. Supplementary data are available at Bioinformatics online.

PSPP: A Protein Structure Prediction Pipeline for Computing Clusters

DTIC Science & Technology

2009-07-01

Evanseck JD, et al. (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. Journal of Physical Chemistry B 102...dimensional (3-D) protein structures are critical for the understanding of molecular mechanisms of living systems. Traditionally, X-ray crystallography...disordered proteins are often responsible for molecular recognition, molecular assembly, protein modifica- tion, and entropic chain activities in organisms [26

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

PubMed Central

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T.; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J.

2017-01-01

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival. PMID:28401902

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

PubMed

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

2017-04-12

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

Modeling of metal thin film growth: Linking angstrom-scale molecular dynamics results to micron-scale film topographies

NASA Astrophysics Data System (ADS)

Hansen, U.; Rodgers, S.; Jensen, K. F.

2000-07-01

A general method for modeling ionized physical vapor deposition is presented. As an example, the method is applied to growth of an aluminum film in the presence of an ionized argon flux. Molecular dynamics techniques are used to examine the surface adsorption, reflection, and sputter reactions taking place during ionized physical vapor deposition. We predict their relative probabilities and discuss their dependence on energy and incident angle. Subsequently, we combine the information obtained from molecular dynamics with a line of sight transport model in a two-dimensional feature, incorporating all effects of reemission and resputtering. This provides a complete growth rate model that allows inclusion of energy- and angular-dependent reaction rates. Finally, a level-set approach is used to describe the morphology of the growing film. We thus arrive at a computationally highly efficient and accurate scheme to model the growth of thin films. We demonstrate the capabilities of the model predicting the major differences on Al film topographies between conventional and ionized sputter deposition techniques studying thin film growth under ionized physical vapor deposition conditions with different Ar fluxes.

Prediction of purification of biopharmeceuticals with molecular dynamics

NASA Astrophysics Data System (ADS)

Ustach, Vincent; Faller, Roland

Purification of biopharmeceuticals remains the most expensive part of protein-based drug production. In ion exchange chromatography (IEX), prediction of the elution ionic strength of host cell and target proteins has the potential to reduce the parameter space for scale-up of protein production. The complex shape and charge distribution of proteins and pores complicates predictions of the interactions in these systems. All-atom molecular dynamics methods are beyond the scope of computational limits for mass transport regimes. We present a coarse-grained model for proteins for prediction of elution pH and ionic strength. By extending the raspberry model for colloid particles to surface shapes and charge distributions of proteins, we can reproduce the behavior of proteins in IEX. The average charge states of titratatable amino acid residues at relevant pH values are determined by extrapolation from all-atom molecular dynamics at pH 7. The pH specific all-atom electrostatic field is then mapped onto the coarse-grained surface beads of the raspberry particle. The hydrodynamics are reproduced with the lattice-Boltzmann scheme. This combination of methods allows very long simulation times. The model is being validated for known elution procedures by comparing the data with experiments. Defense Threat Reduction Agency (Grant Number HDTRA1-15-1-0054).

Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

PubMed

Yoo, Byong Chul; Yeo, Seung-Gu

2017-03-01

Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

Predicting critical micelle concentration and micelle molecular weight of polysorbate 80 using compendial methods.

PubMed

Braun, Alexandra C; Ilko, David; Merget, Benjamin; Gieseler, Henning; Germershaus, Oliver; Holzgrabe, Ulrike; Meinel, Lorenz

2015-08-01

This manuscript addresses the capability of compendial methods in controlling polysorbate 80 (PS80) functionality. Based on the analysis of sixteen batches, functionality related characteristics (FRC) including critical micelle concentration (CMC), cloud point, hydrophilic-lipophilic balance (HLB) value and micelle molecular weight were correlated to chemical composition including fatty acids before and after hydrolysis, content of non-esterified polyethylene glycols and sorbitan polyethoxylates, sorbitan- and isosorbide polyethoxylate fatty acid mono- and diesters, polyoxyethylene diesters, and peroxide values. Batches from some suppliers had a high variability in functionality related characteristic (FRC), questioning the ability of the current monograph in controlling these. Interestingly, the combined use of the input parameters oleic acid content and peroxide value - both of which being monographed methods - resulted in a model adequately predicting CMC. Confining the batches to those complying with specifications for peroxide value proved oleic acid content alone as being predictive for CMC. Similarly, a four parameter model based on chemical analyses alone was instrumental in predicting the molecular weight of PS80 micelles. Improved models based on analytical outcome from fingerprint analyses are also presented. A road map controlling PS80 batches with respect to FRC and based on chemical analyses alone is provided for the formulator. Copyright © 2014 Elsevier B.V. All rights reserved.

Molecular modeling-driven approach for identification of Janus kinase 1 inhibitors through 3D-QSAR, docking and molecular dynamics simulations.

PubMed

Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

2017-10-01

Janus kinase 1 (JAK 1) belongs to the JAK family of intracellular nonreceptor tyrosine kinase. JAK-signal transducer and activator of transcription (JAK-STAT) pathway mediate signaling by cytokines, which control survival, proliferation and differentiation of a variety of cells. Three-dimensional quantitative structure activity relationship (3 D-QSAR), molecular docking and molecular dynamics (MD) methods was carried out on a dataset of Janus kinase 1(JAK 1) inhibitors. Ligands were constructed and docked into the active site of protein using GLIDE 5.6. Best docked poses were selected after analysis for further 3 D-QSAR analysis using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodology. Employing 60 molecules in the training set, 3 D-QSAR models were generate that showed good statistical reliability, which is clearly observed in terms of r 2 ncv and q 2 loo values. The predictive ability of these models was determined using a test set of 25 molecules that gave acceptable predictive correlation (r 2 Pred ) values. The key amino acid residues were identified by means of molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good consonance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the reasonable modification of molecules in order to design more efficient JAK 1 inhibitors. The developed models are expected to provide some directives for further synthesis of highly effective JAK 1 inhibitors.

Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers

PubMed Central

Xu, Li; Fengji, Liang; Changning, Liu; Liangcai, Zhang; Yinghui, Li; Yu, Li; Shanguang, Chen; Jianghui, Xiong

2015-01-01

Introduction Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored. Materials and Methods We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome. Results Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71–0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables. Conclusion Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies. PMID:26606135

Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions.

PubMed

Luo, Heng; Fokoue-Nkoutche, Achille; Singh, Nalini; Yang, Lun; Hu, Jianying; Zhang, Ping

2018-05-23

Adverse drug reactions (ADRs) present a major burden for patients and the healthcare industry. Various computational methods have been developed to predict ADRs for drug molecules. However, many of these methods require experimental or surveillance data and cannot be used when only structural information is available. We collected 1,231 small molecule drugs and 600 human proteins and utilized molecular docking to generate binding features among them. We developed machine learning models that use these docking features to make predictions for 1,533 ADRs. These models obtain an overall area under the receiver operating characteristic curve (AUROC) of 0.843 and an overall area under the precision-recall curve (AUPR) of 0.395, outperforming seven structural fingerprint-based prediction models. Using the method, we predicted skin striae for fluticasone propionate, dermatitis acneiform for mometasone, and decreased libido for irinotecan, as demonstrations. Furthermore, we analyzed the top binding proteins associated with some of the ADRs, which can help to understand and/or generate hypotheses for underlying mechanisms of ADRs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Microarray-based cancer prediction using soft computing approach.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

Geometric and electrostatic modeling using molecular rigidity functions

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

Mu, Lin; Xia, Kelin; Wei, Guowei

Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins.more » Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.« less

Geometric and electrostatic modeling using molecular rigidity functions

DOE PAGES

Mu, Lin; Xia, Kelin; Wei, Guowei

2017-03-01

Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins.more » Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.« less

Incorporation of Predictive Population Modeling into the AOP Famework: A Case Study with White Suckers Exposed to Pulp Effluent

EPA Science Inventory

A need in ecological risk assessment is the ability to create linkages between chemically-induced alterations at molecular and biochemical levels of organization with adverse outcomes in whole organisms and populations. A predictive model was developed to translate changes in th...

Isochoric structural recovery in molecular glasses and its analog in colloidal glasses

NASA Astrophysics Data System (ADS)

Banik, Sourya; McKenna, Gregory B.

2018-06-01

Concentrated colloidal dispersions have been regarded as models for molecular glasses. One of the many ways to compare the behavior in these two different systems is by comparing the structural recovery or the physical aging behavior. However, recent investigations from our group to examine structural recovery in thermosensitive colloidal dispersions have shown contrasting results between the colloidal and the molecular glasses. The differences in the behaviors of the two systems have led us to pose this question: Is structural recovery behavior in colloidal glasses truly distinct from that of molecular glasses or is the conventional experimental condition (isobaric temperature-jumps) in determining the structural recovery in molecular glasses different from the experimental condition in the colloidal experiments (concentration- or volume fraction-jumps); i.e., are colloidal glasses inherently different from molecular glasses or not? To address the question, we resort to model calculations of structural recovery in a molecular glass under constant volume (isochoric) conditions following temperature only- and simultaneous volume- and temperature-jumps, which are closer to the volume fraction-jump conditions used in the thermosensitive-colloidal experiments. The current model predictions are then compared with the signatures of structural recovery under the conventional isobaric state in a molecular glass and with structural recovery behavior in colloidal glasses following volume fraction-jumps. We show that the results obtained from the experiments conducted by our group were contrasting to classical molecular glass behavior because the basis of our comparisons were incorrect (the histories were not analogous). The present calculations (with analogous histories) are qualitatively closer to the colloidal behavior. The signatures of "intrinsic isotherms" and "asymmetry of approach" in the current isochoric model predictions are quite different from those in the classical isobaric conditions while the "memory" signatures remain essentially the same. While there are qualitative similarities between the current isochoric model predictions and results from colloidal glasses, it appears from the calculations that the origins of these are different. The isochoric histories in the molecular glasses have compensating effects of pressure and departure from equilibrium which determines the structure dependence on mobility of the molecules. On the other hand, in the colloids it simply appears that the volume fraction-jump conditions simply do not exhibit such structure mobility dependence. The determining interplay of thermodynamic phase variables in colloidal and molecular systems might be very different or at least their correlations are yet to be ascertained. This topic requires further investigation to bring the similarities and differences between molecular and colloidal glass formers into fuller clarity.

A novel numerical model to predict the morphological behavior of magnetic liquid marbles using coarse grained molecular dynamics concepts

NASA Astrophysics Data System (ADS)

Polwaththe-Gallage, Hasitha-Nayanajith; Sauret, Emilie; Nguyen, Nam-Trung; Saha, Suvash C.; Gu, YuanTong

2018-01-01

Liquid marbles are liquid droplets coated with superhydrophobic powders whose morphology is governed by the gravitational and surface tension forces. Small liquid marbles take spherical shapes, while larger liquid marbles exhibit puddle shapes due to the dominance of gravitational forces. Liquid marbles coated with hydrophobic magnetic powders respond to an external magnetic field. This unique feature of magnetic liquid marbles is very attractive for digital microfluidics and drug delivery systems. Several experimental studies have reported the behavior of the liquid marbles. However, the complete behavior of liquid marbles under various environmental conditions is yet to be understood. Modeling techniques can be used to predict the properties and the behavior of the liquid marbles effectively and efficiently. A robust liquid marble model will inspire new experiments and provide new insights. This paper presents a novel numerical modeling technique to predict the morphology of magnetic liquid marbles based on coarse grained molecular dynamics concepts. The proposed model is employed to predict the changes in height of a magnetic liquid marble against its width and compared with the experimental data. The model predictions agree well with the experimental findings. Subsequently, the relationship between the morphology of a liquid marble with the properties of the liquid is investigated. Furthermore, the developed model is capable of simulating the reversible process of opening and closing of the magnetic liquid marble under the action of a magnetic force. The scaling analysis shows that the model predictions are consistent with the scaling laws. Finally, the proposed model is used to assess the compressibility of the liquid marbles. The proposed modeling approach has the potential to be a powerful tool to predict the behavior of magnetic liquid marbles serving as bioreactors.

From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors.

PubMed

Eren, Gokcen; Macchiarulo, Antonio; Banoglu, Erden

2012-02-01

Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Diffusion Coefficients from Molecular Dynamics Simulations in Binary and Ternary Mixtures

NASA Astrophysics Data System (ADS)

Liu, Xin; Schnell, Sondre K.; Simon, Jean-Marc; Krüger, Peter; Bedeaux, Dick; Kjelstrup, Signe; Bardow, André; Vlugt, Thijs J. H.

2013-07-01

Multicomponent diffusion in liquids is ubiquitous in (bio)chemical processes. It has gained considerable and increasing interest as it is often the rate limiting step in a process. In this paper, we review methods for calculating diffusion coefficients from molecular simulation and predictive engineering models. The main achievements of our research during the past years can be summarized as follows: (1) we introduced a consistent method for computing Fick diffusion coefficients using equilibrium molecular dynamics simulations; (2) we developed a multicomponent Darken equation for the description of the concentration dependence of Maxwell-Stefan diffusivities. In the case of infinite dilution, the multicomponent Darken equation provides an expression for [InlineEquation not available: see fulltext.] which can be used to parametrize the generalized Vignes equation; and (3) a predictive model for self-diffusivities was proposed for the parametrization of the multicomponent Darken equation. This equation accurately describes the concentration dependence of self-diffusivities in weakly associating systems. With these methods, a sound framework for the prediction of mutual diffusion in liquids is achieved.

A hybrid approach to survival model building using integration of clinical and molecular information in censored data.

PubMed

Choi, Ickwon; Kattan, Michael W; Wells, Brian J; Yu, Changhong

2012-01-01

In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain treatment, have been externally validated and used in practice. In recent years, most research has focused on high dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary, is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-genomic models due to the P ≥ N problem, in particular, for a parsimonious model. We propose a methodology to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which uses several dimension reduction techniques, L₂ penalized maximum likelihood estimation (PMLE), and resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final model with a hybrid signature that is parsimonious when integrating both types of variables.

Prognostic factors, predictive markers and cancer biology: the triad for successful oral cancer chemoprevention.

PubMed

Monteiro de Oliveira Novaes, Jose Augusto; William, William N

2016-10-01

Oral squamous cell carcinomas represent a significant cancer burden worldwide. Unfortunately, chemoprevention strategies investigated to date have failed to produce an agent considered standard of care to prevent oral cancers. Nonetheless, recent advances in clinical trial design may streamline drug development in this setting. In this manuscript, we review some of these improvements, including risk prediction tools based on molecular markers that help select patients most suitable for chemoprevention. We also discuss the opportunities that novel preclinical models and modern molecular profiling techniques will bring to the prevention field in the near future, and propose a clinical trials framework that incorporates molecular prognostic factors, predictive markers and cancer biology as a roadmap to improve chemoprevention strategies for oral cancers.

Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

NASA Astrophysics Data System (ADS)

Basant, Nikita; Gupta, Shikha

2018-03-01

The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

Proposing Novel MAO-B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of Their Therapeutic Activity, Pharmacokinetic and Toxicity Properties.

PubMed

Is, Yusuf Serhat; Durdagi, Serdar; Aksoydan, Busecan; Yurtsever, Mine

2018-05-07

Monoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256 750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary quantitative structure-activity relationship (QSAR) models. Therapeutic activity prediction as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular dynamics (MD) simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if studied molecules possess the potential to be a MAO-B inhibitor but also to find out whether they carry MAO-B selectivity versus MAO-A. The evaluation of docking results and pharmacokinetic profile predictions together with the MD simulations enabled us to identify one hit molecule (ligand 1, Otava ID: 3463218) which displayed higher selectivity toward MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes.

Assessing the clinical utility of cancer genomic and proteomic data across tumor types.

PubMed

Yuan, Yuan; Van Allen, Eliezer M; Omberg, Larsson; Wagle, Nikhil; Amin-Mansour, Ali; Sokolov, Artem; Byers, Lauren A; Xu, Yanxun; Hess, Kenneth R; Diao, Lixia; Han, Leng; Huang, Xuelin; Lawrence, Michael S; Weinstein, John N; Stuart, Josh M; Mills, Gordon B; Garraway, Levi A; Margolin, Adam A; Getz, Gad; Liang, Han

2014-07-01

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.

Dielectric Properties of Piezoelectric Polyimides

NASA Technical Reports Server (NTRS)

Ounaies, Z.; Young, J. A.; Simpson, J. O.; Farmer, B. L.

1997-01-01

Molecular modeling and dielectric measurements are being used to identify mechanisms governing piezoelectric behavior in polyimides such as dipole orientation during poling, as well as degree of piezoelectricity achievable. Molecular modeling on polyimides containing pendant, polar nitrile (CN) groups has been completed to determine their remanent polarization. Experimental investigation of their dielectric properties evaluated as a function of temperature and frequency has substantiated numerical predictions. With this information in hand, we are then able to suggest changes in the molecular structures, which will then improve upon the piezoelectric response.

Medicinal Chemistry and Molecular Modeling: An Integration to Teach Drug Structure-Activity Relationship and the Molecular Basis of Drug Action

ERIC Educational Resources Information Center

Carvalho, Ivone; Borges, Aurea D. L.; Bernardes, Lilian S. C.

2005-01-01

The use of computational chemistry and the protein data bank (PDB) to understand and predict the chemical and molecular basis involved in the drug-receptor interactions is discussed. A geometrical and chemical overview of the great structural similarity in the substrate and inhibitor is provided.

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

Application of Molecular Interaction Volume Model for Phase Equilibrium of Sn-Based Binary System in Vacuum Distillation

NASA Astrophysics Data System (ADS)

Kong, Lingxin; Yang, Bin; Xu, Baoqiang; Li, Yifu

2014-09-01

Based on the molecular interaction volume model (MIVM), the activities of components of Sn-Sb, Sb-Bi, Sn-Zn, Sn-Cu, and Sn-Ag alloys were predicted. The predicted values are in good agreement with the experimental data, which indicate that the MIVM is of better stability and reliability due to its good physical basis. A significant advantage of the MIVM lies in its ability to predict the thermodynamic properties of liquid alloys using only two parameters. The phase equilibria of Sn-Sb and Sn-Bi alloys were calculated based on the properties of pure components and the activity coefficients, which indicates that Sn-Sb and Sn-Bi alloys can be separated thoroughly by vacuum distillation. This study extends previous investigations and provides an effective and convenient model on which to base refining simulations for Sn-based alloys.

From Reactor to Rheology in LDPE Modeling

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

Read, Daniel J.; Das, Chinmay; Auhl, Dietmar

2008-07-07

In recent years the association between molecular structure and linear rheology has been established and well-understood through the tube concept and its extensions for well-characterized materials (e.g. McLeish, Adv. Phys. 2002). However, for industrial branched polymeric material at processing conditions this piece of information is missing. A large number of phenomenological models have been developed to describe the nonlinear response of polymers. But none of these models takes into account the underlying molecular structure, leading to a fitting procedure with arbitrary fitting parameters. The goal of applied molecular rheology is a predictive scheme that runs in its entirety from themore » molecular structure from the reactor to the non-linear rheology of the resin. In our approach, we use a model for the industrial reactor to explicitly generate the molecular structure ensemble of LDPE's, (Tobita, J. Polym. Sci. B 2001), which are consistent with the analytical information. We calculate the linear rheology of the LDPE ensemble with the use of a tube model for branched polymers (Das et al., J. Rheol. 2006). We then, separate the contribution of the stress decay to a large number of pompom modes (McLeish et al., J. Rheol. 1998 and Inkson et al., J. Rheol. 1999) with the stretch time and the priority variables corresponding to the actual ensemble of molecules involved. This multimode pompom model allows us to predict the nonlinear properties without any fitting parameter. We present and analyze our results in comparison with experimental data on industrial materials.« less

Extensive Evaluation of the Conductor-like Screening Model for Real Solvents Method in Predicting Liquid-Liquid Equilibria in Ternary Systems of Ionic Liquids with Molecular Compounds.

PubMed

Paduszyński, Kamil

2018-04-12

A conductor-like screening model for real solvents (COSMO-RS) is nowadays one of the most popular and commonly applied tools for the estimation of thermodynamic properties of complex fluids. The goal of this work is to provide a comprehensive review and analysis of the performance of this approach in calculating liquid-liquid equilibrium (LLE) phase diagrams in ternary systems composed of ionic liquid and two molecular compounds belonging to diverse families of chemicals (alkanes, aromatics, S/N-compounds, alcohols, ketones, ethers, carboxylic acid, esters, and water). The predictions are presented for extensive experimental database, including 930 LLE data sets and more than 9000 data points (LLE tie lines) reported for 779 unique ternary mixtures. An impact of the type of molecular binary subsystem on the accuracy of predictions is demonstrated and discussed on the basis of representative examples. The model's capability of capturing qualitative trends in the LLE distribution ratio and selectivity is also checked for a number of structural effects. Comparative analysis of two levels of quantum chemical theory (BP-TZVP-COSMO vs BP-TZVPD-FINE) for the input molecular data for COSMO-RS is presented. Finally, some general recommendations for the applicability of the model are indicated based on the analysis of the global performance as well as on the results obtained for systems relevant from the point of view of important separation problems.

Predicting the stability of nanodevices

NASA Astrophysics Data System (ADS)

Lin, Z. Z.; Yu, W. F.; Wang, Y.; Ning, X. J.

2011-05-01

A simple model based on the statistics of single atoms is developed to predict the stability or lifetime of nanodevices without empirical parameters. Under certain conditions, the model produces the Arrhenius law and the Meyer-Neldel compensation rule. Compared with the classical molecular-dynamics simulations for predicting the stability of monatomic carbon chain at high temperature, the model is proved to be much more accurate than the transition state theory. Based on the ab initio calculation of the static potential, the model can give out a corrected lifetime of monatomic carbon and gold chains at higher temperature, and predict that the monatomic chains are very stable at room temperature.

In silico environmental chemical science: properties and processes from statistical and computational modelling

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

Tratnyek, Paul G.; Bylaska, Eric J.; Weber, Eric J.

2017-01-01

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs usingmore » descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.« less

A combined pharmacophore modeling, 3D-QSAR and molecular docking study of substituted bicyclo-[3.3.0]oct-2-enes as liver receptor homolog-1 (LRH-1) agonists

NASA Astrophysics Data System (ADS)

Lalit, Manisha; Gangwal, Rahul P.; Dhoke, Gaurao V.; Damre, Mangesh V.; Khandelwal, Kanchan; Sangamwar, Abhay T.

2013-10-01

A combined pharmacophore modelling, 3D-QSAR and molecular docking approach was employed to reveal structural and chemical features essential for the development of small molecules as LRH-1 agonists. The best HypoGen pharmacophore hypothesis (Hypo1) consists of one hydrogen-bond donor (HBD), two general hydrophobic (H), one hydrophobic aromatic (HYAr) and one hydrophobic aliphatic (HYA) feature. It has exhibited high correlation coefficient of 0.927, cost difference of 85.178 bit and low RMS value of 1.411. This pharmacophore hypothesis was cross-validated using test set, decoy set and Cat-Scramble methodology. Subsequently, validated pharmacophore hypothesis was used in the screening of small chemical databases. Further, 3D-QSAR models were developed based on the alignment obtained using substructure alignment. The best CoMFA and CoMSIA model has exhibited excellent rncv2 values of 0.991 and 0.987, and rcv2 values of 0.767 and 0.703, respectively. CoMFA predicted rpred2 of 0.87 and CoMSIA predicted rpred2 of 0.78 showed that the predicted values were in good agreement with the experimental values. Molecular docking analysis reveals that π-π interaction with His390 and hydrogen bond interaction with His390/Arg393 is essential for LRH-1 agonistic activity. The results from pharmacophore modelling, 3D-QSAR and molecular docking are complementary to each other and could serve as a powerful tool for the discovery of potent small molecules as LRH-1 agonists.

Predicting when biliary excretion of parent drug is a major route of elimination in humans.

PubMed

Hosey, Chelsea M; Broccatelli, Fabio; Benet, Leslie Z

2014-09-01

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

A Critical Assessment of Combined Ligand-based and Structure-based Approaches to hERG Channel Blocker Modeling

PubMed Central

Du-Cuny, Lei; Chen, Lu; Zhang, Shuxing

2014-01-01

Blockade of hERG channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining QSAR modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high predictive capacity and improved enrichment, and they could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed and molecular docking studies were conducted, resulting in high correlations (R2=0.81) between predicted and experimental binding affinities. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities. PMID:21902220

Molecular motors that digest their track to rectify Brownian motion: processive movement of exonuclease enzymes.

PubMed

Xie, Ping

2009-09-16

A general model is presented for the processive movement of molecular motors such as λ-exonuclease, RecJ and exonuclease I that use digestion of a DNA track to rectify Brownian motion along this track. Using this model, the translocation dynamics of these molecular motors is studied. The sequence-dependent pausing of λ-exonuclease, which results from a site-specific high affinity DNA interaction, is also studied. The theoretical results are consistent with available experimental data. Moreover, the model is used to predict the lifetime distribution and force dependence of these paused states.

Molecular models of alginic acid: Interactions with calcium ions and calcite surfaces

NASA Astrophysics Data System (ADS)

Perry, Thomas D.; Cygan, Randall T.; Mitchell, Ralph

2006-07-01

Cation binding by polysaccharides is observed in many environments and is important for predictive environmental modeling, and numerous industrial and food technology applications. The complexities of these cation-organic interactions are well suited for predictive molecular modeling and the analysis of conformation and configuration of polysaccharides and their influence on cation binding. In this study, alginic acid was chosen as a model polymer system and representative disaccharide and polysaccharide subunits were developed. Molecular dynamics simulation of the torsion angles of the ether linkage between various monomeric subunits identified local and global energy minima for selected disaccharides. The simulations indicate stable disaccharide configurations and a common global energy minimum for all disaccharide models at Φ = 274 ± 7°, Ψ = 227 ± 5°, where Φ and Ψ are the torsion angles about the ether linkage. The ability of disaccharide subunits to bind calcium ions and to associate with the (101¯4) surface of calcite was also investigated. Molecular models of disaccharide interactions with calcite provide binding energy differences for conformations that are related to the proximity and residence densities of the electron-donating moieties with calcium ions on the calcite surface, which are controlled, in part, by the torsion of the ether linkage between monosaccharide units. Dynamically optimized configurations for polymer alginate models with calcium ions were also derived.

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

PubMed

Fletcher, Timothy L; Popelier, Paul L A

2016-06-14

A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry only. These models then predict molecular electrostatic interaction energies. On the basis of 200 unseen test geometries for each amino acid, no amino acid shows a mean prediction error above 5.3 kJ mol(-1), while the lowest error observed is 2.8 kJ mol(-1). The mean error across the entire set is only 4.2 kJ mol(-1) (or 1 kcal mol(-1)). Charged systems are created by protonating or deprotonating selected amino acids, and these show no significant deviation in prediction error over their neutral counterparts. Similarly, the proposed methodology can also handle amino acids with aromatic side chains, without the need for modification. Thus, we present a generic method capable of accurately capturing multipolar polarizable electrostatics in amino acids.

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

DOE PAGES

Henry, Michael M.; Jones, Matthew L.; Oosterhout, Stefan D.; ...

2017-11-08

We perform molecular dynamics simulations of poly(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 +/- 32 K) is found to be in agreement withmore » experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (p-p stacking around 3.9 A), and long-range spatial correlations due to the self-organization of backbone stacks into 'ribbons' (lamellar ordering around 20.9 A), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.« less

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

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

Henry, Michael M.; Jones, Matthew L.; Oosterhout, Stefan D.

We perform molecular dynamics simulations of poly(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 +/- 32 K) is found to be in agreement withmore » experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (p-p stacking around 3.9 A), and long-range spatial correlations due to the self-organization of backbone stacks into 'ribbons' (lamellar ordering around 20.9 A), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.« less

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED).

PubMed

Li, ZhiLiang; Wu, ShiRong; Chen, ZeCong; Ye, Nancy; Yang, ShengXi; Liao, ChunYang; Zhang, MengJun; Yang, Li; Mei, Hu; Yang, Yan; Zhao, Na; Zhou, Yuan; Zhou, Ping; Xiong, Qing; Xu, Hong; Liu, ShuShen; Ling, ZiHua; Chen, Gang; Li, GenRong

2007-10-01

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

NASA Astrophysics Data System (ADS)

Guha, Rajarshi; Schürer, Stephan C.

2008-06-01

Computational toxicology is emerging as an encouraging alternative to experimental testing. The Molecular Libraries Screening Center Network (MLSCN) as part of the NIH Molecular Libraries Roadmap has recently started generating large and diverse screening datasets, which are publicly available in PubChem. In this report, we investigate various aspects of developing computational models to predict cell toxicity based on cell proliferation screening data generated in the MLSCN. By capturing feature-based information in those datasets, such predictive models would be useful in evaluating cell-based screening results in general (for example from reporter assays) and could be used as an aid to identify and eliminate potentially undesired compounds. Specifically we present the results of random forest ensemble models developed using different cell proliferation datasets and highlight protocols to take into account their extremely imbalanced nature. Depending on the nature of the datasets and the descriptors employed we were able to achieve percentage correct classification rates between 70% and 85% on the prediction set, though the accuracy rate dropped significantly when the models were applied to in vivo data. In this context we also compare the MLSCN cell proliferation results with animal acute toxicity data to investigate to what extent animal toxicity can be correlated and potentially predicted by proliferation results. Finally, we present a visualization technique that allows one to compare a new dataset to the training set of the models to decide whether the new dataset may be reliably predicted.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data

PubMed Central

Rusyn, Ivan; Sedykh, Alexander; Guyton, Kathryn Z.; Tropsha, Alexander

2012-01-01

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage. PMID:22387746

Research Area 3 - Mathematical Sciences: Multiscale Modeling of the Mechanics of Advanced Energetic Materials Relevant to Detonation Prediction

DTIC Science & Technology

2015-08-24

new energetic materials with enhanced energy release rates and reduced sensitivity to unintentional detonation . The following results have been...Mechanics of Advanced Energetic Materials Relevant to Detonation Prediction The views, opinions and/or findings contained in this report are those of the...modeling, molecular simulations, detonation prediction REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S

Molecular orientation distributions during injection molding of liquid crystalline polymers: Ex situ investigation of partially filled moldings

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

Fang, Jun; Burghardt, Wesley R.; Bubeck, Robert A.

