Protein crystal growth in space

NASA Technical Reports Server (NTRS)

Bugg, C. E.; Clifford, D. W.

1987-01-01

The advantages of protein crystallization in space, and the applications of protein crystallography to drug design, protein engineering, and the design of synthetic vaccines are examined. The steps involved in using protein crystallography to determine the three-dimensional structure of a protein are discussed. The growth chamber design and the hand-held apparatus developed for protein crystal growth by vapor diffusion techniques (hanging-drop method) are described; the experimental data from the four Shuttle missions are utilized to develop hardware for protein crystal growth in space and to evaluate the effects of gravity on protein crystal growth.

Computational approaches for rational design of proteins with novel functionalities

PubMed Central

Tiwari, Manish Kumar; Singh, Ranjitha; Singh, Raushan Kumar; Kim, In-Won; Lee, Jung-Kul

2012-01-01

Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes. PMID:24688643

New valve and bonding designs for microfluidic biochips containing proteins.

PubMed

Lu, Chunmeng; Xie, Yubing; Yang, Yong; Cheng, Mark M-C; Koh, Chee-Guan; Bai, Yunling; Lee, L James; Juang, Yi-Je

2007-02-01

Two major concerns in the design and fabrication of microfluidic biochips are protein binding on the channel surface and protein denaturing during device assembly. In this paper, we describe new methods to solve these problems. A "fishbone" microvalve design based on the concept of superhydrophobicity was developed to replace the capillary valve in applications where the chip surface requires protein blocking to prevent nonspecific binding. Our experimental results show that the valve functions well in a CD-like ELISA device. The packaging of biochips containing pre-loaded proteins is also a challenging task since conventional sealing methods often require the use of high temperatures, electric voltages, or organic solvents that are detrimental to the protein activity. Using CO2 gas to enhance the diffusion of polymer molecules near the device surface can result in good bonding at low temperatures and low pressure. This bonding method has little influence on the activity of the pre-loaded proteins after bonding.

Computational Design of DNA-Binding Proteins.

PubMed

Thyme, Summer; Song, Yifan

2016-01-01

Predicting the outcome of engineered and naturally occurring sequence perturbations to protein-DNA interfaces requires accurate computational modeling technologies. It has been well established that computational design to accommodate small numbers of DNA target site substitutions is possible. This chapter details the basic method of design used in the Rosetta macromolecular modeling program that has been successfully used to modulate the specificity of DNA-binding proteins. More recently, combining computational design and directed evolution has become a common approach for increasing the success rate of protein engineering projects. The power of such high-throughput screening depends on computational methods producing multiple potential solutions. Therefore, this chapter describes several protocols for increasing the diversity of designed output. Lastly, we describe an approach for building comparative models of protein-DNA complexes in order to utilize information from homologous sequences. These models can be used to explore how nature modulates specificity of protein-DNA interfaces and potentially can even be used as starting templates for further engineering.

Protein Design Using Unnatural Amino Acids

NASA Astrophysics Data System (ADS)

Bilgiçer, Basar; Kumar, Krishna

2003-11-01

With the increasing availability of whole organism genome sequences, understanding protein structure and function is of capital importance. Recent developments in the methodology of incorporation of unnatural amino acids into proteins allow the exploration of proteins at a very detailed level. Furthermore, de novo design of novel protein structures and function is feasible with unprecedented sophistication. Using examples from the literature, this article describes the available methods for unnatural amino acid incorporation and highlights some recent applications including the design of hyperstable protein folds.

Computational protein design with backbone plasticity

PubMed Central

MacDonald, James T.; Freemont, Paul S.

2016-01-01

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process. PMID:27911735

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

PubMed

Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

2018-05-08

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

Rational protein design: developing next-generation biological therapeutics and nanobiotechnological tools.

PubMed

Wilson, Corey J

2015-01-01

Proteins are the most functionally diverse macromolecules observed in nature, participating in a broad array of catalytic, biosensing, transport, scaffolding, and regulatory functions. Fittingly, proteins have become one of the most promising nanobiotechnological tools to date, and through the use of recombinant DNA and other laboratory methods we have produced a vast number of biological therapeutics derived from human genes. Our emerging ability to rationally design proteins (e.g., via computational methods) holds the promise of significantly expanding the number and diversity of protein therapies and has opened the gateway to realizing true and uncompromised personalized medicine. In the last decade computational protein design has been transformed from a set of fundamental strategies to stringently test our understanding of the protein structure-function relationship, to practical tools for developing useful biological processes, nano-devices, and novel therapeutics. As protein design strategies improve (i.e., in terms of accuracy and efficiency) clinicians will be able to leverage individual genetic data and biological metrics to develop and deliver personalized protein therapeutics with minimal delay. © 2014 Wiley Periodicals, Inc.

Toward high-resolution computational design of helical membrane protein structure and function

PubMed Central

Barth, Patrick; Senes, Alessandro

2016-01-01

The computational design of α-helical membrane proteins is still in its infancy but has made important progress. De novo design has produced stable, specific and active minimalistic oligomeric systems. Computational re-engineering can improve stability and modulate the function of natural membrane proteins. Currently, the major hurdle for the field is not computational, but the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress PMID:27273630

Toward high-resolution computational design of the structure and function of helical membrane proteins.

PubMed

Barth, Patrick; Senes, Alessandro

2016-06-07

The computational design of α-helical membrane proteins is still in its infancy but has already made great progress. De novo design allows stable, specific and active minimal oligomeric systems to be obtained. Computational reengineering can improve the stability and function of naturally occurring membrane proteins. Currently, the major hurdle for the field is the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress.

Design of structurally distinct proteins using strategies inspired by evolution

DOE PAGES

Jacobs, T. M.; Williams, B.; Williams, T.; ...

2016-05-06

Natural recombination combines pieces of preexisting proteins to create new tertiary structures and functions. In this paper, we describe a computational protocol, called SEWING, which is inspired by this process and builds new proteins from connected or disconnected pieces of existing structures. Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and, in some cases, remain folded at more than 100°C. High-resolution structures of the designed proteins CA01 and DA05R1 were solved by x-ray crystallography (2.2 angstrom resolution) and nuclear magnetic resonance, respectively, and there was excellent agreement with the design models. Finally, thismore » method provides a new strategy to rapidly create large numbers of diverse and designable protein scaffolds.« less

Crysalis: an integrated server for computational analysis and design of protein crystallization.

PubMed

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I; Lin, Donghai; Song, Jiangning

2016-02-24

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.

Crysalis: an integrated server for computational analysis and design of protein crystallization

PubMed Central

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I.; Lin, Donghai; Song, Jiangning

2016-01-01

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/. PMID:26906024

Designing Artificial Enzymes by Intuition and Computation

PubMed Central

Nanda, Vikas; Koder, Ronald L.

2012-01-01

The rational design of artificial enzymes either by applying physio-chemical intuition of protein structure and function or with the aid of computation methods is a promising area of research with the potential to tremendously impact medicine, industrial chemistry and energy production. Designed proteins also provide a powerful platform for dissecting enzyme mechanisms of natural systems. Artificial enzymes have come a long way, from simple α-helical peptide catalysts to proteins that facilitate multi-step chemical reactions designed by state-of-the-art computational methods. Looking forward, we examine strategies employed by natural enzymes which could be used to improve the speed and selectivity of artificial catalysts. PMID:21124375

Protein engineering and the use of molecular modeling and simulation: the case of heterodimeric Fc engineering.

PubMed

Spreter Von Kreudenstein, Thomas; Lario, Paula I; Dixit, Surjit B

2014-01-01

Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs. Copyright © 2013 Elsevier Inc. All rights reserved.

Integrating structure-based and ligand-based approaches for computational drug design.

PubMed

Wilson, Gregory L; Lill, Markus A

2011-04-01

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

Validating a Coarse-Grained Potential Energy Function through Protein Loop Modelling

PubMed Central

MacDonald, James T.; Kelley, Lawrence A.; Freemont, Paul S.

2013-01-01

Coarse-grained (CG) methods for sampling protein conformational space have the potential to increase computational efficiency by reducing the degrees of freedom. The gain in computational efficiency of CG methods often comes at the expense of non-protein like local conformational features. This could cause problems when transitioning to full atom models in a hierarchical framework. Here, a CG potential energy function was validated by applying it to the problem of loop prediction. A novel method to sample the conformational space of backbone atoms was benchmarked using a standard test set consisting of 351 distinct loops. This method used a sequence-independent CG potential energy function representing the protein using -carbon positions only and sampling conformations with a Monte Carlo simulated annealing based protocol. Backbone atoms were added using a method previously described and then gradient minimised in the Rosetta force field. Despite the CG potential energy function being sequence-independent, the method performed similarly to methods that explicitly use either fragments of known protein backbones with similar sequences or residue-specific /-maps to restrict the search space. The method was also able to predict with sub-Angstrom accuracy two out of seven loops from recently solved crystal structures of proteins with low sequence and structure similarity to previously deposited structures in the PDB. The ability to sample realistic loop conformations directly from a potential energy function enables the incorporation of additional geometric restraints and the use of more advanced sampling methods in a way that is not possible to do easily with fragment replacement methods and also enable multi-scale simulations for protein design and protein structure prediction. These restraints could be derived from experimental data or could be design restraints in the case of computational protein design. C++ source code is available for download from http://www.sbg.bio.ic.ac.uk/phyre2/PD2/. PMID:23824634

Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer.

PubMed

Berger, Stephanie; Procko, Erik; Margineantu, Daciana; Lee, Erinna F; Shen, Betty W; Zelter, Alex; Silva, Daniel-Adriano; Chawla, Kusum; Herold, Marco J; Garnier, Jean-Marc; Johnson, Richard; MacCoss, Michael J; Lessene, Guillaume; Davis, Trisha N; Stayton, Patrick S; Stoddard, Barry L; Fairlie, W Douglas; Hockenbery, David M; Baker, David

2016-11-02

Many cancers overexpress one or more of the six human pro-survival BCL2 family proteins to evade apoptosis. To determine which BCL2 protein or proteins block apoptosis in different cancers, we computationally designed three-helix bundle protein inhibitors specific for each BCL2 pro-survival protein. Following in vitro optimization, each inhibitor binds its target with high picomolar to low nanomolar affinity and at least 300-fold specificity. Expression of the designed inhibitors in human cancer cell lines revealed unique dependencies on BCL2 proteins for survival which could not be inferred from other BCL2 profiling methods. Our results show that designed inhibitors can be generated for each member of a closely-knit protein family to probe the importance of specific protein-protein interactions in complex biological processes.

Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis.

PubMed

Bai, Fang; Morcos, Faruck; Cheng, Ryan R; Jiang, Hualiang; Onuchic, José N

2016-12-13

Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

M13 bacteriophage coat proteins engineered for improved phage display.

PubMed

Sidhu, Sachdev S; Feld, Birte K; Weiss, Gregory A

2007-01-01

This chapter describes a method for increasing levels of protein fusions displayed on the surfaces of M13 bacteriophage particles. By introducing mutations into the anchoring M13 coat protein, protein display levels can be increased by up to two orders of magnitude. Experimental methods are presented for the design, construction, and screening of phage-displayed libraries for improved protein display.

Selection of stably folded proteins by phage-display with proteolysis.

PubMed

Bai, Yawen; Feng, Hanqiao

2004-05-01

To facilitate the process of protein design and learn the basic rules that control the structure and stability of proteins, combinatorial methods have been developed to select or screen proteins with desired properties from libraries of mutants. One such method uses phage-display and proteolysis to select stably folded proteins. This method does not rely on specific properties of proteins for selection. Therefore, in principle it can be applied to any protein. Since its first demonstration in 1998, the method has been used to create hyperthermophilic proteins, to evolve novel folded domains from a library generated by combinatorial shuffling of polypeptide segments and to convert a partially unfolded structure to a fully folded protein.

PepComposer: computational design of peptides binding to a given protein surface

PubMed Central

Obarska-Kosinska, Agnieszka; Iacoangeli, Alfredo; Lepore, Rosalba; Tramontano, Anna

2016-01-01

There is a wide interest in designing peptides able to bind to a specific region of a protein with the aim of interfering with a known interaction or as starting point for the design of inhibitors. Here we describe PepComposer, a new pipeline for the computational design of peptides binding to a given protein surface. PepComposer only requires the target protein structure and an approximate definition of the binding site as input. We first retrieve a set of peptide backbone scaffolds from monomeric proteins that harbor the same backbone arrangement as the binding site of the protein of interest. Next, we design optimal sequences for the identified peptide scaffolds. The method is fully automatic and available as a web server at http://biocomputing.it/pepcomposer/webserver. PMID:27131789

Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer

PubMed Central

Berger, Stephanie; Procko, Erik; Margineantu, Daciana; Lee, Erinna F; Shen, Betty W; Zelter, Alex; Silva, Daniel-Adriano; Chawla, Kusum; Herold, Marco J; Garnier, Jean-Marc; Johnson, Richard; MacCoss, Michael J; Lessene, Guillaume; Davis, Trisha N; Stayton, Patrick S; Stoddard, Barry L; Fairlie, W Douglas; Hockenbery, David M; Baker, David

2016-01-01

Many cancers overexpress one or more of the six human pro-survival BCL2 family proteins to evade apoptosis. To determine which BCL2 protein or proteins block apoptosis in different cancers, we computationally designed three-helix bundle protein inhibitors specific for each BCL2 pro-survival protein. Following in vitro optimization, each inhibitor binds its target with high picomolar to low nanomolar affinity and at least 300-fold specificity. Expression of the designed inhibitors in human cancer cell lines revealed unique dependencies on BCL2 proteins for survival which could not be inferred from other BCL2 profiling methods. Our results show that designed inhibitors can be generated for each member of a closely-knit protein family to probe the importance of specific protein-protein interactions in complex biological processes. DOI: http://dx.doi.org/10.7554/eLife.20352.001 PMID:27805565

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method.

PubMed

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

NASA Astrophysics Data System (ADS)

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Protein-Protein Docking in Drug Design and Discovery.

PubMed

Kaczor, Agnieszka A; Bartuzi, Damian; Stępniewski, Tomasz Maciej; Matosiuk, Dariusz; Selent, Jana

2018-01-01

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

Optimizing energy functions for protein-protein interface design.