The development of molecular orientation in thermotropic liquid crystalline polymers (TLCPs) during injection molding has been investigated using two-dimensional wide-angle X-ray scattering coordinated with numerical computations employing the Larson-Doi polydomain model. Orientation distributions were measured in 'short shot' moldings to characterize structural evolution prior to completion of mold filling, in both thin and thick rectangular plaques. Distinct orientation patterns are observed near the filling front. In particular, strong extension at the melt front results in nearly transverse molecular alignment. Far away from the flow front shear competes with extension to produce complex spatial distributions of orientation. The relative influence ofmore » shear is stronger in the thin plaque, producing orientation along the filling direction. Exploiting an analogy between the Larson-Doi model and a fiber orientation model, we test the ability of process simulation tools to predict TLCP orientation distributions during molding. Substantial discrepancies between model predictions and experimental measurements are found near the flow front in partially filled short shots, attributed to the limits of the Hele-Shaw approximation used in the computations. Much of the flow front effect is however 'washed out' by subsequent shear flow as mold filling progresses, leading to improved agreement between experiment and corresponding numerical predictions.« less

Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data

PubMed Central

Lado, Bettina; Matus, Ivan; Rodríguez, Alejandra; Inostroza, Luis; Poland, Jesse; Belzile, François; del Pozo, Alejandro; Quincke, Martín; Castro, Marina; von Zitzewitz, Jarislav

2013-01-01

In crop breeding, the interest of predicting the performance of candidate cultivars in the field has increased due to recent advances in molecular breeding technologies. However, the complexity of the wheat genome presents some challenges for applying new technologies in molecular marker identification with next-generation sequencing. We applied genotyping-by-sequencing, a recently developed method to identify single-nucleotide polymorphisms, in the genomes of 384 wheat (Triticum aestivum) genotypes that were field tested under three different water regimes in Mediterranean climatic conditions: rain-fed only, mild water stress, and fully irrigated. We identified 102,324 single-nucleotide polymorphisms in these genotypes, and the phenotypic data were used to train and test genomic selection models intended to predict yield, thousand-kernel weight, number of kernels per spike, and heading date. Phenotypic data showed marked spatial variation. Therefore, different models were tested to correct the trends observed in the field. A mixed-model using moving-means as a covariate was found to best fit the data. When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment. Multiple genomic selection models were tested, and a Gaussian kernel model was determined to give the highest accuracy. The best predictions between environments were obtained when data from different years were used to train the model. Our results confirm that genotyping-by-sequencing is an effective tool to obtain genome-wide information for crops with complex genomes, that these data are efficient for predicting traits, and that correction of spatial variation is a crucial ingredient to increase prediction accuracy in genomic selection models. PMID:24082033

Defect-induced solid state amorphization of molecular crystals

NASA Astrophysics Data System (ADS)

Lei, Lei; Carvajal, Teresa; Koslowski, Marisol

2012-04-01

We investigate the process of mechanically induced amorphization in small molecule organic crystals under extensive deformation. In this work, we develop a model that describes the amorphization of molecular crystals, in which the plastic response is calculated with a phase field dislocation dynamics theory in four materials: acetaminophen, sucrose, γ-indomethacin, and aspirin. The model is able to predict the fraction of amorphous material generated in single crystals for a given applied stress. Our results show that γ-indomethacin and sucrose demonstrate large volume fractions of amorphous material after sufficient plastic deformation, while smaller amorphous volume fractions are predicted in acetaminophen and aspirin, in agreement with experimental observation.

A molecular topology approach to predicting pesticide pollution of groundwater

USGS Publications Warehouse

Worrall , Fred

2001-01-01

Various models have proposed methods for the discrimination of polluting and nonpolluting compounds on the basis of simple parameters, typically adsorption and degradation constants. However, such attempts are prone to site variability and measurement error to the extent that compounds cannot be reliably classified nor the chemistry of pollution extrapolated from them. Using observations of pesticide occurrence in U.S. groundwater it is possible to show that polluting from nonpolluting compounds can be distinguished purely on the basis of molecular topology. Topological parameters can be derived without measurement error or site-specific variability. A logistic regression model has been developed which explains 97% of the variation in the data, with 86% of the variation being explained by the rule that a compound will be found in groundwater if 6 < 0.55. Where 6χp is the sixth-order molecular path connectivity. One group of compounds cannot be classified by this rule and prediction requires reference to higher order connectivity parameters. The use of molecular approaches for understanding pollution at the molecular level and their application to agrochemical development and risk assessment is discussed.

Generative Recurrent Networks for De Novo Drug Design.

PubMed

Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert

2018-01-01

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Quantitative structure-activity relationship of organosulphur compounds as soybean 15-lipoxygenase inhibitors using CoMFA and CoMSIA.

PubMed

Caballero, Julio; Fernández, Michael; Coll, Deysma

2010-12-01

Three-dimensional quantitative structure-activity relationship studies were carried out on a series of 28 organosulphur compounds as 15-lipoxygenase inhibitors using comparative molecular field analysis and comparative molecular similarity indices analysis. Quantitative information on structure-activity relationships is provided for further rational development and direction of selective synthesis. All models were carried out over a training set including 22 compounds. The best comparative molecular field analysis model only included steric field and had a good Q² = 0.789. Comparative molecular similarity indices analysis overcame the comparative molecular field analysis results: the best comparative molecular similarity indices analysis model also only included steric field and had a Q² = 0.894. In addition, this model predicted adequately the compounds contained in the test set. Furthermore, plots of steric comparative molecular similarity indices analysis field allowed conclusions to be drawn for the choice of suitable inhibitors. In this sense, our model should prove useful in future 15-lipoxygenase inhibitor design studies. © 2010 John Wiley & Sons A/S.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Theoretical Modeling of Interstellar Chemistry

NASA Technical Reports Server (NTRS)

Charnley, Steven

2009-01-01

The chemistry of complex interstellar organic molecules will be described. Gas phase processes that may build large carbon-chain species in cold molecular clouds will be summarized. Catalytic reactions on grain surfaces can lead to a large variety of organic species, and models of molecule formation by atom additions to multiply-bonded molecules will be presented. The subsequent desorption of these mixed molecular ices can initiate a distinctive organic chemistry in hot molecular cores. The general ion-molecule pathways leading to even larger organics will be outlined. The predictions of this theory will be compared with observations to show how possible organic formation pathways in the interstellar medium may be constrained. In particular, the success of the theory in explaining trends in the known interstellar organics, in predicting recently-detected interstellar molecules, and, just as importantly, non-detections, will be discussed.

Study of homogeneous bubble nucleation in liquid carbon dioxide by a hybrid approach combining molecular dynamics simulation and density gradient theory

NASA Astrophysics Data System (ADS)

Langenbach, K.; Heilig, M.; Horsch, M.; Hasse, H.

2018-03-01

A new method for predicting homogeneous bubble nucleation rates of pure compounds from vapor-liquid equilibrium (VLE) data is presented. It combines molecular dynamics simulation on the one side with density gradient theory using an equation of state (EOS) on the other. The new method is applied here to predict bubble nucleation rates in metastable liquid carbon dioxide (CO2). The molecular model of CO2 is taken from previous work of our group. PC-SAFT is used as an EOS. The consistency between the molecular model and the EOS is achieved by adjusting the PC-SAFT parameters to VLE data obtained from the molecular model. The influence parameter of density gradient theory is fitted to the surface tension of the molecular model. Massively parallel molecular dynamics simulations are performed close to the spinodal to compute bubble nucleation rates. From these simulations, the kinetic prefactor of the hybrid nucleation theory is estimated, whereas the nucleation barrier is calculated from density gradient theory. This enables the extrapolation of molecular simulation data to the whole metastable range including technically relevant densities. The results are tested against available experimental data and found to be in good agreement. The new method does not suffer from typical deficiencies of classical nucleation theory concerning the thermodynamic barrier at the spinodal and the bubble size dependence of surface tension, which is typically neglected in classical nucleation theory. In addition, the density in the center of critical bubbles and their surface tension is determined as a function of their radius. The usual linear Tolman correction to the capillarity approximation is found to be invalid.

Study of homogeneous bubble nucleation in liquid carbon dioxide by a hybrid approach combining molecular dynamics simulation and density gradient theory.

PubMed

Langenbach, K; Heilig, M; Horsch, M; Hasse, H

2018-03-28

A new method for predicting homogeneous bubble nucleation rates of pure compounds from vapor-liquid equilibrium (VLE) data is presented. It combines molecular dynamics simulation on the one side with density gradient theory using an equation of state (EOS) on the other. The new method is applied here to predict bubble nucleation rates in metastable liquid carbon dioxide (CO 2 ). The molecular model of CO 2 is taken from previous work of our group. PC-SAFT is used as an EOS. The consistency between the molecular model and the EOS is achieved by adjusting the PC-SAFT parameters to VLE data obtained from the molecular model. The influence parameter of density gradient theory is fitted to the surface tension of the molecular model. Massively parallel molecular dynamics simulations are performed close to the spinodal to compute bubble nucleation rates. From these simulations, the kinetic prefactor of the hybrid nucleation theory is estimated, whereas the nucleation barrier is calculated from density gradient theory. This enables the extrapolation of molecular simulation data to the whole metastable range including technically relevant densities. The results are tested against available experimental data and found to be in good agreement. The new method does not suffer from typical deficiencies of classical nucleation theory concerning the thermodynamic barrier at the spinodal and the bubble size dependence of surface tension, which is typically neglected in classical nucleation theory. In addition, the density in the center of critical bubbles and their surface tension is determined as a function of their radius. The usual linear Tolman correction to the capillarity approximation is found to be invalid.

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4).

PubMed

Sun, Lili; Zhou, Liping; Yu, Yu; Lan, Yukun; Li, Zhiliang

2007-01-01

Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of 209 PCDEs and diphenyl ether. The correlation coefficients (R) and the leave-one-out cross-validation (LOO) correlation coefficients (R(CV)) of all the 6-descriptor models for logP(L), logK(OW) and logS(W,L) are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for logP(L), 4-descriptor model for logK(OW), and 6-descriptor model for logS(W,L), respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for logP(L) is the best (R=0.996, R(CV)=0.996), followed by logK(OW) (R=0.992, R(CV)=0.992) and logS(W,L) (R=0.983, R(CV)=0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.

Molecular markers of neuropsychological functioning and Alzheimer's disease.

PubMed

Edwards, Melissa; Balldin, Valerie Hobson; Hall, James; O'Bryant, Sid

2015-03-01

The current project sought to examine molecular markers of neuropsychological functioning among elders with and without Alzheimer's disease (AD) and determine the predictive ability of combined molecular markers and select neuropsychological tests in detecting disease presence. Data were analyzed from 300 participants (n = 150, AD and n = 150, controls) enrolled in the Texas Alzheimer's Research and Care Consortium. Linear regression models were created to examine the link between the top five molecular markers from our AD blood profile and neuropsychological test scores. Logistical regressions were used to predict AD presence using serum biomarkers in combination with select neuropsychological measures. Using the neuropsychological test with the least amount of variance overlap with the molecular markers, the combined neuropsychological test and molecular markers was highly accurate in detecting AD presence. This work provides the foundation for the generation of a point-of-care device that can be used to screen for AD.

Quantitative Predictions of Binding Free Energy Changes in Drug-Resistant Influenza Neuraminidase

DTIC Science & Technology

2012-08-30

drug resistance to two antiviral drugs, zanamivir and oseltamivir. We augmented molecular dynamics (MD) with Hamiltonian Replica Exchange and...conformations that are virtually identical to WT [10]. Molecular simulations that rigorously model the microscopic structure and thermodynamics PLOS...influenza neuraminidase (NA) that confer drug resistance to two antiviral drugs, zanamivir and oseltamivir. We augmented molecular dynamics (MD) with

A SAR and QSAR study of new artemisinin compounds with antimalarial activity.

PubMed

Santos, Cleydson Breno R; Vieira, Josinete B; Lobato, Cleison C; Hage-Melim, Lorane I S; Souto, Raimundo N P; Lima, Clarissa S; Costa, Elizabeth V M; Brasil, Davi S B; Macêdo, Williams Jorge C; Carvalho, José Carlos T

2013-12-30

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.

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

Larsen, P. E.; Sreedasyam, A.; Trivedi, G

Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models usingmore » a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose. The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.« less

Topology of molecular interaction networks.

PubMed

Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

2013-09-16

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Topology of molecular interaction networks

PubMed Central

2013-01-01

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

PubMed

Daré, Joyce K; Silva, Cristina F; Freitas, Matheus P

2017-10-01

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient. Copyright © 2017 Elsevier Inc. All rights reserved.

Interaction of a sodium ion with the water liquid-vapor interface

NASA Technical Reports Server (NTRS)

Wilson, M. A.; Pohorille, A.; Pratt, L. R.; MacElroy, R. D. (Principal Investigator)

1989-01-01

Molecular dynamics results are presented for the density profile of a sodium ion near the water liquid-vapor interface at 320 K. These results are compared with the predictions of a simple dielectric model for the interaction of a monovalent ion with this interface. The interfacial region described by the model profile is too narrow and the profile decreases too abruptly near the solution interface. Thus, the simple model does not provide a satisfactory description of the molecular dynamics results for ion positions within two molecular diameters from the solution interface where appreciable ion concentrations are observed. These results suggest that surfaces associated with dielectric models of ionic processes at aqueous solution interfaces should be located at least two molecular diameters inside the liquid phase. A free energy expense of about 2 kcal/mol is required to move the ion within two molecular layers of the free water liquid-vapor interface.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

Ngo, Trieu-Du; Tran, Thanh-Dao; Le, Minh-Tri; Thai, Khac-Minh

2016-11-01

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

PubMed

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M; Loza, Maria Isabel; Munteanu, Cristian R; Pazos, Alejandro; García-Mera, Xerardo; González-Díaz, Humbert

2018-06-25

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC 50 , EC 50 , K i , etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

USDA-ARS?s Scientific Manuscript database

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

Quantitative structure-retention relationships of flavonoids unraveled by immobilized artificial membrane chromatography.

PubMed

Santoro, Adriana Leandra; Carrilho, Emanuel; Lanças, Fernando Mauro; Montanari, Carlos Alberto

2016-06-10

The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure-retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R(2)=0.96 and Q(2)=0.85 with four latent variables). Copyright © 2015 Elsevier B.V. All rights reserved.

Mesoscale energy deposition footprint model for kiloelectronvolt cluster bombardment of solids.

PubMed

Russo, Michael F; Garrison, Barbara J

2006-10-15

Molecular dynamics simulations have been performed to model 5-keV C60 and Au3 projectile bombardment of an amorphous water substrate. The goal is to obtain detailed insights into the dynamics of motion in order to develop a straightforward and less computationally demanding model of the process of ejection. The molecular dynamics results provide the basis for the mesoscale energy deposition footprint model. This model provides a method for predicting relative yields based on information from less than 1 ps of simulation time.

In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part II: The body in a Hilbertian space.

PubMed

Jacob, Alexandre; Pratuangdejkul, Jaturong; Buffet, Sébastien; Launay, Jean-Marie; Manivet, Philippe

2009-04-01

We have broken old surviving dogmas and concepts used in computational chemistry and created an efficient in silico ADME-T pharmacological properties modeling and prediction toolbox for any xenobiotic. With the help of an innovative and pragmatic approach combining various in silico techniques, like molecular modeling, quantum chemistry and in-house developed algorithms, the interactions between drugs and those enzymes, transporters and receptors involved in their biotransformation can be studied. ADME-T pharmacological parameters can then be predicted after in vitro and in vivo validations of in silico models.

SAMPL4 & DOCK3.7: lessons for automated docking procedures

NASA Astrophysics Data System (ADS)

Coleman, Ryan G.; Sterling, Teague; Weiss, Dahlia R.

2014-03-01

The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.

Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations.

PubMed

Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

2018-02-01

Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r 2 Pred ) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.

Equilibrium star formation in a constant Q disc: model optimization and initial tests

NASA Astrophysics Data System (ADS)

Zheng, Zheng; Meurer, Gerhardt R.; Heckman, Timothy M.; Thilker, David A.; Zwaan, Martin A.

2013-10-01

We develop a model for the distribution of the interstellar medium (ISM) and star formation in galaxies based on recent studies that indicate that galactic discs stabilize to a constant stability parameter, which we combine with prescriptions of how the phases of the ISM are determined and for the star formation law (SFL). The model predicts the gas surface mass density and star formation intensity of a galaxy given its rotation curve, stellar surface mass density and the gas velocity dispersion. This model is tested on radial profiles of neutral and molecular ISM surface mass density and star formation intensity of 12 galaxies selected from the H I Nearby Galaxy Survey sample. Our tests focus on intermediate radii (0.3 to 1 times the optical radius) because there are insufficient data to test the outer discs and the fits are less accurate in detail in the centre. Nevertheless, the model produces reasonable agreement with the ISM mass and star formation rate integrated over the central region in all but one case. To optimize the model, we evaluate four recipes for the stability parameter, three recipes for apportioning the ISM into molecular and neutral components, and eight versions of the SFL. We find no clear-cut best prescription for the two-fluid (gas and stars) stability parameter Q2f and therefore for simplicity, we use the Wang and Silk approximation (QWS). We found that an empirical scaling between the molecular-to-neutral ISM ratio (Rmol) and the stellar surface mass density proposed by Leroy et al. works marginally better than the other two prescriptions for this ratio in predicting the ISM profiles, and noticeably better in predicting the star formation intensity from the ISM profiles produced by our model with the SFLs we tested. Thus, in the context of our modelled ISM profiles, the linear molecular SFL and the two-component SFL work better than the other prescriptions we tested. We incorporate these relations into our `constant Q disc' model.

Ribonucleotide reductase as a drug target against drug resistance Mycobacterium leprae: A molecular docking study.

PubMed

Mohanty, Partha Sarathi; Bansal, Avi Kumar; Naaz, Farah; Gupta, Umesh Datta; Dwivedi, Vivek Dhar; Yadava, Umesh

2018-06-01

Leprosy is a chronic infection of skin and nerve caused by Mycobacterium leprae. The treatment is based on standard multi drug therapy consisting of dapsone, rifampicin and clofazamine. The use of rifampicin alone or with dapsone led to the emergence of rifampicin-resistant Mycobacterium leprae strains. The emergence of drug-resistant leprosy put a hurdle in the leprosy eradication programme. The present study aimed to predict the molecular model of ribonucleotide reductase (RNR), the enzyme responsible for biosynthesis of nucleotides, to screen new drugs for treatment of drug-resistant leprosy. The study was conducted by retrieving RNR of M. leprae from GenBank. A molecular 3D model of M. leprae was predicted using homology modelling and validated. A total of 325 characters were included in the analysis. The predicted 3D model of RNR showed that the ϕ and φ angles of 251 (96.9%) residues were positioned in the most favoured regions. It was also conferred that 18 α-helices, 6 β turns, 2 γ turns and 48 helix-helix interactions contributed to the predicted 3D structure. Virtual screening of Food and Drug Administration approved drug molecules recovered 1829 drugs of which three molecules, viz., lincomycin, novobiocin and telithromycin, were taken for the docking study. It was observed that the selected drug molecules had a strong affinity towards the modelled protein RNR. This was evident from the binding energy of the drug molecules towards the modelled protein RNR (-6.10, -6.25 and -7.10). Three FDA-approved drugs, viz., lincomycin, novobiocin and telithromycin, could be taken for further clinical studies to find their efficacy against drug resistant leprosy. Copyright © 2018 Elsevier B.V. All rights reserved.

Design, synthesis, pharmacological evaluation and in silico ADMET prediction of novel substituted benzimidazole derivatives as angiotensin II-AT1 receptor antagonists based on predictive 3D QSAR models.

PubMed

Vyas, V K; Gupta, N; Ghate, M; Patel, S

2014-01-01

In this study we designed novel substituted benzimidazole derivatives and predicted their absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, based on a predictive 3D QSAR study on 132 substituted benzimidazoles as AngII-AT1 receptor antagonists. The two best predicted compounds were synthesized and evaluated for AngII-AT1 receptor antagonism. Three different alignment tools for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used. The best 3D QSAR models were obtained using the rigid body (Distill) alignment method. CoMFA and CoMSIA models were found to be statistically significant with leave-one-out correlation coefficients (q(2)) of 0.630 and 0.623, respectively, cross-validated coefficients (r(2)cv) of 0.651 and 0.630, respectively, and conventional coefficients of determination (r(2)) of 0.848 and 0.843, respectively. 3D QSAR models were validated using a test set of 24 compounds, giving satisfactory predicted results (r(2)pred) of 0.727 and 0.689 for the CoMFA and CoMSIA models, respectively. We have identified some key features in substituted benzimidazole derivatives, such as lipophilicity and H-bonding at the 2- and 5-positions of the benzimidazole nucleus, respectively, for AT1 receptor antagonistic activity. We designed 20 novel substituted benzimidazole derivatives and predicted their activity. In silico ADMET properties were also predicted for these designed molecules. Finally, the compounds with best predicted activity were synthesized and evaluated for in vitro angiotensin II-AT1 receptor antagonism.

New mathematic model for predicting chiral separation using molecular docking: mechanism of chiral recognition of triadimenol analogues.

PubMed

Zhang, Guoqing; Sun, Qingyan; Hou, Ying; Hong, Zhanying; Zhang, Jun; Zhao, Liang; Zhang, Hai; Chai, Yifeng

2009-07-01

The purpose of this paper was to study the enantioseparation mechanism of triadimenol compounds by carboxymethylated (CM)-beta-CD mediated CE. All the enantiomers were separated under the same experimental conditions to study the chiral recognition mechanism using a 30 mM sodium dihydrogen phosphate buffer at pH 2.2 adjusted by phosphoric acid. The inclusion courses between CM-beta-CD and enantiomers were investigated by the means of molecular docking technique. It was found that there were at least three points (one hydrophobic bond and two hydrogen bonds) involved in the interaction of each enantiomer with the chiral selectors. A new mathematic model has been built up based on the results of molecular mechanics calculations, which could analyze the relationship between the resolution of enantioseparation and the interaction energy in the docking area. Comparing the results of the separation by CE, the established mathematic model demonstrated good capability to predict chiral separation of triadimenol enantiomers using CM-beta-CD mediated CE.

BDDCS Class Prediction for New Molecular Entities

PubMed Central

Broccatelli, Fabio; Cruciani, Gabriele; Benet, Leslie Z.; Oprea, Tudor I.

2012-01-01

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport are not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the times. The unbalanced stratification of the dataset didn’t affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirmed the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the dataset. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction. PMID:22224483

Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

PubMed

Zhang, Hui; Ding, Lan; Zou, Yi; Hu, Shui-Qing; Huang, Hai-Guo; Kong, Wei-Bao; Zhang, Ji

2016-10-01

Drug-induced liver injury (DILI) is one of the major safety concerns in drug development. Although various toxicological studies assessing DILI risk have been developed, these methods were not sufficient in predicting DILI in humans. Thus, developing new tools and approaches to better predict DILI risk in humans has become an important and urgent task. In this study, we aimed to develop a computational model for assessment of the DILI risk with using a larger scale human dataset and Naïve Bayes classifier. The established Naïve Bayes prediction model was evaluated by 5-fold cross validation and an external test set. For the training set, the overall prediction accuracy of the 5-fold cross validation was 94.0 %. The sensitivity, specificity, positive predictive value and negative predictive value were 97.1, 89.2, 93.5 and 95.1 %, respectively. The test set with the concordance of 72.6 %, sensitivity of 72.5 %, specificity of 72.7 %, positive predictive value of 80.4 %, negative predictive value of 63.2 %. Furthermore, some important molecular descriptors related to DILI risk and some toxic/non-toxic fragments were identified. Thus, we hope the prediction model established here would be employed for the assessment of human DILI risk, and the obtained molecular descriptors and substructures should be taken into consideration in the design of new candidate compounds to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.

A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development.

PubMed

Wang, Na-Na; Yang, Zheng-Jun; Wang, Xue; Chen, Li-Xuan; Zhao, Hong-Meng; Cao, Wen-Feng; Zhang, Bin

2018-04-25

Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort. We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017 at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort. Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0.575-0.817; P < 0.001) were independent statistically significant predictors of non-sentinel lymph node metastasis. Furthermore, luminal B (OR = 3.311, 95% CI 1.593-6.884; P = 0.001) and HER2 overexpression (OR = 4.308, 95% CI 1.097-16.912; P = 0.036) were independent and statistically significant predictor of non-sentinel lymph node metastasis versus luminal A. A regression model based on the results of multivariate analysis was established to predict the risk of non-sentinel lymph node metastasis, which had an AUC of 0.8188. The model was validated in the validation cohort and showed excellent diagnostic performance. The mathematical prediction model that incorporates five variables including breast cancer molecular subtype demonstrates excellent diagnostic performance in assessing the risk of non-sentinel lymph node metastasis in sentinel lymph node-positive patients. The prediction model could be of help surgeons in evaluating the risk of non-sentinel lymph node involvement for breast cancer patients; however, the model requires further validation in prospective studies.

Fragment-based 13C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods

NASA Astrophysics Data System (ADS)

Hartman, Joshua D.; Monaco, Stephen; Schatschneider, Bohdan; Beran, Gregory J. O.

2015-09-01

We assess the quality of fragment-based ab initio isotropic 13C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic 13C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.

Fragment-based (13)C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods.

PubMed

Hartman, Joshua D; Monaco, Stephen; Schatschneider, Bohdan; Beran, Gregory J O

2015-09-14

We assess the quality of fragment-based ab initio isotropic (13)C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic (13)C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.

Modeling Aromatic Liquids:  Toluene, Phenol, and Pyridine.

PubMed

Baker, Christopher M; Grant, Guy H

2007-03-01

Aromatic groups are now acknowledged to play an important role in many systems of interest. However, existing molecular mechanics methods provide a poor representation of these groups. In a previous paper, we have shown that the molecular mechanics treatment of benzene can be improved by the incorporation of an explicit representation of the aromatic π electrons. Here, we develop this concept further, developing charge-separation models for toluene, phenol, and pyridine. Monte Carlo simulations are used to parametrize the models, via the reproduction of experimental thermodynamic data, and our models are shown to outperform an existing atom-centered model. The models are then used to make predictions about the structures of the liquids at the molecular level and are tested further through their application to the modeling of gas-phase dimers and cation-π interactions.

Modeling of coherent ultrafast magneto-optical experiments: Light-induced molecular mean-field model

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

Hinschberger, Y.; Hervieux, P.-A.

2015-12-28

We present calculations which aim to describe coherent ultrafast magneto-optical effects observed in time-resolved pump-probe experiments. Our approach is based on a nonlinear semi-classical Drude-Voigt model and is used to interpret experiments performed on nickel ferromagnetic thin film. Within this framework, a phenomenological light-induced coherent molecular mean-field depending on the polarizations of the pump and probe pulses is proposed whose microscopic origin is related to a spin-orbit coupling involving the electron spins of the material sample and the electric field of the laser pulses. Theoretical predictions are compared to available experimental data. The model successfully reproduces the observed experimental trendsmore » and gives meaningful insight into the understanding of magneto-optical rotation behavior in the ultrafast regime. Theoretical predictions for further experimental studies are also proposed.« less

Evaluation of the phototoxicity of unsubstituted and alkylated polycyclic aromatic hydrocarbons to mysid shrimp (Americamysis bahia): Validation of predictive models.

PubMed

Finch, Bryson E; Marzooghi, Solmaz; Di Toro, Dominic M; Stubblefield, William A

2017-08-01

Crude oils are composed of an assortment of hydrocarbons, some of which are polycyclic aromatic hydrocarbons (PAHs). Polycyclic aromatic hydrocarbons are of particular interest due to their narcotic and potential phototoxic effects. Several studies have examined the phototoxicity of individual PAHs and fresh and weathered crude oils, and several models have been developed to predict PAH toxicity. Fingerprint analyses of oils have shown that PAHs in crude oils are predominantly alkylated. However, current models for estimating PAH phototoxicity assume toxic equivalence between unsubstituted (i.e., parent) and alkyl-substituted compounds. This approach may be incorrect if substantial differences in toxic potency exist between unsubstituted and substituted PAHs. The objective of the present study was to examine the narcotic and photo-enhanced toxicity of commercially available unsubstituted and alkylated PAHs to mysid shrimp (Americamysis bahia). Data were used to validate predictive models of phototoxicity based on the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap approach and to develop relative effect potencies. Results demonstrated that photo-enhanced toxicity increased with increasing methylation and that phototoxic PAH potencies vary significantly among unsubstituted compounds. Overall, predictive models based on the HOMO-LUMO gap were relatively accurate in predicting phototoxicity for unsubstituted PAHs but are limited to qualitative assessments. Environ Toxicol Chem 2017;36:2043-2049. © 2017 SETAC. © 2017 SETAC.

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

PubMed

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

Predicting unfolding thermodynamics and stable intermediates for alanine-rich helical peptides with the aid of coarse-grained molecular simulation.

PubMed

Calero-Rubio, Cesar; Paik, Bradford; Jia, Xinqiao; Kiick, Kristi L; Roberts, Christopher J

2016-10-01

This report focuses on the molecular-level processes and thermodynamics of unfolding of a series of helical peptides using a coarse-grained (CG) molecular model. The CG model was refined to capture thermodynamics and structural changes as a function of temperature for a set of published peptide sequences. Circular dichroism spectroscopy (CD) was used to experimentally monitor the temperature-dependent conformational changes and stability of published peptides and new sequences introduced here. The model predictions were quantitatively or semi-quantitatively accurate in all cases. The simulations and CD results showed that, as expected, in most cases the unfolding of helical peptides is well described by a simply 2-state model, and conformational stability increased with increased length of the helices. A notable exception in a 19-residue helix was when two Ala residues were each replaced with Phe. This stabilized a partly unfolded intermediate state via hydrophobic contacts, and also promoted aggregates at higher peptide concentrations. Copyright © 2016 Elsevier B.V. All rights reserved.

Laser ablation molecular isotopic spectroscopy (LAMIS) towards the determination of multivariate LODs via PLS calibration model of 10B and 11B Boric acid mixtures

NASA Astrophysics Data System (ADS)

Harris, C. D.; Profeta, Luisa T. M.; Akpovo, Codjo A.; Johnson, Lewis; Stowe, Ashley C.

2017-05-01

A calibration model was created to illustrate the detection capabilities of laser ablation molecular isotopic spectroscopy (LAMIS) discrimination in isotopic analysis. The sample set contained boric acid pellets that varied in isotopic concentrations of 10B and 11B. Each sample set was interrogated with a Q-switched Nd:YAG ablation laser operating at 532 nm. A minimum of four band heads of the β system B2∑ -> Χ2∑transitions were identified and verified with previous literature on BO molecular emission lines. Isotopic shifts were observed in the spectra for each transition and used as the predictors in the calibration model. The spectra along with their respective 10/11B isotopic ratios were analyzed using Partial Least Squares Regression (PLSR). An IUPAC novel approach for determining a multivariate Limit of Detection (LOD) interval was used to predict the detection of the desired isotopic ratios. The predicted multivariate LOD is dependent on the variation of the instrumental signal and other composites in the calibration model space.

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.

Nanoindentation of virus capsids in a molecular model

NASA Astrophysics Data System (ADS)

Cieplak, Marek; Robbins, Mark O.

2010-01-01

A molecular-level model is used to study the mechanical response of empty cowpea chlorotic mottle virus (CCMV) and cowpea mosaic virus (CPMV) capsids. The model is based on the native structure of the proteins that constitute the capsids and is described in terms of the Cα atoms. Nanoindentation by a large tip is modeled as compression between parallel plates. Plots of the compressive force versus plate separation for CCMV are qualitatively consistent with continuum models and experiments, showing an elastic region followed by an irreversible drop in force. The mechanical response of CPMV has not been studied, but the molecular model predicts an order of magnitude higher stiffness and a much shorter elastic region than for CCMV. These large changes result from small structural changes that increase the number of bonds by only 30% and would be difficult to capture in continuum models. Direct comparison of local deformations in continuum and molecular models of CCMV shows that the molecular model undergoes a gradual symmetry breaking rotation and accommodates more strain near the walls than the continuum model. The irreversible drop in force at small separations is associated with rupturing nearly all of the bonds between capsid proteins in the molecular model, while a buckling transition is observed in continuum models.

Design of Novel Chemotherapeutic Agents Targeting Checkpoint Kinase 1 Using 3D-QSAR Modeling and Molecular Docking Methods.

PubMed

Balupuri, Anand; Balasubramanian, Pavithra K; Cho, Seung J

2016-01-01

Checkpoint kinase 1 (Chk1) has emerged as a potential therapeutic target for design and development of novel anticancer drugs. Herein, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses on a series of diazacarbazoles to design potent Chk1 inhibitors. 3D-QSAR models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Docking studies were performed using AutoDock. The best CoMFA and CoMSIA models exhibited cross-validated correlation coefficient (q2) values of 0.631 and 0.585, and non-cross-validated correlation coefficient (r2) values of 0.933 and 0.900, respectively. CoMFA and CoMSIA models showed reasonable external predictabilities (r2 pred) of 0.672 and 0.513, respectively. A satisfactory performance in the various internal and external validation techniques indicated the reliability and robustness of the best model. Docking studies were performed to explore the binding mode of inhibitors inside the active site of Chk1. Molecular docking revealed that hydrogen bond interactions with Lys38, Glu85 and Cys87 are essential for Chk1 inhibitory activity. The binding interaction patterns observed during docking studies were complementary to 3D-QSAR results. Information obtained from the contour map analysis was utilized to design novel potent Chk1 inhibitors. Their activities and binding affinities were predicted using the derived model and docking studies. Designed inhibitors were proposed as potential candidates for experimental synthesis.

Reducing aquatic hazards of industrial chemicals: probabilistic assessment of sustainable molecular design guidelines.

PubMed

Connors, Kristin A; Voutchkova-Kostal, Adelina M; Kostal, Jakub; Anastas, Paul; Zimmerman, Julie B; Brooks, Bryan W

2014-08-01

Basic toxicological information is lacking for the majority of industrial chemicals. In addition to increasing empirical toxicity data through additional testing, prospective computational approaches to drug development aim to serve as a rational basis for the design of chemicals with reduced toxicity. Recent work has resulted in the derivation of a "rule of 2," wherein chemicals with an octanol-water partition coefficient (log P) less than 2 and a difference between the lowest unoccupied molecular orbital and the highest occupied molecular orbital (ΔE) greater than 9 (log P<2 and ΔE >9 eV) are predicted to be 4 to 5 times less likely to elicit acute or chronic toxicity to model aquatic organisms. The present study examines potential reduction of aquatic toxicity hazards from industrial chemicals if these 2 molecular design guidelines were employed. Probabilistic hazard assessment approaches were used to model the likelihood of encountering industrial chemicals exceeding toxicological categories of concern both with and without the rule of 2. Modeling predicted that utilization of these molecular design guidelines for log P and ΔE would appreciably decrease the number of chemicals that would be designated to be of "high" and "very high" concern for acute and chronic toxicity to standard model aquatic organisms and end points as defined by the US Environmental Protection Agency. For example, 14.5% of chemicals were categorized as having high and very high acute toxicity to the fathead minnow model, whereas only 3.3% of chemicals conforming to the design guidelines were predicted to be in these categories. Considerations of specific chemical classes (e.g., aldehydes), chemical attributes (e.g., ionization), and adverse outcome pathways in representative species (e.g., receptor-mediated responses) could be used to derive future property guidelines for broader classes of contaminants. © 2014 SETAC.

Multiscale modeling and simulation of embryogenesis for in silico predictive toxicology (WC9)

EPA Science Inventory

Translating big data from alternative and HTS platforms into hazard identification and risk assessment is an important need for predictive toxicology and for elucidating adverse outcome pathways (AOPs) in developmental toxicity. Understanding how chemical disruption of molecular ...

Predicting human skin absorption of chemicals: development of a novel quantitative structure activity relationship.

PubMed

Luo, Wen; Medrek, Sarah; Misra, Jatin; Nohynek, Gerhard J

2007-02-01

The objective of this study was to construct and validate a quantitative structure-activity relationship model for skin absorption. Such models are valuable tools for screening and prioritization in safety and efficacy evaluation, and risk assessment of drugs and chemicals. A database of 340 chemicals with percutaneous absorption was assembled. Two models were derived from the training set consisting 306 chemicals (90/10 random split). In addition to the experimental K(ow) values, over 300 2D and 3D atomic and molecular descriptors were analyzed using MDL's QsarIS computer program. Subsequently, the models were validated using both internal (leave-one-out) and external validation (test set) procedures. Using the stepwise regression analysis, three molecular descriptors were determined to have significant statistical correlation with K(p) (R2 = 0.8225): logK(ow), X0 (quantification of both molecular size and the degree of skeletal branching), and SsssCH (count of aromatic carbon groups). In conclusion, two models to estimate skin absorption were developed. When compared to other skin absorption QSAR models in the literature, our model incorporated more chemicals and explored a large number of descriptors. Additionally, our models are reasonably predictive and have met both internal and external statistical validations.

Predictive Modeling of the CDRA 4BMS

NASA Technical Reports Server (NTRS)

Coker, Robert; Knox, James

2016-01-01

Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

PubMed

Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi

2009-11-01

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

A mechanism-mediated model for carcinogenicity: Model content and prediction of the outcome of rodent carcinogenicity bioassays currently being conducted on 25 organic chemicals

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

Purdy, R.

A hierarchical model consisting of quantitative structure-activity relationships based mainly on chemical reactivity was developed to predict the carcinogenicity of organic chemicals to rodents. The model is comprised of quantitative structure-activity relationships, QSARs based on hypothesized mechanisms of action, metabolism, and partitioning. Predictors included octanol/water partition coefficient, molecular size, atomic partial charge, bond angle strain, atomic acceptor delocalizibility, atomic radical superdelocalizibility, the lowest unoccupied molecular orbital (LUMO) energy of hypothesized intermediate nitrenium ion of primary aromatic amines, difference in charge of ionized and unionized carbon-chlorine bonds, substituent size and pattern on polynuclear aromatic hydrocarbons, the distance between lone electron pairsmore » over a rigid structure, and the presence of functionalities such as nitroso and hydrazine. The model correctly classified 96% of the carcinogens in the training set of 306 chemicals, and 90% of the carcinogens in the test set of 301 chemicals. The test set by chance contained 84% of the positive thiocontaining chemicals. A QSAR for these chemicals was developed. This posttest set modified model correctly predicted 94% of the carcinogens in the test set. This model was used to predict the carcinogenicity of the 25 organic chemicals the U.S. National Toxicology Program was testing at the writing of this article. 12 refs., 3 tabs.« less

Cross-Linked Nanotube Materials with Variable Stiffness Tethers

NASA Technical Reports Server (NTRS)

Frankland, Sarah-Jane V.; Odegard, Gregory M.; Herzog, Matthew N.; Gates, Thomas S.; Fay, Catherine C.

2004-01-01

The constitutive properties of a cross-linked single-walled carbon nanotube material are predicted with a multi-scale model. The material is modeled as a transversely isotropic solid using concepts from equivalent-continuum modeling. The elastic constants are determined using molecular dynamics simulation. Some parameters of the molecular force field are determined specifically for the cross-linker from ab initio calculations. A demonstration of how the cross-linked nanotubes may affect the properties of a nanotube/polyimide composite is included using a micromechanical analysis.

Predicting phenolic acid absorption in Caco-2 cells: a theoretical permeability model and mechanistic study.

PubMed

Farrell, Tracy L; Poquet, Laure; Dew, Tristan P; Barber, Stuart; Williamson, Gary

2012-02-01

There is a considerable need to rationalize the membrane permeability and mechanism of transport for potential nutraceuticals. The aim of this investigation was to develop a theoretical permeability equation, based on a reported descriptive absorption model, enabling calculation of the transcellular component of absorption across Caco-2 monolayers. Published data for Caco-2 permeability of 30 drugs transported by the transcellular route were correlated with the descriptors 1-octanol/water distribution coefficient (log D, pH 7.4) and size, based on molecular mass. Nonlinear regression analysis was used to derive a set of model parameters a', β', and b' with an integrated molecular mass function. The new theoretical transcellular permeability (TTP) model obtained a good fit of the published data (R² = 0.93) and predicted reasonably well (R² = 0.86) the experimental apparent permeability coefficient (P(app)) for nine non-training set compounds reportedly transported by the transcellular route. For the first time, the TTP model was used to predict the absorption characteristics of six phenolic acids, and this original investigation was supported by in vitro Caco-2 cell mechanistic studies, which suggested that deviation of the P(app) value from the predicted transcellular permeability (P(app)(trans)) may be attributed to involvement of active uptake, efflux transporters, or paracellular flux.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity.

PubMed

Anwar-Mohamed, Anwar; Barakat, Khaled H; Bhat, Rakesh; Noskov, Sergei Y; Tyrrell, D Lorne; Tuszynski, Jack A; Houghton, Michael

2014-11-04

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure–toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data,more » optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the constructed (c) DTB and (d) DTF regression models to predict the T. pyriformis toxicity of diverse chemicals. - Highlights: • Ensemble learning (EL) based models constructed for toxicity prediction of chemicals • Predictive models used a few simple non-quantum mechanical molecular descriptors. • EL-based DTB/DTF models successfully discriminated toxic and non-toxic chemicals. • DTB/DTF regression models precisely predicted toxicity of chemicals in multi-species. • Proposed EL based models can be used as tool to predict toxicity of new chemicals.« less

3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

PubMed

Ding, Lina; Wang, Zhi-Zheng; Sun, Xu-Dong; Yang, Jing; Ma, Chao-Ya; Li, Wen; Liu, Hong-Min

2017-08-01

Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q 2 =0.802, r 2 ncv =0.979, and the best CoMSIA model has q 2 =0.799, r 2 ncv =0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design. Copyright © 2017. Published by Elsevier Ltd.

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling

PubMed Central

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

Background: The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. Objective: The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. Materials and Methods: The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. Results: The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. Conclusion: The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates. PMID:24748752

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling.

PubMed

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates.

Tensile Strength of Carbon Nanotubes Under Realistic Temperature and Strain Rate

NASA Technical Reports Server (NTRS)

Wei, Chen-Yu; Cho, Kyeong-Jae; Srivastava, Deepak; Biegel, Bryan (Technical Monitor)

2002-01-01

Strain rate and temperature dependence of the tensile strength of single-wall carbon nanotubes has been investigated with molecular dynamics simulations. The tensile failure or yield strain is found to be strongly dependent on the temperature and strain rate. A transition state theory based predictive model is developed for the tensile failure of nanotubes. Based on the parameters fitted from high-strain rate and temperature dependent molecular dynamics simulations, the model predicts that a defect free micrometer long single-wall nanotube at 300 K, stretched with a strain rate of 1%/hour, fails at about 9 plus or minus 1% tensile strain. This is in good agreement with recent experimental findings.

Constructing a molecular theory of self-assembly: Interplay of ideas from surfactants and block copolymers.

PubMed

Nagarajan, Ramanathan

2017-06-01

Low molecular weight surfactants and high molecular weight block copolymers display analogous self-assembly behavior in solutions and at interfaces, generating nanoscale structures of different shapes. Understanding the link between the molecular structure of these amphiphiles and their self-assembly behavior has been the goal of theoretical studies. Despite the analogies between surfactants and block copolymers, models predicting their self-assembly behavior have evolved independent of one another, each overlooking the molecular feature considered critical to the other. In this review, we focus on the interplay of ideas pertaining to surfactants and block copolymers in three areas of self-assembly. First, we show how improved free energy models have evolved by applying ideas from surfactants to block copolymers and vice versa, giving rise to a unitary theoretical framework and better predictive capabilities for both classes of amphiphiles. Second we show that even though molecular packing arguments are often used to explain aggregate shape transitions resulting from self-assembly, the molecular packing considerations are more relevant in the case of surfactants whereas free energy criteria are relevant for block copolymers. Third, we show that even though the surfactant and block copolymer aggregates are small nanostructures, the size differences between them is significant enough to make the interfacial effects control the solubilization of molecules in surfactant micelles while the bulk interactions control the solubilization in block copolymer micelles. Finally, we conclude by identifying recent theoretical progress in adapting the micelle model to a wide variety of self-assembly phenomena and the challenges to modeling posed by emerging novel classes of amphiphiles with complex biological, inorganic or nanoparticle moieties. Published by Elsevier B.V.

Use of multiple picosecond high-mass molecular dynamics simulations to predict crystallographic B-factors of folded globular proteins.

PubMed

Pang, Yuan-Ping

2016-09-01

Predicting crystallographic B-factors of a protein from a conventional molecular dynamics simulation is challenging, in part because the B-factors calculated through sampling the atomic positional fluctuations in a picosecond molecular dynamics simulation are unreliable, and the sampling of a longer simulation yields overly large root mean square deviations between calculated and experimental B-factors. This article reports improved B-factor prediction achieved by sampling the atomic positional fluctuations in multiple picosecond molecular dynamics simulations that use uniformly increased atomic masses by 100-fold to increase time resolution. Using the third immunoglobulin-binding domain of protein G, bovine pancreatic trypsin inhibitor, ubiquitin, and lysozyme as model systems, the B-factor root mean square deviations (mean ± standard error) of these proteins were 3.1 ± 0.2-9 ± 1 Å 2 for Cα and 7.3 ± 0.9-9.6 ± 0.2 Å 2 for Cγ, when the sampling was done for each of these proteins over 20 distinct, independent, and 50-picosecond high-mass molecular dynamics simulations with AMBER forcefield FF12MC or FF14SB. These results suggest that sampling the atomic positional fluctuations in multiple picosecond high-mass molecular dynamics simulations may be conducive to a priori prediction of crystallographic B-factors of a folded globular protein.

Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations

DOE PAGES

Meng, Zhaoxu; Bessa, Miguel A.; Xia, Wenjie; ...

2016-12-06

Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that themore » fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.« less

Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations

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

Meng, Zhaoxu; Bessa, Miguel A.; Xia, Wenjie

Predicting the macroscopic fracture energy of highly crosslinked glassy polymers from atomistic simulations is challenging due to the size of the process zone being large in these systems. Here, we present a scale-bridging approach that links atomistic molecular dynamics simulations to macroscopic fracture properties on the basis of a continuum fracture mechanics model for two different epoxy materials. Our approach reveals that the fracture energy of epoxy resins strongly depends on the functionality of epoxy resin and the component ratio between the curing agent (amine) and epoxide. The most intriguing part of our study is that we demonstrate that themore » fracture energy exhibits a maximum value within the range of conversion degrees considered (from 65% to 95%), which can be attributed to the combined effects of structural rigidity and post-yield deformability. Our study provides physical insight into the molecular mechanisms that govern the fracture characteristics of epoxy resins and demonstrates the success of utilizing atomistic molecular simulations towards predicting macroscopic material properties.« less

Atmospheric pressure ionization of chlorinated ethanes in ion mobility spectrometry and mass spectrometry

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

Ewing, Robert G.; Atkinson, David A.; Benson, Michael T.

2015-05-16

This study investigates the APCI mechanisms associated with chlorinated ethanes in an attempt to define conditions under which unique pseudo-molecular adducts, in addition to chloride ion, can be produced for analytical measurements using IMS and MS. The ionization chemistry of chlorinated compounds typically leads to the detection of only the halide ions. Using molecular modeling, which provides insights into the ion formation and relative binding energies, predictions for the formation of pseudo-molecular adducts are postulated. Predicted structures of the chloride ion with multiple hydrogens on the ethane backbone was supported by the observation of specific pseudo-molecular adducts in IMS andmore » MS spectra. With the proper instrumental conditions, such as short reaction times and low temp.« less

A computational chemistry perspective on the current status and future direction of hepatitis B antiviral drug discovery.

PubMed

Morgnanesi, Dante; Heinrichs, Eric J; Mele, Anthony R; Wilkinson, Sean; Zhou, Suzanne; Kulp, John L

2015-11-01

Computational chemical biology, applied to research on hepatitis B virus (HBV), has two major branches: bioinformatics (statistical models) and first-principle methods (molecular physics). While bioinformatics focuses on statistical tools and biological databases, molecular physics uses mathematics and chemical theory to study the interactions of biomolecules. Three computational techniques most commonly used in HBV research are homology modeling, molecular docking, and molecular dynamics. Homology modeling is a computational simulation to predict protein structure and has been used to construct conformers of the viral polymerase (reverse transcriptase domain and RNase H domain) and the HBV X protein. Molecular docking is used to predict the most likely orientation of a ligand when it is bound to a protein, as well as determining an energy score of the docked conformation. Molecular dynamics is a simulation that analyzes biomolecule motions and determines conformation and stability patterns. All of these modeling techniques have aided in the understanding of resistance mutations on HBV non-nucleos(t)ide reverse-transcriptase inhibitor binding. Finally, bioinformatics can be used to study the DNA and RNA protein sequences of viruses to both analyze drug resistance and to genotype the viral genomes. Overall, with these techniques, and others, computational chemical biology is becoming more and more necessary in hepatitis B research. This article forms part of a symposium in Antiviral Research on "An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B." Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling side-chains using molecular dynamics improve recognition of binding region in CAPRI targets.

PubMed

Camacho, Carlos J

2005-08-01

The CAPRI-II experiment added an extra level of complexity to the problem of predicting protein-protein interactions by including 5 targets for which participants had to build or complete the 3-dimensional (3D) structure of either the receptor or ligand based on the structure of a close homolog. In this article, we describe how modeling key side-chains using molecular dynamics (MD) in explicit solvent improved the recognition of the binding region of a free energy- based computational docking method. In particular, we show that MD is able to predict with relatively high accuracy the rotamer conformation of the anchor side-chains important for molecular recognition as suggested by Rajamani et al. (Proc Natl Acad Sci USA 2004;101:11287-11292). As expected, the conformations are some of the most common rotamers for the given residue, while latch side-chains that undergo induced fit upon binding are forced into less common conformations. Using these models as starting conformations in conjunction with the rigid-body docking server ClusPro and the flexible docking algorithm SmoothDock, we produced valuable predictions for 6 of the 9 targets in CAPRI-II, missing only the 3 targets that underwent significant structural rearrangements upon binding. We also show that our free energy- based scoring function, consisting of the sum of van der Waals, Coulombic electrostatic with a distance-dependent dielectric, and desolvation free energy successfully discriminates the nativelike conformation of our submitted predictions. The latter emphasizes the critical role that thermodynamics plays on our methodology, and validates the generality of the algorithm to predict protein interactions.

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

PubMed Central

Chao, Keh-Ping; Lu, Yu-Ting; Yang, Hsiu-Wen

2014-01-01

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique. PMID:24534804

QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method.

PubMed

Zeng, Xiao-Lan; Wang, Hong-Jun; Wang, Yan

2012-02-01

The possible molecular geometries of 134 halogenated methyl-phenyl ethers were optimized at B3LYP/6-31G(*) level with Gaussian 98 program. The calculated structural parameters were taken as theoretical descriptors to establish two new novel QSPR models for predicting aqueous solubility (-lgS(w,l)) and n-octanol/water partition coefficient (lgK(ow)) of halogenated methyl-phenyl ethers. The two models achieved in this work both contain three variables: energy of the lowest unoccupied molecular orbital (E(LUMO)), most positive atomic partial charge in molecule (q(+)), and quadrupole moment (Q(yy) or Q(zz)), of which R values are 0.992 and 0.970 respectively, their standard errors of estimate in modeling (SD) are 0.132 and 0.178, respectively. The results of leave-one-out (LOO) cross-validation for training set and validation with external test sets both show that the models obtained exhibited optimum stability and good predictive power. We suggests that two QSPR models derived here can be used to predict S(w,l) and K(ow) accurately for non-tested halogenated methyl-phenyl ethers congeners. Copyright © 2011 Elsevier Ltd. All rights reserved.

Structure- and ligand-based structure-activity relationships for a series of inhibitors of aldolase.

PubMed

Ferreira, Leonardo G; Andricopulo, Adriano D

2012-12-01

Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r²=0.98 and q²=0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015

NASA Astrophysics Data System (ADS)

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015.

PubMed

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

Prediction of boiling points of organic compounds by QSPR tools.

PubMed

Dai, Yi-min; Zhu, Zhi-ping; Cao, Zhong; Zhang, Yue-fei; Zeng, Ju-lan; Li, Xun

2013-07-01

The novel electro-negativity topological descriptors of YC, WC were derived from molecular structure by equilibrium electro-negativity of atom and relative bond length of molecule. The quantitative structure-property relationships (QSPR) between descriptors of YC, WC as well as path number parameter P3 and the normal boiling points of 80 alkanes, 65 unsaturated hydrocarbons and 70 alcohols were obtained separately. The high-quality prediction models were evidenced by coefficient of determination (R(2)), the standard error (S), average absolute errors (AAE) and predictive parameters (Qext(2),RCV(2),Rm(2)). According to the regression equations, the influences of the length of carbon backbone, the size, the degree of branching of a molecule and the role of functional groups on the normal boiling point were analyzed. Comparison results with reference models demonstrated that novel topological descriptors based on the equilibrium electro-negativity of atom and the relative bond length were useful molecular descriptors for predicting the normal boiling points of organic compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Prediction of EPR Spectra of Lyotropic Liquid Crystals using a Combination of Molecular Dynamics Simulations and the Model-Free Approach.

PubMed

Prior, Christopher; Oganesyan, Vasily S

2017-09-21

We report the first application of fully atomistic molecular dynamics (MD) simulations to the prediction of the motional electron paramagnetic resonance (EPR) spectra of lyotropic liquid crystals in different aggregation states doped with a paramagnetic spin probe. The purpose of this study is twofold. First, given that EPR spectra are highly sensitive to the motions and order of the spin probes doped within lyotropic aggregates, simulation of EPR line shapes from the results of MD modelling provides an ultimate test bed for the force fields currently employed to model such systems. Second, the EPR line shapes are simulated using the motional parameters extracted from MD trajectories using the Model-Free (MF) approach. Thus a combined MD-EPR methodology allowed us to test directly the validity of the application of the MF approach to systems with multi-component molecular motions. All-atom MD simulations using the General AMBER Force Field (GAFF) have been performed on sodium dodecyl sulfate (SDS) and dodecyltrimethylammonium chloride (DTAC) liquid crystals. The resulting MD trajectories were used to predict and interpret the EPR spectra of pre-micellar, micellar, rod and lamellar aggregates. The predicted EPR spectra demonstrate good agreement with most of experimental line shapes thus confirming the validity of both the force fields employed and the MF approach for the studied systems. At the same time simulation results confirm that GAFF tends to overestimate the packing and the order of the carbonyl chains of the surfactant molecules. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Three-dimensional quantitative structure-activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA.

PubMed

Gueto, Carlos; Ruiz, José L; Torres, Juan E; Méndez, Jefferson; Vivas-Reyes, Ricardo

2008-03-01

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of benzotriazine derivatives, as Src inhibitors. Ligand molecular superimposition on the template structure was performed by database alignment method. The statistically significant model was established of 72 molecules, which were validated by a test set of six compounds. The CoMFA model yielded a q(2)=0.526, non cross-validated R(2) of 0.781, F value of 88.132, bootstrapped R(2) of 0.831, standard error of prediction=0.587, and standard error of estimate=0.351 while the CoMSIA model yielded the best predictive model with a q(2)=0.647, non cross-validated R(2) of 0.895, F value of 115.906, bootstrapped R(2) of 0.953, standard error of prediction=0.519, and standard error of estimate=0.178. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. Results indicate that small steric volumes in the hydrophobic region, electron-withdrawing groups next to the aryl linker region, and atoms close to the solvent accessible region increase the Src inhibitory activity of the compounds. In fact, adding substituents at positions 5, 6, and 8 of the benzotriazine nucleus were generated new compounds having a higher predicted activity. The data generated from the present study will further help to design novel, potent, and selective Src inhibitors as anticancer therapeutic agents.

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

Calculation of Non-Bonded Forces Due to Sliding of Bundled Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Frankland, S. J. V.; Bandorawalla, T.; Gates, T. S.

2003-01-01

An important consideration for load transfer in bundles of single-walled carbon nanotubes is the nonbonded (van der Waals) forces between the nanotubes and their effect on axial sliding of the nanotubes relative to each other. In this research, the non-bonded forces in a bundle of seven hexagonally packed (10,10) single-walled carbon nanotubes are represented as an axial force applied to the central nanotube. A simple model, based on momentum balance, is developed to describe the velocity response of the central nanotube to the applied force. The model is verified by comparing its velocity predictions with molecular dynamics simulations that were performed on the bundle with different force histories applied to the central nanotube. The model was found to quantitatively predict the nanotube velocities obtained from the molecular dynamics simulations. Both the model and the simulations predict a threshold force at which the nanotube releases from the bundle. This force converts to a shear yield strength of 10.5-11.0 MPa for (10,10) nanotubes in a bundle.

Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

PubMed

Moal, Iain H; Bates, Paul A

2012-01-01

The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.

Final report for the DOE Early Career Award #DE-SC0003912

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

Jayaraman, Arthi

This DoE supported early career project was aimed at developing computational models, theory and simulation methods that would be then be used to predict assembly and morphology in polymer nanocomposites. In particular, the focus was on composites in active layers of devices, containing conducting polymers that act as electron donors and nanoscale additives that act as electron acceptors. During the course this work, we developed the first of its kind molecular models to represent conducting polymers enabling simulations at the experimentally relevant length and time scales. By comparison with experimentally observed morphologies we validated these models. Furthermore, using these modelsmore » and molecular dynamics simulations on graphical processing units (GPUs) we predicted the molecular level design features in polymers and additive that lead to morphologies with optimal features for charge carrier behavior in solar cells. Additionally, we also predicted computationally new design rules for better dispersion of additives in polymers that have been confirmed through experiments. Achieving dispersion in polymer nanocomposites is valuable to achieve controlled macroscopic properties of the composite. The results obtained during the course of this DOE funded project enables optimal design of higher efficiency organic electronic and photovoltaic devices and improve every day life with engineering of these higher efficiency devices.« less

Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

NASA Astrophysics Data System (ADS)

Tan, Wen; Mei, Hu; Chao, Li; Liu, Tengfei; Pan, Xianchao; Shu, Mao; Yang, Li

2013-12-01

P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

Molecular design of anticancer drug leads based on three-dimensional quantitative structure-activity relationship.

PubMed

Huang, Xiao Yan; Shan, Zhi Jie; Zhai, Hong Lin; Li, Li Na; Zhang, Xiao Yun

2011-08-22

Heat shock protein 90 (Hsp90) takes part in the developments of several cancers. Novobiocin, a typically C-terminal inhibitor for Hsp90, will probably used as an important anticancer drug in the future. In this work, we explored the valuable information and designed new novobiocin derivatives based on a three-dimensional quantitative structure-activity relationship (3D QSAR). The comparative molecular field analysis and comparative molecular similarity indices analysis models with high predictive capability were established, and their reliabilities are supported by the statistical parameters. Based on the several important influence factors obtained from these models, six new novobiocin derivatives with higher inhibitory activities were designed and confirmed by the molecular simulation with our models, which provide the potential anticancer drug leads for further research.

QSPR using MOLGEN-QSPR: the challenge of fluoroalkane boiling points.

PubMed

Rücker, Christoph; Meringer, Markus; Kerber, Adalbert

2005-01-01

By means of the new software MOLGEN-QSPR, a multilinear regression model for the boiling points of lower fluoroalkanes is established. The model is based exclusively on simple descriptors derived directly from molecular structure and nevertheless describes a broader set of data more precisely than previous attempts that used either more demanding (quantum chemical) descriptors or more demanding (nonlinear) statistical methods such as neural networks. The model's internal consistency was confirmed by leave-one-out cross-validation. The model was used to predict all unknown boiling points of fluorobutanes, and the quality of predictions was estimated by means of comparison with boiling point predictions for fluoropentanes.