PubMed

Sharabi, Oz; Yanover, Chen; Dekel, Ayelet; Shifman, Julia M

2011-01-15

Protein design methods have been originally developed for the design of monomeric proteins. When applied to the more challenging task of protein–protein complex design, these methods yield suboptimal results. In particular, they often fail to recapitulate favorable hydrogen bonds and electrostatic interactions across the interface. In this work, we aim to improve the energy function of the protein design program ORBIT to better account for binding interactions between proteins. By using the advanced machine learning framework of conditional random fields, we optimize the relative importance of all the terms in the energy function, attempting to reproduce the native side-chain conformations in protein–protein interfaces. We evaluate the performance of several optimized energy functions, each describes the van der Waals interactions using a different potential. In comparison with the original energy function, our best energy function (a) incorporates a much “softer” repulsive van der Waals potential, suitable for the discrete rotameric representation of amino acid side chains; (b) does not penalize burial of polar atoms, reflecting the frequent occurrence of polar buried residues in protein–protein interfaces; and (c) significantly up-weights the electrostatic term, attesting to the high importance of these interactions for protein–protein complex formation. Using this energy function considerably improves side chain placement accuracy for interface residues in a large test set of protein–protein complexes. Moreover, the optimized energy function recovers the native sequences of protein–protein interface at a higher rate than the default function and performs substantially better in predicting changes in free energy of binding due to mutations.

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity.

PubMed

Ollikainen, Noah; de Jong, René M; Kortemme, Tanja

2015-01-01

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein-ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art "fixed backbone" design methods perform poorly on these tests, we develop a new "coupled moves" design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein-ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.

High-resolution protein design with backbone freedom.

PubMed

Harbury, P B; Plecs, J J; Tidor, B; Alber, T; Kim, P S

1998-11-20

Recent advances in computational techniques have allowed the design of precise side-chain packing in proteins with predetermined, naturally occurring backbone structures. Because these methods do not model protein main-chain flexibility, they lack the breadth to explore novel backbone conformations. Here the de novo design of a family of alpha-helical bundle proteins with a right-handed superhelical twist is described. In the design, the overall protein fold was specified by hydrophobic-polar residue patterning, whereas the bundle oligomerization state, detailed main-chain conformation, and interior side-chain rotamers were engineered by computational enumerations of packing in alternate backbone structures. Main-chain flexibility was incorporated through an algebraic parameterization of the backbone. The designed peptides form alpha-helical dimers, trimers, and tetramers in accord with the design goals. The crystal structure of the tetramer matches the designed structure in atomic detail.

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

PubMed

Wei, Qing; La, David; Kihara, Daisuke

2017-01-01

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

Structural test of the parameterized-backbone method for protein design.

PubMed

Plecs, Joseph J; Harbury, Pehr B; Kim, Peter S; Alber, Tom

2004-09-03

Designing new protein folds requires a method for simultaneously optimizing the conformation of the backbone and the side-chains. One approach to this problem is the use of a parameterized backbone, which allows the systematic exploration of families of structures. We report the crystal structure of RH3, a right-handed, three-helix coiled coil that was designed using a parameterized backbone and detailed modeling of core packing. This crystal structure was determined using another rationally designed feature, a metal-binding site that permitted experimental phasing of the X-ray data. RH3 adopted the intended fold, which has not been observed previously in biological proteins. Unanticipated structural asymmetry in the trimer was a principal source of variation within the RH3 structure. The sequence of RH3 differs from that of a previously characterized right-handed tetramer, RH4, at only one position in each 11 amino acid sequence repeat. This close similarity indicates that the design method is sensitive to the core packing interactions that specify the protein structure. Comparison of the structures of RH3 and RH4 indicates that both steric overlap and cavity formation provide strong driving forces for oligomer specificity.

Protein Engineering Approaches in the Post-Genomic Era.

PubMed

Singh, Raushan K; Lee, Jung-Kul; Selvaraj, Chandrabose; Singh, Ranjitha; Li, Jinglin; Kim, Sang-Yong; Kalia, Vipin C

2018-01-01

Proteins are one of the most multifaceted macromolecules in living systems. Proteins have evolved to function under physiological conditions and, therefore, are not usually tolerant of harsh experimental and environmental conditions. The growing use of proteins in industrial processes as a greener alternative to chemical catalysts often demands constant innovation to improve their performance. Protein engineering aims to design new proteins or modify the sequence of a protein to create proteins with new or desirable functions. With the emergence of structural and functional genomics, protein engineering has been invigorated in the post-genomic era. The three-dimensional structures of proteins with known functions facilitate protein engineering approaches to design variants with desired properties. There are three major approaches of protein engineering research, namely, directed evolution, rational design, and de novo design. Rational design is an effective method of protein engineering when the threedimensional structure and mechanism of the protein is well known. In contrast, directed evolution does not require extensive information and a three-dimensional structure of the protein of interest. Instead, it involves random mutagenesis and selection to screen enzymes with desired properties. De novo design uses computational protein design algorithms to tailor synthetic proteins by using the three-dimensional structures of natural proteins and their folding rules. The present review highlights and summarizes recent protein engineering approaches, and their challenges and limitations in the post-genomic era. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

PubMed

Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

2016-06-07

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

PubMed

He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

2012-03-01

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. Copyright © 2011 Wiley Periodicals, Inc.

Computational Design of Ligand Binding Proteins with High Affinity and Selectivity

PubMed Central

Dou, Jiayi; Doyle, Lindsey; Nelson, Jorgen W.; Schena, Alberto; Jankowski, Wojciech; Kalodimos, Charalampos G.; Johnsson, Kai; Stoddard, Barry L.; Baker, David

2014-01-01

The ability to design proteins with high affinity and selectivity for any given small molecule would have numerous applications in biosensing, diagnostics, and therapeutics, and is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition phenomena. Attempts to design ligand binding proteins have met with little success, however, and the computational design of precise molecular recognition between proteins and small molecules remains an “unsolved problem”1. We describe a general method for the computational design of small molecule binding sites with pre-organized hydrogen bonding and hydrophobic interfaces and high overall shape complementary to the ligand, and use it to design protein binding sites for the steroid digoxigenin (DIG). Of 17 designs that were experimentally characterized, two bind DIG; the highest affinity design has the lowest predicted interaction energy and the most pre-organized binding site in the set. A comprehensive binding-fitness landscape of this design generated by library selection and deep sequencing was used to guide optimization of binding affinity to a picomolar level, and two X-ray co-crystal structures of optimized complexes show atomic level agreement with the design models. The designed binder has a high selectivity for DIG over the related steroids digitoxigenin, progesterone, and β-estradiol, which can be reprogrammed through the designed hydrogen-bonding interactions. Taken together, the binding fitness landscape, co-crystal structures, and thermodynamic binding parameters illustrate how increases in binding affinity can result from distal sequence changes that limit the protein ensemble to conformers making the most energetically favorable interactions with the ligand. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics, and diagnostics. PMID:24005320

An accurate binding interaction model in de novo computational protein design of interactions: if you build it, they will bind.

PubMed

London, Nir; Ambroggio, Xavier

2014-02-01

Computational protein design efforts aim to create novel proteins and functions in an automated manner and, in the process, these efforts shed light on the factors shaping natural proteins. The focus of these efforts has progressed from the interior of proteins to their surface and the design of functions, such as binding or catalysis. Here we examine progress in the development of robust methods for the computational design of non-natural interactions between proteins and molecular targets such as other proteins or small molecules. This problem is referred to as the de novo computational design of interactions. Recent successful efforts in de novo enzyme design and the de novo design of protein-protein interactions open a path towards solving this problem. We examine the common themes in these efforts, and review recent studies aimed at understanding the nature of successes and failures in the de novo computational design of interactions. While several approaches culminated in success, the use of a well-defined structural model for a specific binding interaction in particular has emerged as a key strategy for a successful design, and is therefore reviewed with special consideration. Copyright © 2013 Elsevier Inc. All rights reserved.

Ligand-regulated peptides: a general approach for modulating protein-peptide interactions with small molecules.

PubMed

Binkowski, Brock F; Miller, Russell A; Belshaw, Peter J

2005-07-01

We engineered a novel ligand-regulated peptide (LiRP) system where the binding activity of intracellular peptides is controlled by a cell-permeable small molecule. In the absence of ligand, peptides expressed as fusions in an FKBP-peptide-FRB-GST LiRP scaffold protein are free to interact with target proteins. In the presence of the ligand rapamycin, or the nonimmunosuppressive rapamycin derivative AP23102, the scaffold protein undergoes a conformational change that prevents the interaction of the peptide with the target protein. The modular design of the scaffold enables the creation of LiRPs through rational design or selection from combinatorial peptide libraries. Using these methods, we identified LiRPs that interact with three independent targets: retinoblastoma protein, c-Src, and the AMP-activated protein kinase. The LiRP system should provide a general method to temporally and spatially regulate protein function in cells and organisms.

Rational design of alpha-helical tandem repeat proteins with closed architectures

PubMed Central

Doyle, Lindsey; Hallinan, Jazmine; Bolduc, Jill; Parmeggiani, Fabio; Baker, David; Stoddard, Barry L.; Bradley, Philip

2015-01-01

Tandem repeat proteins, which are formed by repetition of modular units of protein sequence and structure, play important biological roles as macromolecular binding and scaffolding domains, enzymes, and building blocks for the assembly of fibrous materials1,2. The modular nature of repeat proteins enables the rapid construction and diversification of extended binding surfaces by duplication and recombination of simple building blocks3,4. The overall architecture of tandem repeat protein structures – which is dictated by the internal geometry and local packing of the repeat building blocks – is highly diverse, ranging from extended, super-helical folds that bind peptide, DNA, and RNA partners5–9, to closed and compact conformations with internal cavities suitable for small molecule binding and catalysis10. Here we report the development and validation of computational methods for de novo design of tandem repeat protein architectures driven purely by geometric criteria defining the inter-repeat geometry, without reference to the sequences and structures of existing repeat protein families. We have applied these methods to design a series of closed alpha-solenoid11 repeat structures (alpha-toroids) in which the inter-repeat packing geometry is constrained so as to juxtapose the N- and C-termini; several of these designed structures have been validated by X-ray crystallography. Unlike previous approaches to tandem repeat protein engineering12–20, our design procedure does not rely on template sequence or structural information taken from natural repeat proteins and hence can produce structures unlike those seen in nature. As an example, we have successfully designed and validated closed alpha-solenoid repeats with a left-handed helical architecture that – to our knowledge – is not yet present in the protein structure database21. PMID:26675735

Increasing Sequence Diversity with Flexible Backbone Protein Design: The Complete Redesign of a Protein Hydrophobic Core

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

Murphy, Grant S.; Mills, Jeffrey L.; Miley, Michael J.

2015-10-15

Protein design tests our understanding of protein stability and structure. Successful design methods should allow the exploration of sequence space not found in nature. However, when redesigning naturally occurring protein structures, most fixed backbone design algorithms return amino acid sequences that share strong sequence identity with wild-type sequences, especially in the protein core. This behavior places a restriction on functional space that can be explored and is not consistent with observations from nature, where sequences of low identity have similar structures. Here, we allow backbone flexibility during design to mutate every position in the core (38 residues) of a four-helixmore » bundle protein. Only small perturbations to the backbone, 12 {angstrom}, were needed to entirely mutate the core. The redesigned protein, DRNN, is exceptionally stable (melting point >140C). An NMR and X-ray crystal structure show that the side chains and backbone were accurately modeled (all-atom RMSD = 1.3 {angstrom}).« less

Elfin: An algorithm for the computational design of custom three-dimensional structures from modular repeat protein building blocks.

PubMed

Yeh, Chun-Ting; Brunette, T J; Baker, David; McIntosh-Smith, Simon; Parmeggiani, Fabio

2018-02-01

Computational protein design methods have enabled the design of novel protein structures, but they are often still limited to small proteins and symmetric systems. To expand the size of designable proteins while controlling the overall structure, we developed Elfin, a genetic algorithm for the design of novel proteins with custom shapes using structural building blocks derived from experimentally verified repeat proteins. By combining building blocks with compatible interfaces, it is possible to rapidly build non-symmetric large structures (>1000 amino acids) that match three-dimensional geometric descriptions provided by the user. A run time of about 20min on a laptop computer for a 3000 amino acid structure makes Elfin accessible to users with limited computational resources. Protein structures with controlled geometry will allow the systematic study of the effect of spatial arrangement of enzymes and signaling molecules, and provide new scaffolds for functional nanomaterials. Copyright © 2017 Elsevier Inc. All rights reserved.

A global optimization algorithm for protein surface alignment

PubMed Central

2010-01-01

Background A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved. Results In this paper we propose a new method for local structural alignment of protein surfaces based on continuous global optimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach. Conclusions Our method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites. PMID:20920230

Fast computational methods for predicting protein structure from primary amino acid sequence

DOEpatents

Agarwal, Pratul Kumar [Knoxville, TN

2011-07-19

The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design.

PubMed

Ludwiczak, Jan; Jarmula, Adam; Dunin-Horkawicz, Stanislaw

2018-07-01

Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20-30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences. Copyright © 2018 Elsevier Inc. All rights reserved.

Putting engineering back into protein engineering: bioinformatic approaches to catalyst design.

PubMed

Gustafsson, Claes; Govindarajan, Sridhar; Minshull, Jeremy

2003-08-01

Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.

Designed protein reveals structural determinants of extreme kinetic stability

PubMed Central

Broom, Aron; Ma, S. Martha; Xia, Ke; Rafalia, Hitesh; Trainor, Kyle; Colón, Wilfredo; Gosavi, Shachi; Meiering, Elizabeth M.