Anharmonic and Quantum Fluctuations in Molecular Crystals: A First-Principles Study of the Stability of Paracetamol

NASA Astrophysics Data System (ADS)

Rossi, Mariana; Gasparotto, Piero; Ceriotti, Michele

2016-09-01

Molecular crystals often exist in multiple competing polymorphs, showing significantly different physicochemical properties. Computational crystal structure prediction is key to interpret and guide the search for the most stable or useful form, a real challenge due to the combinatorial search space, and the complex interplay of subtle effects that work together to determine the relative stability of different structures. Here we take a comprehensive approach based on different flavors of thermodynamic integration in order to estimate all contributions to the free energies of these systems with density-functional theory, including the oft-neglected anharmonic contributions and nuclear quantum effects. We take the two main stable forms of paracetamol as a paradigmatic example. We find that anharmonic contributions, different descriptions of van der Waals interactions, and nuclear quantum effects all matter to quantitatively determine the stability of different phases. Our analysis highlights the many challenges inherent in the development of a quantitative and predictive framework to model molecular crystals. However, it also indicates which of the components of the free energy can benefit from a cancellation of errors that can redeem the predictive power of approximate models, and suggests simple steps that could be taken to improve the reliability of ab initio crystal structure prediction.

In silico simulations of tunneling barrier measurements for molecular orbital-mediated junctions: A molecular orbital theory approach to scanning tunneling microscopy

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

Terryn, Raymond J.; Sriraman, Krishnan; Olson, Joel A., E-mail: jolson@fit.edu

A new simulator for scanning tunneling microscopy (STM) is presented based on the linear combination of atomic orbitals molecular orbital (LCAO-MO) approximation for the effective tunneling Hamiltonian, which leads to the convolution integral when applied to the tip interaction with the sample. This approach intrinsically includes the structure of the STM tip. Through this mechanical emulation and the tip-inclusive convolution model, dI/dz images for molecular orbitals (which are closely associated with apparent barrier height, ϕ{sub ap}) are reported for the first time. For molecular adsorbates whose experimental topographic images correspond well to isolated-molecule quantum chemistry calculations, the simulator makes accuratemore » predictions, as illustrated by various cases. Distortions in these images due to the tip are shown to be in accord with those observed experimentally and predicted by other ab initio considerations of tip structure. Simulations of the tunneling current dI/dz images are in strong agreement with experiment. The theoretical framework provides a solid foundation which may be applied to LCAO cluster models of adsorbate–substrate systems, and is extendable to emulate several aspects of functional STM operation.« less

Processing-microstructure relationships in thermotropic liquid crystalline polymers: Experimental and numerical modeling studies

NASA Astrophysics Data System (ADS)

Fang, Jun

Thermotropic liquid crystalline polymers (TLCPs) are a class of promising engineering materials for high-demanding structural applications. Their excellent mechanical properties are highly correlated to the underlying molecular orientation states, which may be affected by complex flow fields during melt processing. Thus, understanding and eventually predicting how processing flows impact molecular orientation is a critical step towards rational design work in order to achieve favorable, balanced physical properties in finished products. This thesis aims to develop deeper understanding of orientation development in commercial TLCPs during processing by coordinating extensive experimental measurements with numerical computations. In situ measurements of orientation development of LCPs during processing are a focal point of this thesis. An x-ray capable injection molding apparatus is enhanced and utilized for time-resolved measurements of orientation development in multiple commercial TLCPs during injection molding. Ex situ wide angle x-ray scattering is also employed for more thorough characterization of molecular orientation distributions in molded plaques. Incompletely injection molded plaques ("short shots") are studied to gain further insights into the intermediate orientation states during mold filling. Finally, two surface orientation characterization techniques, near edge x-ray absorption fine structure (NEXAFS) and infrared attenuated total reflectance (FTIR-ATR) are combined to investigate the surface orientation distribution of injection molded plaques. Surface orientation states are found to be vastly different from their bulk counterparts due to different kinematics involved in mold filling. In general, complex distributions of orientation in molded plaques reflect the spatially varying competition between shear and extension during mold filling. To complement these experimental measurements, numerical calculations based on the Larson-Doi polydomain model are performed. The implementation of the Larson-Doi in complex processing flows is performed using a commercial process modeling software suite (MOLDFLOWRTM), exploiting a nearly exact analogy between the Larson-Doi model and a fiber orientation model that has been widely used in composites processing simulations. The modeling scheme is first verified by predicting many qualitative and quantitative features of molecular orientation distributions in isothermal extrusion-fed channel flows. In coordination with experiments, the model predictions are found to capture many qualitative features observed in injection molded plaques (including short shots). The final, stringent test of Larson-Doi model performance is prediction of in situ transient orientation data collected during mold filling. The model yields satisfactory results, though certain numerical approximations limit performance near the mold front.

The Space Shuttle Orbiter molecular environment induced by the supplemental flash evaporator system

NASA Technical Reports Server (NTRS)

Ehlers, H. K. F.

1985-01-01

The water vapor environment of the Space Shuttle Orbiter induced by the supplemental flash evaporator during the on-orbit flight phase has been analyzed based on Space II model predictions and orbital flight measurements. Model data of local density, column density, and return flux are presented. Results of return flux measurements with a mass spectrometer during STS-2 and of direct flux measurements during STS-4 are discussed and compared with model predictions.

Molecular Modeling of Environmentally Important Processes: Reduction Potentials

ERIC Educational Resources Information Center

Lewis, Anne; Bumpus, John A.; Truhlar, Donald G.; Cramer, Christopher J.

2004-01-01

The increasing use of computational quantum chemistry in the modeling of environmentally important processes is described. The employment of computational quantum mechanics for the prediction of oxidation-reduction potential for solutes in an aqueous medium is discussed.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

PubMed

Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio

2017-11-27

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants

NASA Astrophysics Data System (ADS)

Gramatica, Paola

This chapter surveys the QSAR modeling approaches (developed by the author's research group) for the validated prediction of environmental properties of organic pollutants. Various chemometric methods, based on different theoretical molecular descriptors, have been applied: explorative techniques (such as PCA for ranking, SOM for similarity analysis), modeling approaches by multiple-linear regression (MLR, in particular OLS), and classification methods (mainly k-NN, CART, CP-ANN). The focus of this review is on the main topics of environmental chemistry and ecotoxicology, related to the physico-chemical properties, the reactivity, and biological activity of chemicals of high environmental concern. Thus, the review deals with atmospheric degradation reactions of VOCs by tropospheric oxidants, persistence and long-range transport of POPs, sorption behavior of pesticides (Koc and leaching), bioconcentration, toxicity (acute aquatic toxicity, mutagenicity of PAHs, estrogen binding activity for endocrine disruptors compounds (EDCs)), and finally persistent bioaccumulative and toxic (PBT) behavior for the screening and prioritization of organic pollutants. Common to all the proposed models is the attention paid to model validation for predictive ability (not only internal, but also external for chemicals not participating in the model development) and checking of the chemical domain of applicability. Adherence to such a policy, requested also by the OECD principles, ensures the production of reliable predicted data, useful also in the new European regulation of chemicals, REACH.

Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking

NASA Astrophysics Data System (ADS)

Li, Peizhen; Tian, Yueli; Zhai, Honglin; Deng, Fangfang; Xie, Meihong; Zhang, Xiaoyun

2013-11-01

Non-purine derivatives have been shown to be promising novel drug candidates as xanthine oxidase inhibitors. Based on three-dimensional quantitative structure-activity relationship (3D-QSAR) methods including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), two 3D-QSAR models for a series of non-purine xanthine oxidase (XO) inhibitors were established, and their reliability was supported by statistical parameters. Combined 3D-QSAR modeling and the results of molecular docking between non-purine xanthine oxidase inhibitors and XO, the main factors that influenced activity of inhibitors were investigated, and the obtained results could explain known experimental facts. Furthermore, several new potential inhibitors with higher activity predicted were designed, which based on our analyses, and were supported by the simulation of molecular docking. This study provided some useful information for the development of non-purine xanthine oxidase inhibitors with novel structures.

Unfolding and melting of DNA (RNA) hairpins: the concept of structure-specific 2D dynamic landscapes.

PubMed

Lin, Milo M; Meinhold, Lars; Shorokhov, Dmitry; Zewail, Ahmed H

2008-08-07

A 2D free-energy landscape model is presented to describe the (un)folding transition of DNA/RNA hairpins, together with molecular dynamics simulations and experimental findings. The dependence of the (un)folding transition on the stem sequence and the loop length is shown in the enthalpic and entropic contributions to the free energy. Intermediate structures are well defined by the two coordinates of the landscape during (un)zipping. Both the free-energy landscape model and the extensive molecular dynamics simulations totaling over 10 mus predict the existence of temperature-dependent kinetic intermediate states during hairpin (un)zipping and provide the theoretical description of recent ultrafast temperature-jump studies which indicate that hairpin (un)zipping is, in general, not a two-state process. The model allows for lucid prediction of the collapsed state(s) in simple 2D space and we term it the kinetic intermediate structure (KIS) model.

Comparison of 3D quantitative structure-activity relationship methods: Analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis

NASA Astrophysics Data System (ADS)

Woolfrey, John R.; Avery, Mitchell A.; Doweyko, Arthur M.

1998-03-01

Two three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and hypothetical active site lattice (HASL), were compared with respect to the analysis of a training set of 154 artemisinin analogues. Five models were created, including a complete HASL and two trimmed versions, as well as two CoMFA models (leave-one-out standard CoMFA and the guided-region selection protocol). Similar r2 and q2 values were obtained by each method, although some striking differences existed between CoMFA contour maps and the HASL output. Each of the four predictive models exhibited a similar ability to predict the activity of a test set of 23 artemisinin analogues, although some differences were noted as to which compounds were described well by either model.

In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ).

PubMed

Kumar, Ambuj; Rajendran, Vidya; Sethumadhavan, Rao; Purohit, Rituraj

2012-01-01

Human STIL (SCL/TAL1 interrupting locus) protein maintains centriole stability and spindle pole localisation. It helps in recruitment of CENPJ (Centromere protein J)/CPAP (centrosomal P4.1-associated protein) and other centrosomal proteins. Mutations in STIL protein are reported in several disorders, especially in deregulation of cell cycle cascades. In this work, we examined the non-synonymous single nucleotide polymorphisms (nsSNPs) reported in STIL protein for their disease association. Different SNP prediction tools were used to predict disease-associated nsSNPs. Our evaluation technique predicted rs147744459 (R242C) as a highly deleterious disease-associated nsSNP and its interaction behaviour with CENPJ protein. Molecular modelling, docking and molecular dynamics simulation were conducted to examine the structural consequences of the predicted disease-associated mutation. By molecular dynamic simulation we observed structural consequences of R242C mutation which affects interaction of STIL and CENPJ functional domains. The result obtained in this study will provide a biophysical insight into future investigations of pathological nsSNPs using a computational platform.

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Stagnation Point Nonequilibrium Radiative Heating and the Influence of Energy Exchange Models

NASA Technical Reports Server (NTRS)

Hartung, Lin C.; Mitcheltree, Robert A.; Gnoffo, Peter A.

1991-01-01

A nonequilibrium radiative heating prediction method has been used to evaluate several energy exchange models used in nonequilibrium computational fluid dynamics methods. The radiative heating measurements from the FIRE II flight experiment supply an experimental benchmark against which different formulations for these exchange models can be judged. The models which predict the lowest radiative heating are found to give the best agreement with the flight data. Examination of the spectral distribution of radiation indicates that despite close agreement of the total radiation, many of the models examined predict excessive molecular radiation. It is suggested that a study of the nonequilibrium chemical kinetics may lead to a correction for this problem.

Application of the AMPLE cluster-and-truncate approach to NMR structures for molecular replacement

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

Bibby, Jaclyn; Keegan, Ronan M.; Mayans, Olga

2013-11-01

Processing of NMR structures for molecular replacement by AMPLE works well. AMPLE is a program developed for clustering and truncating ab initio protein structure predictions into search models for molecular replacement. Here, it is shown that its core cluster-and-truncate methods also work well for processing NMR ensembles into search models. Rosetta remodelling helps to extend success to NMR structures bearing low sequence identity or high structural divergence from the target protein. Potential future routes to improved performance are considered and practical, general guidelines on using AMPLE are provided.

High-excitation lines of molecular hydrogen: A discriminant between shock models

NASA Technical Reports Server (NTRS)

Burton, M.; Brand, P.; Moorhouse, A.; Geballe, T.

1989-01-01

The results of column densities of molecular hydrogen, calculated from nineteen infrared line intensities, are discussed. They were measured at peak 1 of the outflow of the Orion molecular cloud OMC-1. The 1-0 O(7) and 0-0 S(13) lines of H2, at 3.8 microns, are mapped over the source. Their intensity ratio is found to be independent of position in the outflow. These observations are well fitted by a simple cooling-flow model of the line emitting region, but seem to be at variance with predictions of C-shocks current in the literature.

Application of simple mathematical expressions to relate the half-lives of xenobiotics in rats to values in humans.

PubMed

Ward, Keith W; Erhardt, Paul; Bachmann, Kenneth

2005-01-01

Previous publications from GlaxoSmithKline and University of Toledo laboratories convey our independent attempts to predict the half-lives of xenobiotics in humans using data obtained from rats. The present investigation was conducted to compare the performance of our published models against a common dataset obtained by merging the two sets of rat versus human half-life (hHL) data previously used by each laboratory. After combining data, mathematical analyses were undertaken by deploying both of our previous models, namely the use of an empirical algorithm based on a best-fit model and the use of rat-to-human liver blood flow ratios as a half-life correction factor. Both qualitative and quantitative analyses were performed, as well as evaluation of the impact of molecular properties on predictability. The merged dataset was remarkably diverse with respect to physiochemical and pharmacokinetic (PK) properties. Application of both models revealed similar predictability, depending upon the measure of stipulated accuracy. Certain molecular features, particularly rotatable bond count and pK(a), appeared to influence the accuracy of prediction. This collaborative effort has resulted in an improved understanding and appreciation of the value of rats to serve as a surrogate for the prediction of xenobiotic half-lives in humans when clinical pharmacokinetic studies are not possible or practicable.

Predicting ecological roles in the rhizosphere using metabolome and transportome modeling

DOE PAGES

Larsen, Peter E.; Collart, Frank R.; Dai, Yang; ...

2015-09-02

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter ofmore » plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

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

Larsen, Peter E.; Collart, Frank R.; Dai, Yang

2015-09-02

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promotermore » of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

Structural features that predict real-value fluctuations of globular proteins

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-01-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics trajectories of non-homologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real-value of residue fluctuations using the support vector regression. It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in molecular dynamics trajectories. Moreover, support vector regression that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson’s correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed for the prediction by the Gaussian network model. An advantage of the developed method over the Gaussian network models is that the former predicts the real-value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. PMID:22328193

Development of Short-term Molecular Thresholds to Predict Long-term Mouse Liver Tumor Outcomes: Phthalate Case Study

EPA Science Inventory

Short-term molecular profiles are a central component of strategies to model health effects of environmental chemicals. In this study, a 7 day mouse assay was used to evaluate transcriptomic and proliferative responses in the liver for a hepatocarcinogenic phthalate, di (2-ethylh...

Drift mobility of photo-electrons in organic molecular crystals: Quantitative comparison between theory and experiment

NASA Astrophysics Data System (ADS)

Reineker, P.; Kenkre, V. M.; Kühne, R.

1981-08-01

A quantitative comparison of a simple theoretical prediction for the drift mobility of photo-electrons in organic molecular crystals, calculated within the model of the coupled band-like and hopping motion, with experiments in napthalene of Schein et al. and Karl et al. is given.

Massive Molecular Outflows and Evidence for AGN Feedback from CO Observations

DTIC Science & Technology

2013-11-13

J. Thomson Avenue, Cambridge CB3 0HE, UK e-mail: c.cicone@mrao.cam.ac.uk 2 Kavli Institute for Cosmology , University of Cambridge, Madingley Road...molecular outflow as a function of AGN luminosity. Theoretical models of AGN feed- back and cosmological simulations predict a coupling efficiency between AGN

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

DOE PAGES

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

2015-06-04

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

PubMed Central

2015-01-01

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

Thermodynamic and mechanical properties of epoxy resin DGEBF crosslinked with DETDA by molecular dynamics.

PubMed

Tack, Jeremy L; Ford, David M

2008-06-01

Fully atomistic molecular dynamics (MD) simulations were used to predict the properties of diglycidyl ether of bisphenol F (DGEBF) crosslinked with curing agent diethyltoluenediamine (DETDA). This polymer is a commercially important epoxy resin and a candidate for applications in nanocomposites. The calculated properties were density and bulk modulus (at near-ambient pressure and temperature) and glass transition temperature (at near-ambient pressure). The molecular topology, degree of curing, and MD force-field were investigated as variables. The models were created by densely packing pre-constructed oligomers of different composition and connectivity into a periodic simulation box. For high degrees of curing (greater than 90%), the density was found to be insensitive to the molecular topology and precise value of degree of curing. Of the two force-fields that were investigated, cff91 and COMPASS, the latter clearly gave more accurate values for the density as compared to experiment. In fact, the density predicted by COMPASS was within 6% of reported experimental values for the highly crosslinked polymer. The predictions of both force-fields for glass transition temperature were within the range of reported experimental values, with the predictions of cff91 being more consistent with a highly cured resin.

Evolution of the Radial Abundance Gradient and Cold Gas along the Milky Way Disk

NASA Astrophysics Data System (ADS)

Chen, Q. S.; Chang, R. X.; Yin, J.

2014-03-01

We have constructed a phenomenological model of the chemical evolution of the Milky Way disk, and treated the molecular and atomic gas separately. Using this model, we explore the radial profiles of oxygen abundance, the surface density of cold gas, and their time evolutions. It is shown that the model predictions are very sensitive to the adopted infall time-scale. By comparing the model predictions with the observations, we find that the model adopting the star formation law based on H_2 can properly predict the observed radial distributions of cold gas and oxygen abundance gradient along the disk. We also compare the model results with the predictions of the model which adopts the instantaneous recycling approximation (IRA), and find that the IRA assumption has little influence on the model results, especially in the low-density gas region.

Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics.

PubMed

Kumar, Avishek; Campitelli, Paul; Thorpe, M F; Ozkan, S Banu

2015-12-01

The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled. © 2015 Wiley Periodicals, Inc.

An orientation sensitive approach in biomolecule interaction quantitative structure-activity relationship modeling and its application in ion-exchange chromatography.

PubMed

Kittelmann, Jörg; Lang, Katharina M H; Ottens, Marcel; Hubbuch, Jürgen

2017-01-27

Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases. Copyright © 2016 Elsevier B.V. All rights reserved.

Bayesian molecular design with a chemical language model

NASA Astrophysics Data System (ADS)

Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo

2017-04-01

The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.

Bayesian molecular design with a chemical language model.

PubMed

Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo

2017-04-01

The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.

Virtual Institute of Microbial Stress and Survival: Deduction of Stress Response Pathways in Metal and Radionuclide Reducing Microorganisms

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

None

2004-04-17

The projects application goals are to: (1) To understand bacterial stress-response to the unique stressors in metal/radionuclide contamination sites; (2) To turn this understanding into a quantitative, data-driven model for exploring policies for natural and biostimulatory bioremediation; (3) To implement proposed policies in the field and compare results to model predictions; and (4) Close the experimental/computation cycle by using discrepancies between models and predictions to drive new measurements and construction of new models. The projects science goals are to: (1) Compare physiological and molecular response of three target microorganisms to environmental perturbation; (2) Deduce the underlying regulatory pathways that controlmore » these responses through analysis of phenotype, functional genomic, and molecular interaction data; (3) Use differences in the cellular responses among the target organisms to understand niche specific adaptations of the stress and metal reduction pathways; (4) From this analysis derive an understanding of the mechanisms of pathway evolution in the environment; and (5) Ultimately, derive dynamical models for the control of these pathways to predict how natural stimulation can optimize growth and metal reduction efficiency at field sites.« less

Predicting CO2-H2O Interfacial Tension Using COSMO-RS.

PubMed

Silvestri, A; Stipp, S L S; Andersson, M P

2017-02-14

Knowledge about the interaction between fluids and solids and the interfacial tension (IFT) that results is important for predicting behavior and properties in industrial systems and in nature, such as in rock formations before, during, and after CO 2 injection for long-term storage. Many authors have studied the effect of the environmental variables on the IFT in the CO 2 -H 2 O system. However, experimental measurements above CO 2 supercritical conditions are scarce and sometimes contradictory. Molecular modeling is a valuable tool for complementing experimental IFT determination, and it can help us interpret results and gain insight under conditions where experiments are difficult or impossible. Here, we report predictions for CO 2 -water interfacial tension performed using density functional theory (DFT) combined with the COSMO-RS implicit solvent model. We predicted the IFT dependence as a function of pressure (0-50 MPa), temperature (273-383 K), and salinity (0-5 M NaCl). The results agree well with literature data, within the estimated uncertainty for experiments and for molecular dynamics (MD) simulations, suggesting that the model can be used as a fast alternative to time-consuming computational approaches for predicting the CO 2 -water IFT over a range of pressures, temperatures, and salinities.

Screening of photosynthetic pigments for herbicidal activity with a new computational molecular approach.

PubMed

Krishnaraj, R Navanietha; Chandran, Saravanan; Pal, Parimal; Berchmans, Sheela

2013-12-01

There is an immense interest among the researchers to identify new herbicides which are effective against the herbs without affecting the environment. In this work, photosynthetic pigments are used as the ligands to predict their herbicidal activity. The enzyme 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase is a good target for the herbicides. Homology modeling of the target enzyme is done using Modeler 9.11 and the model is validated. Docking studies were performed with AutoDock Vina algorithm to predict the binding of the natural pigments such as β-carotene, chlorophyll a, chlorophyll b, phycoerythrin and phycocyanin to the target. β-carotene, phycoerythrin and phycocyanin have higher binding energies indicating the herbicidal activity of the pigments. This work reports a procedure to screen herbicides with computational molecular approach. These pigments will serve as potential bioherbicides in the future.

Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

NASA Astrophysics Data System (ADS)

Kovalenko, Andriy

2014-08-01

Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology enables rational design of CNC-based bionanocomposite materials and systems. Furthermore, the 3D-RISM-KH based multiscale modeling addresses the effect of hemicellulose and lignin composition on nanoscale forces that control cell wall strength towards overcoming plant biomass recalcitrance. It reveals molecular forces maintaining the cell wall structure and provides directions for genetic modulation of plants and pretreatment design to render biomass more amenable to processing. We envision integrated biomass valorization based on extracting and decomposing the non-cellulosic components to low molecular weight chemicals and utilizing the cellulose microfibrils to make CNC. This is an important alternative to approaches of full conversion of lignocellulose to biofuels that face challenges arising from the deleterious impact of cellulose crystallinity on enzymatic processing.

Fine- and hyperfine-structure effects in molecular photoionization. II. Resonance-enhanced multiphoton ionization and hyperfine-selective generation of molecular cations

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

Germann, Matthias; Willitsch, Stefan, E-mail: stefan.willitsch@unibas.ch

2016-07-28

Resonance-enhanced multiphoton ionization (REMPI) is a widely used technique for studying molecular photoionization and producing molecular cations for spectroscopy and dynamics studies. Here, we present a model for describing hyperfine-structure effects in the REMPI process and for predicting hyperfine populations in molecular ions produced by this method. This model is a generalization of our model for fine- and hyperfine-structure effects in one-photon ionization of molecules presented in Paper I [M. Germann and S. Willitsch, J. Chem. Phys. 145, 044314 (2016)]. This generalization is achieved by covering two main aspects: (1) treatment of the neutral bound-bound transition including the hyperfine structuremore » that makes up the first step of the REMPI process and (2) modification of our ionization model to account for anisotropic populations resulting from this first excitation step. Our findings may be used for analyzing results from experiments with molecular ions produced by REMPI and may serve as a theoretical background for hyperfine-selective ionization experiments.« less

Progress toward bridging from atomistic to continuum modeling to predict nuclear waste glass dissolution.

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

Zapol, Peter; Bourg, Ian; Criscenti, Louise Jacqueline

2011-10-01

This report summarizes research performed for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Subcontinuum and Upscaling Task. The work conducted focused on developing a roadmap to include molecular scale, mechanistic information in continuum-scale models of nuclear waste glass dissolution. This information is derived from molecular-scale modeling efforts that are validated through comparison with experimental data. In addition to developing a master plan to incorporate a subcontinuum mechanistic understanding of glass dissolution into continuum models, methods were developed to generate constitutive dissolution rate expressions from quantum calculations, force field models were selected to generate multicomponent glass structures and gel layers,more » classical molecular modeling was used to study diffusion through nanopores analogous to those in the interfacial gel layer, and a micro-continuum model (K{mu}C) was developed to study coupled diffusion and reaction at the glass-gel-solution interface.« less

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

PubMed

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

Fragment-based {sup 13}C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods

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

Hartman, Joshua D.; Beran, Gregory J. O., E-mail: gregory.beran@ucr.edu; Monaco, Stephen

2015-09-14

We assess the quality of fragment-based ab initio isotropic {sup 13}C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic {sup 13}C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readilymore » in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.« less

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds.

PubMed

Pérez-Garrido, Alfonso; Helguera, Aliuska Morales; Rodríguez, Francisco Girón; Cordeiro, M Natália D S

2010-05-01

The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals. Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software. For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers. These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk. 2010 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Transport properties of carbon dioxide and methane from molecular dynamics simulations.

PubMed

Aimoli, C G; Maginn, E J; Abreu, C R A

2014-10-07

Transport properties of carbon dioxide and methane are predicted for temperatures between (273.15 and 573.15) K and pressures up to 800 MPa by molecular dynamics simulations. Viscosities and thermal conductivities were obtained through the Green-Kubo formalism, whereas the Einstein relation was used to provide self-diffusion coefficient estimates. The differences in property predictions due to the force field nature and parametrization were investigated by the comparison of seven different CO2 models (two single-site models, three rigid three-site models, and two fully flexible three-site models) and three different CH4 models (two single-site models and one fully flexible five-site model). The simulation results show good agreement with experimental data, except for thermal conductivities at low densities. The molecular structure and force field parameters play an important role in the accuracy of the simulations, which is within the experimental deviations reported for viscosities and self-diffusion coefficients considering the most accurate CO2 and CH4 models studied. On the other hand, the molecular flexibility does not seem to improve accuracy, since the explicit account of vibrational and bending degrees of freedom in the CO2 flexible models leads to slightly less accurate results. Nonetheless, the use of a correctional term to account for vibrational modes in rigid models generally improves estimations of thermal conductivity values. At extreme densities, the caging effect observed with single-site representations of the molecules restrains mobility and leads to an unphysical overestimation of viscosities and, conversely, to the underestimation of self-diffusion coefficients. This result may help to better understand the limits of applicability of such force fields concerning structural and transport properties of dense systems.

Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K as anti-malarial agents

NASA Astrophysics Data System (ADS)

Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

2017-06-01

The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents.

PubMed

Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

2017-01-01

The Plasmodium falciparum Lactate Dehydrogenase enzyme ( Pf LDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The Pf LDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of Pf LDH, we have used Discovery studio to perform molecular docking in the active binding pocket of Pf LDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q 2 of 0.516. The model has predicted r 2 of 0.91 showing that predicted IC 50 values are in good agreement with experimental IC 50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents

PubMed Central

Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

2017-01-01

The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors. PMID:28664157

UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics.

PubMed

Czaplewski, Cezary; Karczynska, Agnieszka; Sieradzan, Adam K; Liwo, Adam

2018-04-30

A server implementation of the UNRES package (http://www.unres.pl) for coarse-grained simulations of protein structures with the physics-based UNRES model, coined a name UNRES server, is presented. In contrast to most of the protein coarse-grained models, owing to its physics-based origin, the UNRES force field can be used in simulations, including those aimed at protein-structure prediction, without ancillary information from structural databases; however, the implementation includes the possibility of using restraints. Local energy minimization, canonical molecular dynamics simulations, replica exchange and multiplexed replica exchange molecular dynamics simulations can be run with the current UNRES server; the latter are suitable for protein-structure prediction. The user-supplied input includes protein sequence and, optionally, restraints from secondary-structure prediction or small x-ray scattering data, and simulation type and parameters which are selected or typed in. Oligomeric proteins, as well as those containing D-amino-acid residues and disulfide links can be treated. The output is displayed graphically (minimized structures, trajectories, final models, analysis of trajectory/ensembles); however, all output files can be downloaded by the user. The UNRES server can be freely accessed at http://unres-server.chem.ug.edu.pl.

Prediction of consensus binding mode geometries for related chemical series of positive allosteric modulators of adenosine and muscarinic acetylcholine receptors.

PubMed

Sakkal, Leon A; Rajkowski, Kyle Z; Armen, Roger S

2017-06-05

Following insights from recent crystal structures of the muscarinic acetylcholine receptor, binding modes of Positive Allosteric Modulators (PAMs) were predicted under the assumption that PAMs should bind to the extracellular surface of the active state. A series of well-characterized PAMs for adenosine (A 1 R, A 2A R, A 3 R) and muscarinic acetylcholine (M 1 R, M 5 R) receptors were modeled using both rigid and flexible receptor CHARMM-based molecular docking. Studies of adenosine receptors investigated the molecular basis of the probe-dependence of PAM activity by modeling in complex with specific agonist radioligands. Consensus binding modes map common pharmacophore features of several chemical series to specific binding interactions. These models provide a rationalization of how PAM binding slows agonist radioligand dissociation kinetics. M 1 R PAMs were predicted to bind in the analogous M 2 R PAM LY2119620 binding site. The M 5 R NAM (ML-375) was predicted to bind in the PAM (ML-380) binding site with a unique induced-fit receptor conformation. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Validation, acceptance, and extension of a predictive model of reproductive toxicity using ToxCast data

EPA Science Inventory

The EPA ToxCast research program uses a high-throughput screening (HTS) approach for predicting the toxicity of large numbers of chemicals. Phase-I tested 309 well-characterized chemicals (mostly pesticides) in over 500 assays of different molecular targets, cellular responses an...

Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening

EPA Science Inventory

The EPA ToxCast research program uses high throughput screening for bioactivity profiling and predicting the toxicity of large numbers of chemicals. ToxCast Phase‐I tested 309 well‐characterized chemicals in over 500 assays for a wide range of molecular targets and cellular respo...