2015-01-01

The design of stable, functional proteins is difficult. Improved design requires a deeper knowledge of the molecular basis for design outcomes and properties. We previously used a bioinformatics and energy function method to design a symmetric superfold protein composed of repeating structural elements with multivalent carbohydrate-binding function, called ThreeFoil. This and similar methods have produced a notably high yield of stable proteins. Using a battery of experimental and computational analyses we show that despite its small size and lack of disulfide bonds, ThreeFoil has remarkably high kinetic stability and its folding is specifically chaperoned by carbohydrate binding. It is also extremely stable against thermal and chemical denaturation and proteolytic degradation. We demonstrate that the kinetic stability can be predicted and modeled using absolute contact order (ACO) and long-range order (LRO), as well as coarse-grained simulations; the stability arises from a topology that includes many long-range contacts which create a large and highly cooperative energy barrier for unfolding and folding. Extensive data from proteomic screens and other experiments reveal that a high ACO/LRO is a general feature of proteins with strong resistances to denaturation and degradation. These results provide tractable approaches for predicting resistance and designing proteins with sufficient topological complexity and long-range interactions to accommodate destabilizing functional features as well as withstand chemical and proteolytic challenge. PMID:26554002

Symmetry based assembly of a 2 dimensional protein lattice

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

Poulos, Sandra; Agah, Sayeh; Jallah, Nikardi

2017-04-18

The design of proteins that self-assemble into higher order architectures is of great interest due to their potential application in nanotechnology. Specifically, the self-assembly of proteins into ordered lattices is of special interest to the field of structural biology. Here we designed a 2 dimensional (2D) protein lattice using a fusion of a tandem repeat of three TelSAM domains (TTT) to the Ferric uptake regulator (FUR) domain. We determined the structure of the designed (TTT-FUR) fusion protein to 2.3 Å by X-ray crystallographic methods. In agreement with the design, a 2D lattice composed of TelSAM fibers interdigitated by the FURmore » domain was observed. As expected, the fusion of a tandem repeat of three TelSAM domains formed 21 screw axis, and the self-assembly of the ordered oligomer was under pH control. We demonstrated that the fusion of TTT to a domain having a 2-fold symmetry, such as the FUR domain, can produce an ordered 2D lattice. The TTT-FUR system combines features from the rotational symmetry matching approach with the oligomer driven crystallization method. This TTT-FUR fusion was amenable to X-ray crystallographic methods, and is a promising crystallization chaperone.« less

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

Parallel Computational Protein Design.

PubMed

Zhou, Yichao; Donald, Bruce R; Zeng, Jianyang

2017-01-01

Computational structure-based protein design (CSPD) is an important problem in computational biology, which aims to design or improve a prescribed protein function based on a protein structure template. It provides a practical tool for real-world protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational protein design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab (Gainza et al., Methods Enzymol 523:87, 2013) to implement a GPU-based massively parallel A* algorithm for improving protein design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large protein design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE (Gainza et al., PLoS Comput Biol 8:e1002335, 2012) and DEEPer (Hallen et al., Proteins 81:18-39, 2013) to also consider continuous backbone and side-chain flexibility.

Rational design of crystal contact-free space in protein crystals for analyzing spatial distribution of motions within protein molecules.

PubMed

Matsuoka, Rei; Shimada, Atsushi; Komuro, Yasuaki; Sugita, Yuji; Kohda, Daisuke

2016-03-01

Contacts with neighboring molecules in protein crystals inevitably restrict the internal motions of intrinsically flexible proteins. The resultant clear electron densities permit model building, as crystallographic snapshot structures. Although these still images are informative, they could provide biased pictures of the protein motions. If the mobile parts are located at a site lacking direct contacts in rationally designed crystals, then the amplitude of the movements can be experimentally analyzed. We propose a fusion protein method, to create crystal contact-free space (CCFS) in protein crystals and to place the mobile parts in the CCFS. Conventional model building fails when large amplitude motions exist. In this study, the mobile parts appear as smeared electron densities in the CCFS, by suitable processing of the X-ray diffraction data. We applied the CCFS method to a highly mobile presequence peptide bound to the mitochondrial import receptor, Tom20, and a catalytically relevant flexible segment in the oligosaccharyltransferase, AglB. These two examples demonstrated the general applicability of the CCFS method to the analysis of the spatial distribution of motions within protein molecules. © 2016 The Protein Society.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

PubMed Central

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-01-01

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. PMID:27958331

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

PubMed

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-12-13

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

Application of Box-Behnken experimental design to optimize the extraction of insecticidal Cry1Ac from soil.

PubMed

Li, Yan-Liang; Fang, Zhi-Xiang; You, Jing

2013-02-20

A validated method for analyzing Cry proteins is a premise to study the fate and ecological effects of contaminants associated with genetically engineered Bacillus thuringiensis crops. The current study has optimized the extraction method to analyze Cry1Ac protein in soil using a response surface methodology with a three-level-three-factor Box-Behnken experimental design (BBD). The optimum extraction conditions were at 21 °C and 630 rpm for 2 h. Regression analysis showed a good fit of the experimental data to the second-order polynomial model with a coefficient of determination of 0.96. The method was sensitive and precise with a method detection limit of 0.8 ng/g dry weight and relative standard deviations at 7.3%. Finally, the established method was applied for analyzing Cry1Ac protein residues in field-collected soil samples. Trace amounts of Cry1Ac protein were detected in the soils where transgenic crops have been planted for 8 and 12 years.

Artificial enzymes with protein scaffolds: structural design and modification.

PubMed

Matsuo, Takashi; Hirota, Shun

2014-10-15

Recent development in biochemical experiment techniques and bioinformatics has enabled us to create a variety of artificial biocatalysts with protein scaffolds (namely 'artificial enzymes'). The construction methods of these catalysts include genetic mutation, chemical modification using synthetic molecules and/or a combination of these methods. Designed evolution strategy based on the structural information of host proteins has become more and more popular as an effective approach to construct artificial protein-based biocatalysts with desired reactivities. From the viewpoint of application of artificial enzymes for organic synthesis, recently constructed artificial enzymes mediating oxidation, reduction and C-C bond formation/cleavage are introduced in this review article. Copyright © 2014 Elsevier Ltd. All rights reserved.

Redesigning the specificity of protein-DNA interactions with Rosetta.

PubMed

Thyme, Summer; Baker, David

2014-01-01

Building protein tools that can selectively bind or cleave specific DNA sequences requires efficient technologies for modifying protein-DNA interactions. Computational design is one method for accomplishing this goal. In this chapter, we present the current state of protein-DNA interface design with the Rosetta macromolecular modeling program. The LAGLIDADG endonuclease family of DNA-cleaving enzymes, under study as potential gene therapy reagents, has been the main testing ground for these in silico protocols. At this time, the computational methods are most useful for designing endonuclease variants that can accommodate small numbers of target site substitutions. Attempts to engineer for more extensive interface changes will likely benefit from an approach that uses the computational design results in conjunction with a high-throughput directed evolution or screening procedure. The family of enzymes presents an engineering challenge because their interfaces are highly integrated and there is significant coordination between the binding and catalysis events. Future developments in the computational algorithms depend on experimental feedback to improve understanding and modeling of these complex enzymatic features. This chapter presents both the basic method of design that has been successfully used to modulate specificity and more advanced procedures that incorporate DNA flexibility and other properties that are likely necessary for reliable modeling of more extensive target site changes.

From Structure to Function: A Comprehensive Compendium of Tools to Unveil Protein Domains and Understand Their Role in Cytokinesis.

PubMed

Rincon, Sergio A; Paoletti, Anne

2016-01-01

Unveiling the function of a novel protein is a challenging task that requires careful experimental design. Yeast cytokinesis is a conserved process that involves modular structural and regulatory proteins. For such proteins, an important step is to identify their domains and structural organization. Here we briefly discuss a collection of methods commonly used for sequence alignment and prediction of protein structure that represent powerful tools for the identification homologous domains and design of structure-function approaches to test experimentally the function of multi-domain proteins such as those implicated in yeast cytokinesis.

"Print-n-Shrink" technology for the rapid production of microfluidic chips and protein microarrays.

PubMed

Sollier, Kevin; Mandon, Céline A; Heyries, Kevin A; Blum, Loïc J; Marquette, Christophe A

2009-12-21

An innovative method for the production of microfluidic chips integrating protein spots is described. The technology, called "Print-n-Shrink", is based on the screen-printing of a microfluidic design (using a dielectric ink) onto Polyshrink polystyrene sheets. The initial print which has a minimum size of 15 microm (height) x 230 microm (width) is thermally treated (30 seconds, 163 degrees C) to shrink and generate features of 85 microm (height) x 100 microm (width). Concomitantly, proteins such as monoclonal antibodies or cellular adhesion proteins are spotted onto the Polyshrink sheets and shrunk together with the microfluidic design, creating a complete biochip integrating both complex microfluidic designs and protein spots for bioanalytical applications.

Protein-protein interface analysis and hot spots identification for chemical ligand design.

PubMed

Chen, Jing; Ma, Xiaomin; Yuan, Yaxia; Pei, Jianfeng; Lai, Luhua

2014-01-01

Rational design for chemical compounds targeting protein-protein interactions has grown from a dream to reality after a decade of efforts. There are an increasing number of successful examples, though major challenges remain in the field. In this paper, we will first give a brief review of the available methods that can be used to analyze protein-protein interface and predict hot spots for chemical ligand design. New developments of binding sites detection, ligandability and hot spots prediction from the author's group will also be described. Pocket V.3 is an improved program for identifying hot spots in protein-protein interface using only an apo protein structure. It has been developed based on Pocket V.2 that can derive receptor-based pharmacophore model for ligand binding cavity. Given similarities and differences between the essence of pharmacophore and hot spots for guiding design of chemical compounds, not only energetic but also spatial properties of protein-protein interface are used in Pocket V.3 for dealing with protein-protein interface. In order to illustrate the capability of Pocket V.3, two datasets have been used. One is taken from ASEdb and BID having experimental alanine scanning results for testing hot spots prediction. The other is taken from the 2P2I database containing complex structures of protein-ligand binding at the original protein-protein interface for testing hot spots application in ligand design.

A Novel Domain Assembly Routine for Creating Full-Length Models of Membrane Proteins from Known Domain Structures.

PubMed

Koehler Leman, Julia; Bonneau, Richard

2018-04-03

Membrane proteins composed of soluble and membrane domains are often studied one domain at a time. However, to understand the biological function of entire protein systems and their interactions with each other and drugs, knowledge of full-length structures or models is required. Although few computational methods exist that could potentially be used to model full-length constructs of membrane proteins, none of these methods are perfectly suited for the problem at hand. Existing methods require an interface or knowledge of the relative orientations of the domains or are not designed for domain assembly, and none of them are developed for membrane proteins. Here we describe the first domain assembly protocol specifically designed for membrane proteins that assembles intra- and extracellular soluble domains and the transmembrane domain into models of the full-length membrane protein. Our protocol does not require an interface between the domains and samples possible domain orientations based on backbone dihedrals in the flexible linker regions, created via fragment insertion, while keeping the transmembrane domain fixed in the membrane. For five examples tested, our method mp_domain_assembly, implemented in RosettaMP, samples domain orientations close to the known structure and is best used in conjunction with experimental data to reduce the conformational search space.

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

PubMed

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

2015-04-01

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

Terminal sequence importance of de novo proteins from binary-patterned library: stable artificial proteins with 11- or 12-amino acid alphabet.

PubMed

Okura, Hiromichi; Takahashi, Tsuyoshi; Mihara, Hisakazu

2012-06-01

Successful approaches of de novo protein design suggest a great potential to create novel structural folds and to understand natural rules of protein folding. For these purposes, smaller and simpler de novo proteins have been developed. Here, we constructed smaller proteins by removing the terminal sequences from stable de novo vTAJ proteins and compared stabilities between mutant and original proteins. vTAJ proteins were screened from an α3β3 binary-patterned library which was designed with polar/ nonpolar periodicities of α-helix and β-sheet. vTAJ proteins have the additional terminal sequences due to the method of constructing the genetically repeated library sequences. By removing the parts of the sequences, we successfully obtained the stable smaller de novo protein mutants with fewer amino acid alphabets than the originals. However, these mutants showed the differences on ANS binding properties and stabilities against denaturant and pH change. The terminal sequences, which were designed just as flexible linkers not as secondary structure units, sufficiently affected these physicochemical details. This study showed implications for adjusting protein stabilities by designing N- and C-terminal sequences.

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

PubMed

Lee, Hui Sun; Im, Wonpil

2016-04-01

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. © 2016 The Protein Society.

Hot-spot analysis for drug discovery targeting protein-protein interactions.

PubMed

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Screening of the binding of small molecules to proteins by desorption electrospray ionization mass spectrometry combined with protein microarray.

PubMed

Yao, Chenxi; Wang, Tao; Zhang, Buqing; He, Dacheng; Na, Na; Ouyang, Jin

2015-11-01

The interaction between bioactive small molecule ligands and proteins is one of the important research areas in proteomics. Herein, a simple and rapid method is established to screen small ligands that bind to proteins. We designed an agarose slide to immobilize different proteins. The protein microarrays were allowed to interact with different small ligands, and after washing, the microarrays were screened by desorption electrospray ionization mass spectrometry (DESI MS). This method can be applied to screen specific protein binding ligands and was shown for seven proteins and 34 known ligands for these proteins. In addition, a high-throughput screening was achieved, with the analysis requiring approximately 4 s for one sample spot. We then applied this method to determine the binding between the important protein matrix metalloproteinase-9 (MMP-9) and 88 small compounds. The molecular docking results confirmed the MS results, demonstrating that this method is suitable for the rapid and accurate screening of ligands binding to proteins. Graphical Abstract ᅟ.

Electrostatic Similarities between Protein and Small Molecule Ligands Facilitate the Design of Protein-Protein Interaction Inhibitors

PubMed Central

Zhang, Kam Y. J.

2013-01-01

One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs). We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research) SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs. PMID:24130741

Designed Proteins as Novel Imaging Reagents in Living Escherichia coli.

PubMed

Pratt, Susan E; Speltz, Elizabeth B; Mochrie, Simon G J; Regan, Lynne

2016-09-02

Fluorescence imaging is a powerful tool to study protein function in living cells. Here, we introduce a novel imaging strategy that is fully genetically encodable, does not require the use of exogenous substrates, and adds a minimally disruptive tag to the protein of interest (POI). Our method was based on a set of designed tetratricopeptide repeat affinity proteins (TRAPs) that specifically and reversibly interact with a short, extended peptide tag. We co-expressed the TRAPs fused to fluorescent proteins (FPs) and the peptide tags fused to the POIs. We illustrated the method using the Escherichia coli protein FtsZ and showed that our system could track distinct FtsZ structures under both low and high expression conditions in live cells. We anticipate that our imaging strategy will be a useful tool for imaging the subcellular localization of many proteins, especially those recalcitrant to imaging by direct tagging with FPs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Beyond directed evolution - semi-rational protein engineering and design

PubMed Central

Lutz, Stefan

2010-01-01

Over the last two decades, directed evolution has transformed the field of protein engineering. The advances in understanding protein structure and function, in no insignificant part a result of directed evolution studies, are increasingly empowering scientists and engineers to device more effective methods for manipulating and tailoring biocatalysts. Abandoning large combinatorial libraries, the focus has shifted to small, functionally-rich libraries and rational design. A critical component to the success of these emerging engineering strategies are computational tools for the evaluation of protein sequence datasets and the analysis of conformational variations of amino acids in proteins. Highlighting the opportunities and limitations of such approaches, this review focuses on recent engineering and design examples that require screening or selection of small libraries. PMID:20869867

Promoting protein crystallization using a plate with simple geometry.

PubMed

Chen, Rui-Qing; Yin, Da-Chuan; Liu, Yong-Ming; Lu, Qin-Qin; He, Jin; Liu, Yue

2014-03-01

Increasing the probability of obtaining protein crystals in crystallization screening is always an important goal for protein crystallography. In this paper, a new method called the cross-diffusion microbatch (CDM) method is presented, which aims to efficiently promote protein crystallization and increase the chance of obtaining protein crystals. In this method, a very simple crystallization plate was designed in which all crystallization droplets are in one sealed space, so that a variety of volatile components from one droplet can diffuse into any other droplet via vapour diffusion. Crystallization screening and reproducibility tests indicate that this method could be a potentially powerful technique in practical protein crystallization screening. It can help to obtain crystals with higher probability and at a lower cost, while using a simple and easy procedure.

Prediction of essential proteins based on gene expression programming.

PubMed

Zhong, Jiancheng; Wang, Jianxin; Peng, Wei; Zhang, Zhen; Pan, Yi

2013-01-01

Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.

Electrostatic design of protein-protein association rates.

PubMed

Schreiber, Gideon; Shaul, Yossi; Gottschalk, Kay E

2006-01-01

De novo design and redesign of proteins and protein complexes have made promising progress in recent years. Here, we give an overview of how to use available computer-based tools to design proteins to bind faster and tighter to their protein-complex partner by electrostatic optimization between the two proteins. Electrostatic optimization is possible because of the simple relation between the Debye-Huckel energy of interaction between a pair of proteins and their rate of association. This can be used for rapid, structure-based calculations of the electrostatic attraction between the two proteins in the complex. Using these principles, we developed two computer programs that predict the change in k(on), and as such the affinity, on introducing charged mutations. The two programs have a web interface that is available at www.weizmann.ac.il/home/bcges/PARE.html and http://bip.weizmann.ac.il/hypare. When mutations leading to charge optimization are introduced outside the physical binding site, the rate of dissociation is unchanged and therefore the change in k(on) parallels that of the affinity. This design method was evaluated on a number of different protein complexes resulting in binding rates and affinities of hundreds of fold faster and tighter compared to wild type. In this chapter, we demonstrate the procedure and go step by step over the methodology of using these programs for protein-association design. Finally, the way to easily implement the principle of electrostatic design for any protein complex of choice is shown.

Computational design of water-soluble α-helical barrels.

PubMed

Thomson, Andrew R; Wood, Christopher W; Burton, Antony J; Bartlett, Gail J; Sessions, Richard B; Brady, R Leo; Woolfson, Derek N

2014-10-24

The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function. Copyright © 2014, American Association for the Advancement of Science.

Assessment of Protein Side-Chain Conformation Prediction Methods in Different Residue Environments

PubMed Central

Peterson, Lenna X.; Kang, Xuejiao; Kihara, Daisuke

2016-01-01

Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers. PMID:24619909

Achievements and Challenges in Computational Protein Design.

PubMed

Samish, Ilan

2017-01-01

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Benchmark analysis of native and artificial NAD+-dependent enzymes generated by a sequence based design method with or without phylogenetic data.

PubMed

Nakano, Shogo; Motoyama, Tomoharu; Miyashita, Yurina; Ishizuka, Yuki; Matsuo, Naoya; Tokiwa, Hiroaki; Shinoda, Suguru; Asano, Yasuhisa; Ito, Sohei

2018-05-22

The expansion of protein sequence databases has enabled us to design artificial proteins by sequence-based design methods, such as full consensus design (FCD) and ancestral sequence reconstruction (ASR). Artificial proteins with enhanced activity levels compared with native ones can potentially be generated by such methods, but successful design is rare because preparing a sequence library by curating the database and selecting a method is difficult. Utilizing a curated library prepared by reducing conservation energies, we successfully designed two artificial L-threonine 3-dehydrogenase (SDR-TDH) with higher activity levels than native SDR-TDH, FcTDH-N1 and AncTDH, using FCD and ASR, respectively. The artificial SDR-TDHs had excellent thermal stability and NAD+ recognition compared to native SDR-TDH from Cupriavidus necator (CnTDH): the melting temperatures of FcTDH-N1 and AncTDH were about 10 and 5°C higher than CnTDH, respectively, and the dissociation constants toward NAD+ of FcTDH-N1 and AncTDH were two- and seven-fold lower than that of CnTDH, respectively. Enzymatic efficiency of the artificial SDR-TDHs were comparable to that of CnTDH. Crystal structures of FcTDH-N1 and AncTDH were determined at 2.8 and 2.1 Å resolution, respectively. Structural and MD simulation analysis of the SDR-TDHs indicated that only the flexibility at specific regions was changed, suggesting that multiple mutations introduced in the artificial SDR-TDHs altered their flexibility and thereby affected their enzymatic properties. Benchmark analysis of the SDR-TDHs indicated that both FCD and ASR can generate highly functional proteins if a curated library is prepared appropriately.

Predicting Silk Fiber Mechanical Properties through Multiscale Simulation and Protein Design.

PubMed

Rim, Nae-Gyune; Roberts, Erin G; Ebrahimi, Davoud; Dinjaski, Nina; Jacobsen, Matthew M; Martín-Moldes, Zaira; Buehler, Markus J; Kaplan, David L; Wong, Joyce Y

2017-08-14

Silk is a promising material for biomedical applications, and much research is focused on how application-specific, mechanical properties of silk can be designed synthetically through proper amino acid sequences and processing parameters. This protocol describes an iterative process between research disciplines that combines simulation, genetic synthesis, and fiber analysis to better design silk fibers with specific mechanical properties. Computational methods are used to assess the protein polymer structure as it forms an interconnected fiber network through shearing and how this process affects fiber mechanical properties. Model outcomes are validated experimentally with the genetic design of protein polymers that match the simulation structures, fiber fabrication from these polymers, and mechanical testing of these fibers. Through iterative feedback between computation, genetic synthesis, and fiber mechanical testing, this protocol will enable a priori prediction capability of recombinant material mechanical properties via insights from the resulting molecular architecture of the fiber network based entirely on the initial protein monomer composition. This style of protocol may be applied to other fields where a research team seeks to design a biomaterial with biomedical application-specific properties. This protocol highlights when and how the three research groups (simulation, synthesis, and engineering) should be interacting to arrive at the most effective method for predictive design of their material.

BcL-xL Conformational Changes upon Fragment Binding Revealed by NMR

PubMed Central

Aguirre, Clémentine; ten Brink, Tim; Walker, Olivier; Guillière, Florence; Davesne, Dany; Krimm, Isabelle

2013-01-01

Protein-protein interactions represent difficult but increasingly important targets for the design of therapeutic compounds able to interfere with biological processes. Recently, fragment-based strategies have been proposed as attractive approaches for the elaboration of protein-protein surface inhibitors from fragment-like molecules. One major challenge in targeting protein-protein interactions is related to the structural adaptation of the protein surface upon molecular recognition. Methods capable of identifying subtle conformational changes of proteins upon fragment binding are therefore required at the early steps of the drug design process. In this report we present a fast NMR method able to probe subtle conformational changes upon fragment binding. The approach relies on the comparison of experimental fragment-induced Chemical Shift Perturbation (CSP) of amine protons to CSP simulated for a set of docked fragment poses, considering the ring-current effect from fragment binding. We illustrate the method by the retrospective analysis of the complex between the anti-apoptotic Bcl-xL protein and the fragment 4′-fluoro-[1,1′-biphenyl]-4-carboxylic acid that was previously shown to bind one of the Bcl-xL hot spots. The CSP-based approach shows that the protein undergoes a subtle conformational rearrangement upon interaction, for residues located in helices 2, 3 and the very beginning of 5. Our observations are corroborated by residual dipolar coupling measurements performed on the free and fragment-bound forms of the Bcl-xL protein. These NMR-based results are in total agreement with previous molecular dynamic calculations that evidenced a high flexibility of Bcl-xL around the binding site. Here we show that CSP of protein amine protons are useful and reliable structural probes. Therefore, we propose to use CSP simulation to assess protein conformational changes upon ligand binding in the fragment-based drug design approach. PMID:23717610

Theoretical and computational studies in protein folding, design, and function

NASA Astrophysics Data System (ADS)

Morrissey, Michael Patrick

2000-10-01

In this work, simplified statistical models are used to understand an array of processes related to protein folding and design. In Part I, lattice models are utilized to test several theories about the statistical properties of protein-like systems. In Part II, sequence analysis and all-atom simulations are used to advance a novel theory for the behavior of a particular protein. Part I is divided into five chapters. In Chapter 2, a method of sequence design for model proteins, based on statistical mechanical first-principles, is developed. The cumulant design method uses a mean-field approximation to expand the free energy of a sequence in temperature. The method successfully designs sequences which fold to a target lattice structure at a specific temperature, a feat which was not possible using previous design methods. The next three chapters are computational studies of the double mutant cycle, which has been used experimentally to predict intra-protein interactions. Complete structure prediction is demonstrated for a model system using exhaustive, and also sub-exhaustive, double mutants. Nonadditivity of enthalpy, rather than of free energy, is proposed and demonstrated to be a superior marker for inter-residue contact. Next, a new double mutant protocol, called exchange mutation, is introduced. Although simple statistical arguments predict exchange mutation to be a more accurate contact predictor than standard mutant cycles, this hypothesis was not upheld in lattice simulations. Reasons for this inconsistency will be discussed. Finally, a multi-chain folding algorithm is introduced. Known as LINKS, this algorithm was developed to test a method of structure prediction which utilizes chain-break mutants. While structure prediction was not successful, LINKS should nevertheless be a useful tool for the study of protein-protein and protein-ligand interactions. The last chapter of Part I utilizes the lattice to explore the differences between standard folding, from the fully denatured state, and cotranslational folding, whereby one end of a protein is synthesized and released before the other. Cotranslational folding is shown to accelerate folding kinetics, particularly when the target backbone contains many local contacts. Additionally, cotranslation is shown capable of "guiding" a model protein into a metastable, local contact-rich state, despite the existence of a true native state of much lower energy. In Part II, a model is developed for the behavior of PrP, a unique mammalian protein which has been shown to possess two native states. The pathogenic "scrapie" state PrPSc, which has not been structurally characterized, is known to trigger conversion of the characterized endogenous conformation PrPC into additional PrPSc, Residues 144--153 are shown to form the most hydrophilic naturally occurring alpha-helix, out of a broad database with more than 10,000 candidates. The novel beta-nucleation model proposes that PrPSc, is not a distinct mono-molecular state, but is rather a beta-sheet-like aggregate centered around helix-1 components of multiple PrP molecules. The remainder of Part II uses molecular dynamics simulations to support the beta-nucleation hypothesis, and to propose a system of peptide ligands which may arrest the process of prion propagation.

Protein Crystal Growth With the Aid of Microfluidics

NASA Technical Reports Server (NTRS)

vanderWoerd, Mark

2003-01-01

Protein crystallography is one of three well-known methods to obtain the structure of proteins. A major rate limiting step in protein crystallography is protein crystal nucleation and growth, which is still largely a process conducted by trial-and-error methods. Many attempts have been made to improve protein crystal growth by performing growth in microgravity. Although the use of microgravity appears to improve crystal quality in some attempts, this method has been inefficient because several reasons: we lack a fundamental understanding of macromolecular crystal growth in general and of the influence of microgravity in particular, we have to start with crystal growth conditions in microgravity based on conditions on the ground and finally the hardware does not allow for experimental iteration without reloading samples on the ground. To partially accommodate the disadvantages of the current hardware, we have used microfluidic technology (Lab-on-a-Chip devices) to design the concept of a more efficient crystallization device, suitable for use on the International Space Station and in high-throughput applications on the ground. The concept and properties of microfluidics, the application design process, and the advances in protein crystal growth hardware will be discussed in this presentation. Some examples of proteins crystallized in the new hardware will be discussed, including the differences between conventional crystallization versus crystallization in microfluidics.

Interrogation of an autofluorescence-based method for protein fingerprinting.