PREDICTION OF THE SOLUBILITY, ACTIVITY COEFFICIENT AND LIQUID/LIQUID PARTITION COEFFICIENT OF ORGANIC COMPOUNDS

EPA Science Inventory

Solvation models, based on fundamental chemical structure theory, were developed in the SPARC mechanistic tool box to predict a large array of physical properties of organic compounds in water and in non-aqueous solvents strictly from molecular structure. The SPARC self-interact...

Predictive Signatures from ToxCast Data for Chronic, Developmental and Reproductive Toxicity Endpoints

EPA Science Inventory

The EPA ToxCast program is using in vitro assay data and chemical descriptors to build predictive models for in vivo toxicity endpoints. In vitro assays measure activity of chemicals against molecular targets such as enzymes and receptors (measured in cell-free and cell-based sys...

Predictive Signatures of Developmental Toxicity Modeled with HTS Data from ToxCast™ Bioactivity Profiles

EPA Science Inventory

The EPA ToxCast™ research program uses a high-throughput screening (HTS) approach for predicting the toxicity of large numbers of chemicals. Phase-I contains 309 well-characterized chemicals which are mostly pesticides tested in over 600 assays of different molecular targets, cel...

Bioethanol production optimization: a thermodynamic analysis.

PubMed

Alvarez, Víctor H; Rivera, Elmer Ccopa; Costa, Aline C; Filho, Rubens Maciel; Wolf Maciel, Maria Regina; Aznar, Martín

2008-03-01

In this work, the phase equilibrium of binary mixtures for bioethanol production by continuous extractive process was studied. The process is composed of four interlinked units: fermentor, centrifuge, cell treatment unit, and flash vessel (ethanol-congener separation unit). A proposal for modeling the vapor-liquid equilibrium in binary mixtures found in the flash vessel has been considered. This approach uses the Predictive Soave-Redlich-Kwong equation of state, with original and modified molecular parameters. The congeners considered were acetic acid, acetaldehyde, furfural, methanol, and 1-pentanol. The results show that the introduction of new molecular parameters r and q in the UNIFAC model gives more accurate predictions for the concentration of the congener in the gas phase for binary and ternary systems.

Molecular dynamics simulation of real-fluid mutual diffusion coefficients with the Lennard-Jones potential model

NASA Astrophysics Data System (ADS)

Stoker, J. M.; Rowley, R. L.

1989-09-01

Mutual diffusion coefficients for selected alkanes in carbon tetrachloride were calculated using molecular dynamics and Lennard-Jones (LJ) potentials. Use of effective spherical LJ parameters is desirable when possible for two reasons: (i) computer time is saved due to the simplicity of the model and (ii) the number of parameters in the model is kept to a minimum. Results of this study indicate that mutual diffusivity is particularly sensitive to the molecular size cross parameter, σ12, and that the commonly used Lorentz-Berthelot rules are inadequate for mixtures in which the component structures differ significantly. Good agreement between simulated and experimental mutual diffusivities is obtained with a combining rule for σ12 which better represents these asymmetric mixtures using pure component LJ parameters obtained from self-diffusion coefficient data. The effect of alkane chain length on the mutual diffusion coefficient is correctly predicted. While the effects of alkane branching upon the diffusion coefficient are comparable in size to the uncertainty of these calculations, the qualitative trend due to branching is also correctly predicted by the MD results.

Predicting Biological Information Flow in a Model Oxygen Minimum Zone

NASA Astrophysics Data System (ADS)

Louca, S.; Hawley, A. K.; Katsev, S.; Beltran, M. T.; Bhatia, M. P.; Michiels, C.; Capelle, D.; Lavik, G.; Doebeli, M.; Crowe, S.; Hallam, S. J.

2016-02-01

Microbial activity drives marine biochemical fluxes and nutrient cycling at global scales. Geochemical measurements as well as molecular techniques such as metagenomics, metatranscriptomics and metaproteomics provide great insight into microbial activity. However, an integration of molecular and geochemical data into mechanistic biogeochemical models is still lacking. Recent work suggests that microbial metabolic pathways are, at the ecosystem level, strongly shaped by stoichiometric and energetic constraints. Hence, models rooted in fluxes of matter and energy may yield a holistic understanding of biogeochemistry. Furthermore, such pathway-centric models would allow a direct consolidation with meta'omic data. Here we present a pathway-centric biogeochemical model for the seasonal oxygen minimum zone in Saanich Inlet, a fjord off the coast of Vancouver Island. The model considers key dissimilatory nitrogen and sulfur fluxes, as well as the population dynamics of the genes that mediate them. By assuming a direct translation of biocatalyzed energy fluxes to biosynthesis rates, we make predictions about the distribution and activity of the corresponding genes. A comparison of the model to molecular measurements indicates that the model explains observed DNA, RNA, protein and cell depth profiles. This suggests that microbial activity in marine ecosystems such as oxygen minimum zones is well described by DNA abundance, which, in conjunction with geochemical constraints, determines pathway expression and process rates. Our work further demonstrates how meta'omic data can be mechanistically linked to environmental redox conditions and biogeochemical processes.

A molecular model for cohesive slip at polymer melt/solid interfaces.

PubMed

Tchesnokov, M A; Molenaar, J; Slot, J J M; Stepanyan, R

2005-06-01

A molecular model is proposed which predicts wall slip by disentanglement of polymer chains adsorbed on a wall from those in the polymer bulk. The dynamics of the near-wall boundary layer is found to be governed by a nonlinear equation of motion, which accounts for such mechanisms on surface chains as convection, retraction, constraint release, and thermal fluctuations. This equation is valid over a wide range of grafting regimes, including those in which interactions between neighboring adsorbed molecules become essential. It is not closed since the dynamics of adsorbed chains is shown to be coupled to that of polymer chains in the bulk via constraint release. The constitutive equations for the layer and bulk, together with continuity of stress and velocity, are found to form a closed system of equations which governs the dynamics of the whole "bulk+boundary layer" ensemble. Its solution provides a stick-slip law in terms of the molecular parameters and extruder geometry. The model is quantitative and contains only those parameters that can be measured directly, or extracted from independent rheological measurements. The model predictions show a good agreement with available experimental data.

Towards the chemometric dissection of peptide - HLA-A*0201 binding affinity: comparison of local and global QSAR models

NASA Astrophysics Data System (ADS)

Doytchinova, Irini A.; Walshe, Valerie; Borrow, Persephone; Flower, Darren R.

2005-03-01

The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity χ indices, κ shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

Mechanical response of two polyimides through coarse-grained molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Sudarkodi, V.; Sooraj, K.; Nair, Nisanth N.; Basu, Sumit; Parandekar, Priya V.; Sinha, Nishant K.; Prakash, Om; Tsotsis, Tom

2018-03-01

Coarse-grained molecular dynamics (MD) simulations allow us to predict the mechanical responses of polymers, starting merely with a description of their molecular architectures. It is interesting to ask whether, given two competing molecular architectures, coarse-grained MD simulations can predict the differences that can be expected in their mechanical responses. We have studied two crosslinked polyimides PMR15 and HFPE52—both used in high- temperature applications—to assess whether the subtle differences in their uniaxial stress-strain responses, revealed by experiments, can be reproduced by carefully coarse-grained MD models. The coarse graining procedure for PMR15 is outlined in this work, while the coarse grain forcefields for HFPE52 are borrowed from an earlier one (Pandiyan et al 2015 Macromol. Theory Simul. 24 513-20). We show that the stress-strain responses of both these polyimides are qualitatively reproduced, and important insights into their deformation and failure mechanisms are obtained. More importantly, the differences in the molecular architecture between the polyimides carry over to the differences in the stress-strain responses in a manner that parallels the experimental results. A critical assessment of the successes and shortcomings of predicting mechanical responses through coarse-grained MD simulations has been made.

Investigations of FAK inhibitors: a combination of 3D-QSAR, docking, and molecular dynamics simulations studies.

PubMed

Cheng, Peng; Li, Jiaojiao; Wang, Juan; Zhang, Xiaoyun; Zhai, Honglin

2018-05-01

Focal adhesion kinase (FAK) is one kind of tyrosine kinases that modulates integrin and growth factor signaling pathways, which is a promising therapeutic target because of involving in cancer cell migration, proliferation, and survival. To investigate the mechanism between FAK and triazinic inhibitors and design high activity inhibitors, a molecular modeling integrated with 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy calculations was performed. The optimum CoMFA and CoMSIA models showed good reliability and satisfactory predictability (with Q 2  = 0.663, R 2  = 0.987, [Formula: see text] = 0.921 and Q 2  = 0.670, R 2  = 0.981, [Formula: see text] = 0.953). Its contour maps could provide structural features to improve inhibitory activity. Furthermore, a good consistency between contour maps, docking, and molecular dynamics simulations strongly demonstrates that the molecular modeling is reliable. Based on it, we designed several new compounds and their inhibitory activities were validated by the molecular models. We expect our studies could bring new ideas to promote the development of novel inhibitors with higher inhibitory activity for FAK.

Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes

NASA Astrophysics Data System (ADS)

Toropov, Andrey A.; Toropova, Alla P.

2018-06-01

Predictive model of logP for Pt(II) and Pt(IV) complexes built up with the Monte Carlo method using the CORAL software has been validated with six different splits into the training and validation sets. The improving of the predictive potential of models for six different splits has been obtained using so-called index of ideality of correlation. The suggested models give possibility to extract molecular features, which cause the increase or vice versa decrease of the logP.

Prediction of Rate Constant for Supramolecular Systems with Multiconfigurations.

PubMed

Guo, Tao; Li, Haiyan; Wu, Li; Guo, Zhen; Yin, Xianzhen; Wang, Caifen; Sun, Lixin; Shao, Qun; Gu, Jingkai; York, Peter; Zhang, Jiwen

2016-02-25

The control of supramolecular systems requires a thorough understanding of their dynamics, especially on a molecular level. It is extremely difficult to determine the thermokinetic parameters of supramolecular systems, such as drug-cyclodextrin complexes with fast association/dissociation processes by experimental techniques. In this paper, molecular modeling combined with novel mathematical relationships integrating the thermodynamic/thermokinetic parameters of a series of isomeric multiconfigurations to predict the overall parameters in a range of pH values have been employed to study supramolecular dynamics at the molecular level. A suitable form of Eyring's equation was derived and a two-stage model was introduced. The new approach enabled accurate prediction of the apparent dissociation/association (k(off)/k(on)) and unbinding/binding (k-r/kr) rate constants of the ubiquitous multiconfiguration complexes of the supramolecular system. The pyronine Y (PY) was used as a model system for the validation of the presented method. Interestingly, the predicted k(off) value ((40 ± 1) × 10(5) s(-1), 298 K) of PY is largely in agreement with that previously determined by fluorescence correlation spectroscopy ((5 ± 3) × 10(5) s(-1), 298 K). Moreover, the k(off)/k(on) and k-r/kr for flurbiprofen-β-cylcodextrin and ibuprofen-β-cyclodextrin systems were also predicted and suggested that the association processes are diffusion-controlled. The methodology is considered to be especially useful in the design and selection of excipients for a supramolecular system with preferred association and dissociation rate constants and understanding their mechanisms. It is believed that this new approach could be applicable to a wide range of ligand-receptor supramolecular systems and will surely help in understanding their complex mechanism.

Predicting crystal growth via a unified kinetic three-dimensional partition model

NASA Astrophysics Data System (ADS)

Anderson, Michael W.; Gebbie-Rayet, James T.; Hill, Adam R.; Farida, Nani; Attfield, Martin P.; Cubillas, Pablo; Blatov, Vladislav A.; Proserpio, Davide M.; Akporiaye, Duncan; Arstad, Bjørnar; Gale, Julian D.

2017-04-01

Understanding and predicting crystal growth is fundamental to the control of functionality in modern materials. Despite investigations for more than one hundred years, it is only recently that the molecular intricacies of these processes have been revealed by scanning probe microscopy. To organize and understand this large amount of new information, new rules for crystal growth need to be developed and tested. However, because of the complexity and variety of different crystal systems, attempts to understand crystal growth in detail have so far relied on developing models that are usually applicable to only one system. Such models cannot be used to achieve the wide scope of understanding that is required to create a unified model across crystal types and crystal structures. Here we describe a general approach to understanding and, in theory, predicting the growth of a wide range of crystal types, including the incorporation of defect structures, by simultaneous molecular-scale simulation of crystal habit and surface topology using a unified kinetic three-dimensional partition model. This entails dividing the structure into ‘natural tiles’ or Voronoi polyhedra that are metastable and, consequently, temporally persistent. As such, these units are then suitable for re-construction of the crystal via a Monte Carlo algorithm. We demonstrate our approach by predicting the crystal growth of a diverse set of crystal types, including zeolites, metal-organic frameworks, calcite, urea and L-cystine.

Modelling the molecular mechanisms of aging

PubMed Central

Mc Auley, Mark T.; Guimera, Alvaro Martinez; Hodgson, David; Mcdonald, Neil; Mooney, Kathleen M.; Morgan, Amy E.

2017-01-01

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field. PMID:28096317

X-ray diffraction from nonuniformly stretched helical molecules

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

Prodanovic, Momcilo; Irving, Thomas C.; Mijailovich, Srboljub M.

2016-04-18

The fibrous proteins in living cells are exposed to mechanical forces interacting with other subcellular structures. X-ray fiber diffraction is often used to assess deformation and movement of these proteins, but the analysis has been limited to the theory for fibrous molecular systems that exhibit helical symmetry. However, this approach cannot adequately interpret X-ray data from fibrous protein assemblies where the local strain varies along the fiber length owing to interactions of its molecular constituents with their binding partners. To resolve this problem a theoretical formulism has been developed for predicting the diffraction from individual helical molecular structures nonuniformly strainedmore » along their lengths. This represents a critical first step towards modeling complex dynamical systems consisting of multiple helical structures using spatially explicit, multi-scale Monte Carlo simulations where predictions are compared with experimental data in a `forward' process to iteratively generate ever more realistic models. Here the effects of nonuniform strains and the helix length on the resulting magnitude and phase of diffraction patterns are quantitatively assessed. Examples of the predicted diffraction patterns of nonuniformly deformed double-stranded DNA and actin filaments in contracting muscle are presented to demonstrate the feasibly of this theoretical approach.« less

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

Prediction of Material Properties of Nanostructured Polymer Composites Using Atomistic Simulations

NASA Technical Reports Server (NTRS)

Hinkley, J.A.; Clancy, T.C.; Frankland, S.J.V.

2009-01-01

Atomistic models of epoxy polymers were built in order to assess the effect of structure at the nanometer scale on the resulting bulk properties such as elastic modulus and thermal conductivity. Atomistic models of both bulk polymer and carbon nanotube polymer composites were built. For the bulk models, the effect of moisture content and temperature on the resulting elastic constants was calculated. A relatively consistent decrease in modulus was seen with increasing temperature. The dependence of modulus on moisture content was less consistent. This behavior was seen for two different epoxy systems, one containing a difunctional epoxy molecule and the other a tetrafunctional epoxy molecule. Both epoxy structures were crosslinked with diamine curing agents. Multifunctional properties were calculated with the nanocomposite models. Molecular dynamics simulation was used to estimate the interfacial thermal (Kapitza) resistance between the carbon nanotube and the surrounding epoxy matrix. These estimated values were used in a multiscale model in order to predict the thermal conductivity of a nanocomposite as a function of the nanometer scaled molecular structure.

Predicting the Highly Nonlinear Mechanical Properties of Polymeric Materials

NASA Astrophysics Data System (ADS)

Porter, David

2009-06-01

Over the past few years, we have developed models that calculate the highly nonlinear mechanical properties of polymers as a function of temperature, strain and strain rate from their molecular and morphological structure. A review of these models is presented here, with emphasis on combining the fundamental aspects of molecular physics that dictate these properties and the pragmatic need to make realistic predictions for our customers; the designer of new materials and the engineers who use these materials. The models calculate the highly nonlinear mechanical properties of polymers as a function of temperature, strain and strain rate from their molecular structure. The model is based upon the premise that mechanical properties are a direct consequence of energy stored and energy dissipated during deformation of a material. This premise is transformed into a consistent set of structure-property relations for the equation of state, EoS, and the engineering constitutive relations in a polymer by quantifying energy storage and loss at the molecular level of interactions between characteristic groups of atoms in a polymer. These relations are derived from a simple volumetric mean field Lennard-Jones potential function for the potential energy of intermolecular interactions in a polymer. First, properties such as temperature-volume relations and glass transition temperature are calculated directly from the potential function. Then, the `shock' EoS is derived simply by differentiating the potential function with respect to volume, assuming that the molecules cannot relax in the time scales of the deformation. The energy components are then used to predict the dynamic mechanical spectrum of a polymer in terms of temperature and rate. This can be transformed directly into the highly nonlinear stress-strain relations through yield. The constitutive relations are formulated as a set of analytical equations that predict properties directly in terms of a small set of structural parameters that can be calculated directly and independently from the chemical composition and morphology of a polymer. A number of examples are given to illustrate the model and also to show that the method can be applied, with appropriate modifications, to other materials.

Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study.

PubMed

Dienstmann, R; Mason, M J; Sinicrope, F A; Phipps, A I; Tejpar, S; Nesbakken, A; Danielsen, S A; Sveen, A; Buchanan, D D; Clendenning, M; Rosty, C; Bot, B; Alberts, S R; Milburn Jessup, J; Lothe, R A; Delorenzi, M; Newcomb, P A; Sargent, D; Guinney, J

2017-05-01

TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation. After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782). TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively. Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

An enhanced Petri-net model to predict synergistic effects of pairwise drug combinations from gene microarray data.

PubMed

Jin, Guangxu; Zhao, Hong; Zhou, Xiaobo; Wong, Stephen T C

2011-07-01

Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data. However, such methods cannot be used to identify molecular signaling mechanisms of synergistic drug combinations. In this article, we propose an enhanced Petri-Net (EPN) model to recognize the synergistic effects of drug combinations from the molecular response profiles, i.e. drug-treated microarray data. We addressed the downstream signaling network of the targets for the two individual drugs used in the pairwise combinations and applied EPN to the identified targeted signaling network. In EPN, drugs and signaling molecules are assigned to different types of places, while drug doses and molecular expressions are denoted by color tokens. The changes of molecular expressions caused by treatments of drugs are simulated by two actions of EPN: firing and blasting. Firing is to transit the drug and molecule tokens from one node or place to another, and blasting is to reduce the number of molecule tokens by drug tokens in a molecule node. The goal of EPN is to mediate the state characterized by control condition without any treatment to that of treatment and to depict the drug effects on molecules by the drug tokens. We applied EPN to our generated pairwise drug combination microarray data. The synergistic predictions using EPN are consistent with those predicted using phenotypic response data. The molecules responsible for the synergistic effects with their associated feedback loops display the mechanisms of synergism. The software implemented in Python 2.7 programming language is available from request. stwong@tmhs.org.

Experimentally assessing molecular dynamics sampling of the protein native state conformational distribution

PubMed Central

Hernández, Griselda; Anderson, Janet S.; LeMaster, David M.

2012-01-01

The acute sensitivity to conformation exhibited by amide hydrogen exchange reactivity provides a valuable test for the physical accuracy of model ensembles developed to represent the Boltzmann distribution of the protein native state. A number of molecular dynamics studies of ubiquitin have predicted a well-populated transition in the tight turn immediately preceding the primary site of proteasome-directed polyubiquitylation Lys 48. Amide exchange reactivity analysis demonstrates that this transition is 103-fold rarer than these predictions. More strikingly, for the most populated novel conformational basin predicted from a recent 1 ms MD simulation of bovine pancreatic trypsin inhibitor (at 13% of total), experimental hydrogen exchange data indicates a population below 10−6. The most sophisticated efforts to directly incorporate experimental constraints into the derivation of model protein ensembles have been applied to ubiquitin, as illustrated by three recently deposited studies (PDB codes 2NR2, 2K39 and 2KOX). Utilizing the extensive set of experimental NOE constraints, each of these three ensembles yields a modestly more accurate prediction of the exchange rates for the highly exposed amides than does a standard unconstrained molecular simulation. However, for the less frequently exposed amide hydrogens, the 2NR2 ensemble offers no improvement in rate predictions as compared to the unconstrained MD ensemble. The other two NMR-constrained ensembles performed markedly worse, either underestimating (2KOX) or overestimating (2K39) the extent of conformational diversity. PMID:22425325

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

PubMed

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

2009-10-01

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

A model of early formation of uranium molecular oxides in laser-ablated plasmas

NASA Astrophysics Data System (ADS)

Finko, Mikhail S.; Curreli, Davide; Weisz, David G.; Crowhurst, Jonathan C.; Rose, Timothy P.; Koroglu, Batikan; Radousky, Harry B.; Armstrong, Michael R.

2017-12-01

In this work, we present a newly constructed U x O y reaction mechanism that consists of 30 reaction channels (21 of which are reversible channels) for 11 uranium molecular species (including ions). Both the selection of reaction channels and calculation of corresponding rate coefficients is accomplished via a comprehensive literature review and application of basic reaction rate theory. The reaction mechanism is supplemented by a detailed description of oxygen plasma chemistry (19 species and 142 reaction channels) and is used to model an atmospheric laser ablated uranium plume via a 0D (global) model. The global model is used to analyze the evolution of key uranium molecular species predicted by the reaction mechanism, and the initial stage of formation of uranium oxide species.

Tensile strength of Iß crystalline cellulose predicted by molecular dynamics simulation

Treesearch

Xiawa Wu; Robert J. Moon; Ashlie Martini

2014-01-01

The mechanical properties of Iß crystalline cellulose are studied using molecular dynamics simulation. A model Iß crystal is deformed in the three orthogonal directions at three different strain rates. The stress-strain behaviors for each case are analyzed and then used to calculate mechanical properties. The results show that the elastic modulus, Poisson's ratio...

Melting Behavior of a Model Molecular Crystalline GeI4

NASA Astrophysics Data System (ADS)

Fuchizaki, Kazuhiro; Asano, Yuta

2015-06-01

A model molecular crystalline GeI4 was examined using molecular dynamics simulation. The model was constructed in such a way that rigid tetrahedral molecules interact with each other via Lennard-Jones potentials whose centers are located at the vertices of a tetrahedron. Because no other interaction that can "soften" the intermolecular interaction was introduced, the melting curve of the model crystalline material does not exhibit the anomaly that was found for the real substance. However, the current investigation is useful in that it could settle the upper bound of pressure below which the model can predict properties of the molecular liquid. Moreover, singularity-free nature of the melting curve allowed us to analytically treat the melting curve in the light of the Kumari-Dass-Kechin equation. As a result, we could definitely conclude that the well-known Simon equation for the melting curve is merely an approximate expression. The condition for the validity of Simon's equation was identified.

Fast and reliable prediction of domain-peptide binding affinity using coarse-grained structure models.

PubMed

Tian, Feifei; Tan, Rui; Guo, Tailin; Zhou, Peng; Yang, Li

2013-07-01

Domain-peptide recognition and interaction are fundamentally important for eukaryotic signaling and regulatory networks. It is thus essential to quantitatively infer the binding stability and specificity of such interaction based upon large-scale but low-accurate complex structure models which could be readily obtained from sophisticated molecular modeling procedure. In the present study, a new method is described for the fast and reliable prediction of domain-peptide binding affinity with coarse-grained structure models. This method is designed to tolerate strong random noises involved in domain-peptide complex structures and uses statistical modeling approach to eliminate systematic bias associated with a group of investigated samples. As a paradigm, this method was employed to model and predict the binding behavior of various peptides to four evolutionarily unrelated peptide-recognition domains (PRDs), i.e. human amph SH3, human nherf PDZ, yeast syh GYF and yeast bmh 14-3-3, and moreover, we explored the molecular mechanism and biological implication underlying the binding of cognate and noncognate peptide ligands to their domain receptors. It is expected that the newly proposed method could be further used to perform genome-wide inference of domain-peptide binding at three-dimensional structure level. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Nonlinear vibrational excitations in molecular crystals molecular mechanics calculations

NASA Astrophysics Data System (ADS)

Pumilia, P.; Abbate, S.; Baldini, G.; Ferro, D. R.; Tubino, R.

1992-03-01

The coupling constant for vibrational solitons χ has been examined in a molecular mechanics model for acetanilide (ACN) molecular crystal. According to A.C. Scott, solitons can form and propagate in solid acetanilide over a threshold energy value. This can be regarded as a structural model for the spines of hydrogen bond chains stabilizing the α helical structure of proteins. A one dimensional hydrogen bond chain of ACN has been built, for which we have found that, even though experimental parameters are correctly predicted, the excessive rigidity of the isolated chain prevents the formation of a localized distortion around the excitation. Yet, C=O coupling value with softer lattice modes could be rather high, allowing self-trapping to take place.

CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer

PubMed Central

Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

2015-01-01

Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies. PMID:26397224

CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer.

PubMed

Nguyen, Minh Nam; Choi, Tae Gyu; Nguyen, Dinh Truong; Kim, Jin-Hwan; Jo, Yong Hwa; Shahid, Muhammad; Akter, Salima; Aryal, Saurav Nath; Yoo, Ji Youn; Ahn, Yong-Joo; Cho, Kyoung Min; Lee, Ju-Seog; Choe, Wonchae; Kang, Insug; Ha, Joohun; Kim, Sung Soo

2015-10-13

Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies.

Engineering and validation of a novel lipid thin film for biomembrane modeling in lipophilicity determination of drugs and xenobiotics

PubMed Central

Idowu, Sunday Olakunle; Adeyemo, Morenikeji Ambali; Ogbonna, Udochi Ihechiluru

2009-01-01

Background Determination of lipophilicity as a tool for predicting pharmacokinetic molecular behavior is limited by the predictive power of available experimental models of the biomembrane. There is current interest, therefore, in models that accurately simulate the biomembrane structure and function. A novel bio-device; a lipid thin film, was engineered as an alternative approach to the previous use of hydrocarbon thin films in biomembrane modeling. Results Retention behavior of four structurally diverse model compounds; 4-amino-3,5-dinitrobenzoic acid (ADBA), naproxen (NPX), nabumetone (NBT) and halofantrine (HF), representing 4 broad classes of varying molecular polarities and aqueous solubility behavior, was investigated on the lipid film, liquid paraffin, and octadecylsilane layers. Computational, thermodynamic and image analysis confirms the peculiar amphiphilic configuration of the lipid film. Effect of solute-type, layer-type and variables interactions on retention behavior was delineated by 2-way analysis of variance (ANOVA) and quantitative structure property relationships (QSPR). Validation of the lipid film was implemented by statistical correlation of a unique chromatographic metric with Log P (octanol/water) and several calculated molecular descriptors of bulk and solubility properties. Conclusion The lipid film signifies a biomimetic artificial biological interface capable of both hydrophobic and specific electrostatic interactions. It captures the hydrophilic-lipophilic balance (HLB) in the determination of lipophilicity of molecules unlike the pure hydrocarbon film of the prior art. The potentials and performance of the bio-device gives the promise of its utility as a predictive analytic tool for early-stage drug discovery science. PMID:19735551

A Simplified Model of Local Structure in Aqueous Proline Amino Acid Revealed by First-Principles Molecular Dynamics Simulations

PubMed Central

Troitzsch, Raphael Z.; Tulip, Paul R.; Crain, Jason; Martyna, Glenn J.

2008-01-01

Aqueous proline solutions are deceptively simple as they can take on complex roles such as protein chaperones, cryoprotectants, and hydrotropic agents in biological processes. Here, a molecular level picture of proline/water mixtures is developed. Car-Parrinello ab initio molecular dynamics (CPAIMD) simulations of aqueous proline amino acid at the B-LYP level of theory, performed using IBM's Blue Gene/L supercomputer and massively parallel software, reveal hydrogen-bonding propensities that are at odds with the predictions of the CHARMM22 empirical force field but are in better agreement with results of recent neutron diffraction experiments. In general, the CPAIMD (B-LYP) simulations predict a simplified structural model of proline/water mixtures consisting of fewer distinct local motifs. Comparisons of simulation results to experiment are made by direct evaluation of the neutron static structure factor S(Q) from CPAIMD (B-LYP) trajectories as well as to the results of the empirical potential structure refinement reverse Monte Carlo procedure applied to the neutron data. PMID:18790850

Field determination and QSPR prediction of equilibrium-status soil/vegetation partition coefficient of PCDD/Fs.

PubMed

Li, Li; Wang, Qiang; Qiu, Xinghua; Dong, Yian; Jia, Shenglan; Hu, Jianxin

2014-07-15

Characterizing pseudo equilibrium-status soil/vegetation partition coefficient KSV, the quotient of respective concentrations in soil and vegetation of a certain substance at remote background areas, is essential in ecological risk assessment, however few previous attempts have been made for field determination and developing validated and reproducible structure-based estimates. In this study, KSV was calculated based on measurements of seventeen 2,3,7,8-substituted PCDD/F congeners in soil and moss (Dicranum angustum), and rouzi grass (Thylacospermum caespitosum) of two background sites, Ny-Ålesund of the Arctic and Zhangmu-Nyalam region of the Tibet Plateau, respectively. By both fugacity modeling and stepwise regression of field data, the air-water partition coefficient (KAW) and aqueous solubility (SW) were identified as the influential physicochemical properties. Furthermore, validated quantitative structure-property relationship (QSPR) model was developed to extrapolate the KSV prediction to all 210 PCDD/F congeners. Molecular polarizability, molecular size and molecular energy demonstrated leading effects on KSV. Copyright © 2014 Elsevier B.V. All rights reserved.

A simplified model of local structure in aqueous proline amino acid revealed by first-principles molecular dynamics simulations.

PubMed

Troitzsch, Raphael Z; Tulip, Paul R; Crain, Jason; Martyna, Glenn J

2008-12-01

Aqueous proline solutions are deceptively simple as they can take on complex roles such as protein chaperones, cryoprotectants, and hydrotropic agents in biological processes. Here, a molecular level picture of proline/water mixtures is developed. Car-Parrinello ab initio molecular dynamics (CPAIMD) simulations of aqueous proline amino acid at the B-LYP level of theory, performed using IBM's Blue Gene/L supercomputer and massively parallel software, reveal hydrogen-bonding propensities that are at odds with the predictions of the CHARMM22 empirical force field but are in better agreement with results of recent neutron diffraction experiments. In general, the CPAIMD (B-LYP) simulations predict a simplified structural model of proline/water mixtures consisting of fewer distinct local motifs. Comparisons of simulation results to experiment are made by direct evaluation of the neutron static structure factor S(Q) from CPAIMD (B-LYP) trajectories as well as to the results of the empirical potential structure refinement reverse Monte Carlo procedure applied to the neutron data.

Molecular modeling studies of structural properties of polyvinyl alcohol: a comparative study using INTERFACE force field.

PubMed

Radosinski, Lukasz; Labus, Karolina

2017-10-05

Polyvinyl alcohol (PVA) is a material with a variety of applications in separation, biotechnology, and biomedicine. Using combined Monte Carlo and molecular dynamics techniques, we present an extensive comparative study of second- and third-generation force fields Universal, COMPASS, COMPASS II, PCFF, and the newly developed INTERFACE, as applied to this system. In particular, we show that an INTERFACE force field provides a possibility of composing a reliable atomistic model to reproduce density change of PVA matrix in a narrow temperature range (298-348 K) and calculate a thermal expansion coefficient with reasonable accuracy. Thus, the INTERFACE force field may be used to predict mechanical properties of the PVA system, being a scaffold for hydrogels, with much greater accuracy than latter approaches. Graphical abstract Molecular Dynamics and Monte Carlo studies indicate that it is possible to predict properties of the PVA in narrow temperature range by using the INTERFACE force field.

Fold Prediction of VP24 Protein of Ebola and Marburg Viruses using de novo Fragment Assembly

DTIC Science & Technology

2009-05-15

Watanabe, M., Wiorkiewicz-Kuczera, J., Yin, D., Karplus, M., 1998. All-atom empirical potential for molecular modeling and dynamics studies of...aqueous forms that may be related to its role as a matrix protein. Specifically, the molecular weight of VP24 oligomers was determined using differ...dielectric electrostatic function. Next, the PARAM22 plus generalized Born molecular volume solvation (GBMV2) (Lee et al., 2003) energy (including a 15

Predicted molecular signaling guiding photoreceptor cell migration following transplantation into damaged retina

NASA Astrophysics Data System (ADS)

Unachukwu, Uchenna John; Warren, Alice; Li, Ze; Mishra, Shawn; Zhou, Jing; Sauane, Moira; Lim, Hyungsik; Vazquez, Maribel; Redenti, Stephen

2016-03-01

To replace photoreceptors lost to disease or trauma and restore vision, laboratories around the world are investigating photoreceptor replacement strategies using subretinal transplantation of photoreceptor precursor cells (PPCs) and retinal progenitor cells (RPCs). Significant obstacles to advancement of photoreceptor cell-replacement include low migration rates of transplanted cells into host retina and an absence of data describing chemotactic signaling guiding migration of transplanted cells in the damaged retinal microenvironment. To elucidate chemotactic signaling guiding transplanted cell migration, bioinformatics modeling of PPC transplantation into light-damaged retina was performed. The bioinformatics modeling analyzed whole-genome expression data and matched PPC chemotactic cell-surface receptors to cognate ligands expressed in the light-damaged retinal microenvironment. A library of significantly predicted chemotactic ligand-receptor pairs, as well as downstream signaling networks was generated. PPC and RPC migration in microfluidic ligand gradients were analyzed using a highly predicted ligand-receptor pair, SDF-1α - CXCR4, and both PPCs and RPCs exhibited significant chemotaxis. This work present a systems level model and begins to elucidate molecular mechanisms involved in PPC and RPC migration within the damaged retinal microenvironment.

Binding Affinity prediction with Property Encoded Shape Distribution signatures

PubMed Central

Das, Sourav; Krein, Michael P.

2010-01-01

We report the use of the molecular signatures known as “Property-Encoded Shape Distributions” (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This “PESD-SVM” method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore – a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously. PMID:20095526

Ab Initio Molecular Dynamics and Lattice Dynamics-Based Force Field for Modeling Hexagonal Boron Nitride in Mechanical and Interfacial Applications.

PubMed

Govind Rajan, Ananth; Strano, Michael S; Blankschtein, Daniel

2018-04-05

Hexagonal boron nitride (hBN) is an up-and-coming two-dimensional material, with applications in electronic devices, tribology, and separation membranes. Herein, we utilize density-functional-theory-based ab initio molecular dynamics (MD) simulations and lattice dynamics calculations to develop a classical force field (FF) for modeling hBN. The FF predicts the crystal structure, elastic constants, and phonon dispersion relation of hBN with good accuracy and exhibits remarkable agreement with the interlayer binding energy predicted by random phase approximation calculations. We demonstrate the importance of including Coulombic interactions but excluding 1-4 intrasheet interactions to obtain the correct phonon dispersion relation. We find that improper dihedrals do not modify the bulk mechanical properties and the extent of thermal vibrations in hBN, although they impact its flexural rigidity. Combining the FF with the accurate TIP4P/Ice water model yields excellent agreement with interaction energies predicted by quantum Monte Carlo calculations. Our FF should enable an accurate description of hBN interfaces in classical MD simulations.

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

PubMed

Cao, D-S; Zhao, J-C; Yang, Y-N; Zhao, C-X; Yan, J; Liu, S; Hu, Q-N; Xu, Q-S; Liang, Y-Z

2012-01-01

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.

Combined 3D-QSAR and molecular docking study on 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] Quinazoline series compounds to understand the binding mechanism of DHFR inhibitors

NASA Astrophysics Data System (ADS)

Aouidate, Adnane; Ghaleb, Adib; Ghamali, Mounir; Chtita, Samir; Choukrad, M'barek; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

2017-07-01

A series of nineteen DHFR inhibitors was studied based on the combination of two computational techniques namely, three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were developed using 19 molecules having pIC50 ranging from 9.244 to 5.839. The best CoMFA and CoMSIA models show conventional determination coefficients R2 of 0.96 and 0.93 as well as the Leave One Out cross-validation determination coefficients Q2 of 0.64 and 0.72, respectively. The predictive ability of those models was evaluated by the external validation using a test set of five compounds with predicted determination coefficients R2test of 0.92 and 0.94, respectively. The binding mode between this kind of compounds and the DHFR enzyme in addition to the key amino acid residues were explored by molecular docking simulation. Contour maps and molecular docking identified that the R1 and R2 natures at the pyrazole moiety are the important features for the optimization of the binding affinity to the DHFR receptor. According to the good concordance between the CoMFA/CoMSIA contour maps and docking results, the obtained information was explored to design novel molecules.

Ex vivo inhibition of Clostridium botulinum neurotoxin types B, C, E, and F by small molecular weight inhibitors.

PubMed

Montgomery, Vicki A; Ahmed, S Ashraf; Olson, Mark A; Mizanur, Rahman M; Stafford, Robert G; Roxas-Duncan, Virginia I; Smith, Leonard A

2015-05-01

Two small molecular weight inhibitors, compounds CB7969312 and CB7967495, that displayed inhibition of botulinum neurotoxin serotype A in a previous study, were evaluated for inhibition of botulinum neurotoxin serotypes B, C, E, and F. The small molecular weight inhibitors were assessed by molecular modeling, UPLC-based peptide cleavage assay; and an ex vivo assay, the mouse phrenic nerve - hemidiaphragm assay (MPNHDA). While both compounds were inhibitors of botulinum neurotoxin (BoNT) serotypes B, C, and F in the MPNHDA, compound CB7969312 was effective at lower molar concentrations than compound CB7967495. However, compound CB7967495 was significantly more effective at preventing BoNTE intoxication than compound CB7969312. In the UPLC-based peptide cleavage assay, CB7969312 was also more effective against LcC. Both compounds inhibited BoNTE, but not BoNTF, LcE, or LcF in the UPLC-based peptide cleavage assay. Molecular modeling studies predicted that both compounds would be effective inhibitors of BoNTs B, C, E, and F. But CB7967495 was predicted to be a more effective inhibitor of the four serotypes (B, C, E, and F) than CB7969312. This is the first report of a small molecular weight compound that inhibits serotypes B, C, E, and F in the ex vivo assay. Published by Elsevier Ltd.

Modeling the relationship between body weight and energy intake: A molecular diffusion-based approach

PubMed Central

2012-01-01

Background Body weight is at least partly controlled by the choices made by a human in response to external stimuli. Changes in body weight are mainly caused by energy intake. By analyzing the mechanisms involved in food intake, we considered that molecular diffusion plays an important role in body weight changes. We propose a model based on Fick's second law of diffusion to simulate the relationship between energy intake and body weight. Results This model was applied to food intake and body weight data recorded in humans; the model showed a good fit to the experimental data. This model was also effective in predicting future body weight. Conclusions In conclusion, this model based on molecular diffusion provides a new insight into the body weight mechanisms. Reviewers This article was reviewed by Dr. Cabral Balreira (nominated by Dr. Peter Olofsson), Prof. Yang Kuang and Dr. Chao Chen. PMID:22742862

Simulating the flow of entangled polymers.

PubMed

Masubuchi, Yuichi

2014-01-01

To optimize automation for polymer processing, attempts have been made to simulate the flow of entangled polymers. In industry, fluid dynamics simulations with phenomenological constitutive equations have been practically established. However, to account for molecular characteristics, a method to obtain the constitutive relationship from the molecular structure is required. Molecular dynamics simulations with atomic description are not practical for this purpose; accordingly, coarse-grained models with reduced degrees of freedom have been developed. Although the modeling of entanglement is still a challenge, mesoscopic models with a priori settings to reproduce entangled polymer dynamics, such as tube models, have achieved remarkable success. To use the mesoscopic models as staging posts between atomistic and fluid dynamics simulations, studies have been undertaken to establish links from the coarse-grained model to the atomistic and macroscopic simulations. Consequently, integrated simulations from materials chemistry to predict the macroscopic flow in polymer processing are forthcoming.

Molecular modeling of polycarbonate materials: Glass transition and mechanical properties

NASA Astrophysics Data System (ADS)

Palczynski, Karol; Wilke, Andreas; Paeschke, Manfred; Dzubiella, Joachim

2017-09-01

Linking the experimentally accessible macroscopic properties of thermoplastic polymers to their microscopic static and dynamic properties is a key requirement for targeted material design. Classical molecular dynamics simulations enable us to study the structural and dynamic behavior of molecules on microscopic scales, and statistical physics provides a framework for relating these properties to the macroscopic properties. We take a first step toward creating an automated workflow for the theoretical prediction of thermoplastic material properties by developing an expeditious method for parameterizing a simple yet surprisingly powerful coarse-grained bisphenol-A polycarbonate model which goes beyond previous coarse-grained models and successfully reproduces the thermal expansion behavior, the glass transition temperature as a function of the molecular weight, and several elastic properties.

Physical models have gender-specific effects on student understanding of protein structure-function relationships.

PubMed

Forbes-Lorman, Robin M; Harris, Michelle A; Chang, Wesley S; Dent, Erik W; Nordheim, Erik V; Franzen, Margaret A

2016-07-08

Understanding how basic structural units influence function is identified as a foundational/core concept for undergraduate biological and biochemical literacy. It is essential for students to understand this concept at all size scales, but it is often more difficult for students to understand structure-function relationships at the molecular level, which they cannot as effectively visualize. Students need to develop accurate, 3-dimensional mental models of biomolecules to understand how biomolecular structure affects cellular functions at the molecular level, yet most traditional curricular tools such as textbooks include only 2-dimensional representations. We used a controlled, backward design approach to investigate how hand-held physical molecular model use affected students' ability to logically predict structure-function relationships. Brief (one class period) physical model use increased quiz score for females, whereas there was no significant increase in score for males using physical models. Females also self-reported higher learning gains in their understanding of context-specific protein function. Gender differences in spatial visualization may explain the gender-specific benefits of physical model use observed. © 2016 The Authors Biochemistry and Molecular Biology Education published by Wiley Periodicals, Inc. on behalf of International Union of Biochemistry and Molecular Biology, 44(4):326-335, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

Kinetic modeling of ethane pyrolysis at high conversion.

PubMed

Xu, Chen; Al Shoaibi, Ahmed Sultan; Wang, Chenguang; Carstensen, Hans-Heinrich; Dean, Anthony M

2011-09-29

The primary objective of this study is to develop an improved first-principle-based mechanism that describes the molecular weight growth kinetics observed during ethane pyrolysis. A proper characterization of the kinetics of ethane pyrolysis is a prerequisite for any analysis of hydrocarbon pyrolysis and oxidation. Flow reactor experiments were performed with ~50/50 ethane/nitrogen mixtures with temperatures ranging from 550 to 850 °C at an absolute pressure of ~0.8 atm and a residence time of ~5 s. These conditions result in ethane conversions ranging from virtually no reaction to ~90%. Comparisons of predictions using our original mechanism to these data yielded very satisfactory results in terms of the temperature dependence of ethane conversion and prediction of the major products ethylene and hydrogen. However, there were discrepancies in some of the minor species concentrations that are involved in the molecular weight growth kinetics. We performed a series of CBS-QB3 analyses for the C(3)H(7), C(4)H(7), and C(4)H(9) potential energy surfaces to better characterize the radical addition reactions that lead to molecular weight growth. We also extended a published C(6)H(9) PES to include addition of vinyl to butadiene. The results were then used to calculate pressure-dependent rate constants for the multiple reaction pathways of these addition reactions. Inclusion of the unadjusted rate constants resulting from these analyses in the mechanism significantly improved the description of several of the species involved in molecular weight growth kinetics. We compare the predictions of this improved model to those obtained with a consensus model recently published as well as to ethane steam cracking data. We find that a particularly important reaction is that of vinyl addition to butadiene. Another important observation is that several radical addition reactions are partially equilibrated. Not only does this mean that reliable thermodynamic parameters are essential for an accurate model, but also that the reaction set describing molecular weight growth chemistry must include a final product that is sufficiently stable to shift the equilibrium toward this product despite the decrease in entropy that accompanies molecular weight growth. Another reaction, H addition to olefins, was found to inhibit molecular weight growth by leading to the production of a lower olefin plus methyl radicals.

Testing the role of molecular physics in dissipative divertor operations through helium plasmas at DIII-D

DOE PAGES

Canik, John M.; Briesemeister, Alexis R.; McLean, Adam G.; ...

2017-05-10

Recent experiments in DIII-D helium plasmas are examined to resolve the role of atomic and molecular physics in major discrepancies between experiment and modeling of dissipative divertor operation. Helium operation removes the complicated molecular processes of deuterium plasmas that are a prime candidate for the inability of standard fluid models to reproduce dissipative divertor operation, primarily the consistent under-prediction of radiated power. Modeling of these experiments shows that the full divertor radiation can be accounted for, but only if measures are taken to ensure that the model reproduces the measured divertor density. Relying on upstream measurements instead results in amore » lower divertor density and radiation than is measured, indicating a need for improved modeling of the connection between the diverter and the upstream scrape-off layer. Furthermore, these results show that fluid models are able to quantitatively describe the divertor-region plasma, including radiative losses, and indicate that efforts to improve the fidelity of the molecular deuterium models are likely to help resolve the discrepancy in radiation for deuterium plasmas.« less

A Stochastic Mixing Model for Predicting Emissions in a Direct Injection Diesel Engine.

DTIC Science & Technology

1986-09-01

of chemical reactors. The fundamental concept of these models is coalescence/dis- persion micromixing . C1] Details of this method are provided in Appen...Togby,A.H., "Monte Carlo Methods of Simulating Micromixing in Chemical Reactors", Chemical Engineering Science, Vol.27, p.1 4 97, 1972. 46. Kattan,A...on a molecular level. 2. Micromixing or stream mixing refers to the mixing of particles on a molecular level. Until the coalescence and dispersion

CALCULATING PHYSICAL PROPERTIES OF ORGANIC COMPOUNDS FOR ENVIRONMENTAL MODELING FROM MOLECULAR STRUCTURE

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values-- that is value of the physical and chemical constants that govern reactivity. Although empirical structure activity relationships have been developed t...

Mechanics of rolling of nanoribbon on tube and sphere.

PubMed

Yin, Qifang; Shi, Xinghua

2013-06-21

The configuration of graphene nano-ribbon (GNR) assembly on carbon nanotube (CNT) and sphere is studied through theoretical modeling and molecular simulation. The GNR can spontaneously wind onto the CNT due to van der Waals (vdW) interaction and form two basic configurations: helix and scroll. The final configuration arises from the competition among three energy terms: the bending energy of the GNR, the vdW interaction between GNR and CNT, the vdW between the GNR itself. We derive analytical solutions by accounting for the three energy parts, with which we draw phase diagrams and predict the final configuration (helix or scroll) based on the selected parameters. The molecular simulations are conducted to verify the model with the results agree well with the model predicted. Our work can be used to actively control and transfer the tube-like nanoparticles and viruses as well as to assemble ribbon-like nanomaterials.

Prediction of the mechanical properties of zeolite pellets for aerospace molecular decontamination applications.

PubMed

Rioland, Guillaume; Dutournié, Patrick; Faye, Delphine; Daou, T Jean; Patarin, Joël

2016-01-01

Zeolite pellets containing 5 wt % of binder (methylcellulose or sodium metasilicate) were formed with a hydraulic press. This paper describes a mathematical model to predict the mechanical properties (uniaxial and diametric compression) of these pellets for arbitrary dimensions (height and diameter) using a design of experiments (DOE) methodology. A second-degree polynomial equation including interactions was used to approximate the experimental results. This leads to an empirical model for the estimation of the mechanical properties of zeolite pellets with 5 wt % of binder. The model was verified by additional experimental tests including pellets of different dimensions created with different applied pressures. The optimum dimensions were found to be a diameter of 10-23 mm, a height of 1-3.5 mm and an applied pressure higher than 200 MPa. These pellets are promising for technological uses in molecular decontamination for aerospace-based applications.

GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

PubMed

Boudard, Mélanie; Bernauer, Julie; Barth, Dominique; Cohen, Johanne; Denise, Alain

2015-01-01

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

Molecular properties of steroids involved in their effects on the biophysical state of membranes.

PubMed

Wenz, Jorge J

2015-10-01

The activity of steroids on membranes was studied in relation to their ordering, rigidifying, condensing and/or raft promoting ability. The structures of 82 steroids were modeled by a semi-empirical procedure (AM1) and 245 molecular descriptors were next computed on the optimized energy conformations. Principal component analysis, mean contrasting and logistic regression were used to correlate the molecular properties with 212 cases of documented activities. It was possible to group steroids based on their properties and activities, indicating that steroids having similar molecular properties have similar activities on membranes. Steroids having high values of area, partition coefficient, volume, number of rotatable bonds, molar refractivity, polarizability or mass displayed ordering, rigidifying, condensing and/or raft promoting activity on membranes higher than those steroids having low values in such molecular properties. After a variable selection procedure circumventing correlation problems among descriptors, area and log P were found as the most relevant properties in governing and predicting the activity of steroids on membranes. A logistic regression model as a function of the area and log P of the steroids is proposed, which is able to predict correctly 92.5% of the cases. A rationale of the findings is discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

IN SILICO APPROACHES TO MECHANISTIC AND PREDICTIVE TOXICOLOGY: AN INTRODUCTION TO BIOINFORMATICS FOR TOXICOLOGISTS. (R827402)

EPA Science Inventory

Abstract

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new ...

Temperature Effect on Micelle Formation: Molecular Thermodynamic Model Revisited.

PubMed

Khoshnood, Atefeh; Lukanov, Boris; Firoozabadi, Abbas

2016-03-08

Temperature affects the aggregation of macromolecules such as surfactants, polymers, and proteins in aqueous solutions. The effect on the critical micelle concentration (CMC) is often nonmonotonic. In this work, the effect of temperature on the micellization of ionic and nonionic surfactants in aqueous solutions is studied using a molecular thermodynamic model. Previous studies based on this technique have predicted monotonic behavior for ionic surfactants. Our investigation shows that the choice of tail transfer energy to describe the hydrophobic effect between the surfactant tails and the polar solvent molecules plays a key role in the predicted CMC. We modify the tail transfer energy by taking into account the effect of the surfactant head on the neighboring methylene group. The modification improves the description of the CMC and the predicted micellar size for aqueous solutions of sodium n-alkyl sulfate, dodecyl trimethylammonium bromide (DTAB), and n-alkyl polyoxyethylene. The new tail transfer energy describes the nonmonotonic behavior of CMC versus temperature. In the DTAB-water system, we redefine the head size by including the methylene group, next to the nitrogen, in the head. The change in the head size along with our modified tail transfer energy improves the CMC and aggregation size prediction significantly. Tail transfer is a dominant energy contribution in micellar and microemulsion systems. It also promotes the adsorption of surfactants at fluid-fluid interfaces and affects the formation of adsorbed layer at fluid-solid interfaces. Our proposed modifications have direct applications in the thermodynamic modeling of the effect of temperature on molecular aggregation, both in the bulk and at the interfaces.

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

Kim, Jehoon; Wu, Jianzhong, E-mail: jwu@engr.ucr.edu

Self-assembly of capsid proteins and genome encapsidation are two critical steps in the life cycle of most plant and animal viruses. A theoretical description of such processes from a physiochemical perspective may help better understand viral replication and morphogenesis thus provide fresh insights into the experimental studies of antiviral strategies. In this work, we propose a molecular thermodynamic model for predicting the stability of Hepatitis B virus (HBV) capsids either with or without loading nucleic materials. With the key components represented by coarse-grained thermodynamic models, the theoretical predictions are in excellent agreement with experimental data for the formation free energiesmore » of empty T4 capsids over a broad range of temperature and ion concentrations. The theoretical model predicts T3/T4 dimorphism also in good agreement with the capsid formation at in vivo and in vitro conditions. In addition, we have studied the stability of the viral particles in response to physiological cellular conditions with the explicit consideration of the hydrophobic association of capsid subunits, electrostatic interactions, molecular excluded volume effects, entropy of mixing, and conformational changes of the biomolecular species. The course-grained model captures the essential features of the HBV nucleocapsid stability revealed by recent experiments.« less

Three-Dimensional Molecular Modeling of a Diverse Range of SC Clan Serine Proteases

PubMed Central

Laskar, Aparna; Chatterjee, Aniruddha; Chatterjee, Somnath; Rodger, Euan J.

2012-01-01

Serine proteases are involved in a variety of biological processes and are classified into clans sharing structural homology. Although various three-dimensional structures of SC clan proteases have been experimentally determined, they are mostly bacterial and animal proteases, with some from archaea, plants, and fungi, and as yet no structures have been determined for protozoa. To bridge this gap, we have used molecular modeling techniques to investigate the structural properties of different SC clan serine proteases from a diverse range of taxa. Either SWISS-MODEL was used for homology-based structure prediction or the LOOPP server was used for threading-based structure prediction. The predicted models were refined using Insight II and SCRWL and validated against experimental structures. Investigation of secondary structures and electrostatic surface potential was performed using MOLMOL. The structural geometry of the catalytic core shows clear deviations between taxa, but the relative positions of the catalytic triad residues were conserved. Evolutionary divergence was also exhibited by large variation in secondary structure features outside the core, differences in overall amino acid distribution, and unique surface electrostatic potential patterns between species. Encompassing a wide range of taxa, our structural analysis provides an evolutionary perspective on SC clan serine proteases. PMID:23213528

Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

PubMed

Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

2016-05-01

Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

Three-dimensional quantitative structure-activity relationship CoMSIA/CoMFA and LeapFrog studies on novel series of bicyclo [4.1.0] heptanes derivatives as melanin-concentrating hormone receptor R1 antagonists.

PubMed

Morales-Bayuelo, Alejandro; Ayazo, Hernan; Vivas-Reyes, Ricardo

2010-10-01

Comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were performed on a series of bicyclo [4.1.0] heptanes derivatives as melanin-concentrating hormone receptor R1 antagonists (MCHR1 antagonists). Molecular superimposition of antagonists on the template structure was performed by database alignment method. The statistically significant model was established on sixty five molecules, which were validated by a test set of ten molecules. The CoMSIA model yielded the best predictive model with a q(2) = 0.639, non cross-validated R(2) of 0.953, F value of 92.802, bootstrapped R(2) of 0.971, standard error of prediction = 0.402, and standard error of estimate = 0.146 while the CoMFA model yielded a q(2) = 0.680, non cross-validated R(2) of 0.922, F value of 114.351, bootstrapped R(2) of 0.925, standard error of prediction = 0.364, and standard error of estimate = 0.180. CoMFA analysis maps were employed for generating a pseudo cavity for LeapFrog calculation. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. The results show the variability of steric and electrostatic contributions that determine the activity of the MCHR1 antagonist, with these results we proposed new antagonists that may be more potent than previously reported, these novel antagonists were designed from the addition of highly electronegative groups in the substituent di(i-C(3)H(7))N- of the bicycle [4.1.0] heptanes, using the model CoMFA which also was used for the molecular design using the technique LeapFrog. The data generated from the present study will further help to design novel, potent, and selective MCHR1 antagonists. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

Determination of heat capacity of ionic liquid based nanofluids using group method of data handling technique

NASA Astrophysics Data System (ADS)

Sadi, Maryam

2018-01-01

In this study a group method of data handling model has been successfully developed to predict heat capacity of ionic liquid based nanofluids by considering reduced temperature, acentric factor and molecular weight of ionic liquids, and nanoparticle concentration as input parameters. In order to accomplish modeling, 528 experimental data points extracted from the literature have been divided into training and testing subsets. The training set has been used to predict model coefficients and the testing set has been applied for model validation. The ability and accuracy of developed model, has been evaluated by comparison of model predictions with experimental values using different statistical parameters such as coefficient of determination, mean square error and mean absolute percentage error. The mean absolute percentage error of developed model for training and testing sets are 1.38% and 1.66%, respectively, which indicate excellent agreement between model predictions and experimental data. Also, the results estimated by the developed GMDH model exhibit a higher accuracy when compared to the available theoretical correlations.

A Langevin model for fluctuating contact angle behaviour parametrised using molecular dynamics.

PubMed

Smith, E R; Müller, E A; Craster, R V; Matar, O K

2016-12-06

Molecular dynamics simulations are employed to develop a theoretical model to predict the fluid-solid contact angle as a function of wall-sliding speed incorporating thermal fluctuations. A liquid bridge between counter-sliding walls is studied, with liquid-vapour interface-tracking, to explore the impact of wall-sliding speed on contact angle. The behaviour of the macroscopic contact angle varies linearly over a range of capillary numbers beyond which the liquid bridge pinches off, a behaviour supported by experimental results. Nonetheless, the liquid bridge provides an ideal test case to study molecular scale thermal fluctuations, which are shown to be well described by Gaussian distributions. A Langevin model for contact angle is parametrised to incorporate the mean, fluctuation and auto-correlations over a range of sliding speeds and temperatures. The resulting equations can be used as a proxy for the fully-detailed molecular dynamics simulation allowing them to be integrated within a continuum-scale solver.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

PubMed Central

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

PubMed

Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

2016-02-13

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides. Copyright © 2015 Elsevier B.V. All rights reserved.

InterPred: A pipeline to identify and model protein-protein interactions.

PubMed

Mirabello, Claudio; Wallner, Björn

2017-06-01

Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred Proteins 2017; 85:1159-1170. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

In silico modelling and molecular dynamics simulation studies of thiazolidine based PTP1B inhibitors.

PubMed

Mahapatra, Manoj Kumar; Bera, Krishnendu; Singh, Durg Vijay; Kumar, Rajnish; Kumar, Manoj

2018-04-01

Protein tyrosine phosphatase 1B (PTP1B) has been identified as a negative regulator of insulin and leptin signalling pathway; hence, it can be considered as a new therapeutic target of intervention for the treatment of type 2 diabetes. Inhibition of this molecular target takes care of both diabetes and obesity, i.e. diabestiy. In order to get more information on identification and optimization of lead, pharmacophore modelling, atom-based 3D QSAR, docking and molecular dynamics studies were carried out on a set of ligands containing thiazolidine scaffold. A six-point pharmacophore model consisting of three hydrogen bond acceptor (A), one negative ionic (N) and two aromatic rings (R) with discrete geometries as pharmacophoric features were developed for a predictive 3D QSAR model. The probable binding conformation of the ligands within the active site was studied through molecular docking. The molecular interactions and the structural features responsible for PTP1B inhibition and selectivity were further supplemented by molecular dynamics simulation study for a time scale of 30 ns. The present investigation has identified some of the indispensible structural features of thiazolidine analogues which can further be explored to optimize PTP1B inhibitors.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Crystal Structure, Conformational Analysis, and Charge Density Distribution for Eng-Epifisetinidol: An Explanation for Regiospecific Aromatic Substitution of 5-Deoxyflavan

Treesearch

Fred L. Tobiason; Frank R. Fronczek; Jan P. Steynberg; Elizabeth C. Steynberg; Richard W. Hemingway; Wayne L. Mattice

1993-01-01

Molecular modeling and molecular orbital analyses of ent-epifisetinidol gave &ood predictions of the approximate "reverse half-chair" conformation found for the crystal structure. MNDO and AM1 analyses of HOMO electron densities provided an explanation for the stereospecific electrophilic aromatic substitution at C(6) in 5-deoxy-flavans...

Near transferable phenomenological n-body potentials for noble metals

NASA Astrophysics Data System (ADS)

Pontikis, Vassilis; Baldinozzi, Gianguido; Luneville, Laurence; Simeone, David

2017-09-01

We present a semi-empirical model of cohesion in noble metals with suitable parameters reproducing a selected set of experimental properties of perfect and defective lattices in noble metals. It consists of two short-range, n-body terms accounting respectively for attractive and repulsive interactions, the former deriving from the second moment approximation of the tight-binding scheme and the latter from the gas approximation of the kinetic energy of electrons. The stability of the face centred cubic versus the hexagonal compact stacking is obtained via a long-range, pairwise function of customary use with ionic pseudo-potentials. Lattice dynamics, molecular statics, molecular dynamics and nudged elastic band calculations show that, unlike previous potentials, this cohesion model reproduces and predicts quite accurately thermodynamic properties in noble metals. In particular, computed surface energies, largely underestimated by existing empirical cohesion models, compare favourably with measured values, whereas predicted unstable stacking-fault energy profiles fit almost perfectly ab initio evaluations from the literature. All together the results suggest that this semi-empirical model is nearly transferable.

Near transferable phenomenological n-body potentials for noble metals.