PubMed

Siddaramaiah, Manjunath; Rao, Bola Sadashiva S; Joshi, Manjunath B; Datta, Anirbit; Sandya, S; Vishnumurthy, Vasudha; Chandra, Subhash; Nayak, Subramanya G; Satyamoorthy, Kapaettu; Mahato, Krishna K

2018-03-14

In the present study, we have designed a laser-induced fluorescence (LIF) based instrumentation and developed a sensitive methodology for the effective separation, visualization, identification and analysis of proteins on a single platform. In this method, intrinsic fluorescence spectra of proteins were detected after separation on 1 or 2 dimensional Sodium Dodecyl Sulfate-Tris(2-carboxyethyl)phosphine (SDS-TCEP) polyacrylamide gel electrophoresis (PAGE) and the data were analyzed. The MATLAB assisted software was designed for the development of PAGE fingerprint for the visualization of protein after 1- and 2-dimensional protein separation. These provided objective parameters of intrinsic fluorescence intensity, emission peak, molecular weight and isoelectric point using a single platform. Further, the current architecture could differentiate the overlapping proteins in the PAGE gels which otherwise were not identifiable by conventional staining, imaging and tagging methods. Categorization of the proteins based on the presence or absence of tyrosine or tryptophan residues and assigning the corresponding emission peaks (309-356 nm) with pseudo colors allowed the detection of proportion of proteins within the given spectrum. The present methodology doesn't use stains or tags, hence amenable to couple with mass spectroscopic measurements. This technique may have relevance in the field of proteomics that is used for innumerable applications. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Purification of a Recombinant Glutathione Transferase from the Causative Agent of Hydatidosis, "Echinococcus granulosus"

ERIC Educational Resources Information Center

Fleitas, Andrea L.; Randall, Lía M.; Möller, Matías N.; Denicola, Ana

2016-01-01

This practical class activity was designed to introduce students to recombinant protein expression and purification. The principal goal is to shed light on basic aspects concerning recombinant protein production, in particular protein expression, chromatography methods for protein purification, and enzyme activity as a tool to evaluate purity and…

G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

PubMed Central

2016-01-01

Abstract Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G‐LoSA. G‐LoSA aligns protein local structures in a sequence order independent way and provides a GA‐score, a chemical feature‐based and size‐independent structure similarity score. Our benchmark validation shows the robust performance of G‐LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure‐centric comparative biology studies. In particular, G‐LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G‐LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer‐aided drug design. We hope that G‐LoSA can be a useful computational method for exploring interesting biological problems through large‐scale comparison of protein local structures and facilitating drug discovery research and development. G‐LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. PMID:26813336

Spatio-temporal imaging of EGF-induced activation of protein kinase A by FRET in living cells

NASA Astrophysics Data System (ADS)

Wang, Jin Jun; Chen, Xiao-Chuan; Xing, Da

2004-07-01

Intracellular molecular interaction is important for the study of cell physiology, yet current relevant methods require fixation or microinjection and lack temporal or spatial resolution. We introduced a new method -- fluorescence resonance energy transfer (FRET) to detect molecular interaction in living cells. On the basis of FRET principle, A-kinase activity reporter (AKAR) protein was designed to consist of the fusions of cyan fluorescent protein (CFP), a phosphoamino acid binding domain, a consensus substrate for protein kinase-A (PKA), and yellow fluorescent protein (YFP). In this study, the designed pAKAR plasmid was used to transfect a human lung cancer cell line (ASTC-a-1). When the AKAR-transfected cells were treated by forskolin (Fsk), we were able to observe the efficient transfer of energy from excited CFP to YFP within the AKAR molecule by fluorescence microcopy, whereas no FRET was detected in the transfected cells without the treatment of Fsk. When the cells were treated by Epidermal growth factor (EGF), the change of FRET was observed at different subcellular locations, reflecting PKA activation inside the cells upon EGF stimulation. The successful design of a fluorescence reporter of PKA activation and its application demonstrated the superiority of this technology in the research of intracellular protein-protein interaction.

Design of Heteronuclear Metalloenzymes

PubMed Central

Bhagi-Damodaran, Ambika; Hosseinzadeh, Parisa; Mirts, Evan; Reed, Julian; Petrik, Igor D.; Lu, Yi

2016-01-01

Heteronuclear metalloenzymes catalyze some of the most fundamentally interesting and practically useful reactions in nature. However, the presence of two or more metal ions in close proximity in these enzymes makes them more difficult to prepare and study than homonuclear metalloenzymes. To meet these challenges, heteronuclear metal centers have been designed into small and stable proteins with rigid scaffolds to understand how these heteronuclear centers are constructed and the mechanism of their function. This chapter describes methods for designing heterobinuclear metal centers in a protein scaffold by giving specific examples of a few heme-nonheme bimetallic centers engineered in myoglobin and cytochrome c peroxidase. We provide step-by-step procedure on how to choose the protein scaffold, design a heterobinuclear metal center in the protein computationally, incorporate metal centers in the protein and characterize the resulting metalloprotein, both structurally and functionally. Finally, we discuss how an initial design can be further improved by rationally tuning its secondary coordination sphere, electron/proton transfer rates, and the substrate affinity. PMID:27586347

RNA Nanostructures – Methods and Protocols | Center for Cancer Research

Cancer.gov

RNA nanotechnology is a young field with many potential applications. The goal is to utilize designed RNA strands, such that the obtained constructs have specific properties in terms of shape and functionality. RNA has potential functionalities that are comparable to that of proteins, but possesses (compared to proteins) simpler design principles akin to DNA. The promise is

A mobile precursor determines protein resistance on nanostructured surfaces.

PubMed

Wang, Kang; Chen, Ye; Gong, Xiangjun; Xia, Jianlong; Zhao, Junpeng; Shen, Lei

2018-05-09

Biomaterials are often engineered with nanostructured surfaces to control interactions with proteins and thus regulate their biofunctions. However, the mechanism of how nanostructured surfaces resist or attract proteins together with the underlying design rules remains poorly understood at a molecular level, greatly limiting attempts to develop high-performance biomaterials and devices through the rational design of nanostructures. Here, we study the dynamics of nonspecific protein adsorption on block copolymer nanostructures of varying adhesive domain areas in a resistant matrix. Using surface plasmon resonance and single molecule tracking techniques, we show that weakly adsorbed proteins with two-dimensional diffusivity are critical precursors to protein resistance on nanostructured surfaces. The adhesive domain areas must be more than tens or hundreds of times those of the protein footprints to slow down the 2D-mobility of the precursor proteins for their irreversible adsorption. This precursor model can be used to quantitatively analyze the kinetics of nonspecific protein adsorption on nanostructured surfaces. Our method is applicable to precisely manipulate protein adsorption and resistance on various nanostructured surfaces, e.g., amphiphilic, low-surface-energy, and charged nanostructures, for the design of protein-compatible materials.

The Application of Ligand-Mapping Molecular Dynamics Simulations to the Rational Design of Peptidic Modulators of Protein-Protein Interactions.

PubMed

Tan, Yaw Sing; Spring, David R; Abell, Chris; Verma, Chandra S

2015-07-14

A computational ligand-mapping approach to detect protein surface pockets that interact with hydrophobic moieties is presented. In this method, we incorporated benzene molecules into explicit solvent molecular dynamics simulations of various protein targets. The benzene molecules successfully identified the binding locations of hydrophobic hot-spot residues and all-hydrocarbon cross-links from known peptidic ligands. They also unveiled cryptic binding sites that are occluded by side chains and the protein backbone. Our results demonstrate that ligand-mapping molecular dynamics simulations hold immense promise to guide the rational design of peptidic modulators of protein-protein interactions, including that of stapled peptides, which show promise as an exciting new class of cell-penetrating therapeutic molecules.

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

PubMed

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

Computation-Guided Backbone Grafting of a Discontinuous Motif onto a Protein Scaffold

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

Azoitei, Mihai L.; Correia, Bruno E.; Ban, Yih-En Andrew

2012-02-07

The manipulation of protein backbone structure to control interaction and function is a challenge for protein engineering. We integrated computational design with experimental selection for grafting the backbone and side chains of a two-segment HIV gp120 epitope, targeted by the cross-neutralizing antibody b12, onto an unrelated scaffold protein. The final scaffolds bound b12 with high specificity and with affinity similar to that of gp120, and crystallographic analysis of a scaffold bound to b12 revealed high structural mimicry of the gp120-b12 complex structure. The method can be generalized to design other functional proteins through backbone grafting.

Computational design of environmental sensors for the potent opioid fentanyl

DOE PAGES

Bick, Matthew J.; Greisen, Per J.; Morey, Kevin J.; ...

2017-09-19

Here, we describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We also use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.

Computational design of environmental sensors for the potent opioid fentanyl

PubMed Central

Morey, Kevin J; Antunes, Mauricio S; La, David; Sankaran, Banumathi; Reymond, Luc; Johnsson, Kai; Medford, June I

2017-01-01

We describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment. PMID:28925919

Computational design of environmental sensors for the potent opioid fentanyl

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

Bick, Matthew J.; Greisen, Per J.; Morey, Kevin J.

Here, we describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We also use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.

Engineering Designed Proteins for Light Capture, Energy Transfer, and Emissive Sensing In Vivo

NASA Astrophysics Data System (ADS)

Mancini, Joshua A.

Proteins that are used for photosynthetic light harvesting and biological signaling are critical to life. These types of proteins act as scaffolds that hold small, sometimes metal-containing organic molecules in precise locations for light absorption and successive use. For signaling proteins, this energy can be used to induce a photoisomerization of the small molecule that can turn on or off a signaling cascade that controls the physiology of an organism. Alternatively, photosynthetic light-harvesting proteins funnel this energy in a directional manner towards a charge separating catalytic component that can change this light energy into chemical energy. The protein environment also serves to tune the photophysical properties of the small molecules. This is seen extensively with the linear tetrapyrroles that are used in both photosynthetic and signaling proteins. Many efforts have been made to harness these natural proteins for societal use, including improving photophysical properties and interfacing capabilities with manmade catalytic components. Several methods of achieving improvement have entailed structurally guided mutation and directed evolution. However, these methods all have their limitations due to the inherent complexity and fragility of the natural proteins. This work presents an alternative more robust method to natural proteins. My thesis states: that man-made proteins, known as maquettes, employing basic rules of protein folding, can be designed to become light harvesting and signaling proteins that can be assembled fully in vivo providing an alternative, robust, and versatile platform for meeting the diverse array of societal "green chemistry" and biomedical needs. This in vivo assembly is carried out by interacting with cyanobacterial protein and pigment machinery, both as stand-alone units and as protein fusions with natural antenna complexes. Additionally, this work offers insight for fast and tight binding of circular and linear tetrapyrroles to the maquettes both in vitro and in vivo. Design principles are also established for increasing the amount of linear tetrapyrrole attachment to the maquette as well as modulating their photophysical properties. Fast and tight binding of cofactors, high cofactor attachment yields, and control of cofactor photophysical properties are all prerequisites for the maquettes to be successful in vivo photosynthetic light harvesting and signaling proteins.

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

DOE PAGES

Melero, Cristina; Ollikainen, Noah; Harwood, Ian; ...

2014-10-13

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test thismore » strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.« less

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

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

Melero, Cristina; Ollikainen, Noah; Harwood, Ian

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test thismore » strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.« less

Guidelines to reach high-quality purified recombinant proteins.

PubMed

Oliveira, Carla; Domingues, Lucília

2018-01-01

The final goal in recombinant protein production is to obtain high-quality pure protein samples. Indeed, the successful downstream application of a recombinant protein depends on its quality. Besides production, which is conditioned by the host, the quality of a recombinant protein product relies mainly on the purification procedure. Thus, the purification strategy must be carefully designed from the molecular level. On the other hand, the quality control of a protein sample must be performed to ensure its purity, homogeneity and structural conformity, in order to validate the recombinant production and purification process. Therefore, this review aims at providing succinct information on the rational purification design of recombinant proteins produced in Escherichia coli, specifically the tagging purification, as well as on accessible tools for evaluating and optimizing protein quality. The classical techniques for structural protein characterization-denaturing protein gel electrophoresis (SDS-PAGE), size exclusion chromatography (SEC), dynamic light scattering (DLS) and circular dichroism (CD)-are revisited with focus on the protein and their main advantages and disadvantages. Furthermore, methods for determining protein concentration and protein storage are also presented. The guidelines compiled herein will aid preparing pure, soluble and homogeneous functional recombinant proteins from the very beginning of the molecular cloning design.

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

PubMed Central

Seeliger, Daniel; de Groot, Bert L.

2010-01-01

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available. PMID:20066034

A de novo redesign of the WW domain

PubMed Central

Kraemer-Pecore, Christina M.; Lecomte, Juliette T.J.; Desjarlais, John R.

2003-01-01

We have used a sequence prediction algorithm and a novel sampling method to design protein sequences for the WW domain, a small β-sheet motif. The procedure, referred to as SPANS, designs sequences to be compatible with an ensemble of closely related polypeptide backbones, mimicking the inherent flexibility of proteins. Two designed sequences (termed SPANS-WW1 and SPANS-WW2), using only naturally occurring l-amino acids, were selected for study and the corresponding polypeptides were prepared in Escherichia coli. Circular dichroism data suggested that both purified polypeptides adopted secondary structure features related to those of the target without the aid of disulfide bridges or bound cofactors. The structure exhibited by SPANS-WW2 melted cooperatively by raising the temperature of the solution. Further analysis of this polypeptide by proton nuclear magnetic resonance spectroscopy demonstrated that at 5°C, it folds into a structure closely resembling a natural WW domain. This achievement constitutes one of a small number of successful de novo protein designs through fully automated computational methods and highlights the feasibility of including backbone flexibility in the design strategy. PMID:14500877

A de novo redesign of the WW domain.

PubMed

Kraemer-Pecore, Christina M; Lecomte, Juliette T J; Desjarlais, John R

2003-10-01

We have used a sequence prediction algorithm and a novel sampling method to design protein sequences for the WW domain, a small beta-sheet motif. The procedure, referred to as SPANS, designs sequences to be compatible with an ensemble of closely related polypeptide backbones, mimicking the inherent flexibility of proteins. Two designed sequences (termed SPANS-WW1 and SPANS-WW2), using only naturally occurring L-amino acids, were selected for study and the corresponding polypeptides were prepared in Escherichia coli. Circular dichroism data suggested that both purified polypeptides adopted secondary structure features related to those of the target without the aid of disulfide bridges or bound cofactors. The structure exhibited by SPANS-WW2 melted cooperatively by raising the temperature of the solution. Further analysis of this polypeptide by proton nuclear magnetic resonance spectroscopy demonstrated that at 5 degrees C, it folds into a structure closely resembling a natural WW domain. This achievement constitutes one of a small number of successful de novo protein designs through fully automated computational methods and highlights the feasibility of including backbone flexibility in the design strategy.

A computational framework to empower probabilistic protein design

PubMed Central

Fromer, Menachem; Yanover, Chen

2008-01-01

Motivation: The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult. Results: In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future. Contact: fromer@cs.huji.ac.il PMID:18586717

UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks.

PubMed

Peng, Wei; Wang, Jianxin; Cheng, Yingjiao; Lu, Yu; Wu, Fangxiang; Pan, Yi

2015-01-01

Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli.