PubMed

Pontikis, Vassilis; Baldinozzi, Gianguido; Luneville, Laurence; Simeone, David

2017-09-06

We present a semi-empirical model of cohesion in noble metals with suitable parameters reproducing a selected set of experimental properties of perfect and defective lattices in noble metals. It consists of two short-range, n-body terms accounting respectively for attractive and repulsive interactions, the former deriving from the second moment approximation of the tight-binding scheme and the latter from the gas approximation of the kinetic energy of electrons. The stability of the face centred cubic versus the hexagonal compact stacking is obtained via a long-range, pairwise function of customary use with ionic pseudo-potentials. Lattice dynamics, molecular statics, molecular dynamics and nudged elastic band calculations show that, unlike previous potentials, this cohesion model reproduces and predicts quite accurately thermodynamic properties in noble metals. In particular, computed surface energies, largely underestimated by existing empirical cohesion models, compare favourably with measured values, whereas predicted unstable stacking-fault energy profiles fit almost perfectly ab initio evaluations from the literature. All together the results suggest that this semi-empirical model is nearly transferable.

Integral control of plant gravitropism through the interplay of hormone signaling and gene regulation.

PubMed

Rodrigo, Guillermo; Jaramillo, Alfonso; Blázquez, Miguel A

2011-08-17

The interplay between hormone signaling and gene regulatory networks is instrumental in promoting the development of living organisms. In particular, plants have evolved mechanisms to sense gravity and orient themselves accordingly. Here, we present a mathematical model that reproduces plant gravitropic responses based on known molecular genetic interactions for auxin signaling coupled with a physical description of plant reorientation. The model allows one to analyze the spatiotemporal dynamics of the system, triggered by an auxin gradient that induces differential growth of the plant with respect to the gravity vector. Our model predicts two important features with strong biological implications: 1), robustness of the regulatory circuit as a consequence of integral control; and 2), a higher degree of plasticity generated by the molecular interplay between two classes of hormones. Our model also predicts the ability of gibberellins to modulate the tropic response and supports the integration of the hormonal role at the level of gene regulation. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Modelling morphology evolution during solidification of IPP in processing conditions

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

Pantani, R., E-mail: rpantani@unisa.it, E-mail: fedesantis@unisa.it, E-mail: vsperanza@unisa.it, E-mail: gtitomanlio@unisa.it; De Santis, F., E-mail: rpantani@unisa.it, E-mail: fedesantis@unisa.it, E-mail: vsperanza@unisa.it, E-mail: gtitomanlio@unisa.it; Speranza, V., E-mail: rpantani@unisa.it, E-mail: fedesantis@unisa.it, E-mail: vsperanza@unisa.it, E-mail: gtitomanlio@unisa.it

During polymer processing, crystallization takes place during or soon after flow. In most of cases, the flow field dramatically influences both the crystallization kinetics and the crystal morphology. On their turn, crystallinity and morphology affect product properties. Consequently, in the last decade, researchers tried to identify the main parameters determining crystallinity and morphology evolution during solidification In processing conditions. In this work, we present an approach to model flow-induced crystallization with the aim of predicting the morphology after processing. The approach is based on: interpretation of the FIC as the effect of molecular stretch on the thermodynamic crystallization temperature; modelingmore » the molecular stretch evolution by means of a model simple and easy to be implemented in polymer processing simulation codes; identification of the effect of flow on nucleation density and spherulites growth rate by means of simple experiments; determination of the condition under which fibers form instead of spherulites. Model predictions reproduce most of the features of final morphology observed in the samples after solidification.« less

Effects of macromolecular crowding on biochemical reaction equilibria: a molecular thermodynamic perspective.

PubMed

Hu, Zhongqiao; Jiang, Jianwen; Rajagopalan, Raj

2007-09-01

A molecular thermodynamic model is developed to investigate the effects of macromolecular crowding on biochemical reactions. Three types of reactions, representing protein folding/conformational isomerization, coagulation/coalescence, and polymerization/association, are considered. The reactants, products, and crowders are modeled as coarse-grained spherical particles or as polymer chains, interacting through hard-sphere interactions with or without nonbonded square-well interactions, and the effects of crowder size and chain length as well as product size are examined. The results predicted by this model are consistent with experimentally observed crowding effects based on preferential binding or preferential exclusion of the crowders. Although simple hard-core excluded-volume arguments do in general predict the qualitative aspects of the crowding effects, the results show that other intermolecular interactions can substantially alter the extent of enhancement or reduction of the equilibrium and can even change the direction of the shift. An advantage of the approach presented here is that competing reactions can be incorporated within the model.

Structure of a tethered polymer under flow using molecular dynamics and hybrid molecular-continuum simulations

NASA Astrophysics Data System (ADS)

Delgado-Buscalioni, Rafael; Coveney, Peter V.

2006-03-01

We analyse the structure of a single polymer tethered to a solid surface undergoing a Couette flow. We study the problem using molecular dynamics (MD) and hybrid MD-continuum simulations, wherein the polymer and the surrounding solvent are treated via standard MD, and the solvent flow farther away from the polymer is solved by continuum fluid dynamics (CFD). The polymer represents a freely jointed chain (FJC) and is modelled by Lennard-Jones (LJ) beads interacting through the FENE potential. The solvent (modelled as a LJ fluid) and a weakly attractive wall are treated at the molecular level. At large shear rates the polymer becomes more elongated than predicted by existing theoretical scaling laws. Also, along the normal-to-wall direction the structure observed for the FJC is, surprisingly, very similar to that predicted for a semiflexible chain. Comparison with previous Brownian dynamics simulations (which exclude both solvent and wall potential) indicates that these effects are due to the polymer-solvent and polymer-wall interactions. The hybrid simulations are in perfect agreement with the MD simulations, showing no trace of finite size effects. Importantly, the extra cost required to couple the MD and CFD domains is negligible.

A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.

PubMed

Mirkhani, Seyyed Alireza; Gharagheizi, Farhad; Sattari, Mehdi

2012-03-01

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure. Four thousand five hundred and seventy nine organic compounds from broad spectrum of chemical families have been investigated to propose a comprehensive and predictive model. The final model is derived by Genetic Function Approximation (GFA) and contains five descriptors. Using this dedicated model, we obtain satisfactory results quantified by the following statistical results: Squared Correlation Coefficient=0.9723, Standard Deviation Error=0.003 and Average Absolute Relative Deviation=0.3% for the predicted properties from existing experimental values. Copyright © 2011 Elsevier Ltd. All rights reserved.

Logic-based models in systems biology: a predictive and parameter-free network analysis method†

PubMed Central

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

2012-01-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

Raman spectroscopy-based screening of hepatitis C and associated molecular changes

NASA Astrophysics Data System (ADS)

Bilal, Maria; Bilal, M.; Saleem, M.; Khan, Saranjam; Ullah, Rahat; Fatima, Kiran; Ahmed, M.; Hayat, Abbas; Shahzada, Shaista; Ullah Khan, Ehsan

2017-09-01

This study presents the optical screening of hepatitis C and its associated molecular changes in human blood sera using a partial least-squares regression model based on their Raman spectra. In total, 152 samples were tested through enzyme-linked immunosorbent assay for confirmation. This model utilizes minor spectral variations in the Raman spectra of the positive and control groups. Regression coefficients of this model were analyzed with reference to the variations in concentration of associated molecules in these two groups. It was found that trehalose, chitin, ammonia, and cytokines are positively correlated while lipids, beta structures of proteins, and carbohydrate-binding proteins are negatively correlated with hepatitis C. The regression vector yielded by this model is utilized to predict hepatitis C in unknown samples. This model has been evaluated by a cross-validation method, which yielded a correlation coefficient of 0.91. Moreover, 30 unknown samples were screened for hepatitis C infection using this model to test its performance. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve from these predictions were found to be 93.3%, 100%, 96.7%, and 1, respectively.

Reinforcement learning in depression: A review of computational research.

PubMed

Chen, Chong; Takahashi, Taiki; Nakagawa, Shin; Inoue, Takeshi; Kusumi, Ichiro

2015-08-01

Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value ('wanting'), decreased reward sensitivity ('liking') and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exploring the speed and performance of molecular replacement with AMPLE using QUARK ab initio protein models.

PubMed

Keegan, Ronan M; Bibby, Jaclyn; Thomas, Jens; Xu, Dong; Zhang, Yang; Mayans, Olga; Winn, Martyn D; Rigden, Daniel J

2015-02-01

AMPLE clusters and truncates ab initio protein structure predictions, producing search models for molecular replacement. Here, an interesting degree of complementarity is shown between targets solved using the different ab initio modelling programs QUARK and ROSETTA. Search models derived from either program collectively solve almost all of the all-helical targets in the test set. Initial solutions produced by Phaser after only 5 min perform surprisingly well, improving the prospects for in situ structure solution by AMPLE during synchrotron visits. Taken together, the results show the potential for AMPLE to run more quickly and successfully solve more targets than previously suspected.

Elastic properties of graphene: A pseudo-beam model with modified internal bending moment and its application

NASA Astrophysics Data System (ADS)

Xia, Z. M.; Wang, C. G.; Tan, H. F.

2018-04-01

A pseudo-beam model with modified internal bending moment is presented to predict elastic properties of graphene, including the Young's modulus and Poisson's ratio. In order to overcome a drawback in existing molecular structural mechanics models, which only account for pure bending (constant bending moment), the presented model accounts for linear bending moments deduced from the balance equations. Based on this pseudo-beam model, an analytical prediction is accomplished to predict the Young's modulus and Poisson's ratio of graphene based on the equation of the strain energies by using Castigliano second theorem. Then, the elastic properties of graphene are calculated compared with results available in literature, which verifies the feasibility of the pseudo-beam model. Finally, the pseudo-beam model is utilized to study the twisting wrinkling characteristics of annular graphene. Due to modifications of the internal bending moment, the wrinkling behaviors of graphene sheet are predicted accurately. The obtained results show that the pseudo-beam model has a good ability to predict the elastic properties of graphene accurately, especially the out-of-plane deformation behavior.

Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.

PubMed

Wang, Juexin; Luttrell, Joseph; Zhang, Ning; Khan, Saad; Shi, NianQing; Wang, Michael X; Kang, Jing-Qiong; Wang, Zheng; Xu, Dong

2016-01-01

Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.

Alignment-Based Prediction of Sites of Metabolism.

PubMed

de Bruyn Kops, Christina; Friedrich, Nils-Ole; Kirchmair, Johannes

2017-06-26

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

Multicomponent ionic liquid CMC prediction.

PubMed

Kłosowska-Chomiczewska, I E; Artichowicz, W; Preiss, U; Jungnickel, C

2017-09-27

We created a model to predict CMC of ILs based on 704 experimental values published in 43 publications since 2000. Our model was able to predict CMC of variety of ILs in binary or ternary system in a presence of salt or alcohol. The molecular volume of IL (V m ), solvent-accessible surface (Ŝ), solvation enthalpy (Δ solv G ∞ ), concentration of salt (C s ) or alcohol (C a ) and their molecular volumes (V ms and V ma , respectively) were chosen as descriptors, and Kernel Support Vector Machine (KSVM) and Evolutionary Algorithm (EA) as regression methodologies to create the models. Data was split into training and validation set (80/20) and subjected to bootstrap aggregation. KSVM provided better fit with average R 2 of 0.843, and MSE of 0.608, whereas EA resulted in R 2 of 0.794 and MSE of 0.973. From the sensitivity analysis it was shown that V m and Ŝ have the highest impact on ILs micellization in both binary and ternary systems, however surprisingly in the presence of alcohol the V m becomes insignificant/irrelevant. Micelle stabilizing or destabilizing influence of the descriptors depends upon the additives. Previous attempts at modelling the CMC of ILs was generally limited to small number of ILs in simplified (binary) systems. We however showed successful prediction of the CMC over a range of different systems (binary and ternary).

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

PubMed

Sorich, Michael J; McKinnon, Ross A; Miners, John O; Winkler, David A; Smith, Paul A

2004-10-07

This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

Constraint Logic Programming approach to protein structure prediction.

PubMed

Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Predictive Modeling of the CDRA 4BMS

NASA Technical Reports Server (NTRS)

Coker, Robert F.; Knox, James C.

2016-01-01

As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

Nowcasting recreational water quality

USGS Publications Warehouse

Boehm, Alexandria B.; Whitman, Richard L.; Nevers, Meredith; Hou, Deyi; Weisberg, Stephen B.

2007-01-01

Advances in molecular techniques may soon provide new opportunities to provide more timely information on whether recreational beaches are free from fecal contamination. However, an alternative approach is the use of predictive models. This chapter presents a summary of these developing efforts. First, we describe documented physical, chemical, and biological factors that have been demonstrated by researchers to affect bacterial concentrations at beaches and thus represent logical parameters for inclusion in a model. Then, we illustrate how various types of models can be applied to predict water quality at freshwater and marine beaches.

A molecular dynamics study of polymer/graphene interfacial systems

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

Rissanou, Anastassia N.; Harmandaris, Vagelis

2014-05-15

Graphene based polymer nanocomposites are hybrid materials with a very broad range of technological applications. In this work, we study three hybrid polymer/graphene interfacial systems (polystyrene/graphene, poly(methyl methacrylate)/graphene and polyethylene/graphene) through detailed atomistic molecular dynamics (MD) simulations. Density profiles, structural characteristics and mobility aspects are being examined at the molecular level for all model systems. In addition, we compare the properties of the hybrid systems to the properties of the corresponding bulk ones, as well as to theoretical predictions.

A model of early formation of uranium molecular oxides in laser-ablated plasmas

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

Finko, Mikhail S.; Curreli, Davide; Weisz, David G.

Here, in this work, we present a newly constructed U xO y reaction mechanism that consists of 30 reaction channels (21 of which are reversible channels) for 11 uranium molecular species (including ions). Both the selection of reaction channels and calculation of corresponding rate coefficients is accomplished via a comprehensive literature review and application of basic reaction rate theory. The reaction mechanism is supplemented by a detailed description of oxygen plasma chemistry (19 species and 142 reaction channels) and is used to model an atmospheric laser ablated uranium plume via a 0D (global) model. Finally, the global model is usedmore » to analyze the evolution of key uranium molecular species predicted by the reaction mechanism, and the initial stage of formation of uranium oxide species.« less

A model of early formation of uranium molecular oxides in laser-ablated plasmas

DOE PAGES

Finko, Mikhail S.; Curreli, Davide; Weisz, David G.; ...

2017-10-12

Here, in this work, we present a newly constructed U xO y reaction mechanism that consists of 30 reaction channels (21 of which are reversible channels) for 11 uranium molecular species (including ions). Both the selection of reaction channels and calculation of corresponding rate coefficients is accomplished via a comprehensive literature review and application of basic reaction rate theory. The reaction mechanism is supplemented by a detailed description of oxygen plasma chemistry (19 species and 142 reaction channels) and is used to model an atmospheric laser ablated uranium plume via a 0D (global) model. Finally, the global model is usedmore » to analyze the evolution of key uranium molecular species predicted by the reaction mechanism, and the initial stage of formation of uranium oxide species.« less

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.

PubMed

Binder, Harald; Porzelius, Christine; Schumacher, Martin

2011-03-01

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bleomycin induces molecular changes directly relevant to idiopathic pulmonary fibrosis: a model for "active" disease.

PubMed

Peng, Ruoqi; Sridhar, Sriram; Tyagi, Gaurav; Phillips, Jonathan E; Garrido, Rosario; Harris, Paul; Burns, Lisa; Renteria, Lorena; Woods, John; Chen, Leena; Allard, John; Ravindran, Palanikumar; Bitter, Hans; Liang, Zhenmin; Hogaboam, Cory M; Kitson, Chris; Budd, David C; Fine, Jay S; Bauer, Carla M T; Stevenson, Christopher S

2013-01-01

The preclinical model of bleomycin-induced lung fibrosis, used to investigate mechanisms related to idiopathic pulmonary fibrosis (IPF), has incorrectly predicted efficacy for several candidate compounds suggesting that it may be of limited value. As an attempt to improve the predictive nature of this model, integrative bioinformatic approaches were used to compare molecular alterations in the lungs of bleomycin-treated mice and patients with IPF. Using gene set enrichment analysis we show for the first time that genes differentially expressed during the fibrotic phase of the single challenge bleomycin model were significantly enriched in the expression profiles of IPF patients. The genes that contributed most to the enrichment were largely involved in mitosis, growth factor, and matrix signaling. Interestingly, these same mitotic processes were increased in the expression profiles of fibroblasts isolated from rapidly progressing, but not slowly progressing, IPF patients relative to control subjects. The data also indicated that TGFβ was not the sole mediator responsible for the changes observed in this model since the ALK-5 inhibitor SB525334 effectively attenuated some but not all of the fibrosis associated with this model. Although some would suggest that repetitive bleomycin injuries may more effectively model IPF-like changes, our data do not support this conclusion. Together, these data highlight that a single bleomycin instillation effectively replicates several of the specific pathogenic molecular changes associated with IPF, and may be best used as a model for patients with active disease.

Molecular determinants of blood-brain barrier permeation.

PubMed

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.

Molecular determinants of blood–brain barrier permeation

PubMed Central

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood–brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution. PMID:26305616

Ab initio solution of macromolecular crystal structures without direct methods.

PubMed

McCoy, Airlie J; Oeffner, Robert D; Wrobel, Antoni G; Ojala, Juha R M; Tryggvason, Karl; Lohkamp, Bernhard; Read, Randy J

2017-04-04

The majority of macromolecular crystal structures are determined using the method of molecular replacement, in which known related structures are rotated and translated to provide an initial atomic model for the new structure. A theoretical understanding of the signal-to-noise ratio in likelihood-based molecular replacement searches has been developed to account for the influence of model quality and completeness, as well as the resolution of the diffraction data. Here we show that, contrary to current belief, molecular replacement need not be restricted to the use of models comprising a substantial fraction of the unknown structure. Instead, likelihood-based methods allow a continuum of applications depending predictably on the quality of the model and the resolution of the data. Unexpectedly, our understanding of the signal-to-noise ratio in molecular replacement leads to the finding that, with data to sufficiently high resolution, fragments as small as single atoms of elements usually found in proteins can yield ab initio solutions of macromolecular structures, including some that elude traditional direct methods.

Analysis of high vacuum systems using SINDA'85

NASA Technical Reports Server (NTRS)

Spivey, R. A.; Clanton, S. E.; Moore, J. D.

1993-01-01

The theory, algorithms, and test data correlation analysis of a math model developed to predict performance of the Space Station Freedom Vacuum Exhaust System are presented. The theory used to predict the flow characteristics of viscous, transition, and molecular flow is presented in detail. Development of user subroutines which predict the flow characteristics in conjunction with the SINDA'85/FLUINT analysis software are discussed. The resistance-capacitance network approach with application to vacuum system analysis is demonstrated and results from the model are correlated with test data. The model was developed to predict the performance of the Space Station Freedom Vacuum Exhaust System. However, the unique use of the user subroutines developed in this model and written into the SINDA'85/FLUINT thermal analysis model provides a powerful tool that can be used to predict the transient performance of vacuum systems and gas flow in tubes of virtually any geometry. This can be accomplished using a resistance-capacitance (R-C) method very similar to the methods used to perform thermal analyses.

QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

PubMed Central

Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

2011-01-01

Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

Molecular epidemiology, and possible real-world applications in breast cancer.

PubMed

Ito, Hidemi; Matsuo, Keitaro

2016-01-01

Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein.

PubMed

González-Díaz, Humberto; Dea-Ayuela, María A; Pérez-Montoto, Lázaro G; Prado-Prado, Francisco J; Agüero-Chapín, Guillermín; Bolas-Fernández, Francisco; Vazquez-Padrón, Roberto I; Ubeira, Florencio M

2010-05-01

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

Linking short-term responses to ecologically-relevant outcomes

EPA Pesticide Factsheets

Opportunity to participate in the conduct of collaborative integrative lab, field and modelling efforts to characterize molecular-to-organismal level responses and make quantitative testable predictions of population level outcomes

A global resource allocation strategy governs growth transition kinetics of Escherichia coli

PubMed Central

Erickson, David W; Schink, Severin J.; Patsalo, Vadim; Williamson, James R.; Gerland, Ulrich; Hwa, Terence

2018-01-01

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms1–3 and they remain a topic of active investigation4–11. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations12–17, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models15,18–20 from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions. PMID:29072300

Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

PubMed Central

Tamayo, Pablo; Cho, Yoon-Jae; Tsherniak, Aviad; Greulich, Heidi; Ambrogio, Lauren; Schouten-van Meeteren, Netteke; Zhou, Tianni; Buxton, Allen; Kool, Marcel; Meyerson, Matthew; Pomeroy, Scott L.; Mesirov, Jill P.

2011-01-01

Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. Patients and Methods A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression–derived features representing disease subtype–independent (model B) and disease subtype–dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78). Results On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse. Conclusion The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification. PMID:21357789

Iontophoretic transport of oligonucleotides across human epidermal membrane: a study of the Nernst-Planck model.

PubMed

Li, S K; Ghanem, A H; Teng, C L; Hardee, G E; Higuchi, W I

2001-07-01

The objective of this study was to investigate the transport behavior of a series of oligonucleotides with human epidermal membrane (HEM) and to examine the applicability of the modified NERNST-PLANCK model to transdermal iontophoresis of these macromolecules. Iontophoretic transport experiments were first carried out in a synthetic model membrane system (Nuclepore membranes) with a four-electrode potentiostat to examine the baseline modified NERNST-PLANCK model. The modified NERNST-PLANCK model derived from the Einstein relation and the Stokes-Einstein equation taken from previous work did not hold for the oligonucleotides. Results obtained in the Nuclepore studies were, however, consistent with predictions of the modified NERNST-PLANCK model using the experimentally determined electromobilities and diffusion coefficients. The electromobilities of the oligonucleotides (determined by capillary electrophoresis) were found to be more than a factor of two smaller than expected from the Einstein relation between electromobilities and diffusion coefficients (the latter determined in diffusion cell experiments). A correlation between these electromobilities and the theoretical electromobilities estimated by considering the effects of counterion binding and the effects of mobility reduction according to colloid theory was also observed. These results suggest that the modified NERNST-PLANCK model predictions are satisfactory only when the electromobilities and the effective molecular size of the oligonucleotides are known and are used directly to predict the iontophoretically enhanced transport. Results with the HEM experiments generally agreed with model predictions based on the experimental electromobilities. The oligonucleotide HEM flux data also suggest the existence of pores with effective pore radii greater than the effective radii estimated in previous studies with small molecular weight model permeants.

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

PubMed

Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

2015-05-30

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types.

PubMed

Aben, Nanne; Vis, Daniel J; Michaut, Magali; Wessels, Lodewyk F A

2016-09-01

Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression. TANDEM is available as an R package on CRAN (for more information, see http://ccb.nki.nl/software/tandem). m.michaut@nki.nl or l.wessels@nki.nl Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Can we better predict the biologic behavior of incidental IPMN? A comprehensive analysis of molecular diagnostics and biomarkers in intraductal papillary mucinous neoplasms of the pancreas.

PubMed

Tulla, Kiara A; Maker, Ajay V

2018-03-01

Predicting the biologic behavior of intraductal papillary mucinous neoplasm (IPMN) remains challenging. Current guidelines utilize patient symptoms and imaging characteristics to determine appropriate surgical candidates. However, the majority of resected cysts remain low-risk lesions, many of which may be feasible to have under surveillance. We herein characterize the most promising and up-to-date molecular diagnostics in order to identify optimal components of a molecular signature to distinguish levels of IPMN dysplasia. A comprehensive systematic review of pertinent literature, including our own experience, was conducted based on the PRISMA guidelines. Molecular diagnostics in IPMN patient tissue, duodenal secretions, cyst fluid, saliva, and serum were evaluated and organized into the following categories: oncogenes, tumor suppressor genes, glycoproteins, markers of the immune response, proteomics, DNA/RNA mutations, and next-generation sequencing/microRNA. Specific targets in each of these categories, and in aggregate, were identified by their ability to both characterize a cyst as an IPMN and determine the level of cyst dysplasia. Combining molecular signatures with clinical and imaging features in this era of next-generation sequencing and advanced computational analysis will enable enhanced sensitivity and specificity of current models to predict the biologic behavior of IPMN.

WDL-RF: Predicting Bioactivities of Ligand Molecules Acting with G Protein-coupled Receptors by Combining Weighted Deep Learning and Random Forest.

PubMed

Wu, Jiansheng; Zhang, Qiuming; Wu, Weijian; Pang, Tao; Hu, Haifeng; Chan, Wallace K B; Ke, Xiaoyan; Zhang, Yang; Wren, Jonathan

2018-02-08

Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally-determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem. We proposed a new method, WDL-RF, using weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules. The pipeline of our algorithm consists of two consecutive stages: 1) molecular fingerprint generation through a new weighted deep learning method, and 2) bioactivity calculations with a random forest model; where one uniqueness of the approach is that the model allows end-to-end learning of prediction pipelines with input ligands being of arbitrary size. The method was tested on a set of twenty-six non-redundant GPCRs that have a high number of active ligands, each with 200∼4000 ligand associations. The results from our benchmark show that WDL-RF can generate bioactivity predictions with an average root-mean square error 1.33 and correlation coefficient (r2) 0.80 compared to the experimental measurements, which are significantly more accurate than the control predictors with different molecular fingerprints and descriptors. In particular, data-driven molecular fingerprint features, as extracted from the weighted deep learning models, can help solve deficiencies stemming from the use of traditional hand-crafted features and significantly increase the efficiency of short molecular fingerprints in virtual screening. The WDL-RF web server, as well as source codes and datasets of WDL-RF, is freely available at https://zhanglab.ccmb.med.umich.edu/WDL-RF/ for academic purposes. Xiaoyan Ke (kexynj@hotmail.com); Yang Zhang (zhng@umich.edu). Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Theoretical study on the interaction of pyrrolopyrimidine derivatives as LIMK2 inhibitors: insight into structure-based inhibitor design.

PubMed

Shen, Mingyun; Zhou, Shunye; Li, Youyong; Li, Dan; Hou, Tingjun

2013-10-01

LIM kinases (LIMKs), downstream of Rho-associated protein kinases (ROCKs) and p21-activated protein kinases (PAKs), are shown to be promising targets for the treatment of cancers. In this study, the inhibition mechanism of 41 pyrrolopyrimidine derivatives as LIMK2 inhibitors was explored through a series of theoretical approaches. First, a model of LIMK2 was generated through molecular homology modeling, and the studied inhibitors were docked into the binding active site of LIMK2 by the docking protocol, taking into consideration the flexibility of the protein. The binding poses predicted by molecular docking for 17 selected inhibitors with different bioactivities complexed with LIMK2 underwent molecular dynamics (MD) simulations, and the binding free energies for the complexes were predicted by using the molecular mechanics/generalized born surface area (MM/GBSA) method. The predicted binding free energies correlated well with the experimental bioactivities (r(2) = 0.63 or 0.62). Next, the free energy decomposition analysis was utilized to highlight the following key structural features related to biological activity: (1) the important H-bond between Ile408 and pyrrolopyrimidine, (2) the H-bonds between the inhibitors and Asp469 and Gly471 which maintain the stability of the DFG-out conformation, and (3) the hydrophobic interactions between the inhibitors and several key residues (Leu337, Phe342, Ala345, Val358, Lys360, Leu389, Ile408, Leu458 and Leu472). Finally, a variety of LIMK2 inhibitors with a pyrrolopyrimidine scaffold were designed, some of which showed improved potency according to the predictions. Our studies suggest that the use of molecular docking with MD simulations and free energy calculations could be a powerful tool for understanding the binding mechanism of LIMK2 inhibitors and for the design of more potent LIMK2 inhibitors.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

PubMed Central

2011-01-01

Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis. PMID:21375730

Molecular Dynamics Simulations of Hydrophobic Residues

NASA Astrophysics Data System (ADS)

Caballero, Diego; Zhou, Alice; Regan, Lynne; O'Hern, Corey

2013-03-01

Molecular recognition and protein-protein interactions are involved in important biological processes. However, despite recent improvements in computational methods for protein design, we still lack a predictive understanding of protein structure and interactions. To begin to address these shortcomings, we performed molecular dynamics simulations of hydrophobic residues modeled as hard spheres with stereo-chemical constraints initially at high temperature, and then quenched to low temperature to obtain local energy minima. We find that there is a range of quench rates over which the probabilities of side-chain dihedral angles for hydrophobic residues match the probabilities obtained for known protein structures. In addition, we predict the side-chain dihedral angle propensities in the core region of the proteins T4, ROP, and several mutants. These studies serve as a first step in developing the ability to quantitatively rank the energies of designed protein constructs. The success of these studies suggests that only hard-sphere dynamics with geometrical constraints are needed for accurate protein structure prediction in hydrophobic cavities and binding interfaces. NSF Grant PHY-1019147

Structure and Stability of Molecular Crystals with Many-Body Dispersion-Inclusive Density Functional Tight Binding.

PubMed

Mortazavi, Majid; Brandenburg, Jan Gerit; Maurer, Reinhard J; Tkatchenko, Alexandre

2018-01-18

Accurate prediction of structure and stability of molecular crystals is crucial in materials science and requires reliable modeling of long-range dispersion interactions. Semiempirical electronic structure methods are computationally more efficient than their ab initio counterparts, allowing structure sampling with significant speedups. We combine the Tkatchenko-Scheffler van der Waals method (TS) and the many-body dispersion method (MBD) with third-order density functional tight-binding (DFTB3) via a charge population-based method. We find an overall good performance for the X23 benchmark database of molecular crystals, despite an underestimation of crystal volume that can be traced to the DFTB parametrization. We achieve accurate lattice energy predictions with DFT+MBD energetics on top of vdW-inclusive DFTB3 structures, resulting in a speedup of up to 3000 times compared with a full DFT treatment. This suggests that vdW-inclusive DFTB3 can serve as a viable structural prescreening tool in crystal structure prediction.

H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations.

PubMed

Anandakrishnan, Ramu; Aguilar, Boris; Onufriev, Alexey V

2012-07-01

The accuracy of atomistic biomolecular modeling and simulation studies depend on the accuracy of the input structures. Preparing these structures for an atomistic modeling task, such as molecular dynamics (MD) simulation, can involve the use of a variety of different tools for: correcting errors, adding missing atoms, filling valences with hydrogens, predicting pK values for titratable amino acids, assigning predefined partial charges and radii to all atoms, and generating force field parameter/topology files for MD. Identifying, installing and effectively using the appropriate tools for each of these tasks can be difficult for novice and time-consuming for experienced users. H++ (http://biophysics.cs.vt.edu/) is a free open-source web server that automates the above key steps in the preparation of biomolecular structures for molecular modeling and simulations. H++ also performs extensive error and consistency checking, providing error/warning messages together with the suggested corrections. In addition to numerous minor improvements, the latest version of H++ includes several new capabilities and options: fix erroneous (flipped) side chain conformations for HIS, GLN and ASN, include a ligand in the input structure, process nucleic acid structures and generate a solvent box with specified number of common ions for explicit solvent MD.

High-Efficiency Multiscale Modeling of Cell Deformations in Confined Microenvironments in Microcirculation and Microfluidics

NASA Astrophysics Data System (ADS)

Lu, Huijie; Peng, Zhangli

2017-11-01

We developed a high-efficiency multiscale modeling method to predict the stress and deformation of cells during the interactions with their microenvironments in microcirculation and microfluidics, including red blood cells (RBCs) and circulating tumor cells (CTCs). There are more than 1 billion people in the world suffering from RBC diseases. The mechanical properties of RBCs are changed in these diseases due to molecular structure alternations, which is not only important for understanding the disease pathology but also provides an opportunity for diagnostics. On the other hand, the mechanical properties of cancer cells are also altered compared to healthy cells. This can lead to acquired ability to cross the narrow capillary networks and endothelial gaps, which is crucial for metastasis, the leading cause of cancer mortality. Therefore, it is important to predict the deformation and stress of RBCs and CTCs in microcirculations. We develop a high-efficiency multiscale model of cell-fluid interaction. We pass the information from our molecular scale models to the cell scale to study the effect of molecular mutations. Using our high-efficiency boundary element methods of fluids, we will be able to run 3D simulations using a single CPU within several hours, which will enable us to run extensive parametric studies and optimization.

Assessing the Impact of Electrostatic Drag on Processive Molecular Motor Transport.

PubMed

Smith, J Darby; McKinley, Scott A

2018-06-04

The bidirectional movement of intracellular cargo is usually described as a tug-of-war among opposite-directed families of molecular motors. While tug-of-war models have enjoyed some success, recent evidence suggests underlying motor interactions are more complex than previously understood. For example, these tug-of-war models fail to predict the counterintuitive phenomenon that inhibiting one family of motors can decrease the functionality of opposite-directed transport. In this paper, we use a stochastic differential equations modeling framework to explore one proposed physical mechanism, called microtubule tethering, that could play a role in this "co-dependence" among antagonistic motors. This hypothesis includes the possibility of a trade-off: weakly bound trailing molecular motors can serve as tethers for cargoes and processing motors, thereby enhancing motor-cargo run lengths along microtubules; however, this introduces a cost of processing at a lower mean velocity. By computing the small- and large-time mean-squared displacement of our theoretical model and comparing our results to experimental observations of dynein and its "helper protein" dynactin, we find some supporting evidence for microtubule tethering interactions. We extrapolate these findings to predict how dynein-dynactin might interact with the opposite-directed kinesin motors and introduce a criterion for when the trade-off is beneficial in simple systems.

Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors.

PubMed

Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

2016-10-01

Janus kinase 1 (JAK 1) plays a critical role in initiating responses to cytokines by the JAK-signal transducer and activator of transcription (JAK-STAT). This controls survival, proliferation and differentiation of a variety of cells. Docking, 3D quantitative structure activity relationship (3D-QSAR) and molecular dynamics (MD) studies were performed on a series of Imidazo-pyrrolopyridine derivatives reported as JAK 1 inhibitors. QSAR model was generated using 30 molecules in the training set; developed model showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of this model was determined using a test set of 13 molecules that gave acceptable predictive correlation (r 2 Pred ) values. Finally, molecular dynamics simulation was performed to validate docking results and MM/GBSA calculations. This facilitated us to compare binding free energies of cocrystal ligand and newly designed molecule R1. The good concordance between the docking results and CoMFA/CoMSIA contour maps afforded obliging clues for the rational modification of molecules to design more potent JAK 1 inhibitors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.

PubMed

Khan, Tarik A; Friedensohn, Simon; Gorter de Vries, Arthur R; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T

2016-03-01

High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion-the intraclonal diversity index-which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology.

Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting

PubMed Central

Khan, Tarik A.; Friedensohn, Simon; de Vries, Arthur R. Gorter; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T.

2016-01-01

High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion—the intraclonal diversity index—which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

Predicting the chemical stability of monatomic chains

NASA Astrophysics Data System (ADS)

Lin, Zheng-Zhe; Chen, Xi

2013-02-01

A simple model for evaluating the thermal atomic transfer rates in nanosystems (Lin Z.-Z. et al., EPL, 94 (2011) 40002) was developed to predict the chemical reaction rates of nanosystems with small gas molecules. The accuracy of the model was verified by MD simulations for molecular adsorption and desorption on a monatomic chain. By the prediction, a monatomic carbon chain should survive for 1.2 × 102 years in the ambient of 1 atm O2 at room temperature, and it is very invulnerable to N2, H2O, NO2, CO and CO2, while a monatomic gold chain quickly ruptures in vacuum. It is worth noting that since the model can be easily applied via common ab initio calculations, it could be widely used in the prediction of chemical stability of nanosystems.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

PubMed

Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

2014-01-01

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

A REACTIVITY PATTERN OF DISCRIMINATION OF ER AGONISM AND ANTAGONISM BASED ON 3-D MOLECULAR ATTRIBUTES

EPA Science Inventory

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models are important for prioritizing chemicals for screening in biological assays assessing the potential for endocrine disruption. One shortcoming of...

Ligand-based 3D QSAR analysis of reactivation potency of mono- and bis-pyridinium aldoximes toward VX-inhibited rat acetylcholinesterase.

PubMed

Dolezal, Rafael; Korabecny, Jan; Malinak, David; Honegr, Jan; Musilek, Kamil; Kuca, Kamil

2015-03-01

To predict unknown reactivation potencies of 12 mono- and bis-pyridinium aldoximes for VX-inhibited rat acetylcholinesterase (rAChE), three-dimensional quantitative structure-activity relationship (3D QSAR) analysis has been carried out. Utilizing molecular interaction fields (MIFs) calculated by molecular mechanical (MMFF94) and quantum chemical (B3LYP/6-31G*) methods, two satisfactory ligand-based CoMFA models have been developed: 1. R(2)=0.9989, Q(LOO)(2)=0.9090, Q(LTO)(2)=0.8921, Q(LMO(20%))(2)=0.8853, R(ext)(2)=0.9259, SDEP(ext)=6.8938; 2. R(2)=0.9962, Q(LOO)(2)=0.9368, Q(LTO)(2)=0.9298, Q(LMO(20%))(2)=0.9248, R(ext)(2)=0.8905, SDEP(ext)=6.6756. High statistical significance of the 3D QSAR models has been achieved through the application of several data noise reduction techniques (i.e. smart region definition SRD, fractional factor design FFD, uninformative/iterative variable elimination UVE/IVE) on the original MIFs. Besides the ligand-based CoMFA models, an alignment molecular set constructed by flexible molecular docking has been also studied. The contour maps as well as the predicted reactivation potencies resulting from 3D QSAR analyses help better understand which structural features are associated with increased reactivation potency of studied compounds. Copyright © 2014 Elsevier Inc. All rights reserved.

On interfacial properties of tetrahydrofuran: Atomistic and coarse-grained models from molecular dynamics simulation

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

Garrido, J. M.; Algaba, J.; Blas, F. J., E-mail: felipe@uhu.es

2016-04-14

We have determined the interfacial properties of tetrahydrofuran (THF) from direct simulation of the vapor-liquid interface. The molecules are modeled using six different molecular models, three of them based on the united-atom approach and the other three based on a coarse-grained (CG) approach. In the first case, THF is modeled using the transferable parameters potential functions approach proposed by Chandrasekhar and Jorgensen [J. Chem. Phys. 77, 5073 (1982)] and a new parametrization of the TraPPE force fields for cyclic alkanes and ethers [S. J. Keasler et al., J. Phys. Chem. B 115, 11234 (2012)]. In both cases, dispersive and coulombicmore » intermolecular interactions are explicitly taken into account. In the second case, THF is modeled as a single sphere, a diatomic molecule, and a ring formed from three Mie monomers according to the SAFT-γ Mie top-down approach [V. Papaioannou et al., J. Chem. Phys. 140, 054107 (2014)]. Simulations were performed in the molecular dynamics canonical ensemble and the vapor-liquid surface tension is evaluated from the normal and tangential components of the pressure tensor along the simulation box. In addition to the surface tension, we have also obtained density profiles, coexistence densities, critical temperature, density, and pressure, and interfacial thickness as functions of temperature, paying special attention to the comparison between the estimations obtained from different models and literature experimental data. The simulation results obtained from the three CG models as described by the SAFT-γ Mie approach are able to predict accurately the vapor-liquid phase envelope of THF, in excellent agreement with estimations obtained from TraPPE model and experimental data in the whole range of coexistence. However, Chandrasekhar and Jorgensen model presents significant deviations from experimental results. We also compare the predictions for surface tension as obtained from simulation results for all the models with experimental data. The three CG models predict reasonably well (but only qualitatively) the surface tension of THF, as a function of temperature, from the triple point to the critical temperature. On the other hand, only the TraPPE united-atoms models are able to predict accurately the experimental surface tension of the system in the whole temperature range.« less

Photoionization-regulated star formation and the structure of molecular clouds

NASA Technical Reports Server (NTRS)

Mckee, Christopher F.

1989-01-01

A model for the rate of low-mass star formation in Galactic molecular clouds and for the influence of this star formation on the structure and evolution of the clouds is presented. The rate of energy injection by newly formed stars is estimated, and the effect of this energy injection on the size of the cloud is determined. It is shown that the observed rate of star formation appears adequate to support the observed clouds against gravitational collapse. The rate of photoionization-regulated star formation is estimated and it is shown to be in agreement with estimates of the observed rate of star formation if the observed molecular cloud parameters are used. The mean cloud extinction and the Galactic star formation rate per unit mass of molecular gas are predicted theoretically from the condition that photionization-regulated star formation be in equilibrium. A simple model for the evolution of isolated molecular clouds is developed.

Genomic Signal Processing: Predicting Basic Molecular Biological Principles

NASA Astrophysics Data System (ADS)

Alter, Orly

2005-03-01

Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of these two sets of states. Mapping genome-scale protein binding data using pseudoinverse projection onto patterns of RNA expression data that had been extracted by SVD and GSVD, a novel correlation between DNA replication initiation and RNA transcription during the cell cycle in yeast, that might be due to a previously unknown mechanism of regulation, is predicted. (1) Alter & Golub, Proc. Natl. Acad. Sci. USA 101, 16577 (2004). (2) Alter, Golub, Brown & Botstein, Miami Nat. Biotechnol. Winter Symp. 2004 (www.med.miami.edu/mnbws/alter-.pdf)

Study of Variable Turbulent Prandtl Number Model for Heat Transfer to Supercritical Fluids in Vertical Tubes

NASA Astrophysics Data System (ADS)

Tian, Ran; Dai, Xiaoye; Wang, Dabiao; Shi, Lin

2018-06-01

In order to improve the prediction performance of the numerical simulations for heat transfer of supercritical pressure fluids, a variable turbulent Prandtl number (Prt) model for vertical upward flow at supercritical pressures was developed in this study. The effects of Prt on the numerical simulation were analyzed, especially for the heat transfer deterioration conditions. Based on the analyses, the turbulent Prandtl number was modeled as a function of the turbulent viscosity ratio and molecular Prandtl number. The model was evaluated using experimental heat transfer data of CO2, water and Freon. The wall temperatures, including the heat transfer deterioration cases, were more accurately predicted by this model than by traditional numerical calculations with a constant Prt. By analyzing the predicted results with and without the variable Prt model, it was found that the predicted velocity distribution and turbulent mixing characteristics with the variable Prt model are quite different from that predicted by a constant Prt. When heat transfer deterioration occurs, the radial velocity profile deviates from the log-law profile and the restrained turbulent mixing then leads to the deteriorated heat transfer.

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.

PubMed

Oguz, Cihan; Sen, Shurjo K; Davis, Adam R; Fu, Yi-Ping; O'Donnell, Christopher J; Gibbons, Gary H

2017-10-26

One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89 th -99 th CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC). RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and "vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs. We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders, hold promise for deriving predictive disease models and networks.

Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds.

PubMed

Goya Jorge, Elizabeth; Rayar, Anita Maria; Barigye, Stephen J; Jorge Rodríguez, María Elisa; Sylla-Iyarreta Veitía, Maité

2016-06-07

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model's predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors.

PubMed

Yu, Shuling; Yuan, Jintao; Zhang, Yi; Gao, Shufang; Gan, Ying; Han, Meng; Chen, Yuewen; Zhou, Qiaoqiao; Shi, Jiahua

2017-06-01

Sodium-glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure-activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. The models and docking provided important insights into the design of potent inhibitors for SGLT2.

Structure and dynamics of microbe-exuded polymers and their interactions with calcite surfaces.

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

Cygan, Randall Timothy; Mitchell, Ralph; Perry, Thomas D.

2005-12-01

Cation binding by polysaccharides is observed in many environments and is important for predictive environmental modeling, and numerous industrial and food technology applications. The complexities of these organo-cation interactions are well suited to predictive molecular modeling studies for investigating the roles of conformation and configuration of polysaccharides on cation binding. In this study, alginic acid was chosen as a model polymer and representative disaccharide and polysaccharide subunits were modeled. The ability of disaccharide subunits to bind calcium and to associate with the surface of calcite was investigated. The findings were extended to modeling polymer interactions with calcium ions.

Molecular markers of carcinogenesis for risk stratification of individuals with colorectal polyps: a case-control study.

PubMed

Gupta, Samir; Sun, Han; Yi, Sang; Storm, Joy; Xiao, Guanghua; Balasubramanian, Bijal A; Zhang, Song; Ashfaq, Raheela; Rockey, Don C

2014-10-01

Risk stratification using number, size, and histology of colorectal adenomas is currently suboptimal for identifying patients at increased risk for future colorectal cancer. We hypothesized that molecular markers of carcinogenesis in adenomas, measured via immunohistochemistry, may help identify high-risk patients. To test this hypothesis, we conducted a retrospective, 1:1 matched case-control study (n = 216; 46% female) in which cases were patients with colorectal cancer and synchronous adenoma and controls were patients with adenoma but no colorectal cancer at baseline or within 5 years of follow-up. In phase I of analyses, we compared expression of molecular markers of carcinogenesis in case and control adenomas, blind to case status. In phase II of analyses, patients were randomly divided into independent training and validation groups to develop a model for predicting case status. We found that seven markers [p53, p21, Cox-2, β-catenin (BCAT), DNA-dependent protein kinase (DNApkcs), survivin, and O6-methylguanine-DNA methyltransferase (MGMT)] were significantly associated with case status on unadjusted analyses, as well as analyses adjusted for age and advanced adenoma status (P < 0.01 for at least one marker component). When applied to the validation set, a predictive model using these seven markers showed substantial accuracy for identifying cases [area under the receiver operation characteristic curve (AUC), 0.83; 95% confidence interval (CI), 0.74-0.92]. A parsimonious model using three markers performed similarly to the seven-marker model (AUC, 0.84). In summary, we found that molecular markers of carcinogenesis distinguished adenomas from patients with and without colorectal cancer. Furthermore, we speculate that prospective studies using molecular markers to identify individuals with polyps at risk for future neoplasia are warranted. ©2014 American Association for Cancer Research.

Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

PubMed

Singh, Nidhi; Shah, Priyanka; Dwivedi, Hemlata; Mishra, Shikha; Tripathi, Renu; Sahasrabuddhe, Amogh A; Siddiqi, Mohammad Imran

2016-11-15

N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, a TbNMT complex model was generated to establish the relationship between the binding mode of the inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies. A 3D-QSAR model was developed and used to predict the activity of the proposed hits in the subsequent step. The hits classified as active based on the machine learning model were assessed as the potential anti-trypanosomal NMT inhibitors through molecular docking studies, predicted activity using a QSAR model and visual inspection. In the final step, the proposed pipeline was validated through in vitro experiments. A total of seven hits have been proposed and tested in vitro for evaluation of dual inhibitory activity against Leishmania donovani and Trypanosoma brucei. Out of these five compounds showed significant inhibition against both of the organisms. The common topmost active compound SEW04173 belongs to a pyrazole carboxylate scaffold and is anticipated to enrich the chemical space with enhanced potency through optimization.

Isotope Fractionation in the Interstellar Medium

NASA Technical Reports Server (NTRS)

Charnley, Steven

2011-01-01

Anomalously fractionated isotopic material is found in many primitive Solar System objects, such as meteorites and comets. It is thought, in some cases, to trace interstellar matter that was incorporated into the Solar Nebula without undergoing significant processing. We will present the results of models of the nitrogen, oxygen, and carbon fractionation chemistry in dense molecular clouds, particularly in cores where substantial freeze-out of molecules on to dust has occurred. The range of fractionation ratios expected in different interstellar molecules will be discussed and compared to the ratios measured in molecular clouds, comets and meteoritic material. These models make several predictions that can be tested in the near future by molecular line observations, particularly with ALMA.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

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

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsetsmore » driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. - Highlights: • Consensus method to predict ready biodegradability by prioritizing multiple QSARs. • Consensus reduced the amount of unpredictable chemicals to less than 2%. • Performance increased with the number of QSAR models considered. • The absence of 2D atom pairs contributed significantly to the consensus model.« less

Assessment of atmospheric distribution of polycyclic aromatic hydrocarbons using a molecular structure model

NASA Astrophysics Data System (ADS)

Turk Sekulić, Maja; Okuka, Marija; Šenk, Nevena; Radonić, Jelena; Vojinović Miloradov, Mirjana; Vidicki, Branko

2013-07-01

In this paper, a comparison of experimentally obtained and SPARC software v4.6 modelled values of gas/particle partitioning coefficients was conducted to determine whether the evaluation of atmospheric distribution of PAH molecules can be performed using a molecular structure model. Partitioning coefficients were calculated for sixteen EPA PAHs, in thirty-nine samples of ambient air collected at nineteen urban, industrial, highly contaminated and background sites in the Republic of Serbia and Bosnia and Herzegovina. For obtaining samples of ambient air, the conventional high volume (Hi-Vol) methodology was applied, whereby gaseous and particulate phase data collection was conducted simultaneously by glass fibre filters (GFFs) and polyurethane foam filters (PUFs). The best prediction was for PAHs with 5 or more rings (benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, indeno(1,2,3-cd)perylene and benzo(ghi)perylene). For evaluating the applicability of SPARC software predictions of gas/particle partitioning coefficients for the existing conditions, the results were compared with those obtained by applying other frequently used and highly ranked theoretical models of phase distributions, namely Junge-Pankow adsorption model, KOA absorption model, Dachs-Eisenreich dual model and PP-LFER model.

Prediction of individual response to anticancer therapy: historical and future perspectives.

PubMed

Unger, Florian T; Witte, Irene; David, Kerstin A

2015-02-01

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Combined molecular modelling and 3D-QSAR study for understanding the inhibition of NQO1 by heterocyclic quinone derivatives.

PubMed

López-Lira, Claudia; Alzate-Morales, Jans H; Paulino, Margot; Mella-Raipán, Jaime; Salas, Cristian O; Tapia, Ricardo A; Soto-Delgado, Jorge

2018-01-01

A combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular modelling methods were used to understand the potent inhibitory NAD(P)H:quinone oxidoreductase 1 (NQO1) activity of a set of 52 heterocyclic quinones. Molecular docking results indicated that some favourable interactions of key amino acid residues at the binding site of NQO1 with these quinones would be responsible for an improvement of the NQO1 activity of these compounds. The main interactions involved are hydrogen bond of the amino group of residue Tyr128, π-stacking interactions with Phe106 and Phe178, and electrostatic interactions with flavin adenine dinucleotide (FADH) cofactor. Three models were prepared by 3D-QSAR analysis. The models derived from Model I and Model III, shown leave-one-out cross-validation correlation coefficients (q 2 LOO ) of .75 and .73 as well as conventional correlation coefficients (R 2 ) of .93 and .95, respectively. In addition, the external predictive abilities of these models were evaluated using a test set, producing the predicted correlation coefficients (r 2 pred ) of .76 and .74, respectively. The good concordance between the docking results and 3D-QSAR contour maps provides helpful information about a rational modification of new molecules based in quinone scaffold, in order to design more potent NQO1 inhibitors, which would exhibit highly potent antitumor activity. © 2017 John Wiley & Sons A/S.

Multiscale Analysis of Structurally-Graded Microstructures Using Molecular Dynamics, Discrete Dislocation Dynamics and Continuum Crystal Plasticity

NASA Technical Reports Server (NTRS)

Saether, Erik; Hochhalter, Jacob D.; Glaessgen, Edward H.; Mishin, Yuri

2014-01-01

A multiscale modeling methodology is developed for structurally-graded material microstructures. Molecular dynamic (MD) simulations are performed at the nanoscale to determine fundamental failure mechanisms and quantify material constitutive parameters. These parameters are used to calibrate material processes at the mesoscale using discrete dislocation dynamics (DD). Different grain boundary interactions with dislocations are analyzed using DD to predict grain-size dependent stress-strain behavior. These relationships are mapped into crystal plasticity (CP) parameters to develop a computationally efficient finite element-based DD/CP model for continuum-level simulations and complete the multiscale analysis by predicting the behavior of macroscopic physical specimens. The present analysis is focused on simulating the behavior of a graded microstructure in which grain sizes are on the order of nanometers in the exterior region and transition to larger, multi-micron size in the interior domain. This microstructural configuration has been shown to offer improved mechanical properties over homogeneous coarse-grained materials by increasing yield stress while maintaining ductility. Various mesoscopic polycrystal models of structurally-graded microstructures are generated, analyzed and used as a benchmark for comparison between multiscale DD/CP model and DD predictions. A final series of simulations utilize the DD/CP analysis method exclusively to study macroscopic models that cannot be analyzed by MD or DD methods alone due to the model size.

On the validity of time-dependent AUC estimators.

PubMed

Schmid, Matthias; Kestler, Hans A; Potapov, Sergej

2015-01-01

Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker studies. Estimators of discrimination measures are usually based on regularity assumptions such as the proportional hazards assumption. Based on two sets of molecular data and a simulation study, we show that violations of the regularity assumptions may lead to over-optimistic estimates of prediction accuracy and may therefore result in biased conclusions regarding the clinical utility of new biomarkers. In particular, we demonstrate that biased medical decision making is possible even if statistical checks indicate that all regularity assumptions are satisfied. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Insights from molecular modeling and dynamics simulation of pathogen resistance (R) protein from brinjal.

PubMed

Shrivastava, Dipty; Nain, Vikrant; Sahi, Shakti; Verma, Anju; Sharma, Priyanka; Sharma, Prakash Chand; Kumar, Polumetla Ananda

2011-01-22

Resistance (R) protein recognizes molecular signature of pathogen infection and activates downstream hypersensitive response signalling in plants. R protein works as a molecular switch for pathogen defence signalling and represent one of the largest plant gene family. Hence, understanding molecular structure and function of R proteins has been of paramount importance for plant biologists. The present study is aimed at predicting structure of R proteins signalling domains (CC-NBS) by creating a homology model, refining and optimising the model by molecular dynamics simulation and comparing ADP and ATP binding. Based on sequence similarity with proteins of known structures, CC-NBS domains were initially modelled using CED- 4 (cell death abnormality protein) and APAF-1 (apoptotic protease activating factor) as multiple templates. The final CC-NBS structural model was built and optimized by molecular dynamic simulation for 5 nanoseconds (ns). Docking of ADP and ATP at active site shows that both ligand bind specifically with same residues and with minor difference (1 Kcal/mol) in binding energy. Sharing of binding site by ADP and ATP and low difference in their binding site makes CC-NBS suitable for working as molecular switch. Furthermore, structural superimposition elucidate that CC-NBS and CARD (caspase recruitment domains) domain of CED-4 have low RMSD value of 0.9 A° Availability of 3D structural model for both CC and NBS domains will . help in getting deeper insight in these pathogen defence genes.

Modeling beta-adrenergic control of cardiac myocyte contractility in silico.

PubMed

Saucerman, Jeffrey J; Brunton, Laurence L; Michailova, Anushka P; McCulloch, Andrew D

2003-11-28

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Modeling beta-adrenergic control of cardiac myocyte contractility in silico

NASA Technical Reports Server (NTRS)

Saucerman, Jeffrey J.; Brunton, Laurence L.; Michailova, Anushka P.; McCulloch, Andrew D.; McCullough, A. D. (Principal Investigator)

2003-01-01

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

A Multiscale Model for the Quasi-Static Thermo-Plastic Behavior of Highly Cross-Linked Glassy Polymers

DOE PAGES

Vu-Bac, N.; Bessa, M. A.; Rabczuk, Timon; ...

2015-09-10

In this paper, we present experimentally validated molecular dynamics predictions of the quasi- static yield and post-yield behavior for a highly cross-linked epoxy polymer under gen- eral stress states and for different temperatures. In addition, a hierarchical multiscale model is presented where the nano-scale simulations obtained from molecular dynamics were homogenized to a continuum thermoplastic constitutive model for the epoxy that can be used to describe the macroscopic behavior of the material. Three major conclusions were achieved: (1) the yield surfaces generated from the nano-scale model for different temperatures agree well with the paraboloid yield crite- rion, supporting previous macroscopicmore » experimental observations; (2) rescaling of the entire yield surfaces to the quasi-static case is possible by considering Argon’s theoretical predictions for pure compression of the polymer at absolute zero temperature; (3) nano- scale simulations can be used for an experimentally-free calibration of macroscopic con- tinuum models, opening new avenues for the design of materials and structures through multi-scale simulations that provide structure-property-performance relationships.« less

Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

PubMed

Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

2010-11-01

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments.

PubMed

Pandey, Gyanendra; Saxena, Anil K

2006-01-01

A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.

Development and Use of Numerical and Factual Data Bases

DTIC Science & Technology

1983-10-01

the quantitative description of what has been accomplished by their scientific and technical endeavors. 1-3 overhead charge to the national treasury... Molecular properties calculated with the aid of quantum mechanics or the prediction of solar eclipses using celestial mechanics are examples of theoretical...system under study. Examples include phase diagrams, molecular models, geological maps, metabolic pathways. Symbolic data (F3) are data presented in

Molecular opacities for exoplanets.

PubMed

Bernath, Peter F

2014-04-28

Spectroscopic observations of exoplanets are now possible by transit methods and direct emission. Spectroscopic requirements for exoplanets are reviewed based on existing measurements and model predictions for hot Jupiters and super-Earths. Molecular opacities needed to simulate astronomical observations can be obtained from laboratory measurements, ab initio calculations or a combination of the two approaches. This discussion article focuses mainly on laboratory measurements of hot molecules as needed for exoplanet spectroscopy.

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1.

PubMed

Fatima, Sabiha; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

2012-08-01

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r², q²(loo) and r²(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r²(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology.

PubMed

Tsigkinopoulou, Areti; Baker, Syed Murtuza; Breitling, Rainer

2017-06-01

Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology. Copyright © 2016 Elsevier Ltd. All rights reserved.

BitterSweetForest: A random forest based binary classifier to predict bitterness and sweetness of chemical compounds

NASA Astrophysics Data System (ADS)

Banerjee, Priyanka; Preissner, Robert

2018-04-01

Taste of a chemical compounds present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96 % and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10 % of the natural product space as sweet with confidence score of 0.60 and above. 77 % of the approved drug set was predicted as bitter and 2% as sweet with a confidence scores of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds from the feature space of a circular fingerprint.

BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds

PubMed Central

Banerjee, Priyanka; Preissner, Robert

2018-01-01

Taste of a chemical compound present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96% and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10% of the natural product space as sweet with confidence score of 0.60 and above. 77% of the approved drug set was predicted as bitter and 2% as sweet with a confidence score of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds using the feature space of a circular fingerprint. PMID:29696137

Understanding the colloidal dispersion stability of 1D and 2D materials: Perspectives from molecular simulations and theoretical modeling.

PubMed

Lin, Shangchao; Shih, Chih-Jen; Sresht, Vishnu; Govind Rajan, Ananth; Strano, Michael S; Blankschtein, Daniel

2017-06-01

The colloidal dispersion stability of 1D and 2D materials in the liquid phase is critical for scalable nano-manufacturing, chemical modification, composites production, and deployment as conductive inks or nanofluids. Here, we review recent computational and theoretical studies carried out by our group to model the dispersion stability of 1D and 2D materials, including single-walled carbon nanotubes, graphene, and graphene oxide in aqueous surfactant solutions or organic solvents. All-atomistic (AA) molecular dynamics (MD) simulations can probe the molecular level details of the adsorption morphology of surfactants and solvents around these materials, as well as quantify the interaction energy between the nanomaterials mediated by surfactants or solvents. Utilizing concepts from reaction kinetics and diffusion, one can directly predict the rate constants for the aggregation kinetics and dispersion life times using MD outputs. Furthermore, the use of coarse-grained (CG) MD simulations allows quantitative prediction of surfactant adsorption isotherms. Combined with the Poisson-Boltzmann equation, the Langmuir isotherm, and the DLVO theory, one can directly use CGMD outputs to: (i) predict electrostatic potentials around the nanomaterial, (ii) correlate surfactant surface coverages with surfactant concentrations in the bulk dispersion medium, and (iii) determine energy barriers against coagulation. Finally, we discuss challenges associated with studying emerging 2D materials, such as, hexagonal boron nitride (h-BN), phosphorene, and transition metal dichalcogenides (TMDCs), including molybdenum disulfide (MoS 2 ). An outlook is provided to address these challenges with plans to develop force-field parameters for MD simulations to enable predictive modeling of emerging 2D materials in the liquid phase. Copyright © 2016 Elsevier B.V. All rights reserved.