Computational methods in preformulation study for pharmaceutical solid dosage forms of therapeutic proteins

NASA Astrophysics Data System (ADS)

Majee, Sutapa Biswas; Biswas, Gopa Roy

2017-06-01

Design and delivery of protein-based biopharmaceuticals needs detailed planning and strict monitoring of intermediate processing steps, storage conditions and container-closure system to ensure a stable, elegant and biopharmaceutically acceptable dosage form. Selection of manufacturing process variables and conditions along with packaging specifications can be achieved through properly designed preformulation study protocol for the formulation. Thermodynamic stability and biological activity of therapeutic proteins depend on folding-unfolding and three-dimensional packing dynamics of amino acid network in the protein molecule. Lack of favourable environment may cause protein aggregation with loss in activity and even fatal immunological reaction. Although lyophilization can enhance the stability of protein-based formulations in the solid state, it can induce protein unfolding leading to thermodynamic instability. Formulation stabilizers such as preservatives can also result in aggregation of therapeutic proteins. Modern instrumental techniques in conjunction with computational tools enable rapid and accurate prediction of amino acid sequence, thermodynamic parameters associated with protein folding and detection of aggregation "hot-spots." Globular proteins pose a challenge during investigations on their aggregation propensity. Biobetter therapeutic monoclonal antibodies with enhanced stability, solubility and reduced immunogenic potential can be designed through mutation of aggregation-prone zones. The objective of the present review article is to focus on the various analytical methods and computational approaches used in the study of thermodynamic stability and aggregation tendency of therapeutic proteins, with an aim to develop optimal and marketable formulation. Knowledge of protein dynamics through application of computational tools will provide the essential inputs and relevant information for successful and meaningful completion of preformulation studies on solid dosage forms of therapeutic proteins.

Antioxidant activities of two sericin proteins extracted from cocoon of silkworm (Bombyx mori) measured by DPPH, chemiluminescence, ORAC and ESR methods.

PubMed

Takechi, Tayori; Wada, Ritsuko; Fukuda, Tsubasa; Harada, Kazuki; Takamura, Hitoshi

2014-05-01

Recent efforts have focused on the use of sericin proteins extracted from cocoons of silkworm as a healthy food source for human consumption. In this study, we focused on the antioxidative properties of sericin proteins. The antioxidative properties were measured in sericin proteins extracted from the shell of the cocoon, designated hereafter as white sericin protein and yellow-green sericin protein, as well as bread without sericin protein and bread to which white sericin powder had been added using four measurement methods: 1,1-Diphenyl-2-picrylhydrazyl (DPPH), chemiluminescence, oxygen radical absorbance capacity (ORAC) and electron spin resonance (ESR). High antioxidative properties of sericin proteins were indicated by all four methods. A comparison of the two types of sericin proteins revealed that yellow-green sericin protein exhibited high antioxidative properties as indicated by the DPPH, chemiluminescence and ORAC methods. By contrast, a higher antioxidative property was determined in white sericin protein by the ESR method. Consequently, our findings confirmed that sericin proteins have antioxidative properties against multiple radicals. In addition, the antioxidative property of bread was enhanced by the addition of sericin powder to the bread. Therefore, findings of this study suggest that sericin proteins may be efficiently used as beneficial food for human health.

Antioxidant activities of two sericin proteins extracted from cocoon of silkworm (Bombyx mori) measured by DPPH, chemiluminescence, ORAC and ESR methods

PubMed Central

TAKECHI, TAYORI; WADA, RITSUKO; FUKUDA, TSUBASA; HARADA, KAZUKI; TAKAMURA, HITOSHI

2014-01-01

Recent efforts have focused on the use of sericin proteins extracted from cocoons of silkworm as a healthy food source for human consumption. In this study, we focused on the antioxidative properties of sericin proteins. The antioxidative properties were measured in sericin proteins extracted from the shell of the cocoon, designated hereafter as white sericin protein and yellow-green sericin protein, as well as bread without sericin protein and bread to which white sericin powder had been added using four measurement methods: 1,1-Diphenyl-2-picrylhydrazyl (DPPH), chemiluminescence, oxygen radical absorbance capacity (ORAC) and electron spin resonance (ESR). High antioxidative properties of sericin proteins were indicated by all four methods. A comparison of the two types of sericin proteins revealed that yellow-green sericin protein exhibited high antioxidative properties as indicated by the DPPH, chemiluminescence and ORAC methods. By contrast, a higher antioxidative property was determined in white sericin protein by the ESR method. Consequently, our findings confirmed that sericin proteins have antioxidative properties against multiple radicals. In addition, the antioxidative property of bread was enhanced by the addition of sericin powder to the bread. Therefore, findings of this study suggest that sericin proteins may be efficiently used as beneficial food for human health. PMID:24748975

Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

PubMed

Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

2015-06-01

Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Linked Production of Pyroglutamate-Modified Proteins via Self-Cleavage of Fusion Tags with TEV Protease and Autonomous N-Terminal Cyclization with Glutaminyl Cyclase In Vivo

PubMed Central

Shih, Yan-Ping; Chou, Chi-Chi; Chen, Yi-Ling; Huang, Kai-Fa; Wang, Andrew H.- J.

2014-01-01

Overproduction of N-terminal pyroglutamate (pGlu)-modified proteins utilizing Escherichia coli or eukaryotic cells is a challenging work owing to the fact that the recombinant proteins need to be recovered by proteolytic removal of fusion tags to expose the N-terminal glutaminyl or glutamyl residue, which is then converted into pGlu catalyzed by the enzyme glutaminyl cyclase. Herein we describe a new method for production of N-terminal pGlu-containing proteins in vivo via intracellular self-cleavage of fusion tags by tobacco etch virus (TEV) protease and then immediate N-terminal cyclization of passenger target proteins by a bacterial glutaminyl cyclase. To combine with the sticky-end PCR cloning strategy, this design allows the gene of target proteins to be efficiently inserted into the expression vector using two unique cloning sites (i.e., SnaB I and Xho I), and the soluble and N-terminal pGlu-containing proteins are then produced in vivo. Our method has been successfully applied to the production of pGlu-modified enhanced green fluorescence protein and monocyte chemoattractant proteins. This design will facilitate the production of protein drugs and drug target proteins that possess an N-terminal pGlu residue required for their physiological activities. PMID:24733552

Computational design and experimental verification of a symmetric protein homodimer.

PubMed

Mou, Yun; Huang, Po-Ssu; Hsu, Fang-Ciao; Huang, Shing-Jong; Mayo, Stephen L

2015-08-25

Homodimers are the most common type of protein assembly in nature and have distinct features compared with heterodimers and higher order oligomers. Understanding homodimer interactions at the atomic level is critical both for elucidating their biological mechanisms of action and for accurate modeling of complexes of unknown structure. Computation-based design of novel protein-protein interfaces can serve as a bottom-up method to further our understanding of protein interactions. Previous studies have demonstrated that the de novo design of homodimers can be achieved to atomic-level accuracy by β-strand assembly or through metal-mediated interactions. Here, we report the design and experimental characterization of a α-helix-mediated homodimer with C2 symmetry based on a monomeric Drosophila engrailed homeodomain scaffold. A solution NMR structure shows that the homodimer exhibits parallel helical packing similar to the design model. Because the mutations leading to dimer formation resulted in poor thermostability of the system, design success was facilitated by the introduction of independent thermostabilizing mutations into the scaffold. This two-step design approach, function and stabilization, is likely to be generally applicable, especially if the desired scaffold is of low thermostability.

Application of the fragment molecular orbital method analysis to fragment-based drug discovery of BET (bromodomain and extra-terminal proteins) inhibitors.

PubMed

Ozawa, Motoyasu; Ozawa, Tomonaga; Ueda, Kazuyoshi

2017-06-01

The molecular interactions of inhibitors of bromodomains (BRDs) were investigated. BRDs are protein interaction modules that recognizing ε-N-acetyl-lysine (εAc-Lys) motifs found in histone tails and are promising protein-protein interaction (PPI) targets. First, we analyzed a peptide ligand containing εAc-Lys to evaluate native PPIs. We then analyzed tetrahydroquinazoline-6-yl-benzensulfonamide derivatives found by fragment-based drug design (FBDD) and examined their interactions with the protein compared with the peptide ligand in terms of the inter-fragment interaction energy. In addition, we analyzed benzodiazepine derivatives that are high-affinity ligands for BRDs and examined differences in the CH/π interactions of the amino acid residues. We further surveyed changes in the charges of the amino acid residues among individual ligands, performed pair interaction energy decomposition analysis and estimated the water profile within the ligand binding site. Thus, useful insights for drug design were provided. Through these analyses and considerations, we show that the FMO method is a useful drug design tool to evaluate the process of FBDD and to explore PPI inhibitors. Copyright © 2017 Elsevier Inc. All rights reserved.

QM/MM Geometry Optimization on Extensive Free-Energy Surfaces for Examination of Enzymatic Reactions and Design of Novel Functional Properties of Proteins.

PubMed

Hayashi, Shigehiko; Uchida, Yoshihiro; Hasegawa, Taisuke; Higashi, Masahiro; Kosugi, Takahiro; Kamiya, Motoshi

2017-05-05

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

QM/MM Geometry Optimization on Extensive Free-Energy Surfaces for Examination of Enzymatic Reactions and Design of Novel Functional Properties of Proteins

NASA Astrophysics Data System (ADS)

Hayashi, Shigehiko; Uchida, Yoshihiro; Hasegawa, Taisuke; Higashi, Masahiro; Kosugi, Takahiro; Kamiya, Motoshi

2017-05-01

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

Physics-based scoring of protein-ligand interactions: explicit polarizability, quantum mechanics and free energies.

PubMed

Bryce, Richard A

2011-04-01

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein-ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein-ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein-ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein-ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries.

Solid-support immobilization of a "swing" fusion protein for enhanced glucose oxidase catalytic activity.

PubMed

Takatsuji, Yoshiyuki; Yamasaki, Ryota; Iwanaga, Atsushi; Lienemann, Michael; Linder, Markus B; Haruyama, Tetsuya

2013-12-01

The strategic surface immobilization of a protein can add new functionality to a solid substrate; however, protein activity, e.g., enzymatic activity, can be drastically decreased on immobilization onto a solid surface. The concept of a designed and optimized "molecular interface" is herein introduced in order to address this problem. In this study, molecular interface was designed and constructed with the aim of attaining high enzymatic activity of a solid-surface-immobilized a using the hydrophobin HFBI protein in conjunction with a fusion protein of HFBI attached to glucose oxidase (GOx). The ability of HFBI to form a self-organized membrane on a solid surface in addition to its adhesion properties makes it an ideal candidate for immobilization. The developed fusion protein was also able to form an organized membrane, and its structure and immobilized state on a solid surface were investigated using QCM-D measurements. This method of immobilization showed retention of high enzymatic activity and the ability to control the density of the immobilized enzyme. In this study, we demonstrated the importance of the design and construction of molecular interface for numerous purposes. This method of protein immobilization could be utilized for preparation of high throughput products requiring structurally ordered molecular interfaces, in addition to many other applications. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Design and Initial Characterization of the SC-200 Proteomics Standard Mixture

PubMed Central

Bauman, Andrew; Higdon, Roger; Rapson, Sean; Loiue, Brenton; Hogan, Jason; Stacy, Robin; Napuli, Alberto; Guo, Wenjin; van Voorhis, Wesley; Roach, Jared; Lu, Vincent; Landorf, Elizabeth; Stewart, Elizabeth; Kolker, Natali; Collart, Frank; Myler, Peter; van Belle, Gerald

2011-01-01

Abstract High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels. PMID:21250827

Design and initial characterization of the SC-200 proteomics standard mixture.

PubMed

Bauman, Andrew; Higdon, Roger; Rapson, Sean; Loiue, Brenton; Hogan, Jason; Stacy, Robin; Napuli, Alberto; Guo, Wenjin; van Voorhis, Wesley; Roach, Jared; Lu, Vincent; Landorf, Elizabeth; Stewart, Elizabeth; Kolker, Natali; Collart, Frank; Myler, Peter; van Belle, Gerald; Kolker, Eugene

2011-01-01

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.

Computational Redesign of Thioredoxin Is Hypersensitive toward Minor Conformational Changes in the Backbone Template

PubMed Central

Christensen, Signe; Horowitz, Scott; Bardwell, James C.A.; Olsen, Johan G.; Willemoës, Martin; Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper; Hamelryck, Thomas; Winther, Jakob R.

2017-01-01

Despite the development of powerful computational tools, the full-sequence design of proteins still remains a challenging task. To investigate the limits and capabilities of computational tools, we conducted a study of the ability of the program Rosetta to predict sequences that recreate the authentic fold of thioredoxin. Focusing on the influence of conformational details in the template structures, we based our study on 8 experimentally determined template structures and generated 120 designs from each. For experimental evaluation, we chose six sequences from each of the eight templates by objective criteria. The 48 selected sequences were evaluated based on their progressive ability to (1) produce soluble protein in Escherichia coli and (2) yield stable monomeric protein, and (3) on the ability of the stable, soluble proteins to adopt the target fold. Of the 48 designs, we were able to synthesize 32, 20 of which resulted in soluble protein. Of these, only two were sufficiently stable to be purified. An X-ray crystal structure was solved for one of the designs, revealing a close resemblance to the target structure. We found a significant difference among the eight template structures to realize the above three criteria despite their high structural similarity. Thus, in order to improve the success rate of computational full-sequence design methods, we recommend that multiple template structures are used. Furthermore, this study shows that special care should be taken when optimizing the geometry of a structure prior to computational design when using a method that is based on rigid conformations. PMID:27659562

Structure-based design of combinatorial mutagenesis libraries.

PubMed

Verma, Deeptak; Grigoryan, Gevorg; Bailey-Kellogg, Chris

2015-05-01

The development of protein variants with improved properties (thermostability, binding affinity, catalytic activity, etc.) has greatly benefited from the application of high-throughput screens evaluating large, diverse combinatorial libraries. At the same time, since only a very limited portion of sequence space can be experimentally constructed and tested, an attractive possibility is to use computational protein design to focus libraries on a productive portion of the space. We present a general-purpose method, called "Structure-based Optimization of Combinatorial Mutagenesis" (SOCoM), which can optimize arbitrarily large combinatorial mutagenesis libraries directly based on structural energies of their constituents. SOCoM chooses both positions and substitutions, employing a combinatorial optimization framework based on library-averaged energy potentials in order to avoid explicitly modeling every variant in every possible library. In case study applications to green fluorescent protein, β-lactamase, and lipase A, SOCoM optimizes relatively small, focused libraries whose variants achieve energies comparable to or better than previous library design efforts, as well as larger libraries (previously not designable by structure-based methods) whose variants cover greater diversity while still maintaining substantially better energies than would be achieved by representative random library approaches. By allowing the creation of large-scale combinatorial libraries based on structural calculations, SOCoM promises to increase the scope of applicability of computational protein design and improve the hit rate of discovering beneficial variants. While designs presented here focus on variant stability (predicted by total energy), SOCoM can readily incorporate other structure-based assessments, such as the energy gap between alternative conformational or bound states. © 2015 The Protein Society.

Computational Redesign of Thioredoxin Is Hypersensitive toward Minor Conformational Changes in the Backbone Template.

PubMed

Johansson, Kristoffer E; Tidemand Johansen, Nicolai; Christensen, Signe; Horowitz, Scott; Bardwell, James C A; Olsen, Johan G; Willemoës, Martin; Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper; Hamelryck, Thomas; Winther, Jakob R

2016-10-23

Despite the development of powerful computational tools, the full-sequence design of proteins still remains a challenging task. To investigate the limits and capabilities of computational tools, we conducted a study of the ability of the program Rosetta to predict sequences that recreate the authentic fold of thioredoxin. Focusing on the influence of conformational details in the template structures, we based our study on 8 experimentally determined template structures and generated 120 designs from each. For experimental evaluation, we chose six sequences from each of the eight templates by objective criteria. The 48 selected sequences were evaluated based on their progressive ability to (1) produce soluble protein in Escherichia coli and (2) yield stable monomeric protein, and (3) on the ability of the stable, soluble proteins to adopt the target fold. Of the 48 designs, we were able to synthesize 32, 20 of which resulted in soluble protein. Of these, only two were sufficiently stable to be purified. An X-ray crystal structure was solved for one of the designs, revealing a close resemblance to the target structure. We found a significant difference among the eight template structures to realize the above three criteria despite their high structural similarity. Thus, in order to improve the success rate of computational full-sequence design methods, we recommend that multiple template structures are used. Furthermore, this study shows that special care should be taken when optimizing the geometry of a structure prior to computational design when using a method that is based on rigid conformations. Copyright © 2016 Elsevier Ltd. All rights reserved.

The development of a thermostable CiP (Coprinus cinereus peroxidase) through in silico design.

PubMed

Kim, Su Jin; Lee, Jeong Ah; Joo, Jeong Chan; Yoo, Young Je; Kim, Yong Hwan; Song, Bong Keun

2010-01-01

Protein thermostability is a crucial issue in the practical application of enzymes, such as inorganic synthesis and enzymatic polymerization of phenol derivatives. Much attention has been focused on the enhancement and numerous successes have been achieved through protein engineering methods. Despite fruitful results based on random mutagenesis, it was still necessary to develop a novel strategy that can reduce the time and effort involved in this process. In this study, a rapid and effective strategy is described for increasing the thermal stability of a protein. Instead of random mutagenesis, a rational strategy was adopted to theoretically stabilize the thermo labile residues of a protein using computational methods. Protein residues with high flexibility can be thermo labile due to their large range of movement. Here, residue B factor values were used to identify putatively thermo labile residues and the RosettaDesign program was applied to search for stable sequences. Coprinus cinereus (CiP) heme peroxidase was selected as a model protein for its importance in commercial applications, such as the polymerization of phenolic compounds. Eleven CiP residues with the highest B factor values were chosen as target mutation sites for thermostabilization, and then redesigned using RosettaDesign to identify sequences. Eight mutants based on the redesigns, were produced as functional enzymes and two of these (S323Y and E328D) showed increased thermal stability over the wild-type in addition to conserved catalytic activity. Thus, this strategy can be used as a rapid and effective in silico design tool for obtaining thermostable proteins. (c) 2010 American Institute of Chemical Engineers

Quantum mechanics implementation in drug-design workflows: does it really help?

PubMed

Arodola, Olayide A; Soliman, Mahmoud Es

2017-01-01

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Thermodynamically balanced inside-out (TBIO) PCR-based gene synthesis: a novel method of primer design for high-fidelity assembly of longer gene sequences

PubMed Central

Gao, Xinxin; Yo, Peggy; Keith, Andrew; Ragan, Timothy J.; Harris, Thomas K.

2003-01-01

A novel thermodynamically-balanced inside-out (TBIO) method of primer design was developed and compared with a thermodynamically-balanced conventional (TBC) method of primer design for PCR-based gene synthesis of codon-optimized gene sequences for the human protein kinase B-2 (PKB2; 1494 bp), p70 ribosomal S6 subunit protein kinase-1 (S6K1; 1622 bp) and phosphoinositide-dependent protein kinase-1 (PDK1; 1712 bp). Each of the 60mer TBIO primers coded for identical nucleotide regions that the 60mer TBC primers covered, except that half of the TBIO primers were reverse complement sequences. In addition, the TBIO and TBC primers contained identical regions of temperature- optimized primer overlaps. The TBC method was optimized to generate sequential overlapping fragments (∼0.4–0.5 kb) for each of the gene sequences, and simultaneous and sequential combinations of overlapping fragments were tested for their ability to be assembled under an array of PCR conditions. However, no fully synthesized gene sequences could be obtained by this approach. In contrast, the TBIO method generated an initial central fragment (∼0.4–0.5 kb), which could be gel purified and used for further inside-out bidirectional elongation by additional increments of 0.4–0.5 kb. By using the newly developed TBIO method of PCR-based gene synthesis, error-free synthetic genes for the human protein kinases PKB2, S6K1 and PDK1 were obtained with little or no corrective mutagenesis. PMID:14602936

Discriminating between stabilizing and destabilizing protein design mutations via recombination and simulation.

PubMed

Johnson, Lucas B; Gintner, Lucas P; Park, Sehoo; Snow, Christopher D

2015-08-01

Accuracy of current computational protein design (CPD) methods is limited by inherent approximations in energy potentials and sampling. These limitations are often used to qualitatively explain design failures; however, relatively few studies provide specific examples or quantitative details that can be used to improve future CPD methods. Expanding the design method to include a library of sequences provides data that is well suited for discriminating between stabilizing and destabilizing design elements. Using thermophilic endoglucanase E1 from Acidothermus cellulolyticus as a model enzyme, we computationally designed a sequence with 60 mutations. The design sequence was rationally divided into structural blocks and recombined with the wild-type sequence. Resulting chimeras were assessed for activity and thermostability. Surprisingly, unlike previous chimera libraries, regression analysis based on one- and two-body effects was not sufficient for predicting chimera stability. Analysis of molecular dynamics simulations proved helpful in distinguishing stabilizing and destabilizing mutations. Reverting to the wild-type amino acid at destabilized sites partially regained design stability, and introducing predicted stabilizing mutations in wild-type E1 significantly enhanced thermostability. The ability to isolate stabilizing and destabilizing elements in computational design offers an opportunity to interpret previous design failures and improve future CPD methods. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study.

PubMed

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-02-28

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of "chimera proteins." In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study

PubMed Central

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-01-01

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of “chimera proteins.” In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape. PMID:16488978

Neutrality and evolvability of designed protein sequences

NASA Astrophysics Data System (ADS)

Bhattacherjee, Arnab; Biswas, Parbati

2010-07-01

The effect of foldability on protein’s evolvability is analyzed by a two-prong approach consisting of a self-consistent mean-field theory and Monte Carlo simulations. Theory and simulation models representing protein sequences with binary patterning of amino acid residues compatible with a particular foldability criteria are used. This generalized foldability criterion is derived using the high temperature cumulant expansion approximating the free energy of folding. The effect of cumulative point mutations on these designed proteins is studied under neutral condition. The robustness, protein’s ability to tolerate random point mutations is determined with a selective pressure of stability (ΔΔG) for the theory designed sequences, which are found to be more robust than that of Monte Carlo and mean-field-biased Monte Carlo generated sequences. The results show that this foldability criterion selects viable protein sequences more effectively compared to the Monte Carlo method, which has a marked effect on how the selective pressure shapes the evolutionary sequence space. These observations may impact de novo sequence design and its applications in protein engineering.

pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life.

PubMed

Spassov, Velin Z; Yan, Lisa

2013-04-01

Understanding the effects of mutation on pH-dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein-protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH-dependent energy profiles demonstrate excellent agreement with experimentally measured pH-dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH-dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH-selective binding. The method can be used to select mutations that change the pH-dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH-dependent binding behavior of IgG to FcRn with the aim of improving the half-life of therapeutic antibodies in the target organism. Copyright © 2013 Wiley Periodicals, Inc.

Exploring protein kinase conformation using swarm-enhanced sampling molecular dynamics.

PubMed

Atzori, Alessio; Bruce, Neil J; Burusco, Kepa K; Wroblowski, Berthold; Bonnet, Pascal; Bryce, Richard A

2014-10-27

Protein plasticity, while often linked to biological function, also provides opportunities for rational design of selective and potent inhibitors of their function. The application of computational methods to the prediction of concealed protein concavities is challenging, as the motions involved can be significant and occur over long time scales. Here we introduce the swarm-enhanced sampling molecular dynamics (sesMD) method as a tool to improve sampling of conformational landscapes. In this approach, a swarm of replica simulations interact cooperatively via a set of pairwise potentials incorporating attractive and repulsive components. We apply the sesMD approach to explore the conformations of the DFG motif in the protein p38α mitogen-activated protein kinase. In contrast to multiple MD simulations, sesMD trajectories sample a range of DFG conformations, some of which map onto existing crystal structures. Simulated structures intermediate between the DFG-in and DFG-out conformations are predicted to have druggable pockets of interest for structure-based ligand design.

Immobilizing affinity proteins to nitrocellulose: a toolbox for paper-based assay developers.

PubMed

Holstein, Carly A; Chevalier, Aaron; Bennett, Steven; Anderson, Caitlin E; Keniston, Karen; Olsen, Cathryn; Li, Bing; Bales, Brian; Moore, David R; Fu, Elain; Baker, David; Yager, Paul

2016-02-01

To enable enhanced paper-based diagnostics with improved detection capabilities, new methods are needed to immobilize affinity reagents to porous substrates, especially for capture molecules other than IgG. To this end, we have developed and characterized three novel methods for immobilizing protein-based affinity reagents to nitrocellulose membranes. We have demonstrated these methods using recombinant affinity proteins for the influenza surface protein hemagglutinin, leveraging the customizability of these recombinant "flu binders" for the design of features for immobilization. The three approaches shown are: (1) covalent attachment of thiolated affinity protein to an epoxide-functionalized nitrocellulose membrane, (2) attachment of biotinylated affinity protein through a nitrocellulose-binding streptavidin anchor protein, and (3) fusion of affinity protein to a novel nitrocellulose-binding anchor protein for direct coupling and immobilization. We also characterized the use of direct adsorption for the flu binders, as a point of comparison and motivation for these novel methods. Finally, we demonstrated that these novel methods can provide improved performance to an influenza hemagglutinin assay, compared to a traditional antibody-based capture system. Taken together, this work advances the toolkit available for the development of next-generation paper-based diagnostics.

A complementation assay for in vivo protein structure/function analysis in Physcomitrella patens (Funariaceae)

DOE PAGES

Scavuzzo-Duggan, Tess R.; Chaves, Arielle M.; Roberts, Alison W.

2015-07-14

Here, a method for rapid in vivo functional analysis of engineered proteins was developed using Physcomitrella patens. A complementation assay was designed for testing structure/function relationships in cellulose synthase (CESA) proteins. The components of the assay include (1) construction of test vectors that drive expression of epitope-tagged PpCESA5 carrying engineered mutations, (2) transformation of a ppcesa5 knockout line that fails to produce gametophores with test and control vectors, (3) scoring the stable transformants for gametophore production, (4) statistical analysis comparing complementation rates for test vectors to positive and negative control vectors, and (5) analysis of transgenic protein expression by Westernmore » blotting. The assay distinguished mutations that generate fully functional, nonfunctional, and partially functional proteins. In conclusion, compared with existing methods for in vivo testing of protein function, this complementation assay provides a rapid method for investigating protein structure/function relationships in plants.« less

Boosting protein stability with the computational design of β-sheet surfaces.

PubMed

Kim, Doo Nam; Jacobs, Timothy M; Kuhlman, Brian

2016-03-01

β-sheets often have one face packed against the core of the protein and the other facing solvent. Mutational studies have indicated that the solvent-facing residues can contribute significantly to protein stability, and that the preferred amino acid at each sequence position is dependent on the precise structure of the protein backbone and the identity of the neighboring amino acids. This suggests that the most advantageous methods for designing β-sheet surfaces will be approaches that take into account the multiple energetic factors at play including side chain rotamer preferences, van der Waals forces, electrostatics, and desolvation effects. Here, we show that the protein design software Rosetta, which models these energetic factors, can be used to dramatically increase protein stability by optimizing interactions on the surfaces of small β-sheet proteins. Two design variants of the β-sandwich protein from tenascin were made with 7 and 14 mutations respectively on its β-sheet surfaces. These changes raised the thermal midpoint for unfolding from 45°C to 64°C and 74°C. Additionally, we tested an empirical approach based on increasing the number of potential salt bridges on the surfaces of the β-sheets. This was not a robust strategy for increasing stability, as three of the four variants tested were unfolded. © 2016 The Protein Society.

Rapid search for tertiary fragments reveals protein sequence–structure relationships

PubMed Central

Zhou, Jianfu; Grigoryan, Gevorg

2015-01-01

Finding backbone substructures from the Protein Data Bank that match an arbitrary query structural motif, composed of multiple disjoint segments, is a problem of growing relevance in structure prediction and protein design. Although numerous protein structure search approaches have been proposed, methods that address this specific task without additional restrictions and on practical time scales are generally lacking. Here, we propose a solution, dubbed MASTER, that is both rapid, enabling searches over the Protein Data Bank in a matter of seconds, and provably correct, finding all matches below a user-specified root-mean-square deviation cutoff. We show that despite the potentially exponential time complexity of the problem, running times in practice are modest even for queries with many segments. The ability to explore naturally plausible structural and sequence variations around a given motif has the potential to synthesize its design principles in an automated manner; so we go on to illustrate the utility of MASTER to protein structural biology. We demonstrate its capacity to rapidly establish structure–sequence relationships, uncover the native designability landscapes of tertiary structural motifs, identify structural signatures of binding, and automatically rewire protein topologies. Given the broad utility of protein tertiary fragment searches, we hope that providing MASTER in an open-source format will enable novel advances in understanding, predicting, and designing protein structure. PMID:25420575

Direct Measurement of S-Nitrosothiols with an Orbitrap Fusion Mass Spectrometer: S-Nitrosoglutathione Reductase as a Model Protein.

PubMed

Guerra, Damian; Truebridge, Ian; Eyles, Stephen J; Treffon, Patrick; Vierling, Elizabeth

2018-01-01

Recent studies suggest cysteine S-nitrosation of S-nitrosoglutathione reductase (GSNOR) could regulate protein redox homeostasis. "Switch" assays enable discovery of putatively S-nitrosated proteins. However, with few exceptions, researchers have not examined the kinetics and biophysical consequences of S-nitrosation. Methods to quantify protein S-nitrosothiol (SNO) abundance and formation kinetics would bridge this mechanistic gap and allow interpretation of the consequences of specific modifications, as well as facilitate development of specific S-nitrosation inhibitors. Here, we describe a rapid assay to estimate protein SNO abundance with intact protein electrospray ionization mass spectrometry. Originally designed using recombinant GSNOR, these methods are applicable to any purified protein to test for or further study nitrosatable cysteines.

Structural interaction fingerprints: a new approach to organizing, mining, analyzing, and designing protein-small molecule complexes.

PubMed

Singh, Juswinder; Deng, Zhan; Narale, Gaurav; Chuaqui, Claudio

2006-01-01

The combination of advances in structure-based drug design efforts in the pharmaceutical industry in parallel with structural genomics initiatives in the public domain has led to an explosion in the number of structures of protein-small molecule complexes structures. This information has critical importance to both the understanding of the structural basis for molecular recognition in biological systems and the design of better drugs. A significant challenge exists in managing this vast amount of data and fully leveraging it. Here, we review our work to develop a simple, fast way to store, organize, mine, and analyze large numbers of protein-small molecule complexes. We illustrate the utility of the approach to the management of inhibitor complexes from the protein kinase family. Finally, we describe our recent efforts in applying this method to the design of target-focused chemical libraries.

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design.

PubMed

Ó Conchúir, Shane; Barlow, Kyle A; Pache, Roland A; Ollikainen, Noah; Kundert, Kale; O'Meara, Matthew J; Smith, Colin A; Kortemme, Tanja

2015-01-01

The development and validation of computational macromolecular modeling and design methods depend on suitable benchmark datasets and informative metrics for comparing protocols. In addition, if a method is intended to be adopted broadly in diverse biological applications, there needs to be information on appropriate parameters for each protocol, as well as metrics describing the expected accuracy compared to experimental data. In certain disciplines, there exist established benchmarks and public resources where experts in a particular methodology are encouraged to supply their most efficient implementation of each particular benchmark. We aim to provide such a resource for protocols in macromolecular modeling and design. We present a freely accessible web resource (https://kortemmelab.ucsf.edu/benchmarks) to guide the development of protocols for protein modeling and design. The site provides benchmark datasets and metrics to compare the performance of a variety of modeling protocols using different computational sampling methods and energy functions, providing a "best practice" set of parameters for each method. Each benchmark has an associated downloadable benchmark capture archive containing the input files, analysis scripts, and tutorials for running the benchmark. The captures may be run with any suitable modeling method; we supply command lines for running the benchmarks using the Rosetta software suite. We have compiled initial benchmarks for the resource spanning three key areas: prediction of energetic effects of mutations, protein design, and protein structure prediction, each with associated state-of-the-art modeling protocols. With the help of the wider macromolecular modeling community, we hope to expand the variety of benchmarks included on the website and continue to evaluate new iterations of current methods as they become available.

Reference Proteome Extracts for Mass Spec Instrument Performance Validation and Method Development

PubMed Central

Rosenblatt, Mike; Urh, Marjeta; Saveliev, Sergei

2014-01-01

Biological samples of high complexity are required to test protein mass spec sample preparation procedures and validate mass spec instrument performance. Total cell protein extracts provide the needed sample complexity. However, to be compatible with mass spec applications, such extracts should meet a number of design requirements: compatibility with LC/MS (free of detergents, etc.)high protein integrity (minimal level of protein degradation and non-biological PTMs)compatibility with common sample preparation methods such as proteolysis, PTM enrichment and mass-tag labelingLot-to-lot reproducibility Here we describe total protein extracts from yeast and human cells that meet the above criteria. Two extract formats have been developed: Intact protein extracts with primary use for sample preparation method development and optimizationPre-digested extracts (peptides) with primary use for instrument validation and performance monitoring

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

NASA Astrophysics Data System (ADS)

Evensen, Erik; Joseph-McCarthy, Diane; Weiss, Gregory A.; Schreiber, Stuart L.; Karplus, Martin

2007-07-01

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.

QconCATs: design and expression of concatenated protein standards for multiplexed protein quantification.

PubMed

Simpson, Deborah M; Beynon, Robert J

2012-09-01

Systems biology requires knowledge of the absolute amounts of proteins in order to model biological processes and simulate the effects of changes in specific model parameters. Quantification concatamers (QconCATs) are established as a method to provide multiplexed absolute peptide standards for a set of target proteins in isotope dilution standard experiments. Two or more quantotypic peptides representing each of the target proteins are concatenated into a designer gene that is metabolically labelled with stable isotopes in Escherichia coli or other cellular or cell-free systems. Co-digestion of a known amount of QconCAT with the target proteins generates a set of labelled reference peptide standards for the unlabelled analyte counterparts, and by using an appropriate mass spectrometry platform, comparison of the intensities of the peptide ratios delivers absolute quantification of the encoded peptides and in turn the target proteins for which they are surrogates. In this review, we discuss the criteria and difficulties associated with surrogate peptide selection and provide examples in the design of QconCATs for quantification of the proteins of the nuclear factor κB pathway.

Rule-Based Design of Plant Expression Vectors Using GenoCAD.

PubMed

Coll, Anna; Wilson, Mandy L; Gruden, Kristina; Peccoud, Jean

2015-01-01

Plant synthetic biology requires software tools to assist on the design of complex multi-genic expression plasmids. Here a vector design strategy to express genes in plants is formalized and implemented as a grammar in GenoCAD, a Computer-Aided Design software for synthetic biology. It includes a library of plant biological parts organized in structural categories and a set of rules describing how to assemble these parts into large constructs. Rules developed here are organized and divided into three main subsections according to the aim of the final construct: protein localization studies, promoter analysis and protein-protein interaction experiments. The GenoCAD plant grammar guides the user through the design while allowing users to customize vectors according to their needs. Therefore the plant grammar implemented in GenoCAD will help plant biologists take advantage of methods from synthetic biology to design expression vectors supporting their research projects.

Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels

PubMed Central

2014-01-01

Background Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods. Results We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes. Conclusions We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes. PMID:24564744

DNA-binding specificity prediction with FoldX.

PubMed

Nadra, Alejandro D; Serrano, Luis; Alibés, Andreu

2011-01-01

With the advent of Synthetic Biology, a field between basic science and applied engineering, new computational tools are needed to help scientists reach their goal, their design, optimizing resources. In this chapter, we present a simple and powerful method to either know the DNA specificity of a wild-type protein or design new specificities by using the protein design algorithm FoldX. The only basic requirement is having a good resolution structure of the complex. Protein-DNA interaction design may aid the development of new parts designed to be orthogonal, decoupled, and precise in its target. Further, it could help to fine-tune the systems in terms of specificity, discrimination, and binding constants. In the age of newly developed devices and invented systems, computer-aided engineering promises to be an invaluable tool. Copyright © 2011 Elsevier Inc. All rights reserved.

PinaColada: peptide-inhibitor ant colony ad-hoc design algorithm.

PubMed

Zaidman, Daniel; Wolfson, Haim J

2016-08-01

Design of protein-protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5-15 amino acid long), are natural candidates for inhibition of protein-protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations. In this article we present PinaColada, a novel computational method for the design of peptide inhibitors for protein-protein interactions. We employ a version of the ant colony optimization heuristic, which is used to explore the exponential space ([Formula: see text]) of length n peptide sequences, in combination with our fast robotics motivated PepCrawler algorithm, which explores the conformational space for each candidate sequence. PinaColada is being run in parallel, on a DELL PowerEdge 2.8 GHZ computer with 20 cores and 256 GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length. An online server available at: http://bioinfo3d.cs.tau.ac.il/PinaColada/. danielza@post.tau.ac.il; wolfson@tau.ac.il. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Grafting of functional motifs onto protein scaffolds identified by PDB screening--an efficient route to design optimizable protein binders.

PubMed

Tlatli, Rym; Nozach, Hervé; Collet, Guillaume; Beau, Fabrice; Vera, Laura; Stura, Enrico; Dive, Vincent; Cuniasse, Philippe

2013-01-01

Artificial miniproteins that are able to target catalytic sites of matrix metalloproteinases (MMPs) were designed using a functional motif-grafting approach. The motif corresponded to the four N-terminal residues of TIMP-2, a broad-spectrum protein inhibitor of MMPs. Scaffolds that are able to reproduce the functional topology of this motif were obtained by exhaustive screening of the Protein Data Bank (PDB) using STAMPS software (search for three-dimensional atom motifs in protein structures). Ten artificial protein binders were produced. The designed proteins bind catalytic sites of MMPs with affinities ranging from 450 nm to 450 μm prior to optimization. The crystal structure of one artificial binder in complex with the catalytic domain of MMP-12 showed that the inter-molecular interactions established by the functional motif in the artificial binder corresponded to those found in the MMP-14-TIMP-2 complex, albeit with some differences in geometry. Molecular dynamics simulations of the ten binders in complex with MMP-14 suggested that these scaffolds may allow partial reproduction of native inter-molecular interactions, but differences in geometry and stability may contribute to the lower affinity of the artificial protein binders compared to the natural protein binder. Nevertheless, these results show that the in silico design method used provides sets of protein binders that target a specific binding site with a good rate of success. This approach may constitute the first step of an efficient hybrid computational/experimental approach to protein binder design. © 2012 The Authors Journal compilation © 2012 FEBS.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

PubMed

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Computational redesign of a protein-protein interface for high affinity and binding specificity using modular architecture and naturally occurring template fragments.

PubMed

Potapov, V; Reichmann, D; Abramovich, R; Filchtinski, D; Zohar, N; Ben Halevy, D; Edelman, M; Sobolev, V; Schreiber, G

2008-12-05

A new method is presented for the redesign of protein-protein interfaces, resulting in specificity of the designed pair while maintaining high affinity. The design is based on modular interface architecture and was carried out on the interaction between TEM1 beta-lactamase and its inhibitor protein, beta-lactamase inhibitor protein. The interface between these two proteins is composed of several mostly independent modules. We previously showed that it is possible to delete a complete module without affecting the overall structure of the interface. Here, we replace a complete module with structure fragments taken from nonrelated proteins. Nature-optimized fragments were chosen from 10(7) starting templates found in the Protein Data Bank. A procedure was then developed to identify sets of interacting template residues with a backbone arrangement mimicking the original module. This generated a final list of 361 putative replacement modules that were ranked using a novel scoring function based on grouped atom-atom contact surface areas. The top-ranked designed complex exhibited an affinity of at least the wild-type level and a mode of binding that was remarkably specific despite the absence of negative design in the procedure. In retrospect, the combined application of three factors led to the success of the design approach: utilizing the modular construction of the interface, capitalizing on native rather than artificial templates, and ranking with an accurate atom-atom contact surface scoring function.

High-coverage quantitative proteomics using amine-specific isotopic labeling.

PubMed

Melanson, Jeremy E; Avery, Steven L; Pinto, Devanand M

2006-08-01

Peptide dimethylation with isotopically coded formaldehydes was evaluated as a potential alternative to techniques such as the iTRAQ method for comparative proteomics. The isotopic labeling strategy and custom-designed protein quantitation software were tested using protein standards and then applied to measure proteins levels associated with Alzheimer's disease (AD). The method provided high accuracy (10% error), precision (14% RSD) and coverage (70%) when applied to the analysis of a standard solution of BSA by LC-MS/MS. The technique was then applied to measure protein abundance levels in brain tissue afflicted with AD relative to normal brain tissue. 2-D LC-MS analysis identified 548 unique proteins (p<0.05). Of these, 349 were quantified with two or more peptides that met the statistical criteria used in this study. Several classes of proteins exhibited significant changes in abundance. For example, elevated levels of antioxidant proteins and decreased levels of mitochondrial electron transport proteins were observed. The results demonstrate the utility of the labeling method for high-throughput quantitative analysis.

Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition.

PubMed

Schmidt Am Busch, Marcel; Sedano, Audrey; Simonson, Thomas

2010-05-05

Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

Deep sequencing methods for protein engineering and design.

PubMed

Wrenbeck, Emily E; Faber, Matthew S; Whitehead, Timothy A

2017-08-01

The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances. Copyright © 2016 Elsevier Ltd. All rights reserved.

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments.

PubMed

Park, Hahnbeom; Lee, Gyu Rie; Heo, Lim; Seok, Chaok

2014-01-01

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

SIMS: A Hybrid Method for Rapid Conformational Analysis

PubMed Central

Gipson, Bryant; Moll, Mark; Kavraki, Lydia E.

2013-01-01

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of “active” residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems. PMID:23935893

Single-step affinity and cost-effective purification of recombinant proteins using the Sepharose-binding lectin-tag from the mushroom Laetiporus sulphureus as fusion partner.

PubMed

Li, Xiao-Jing; Liu, Jin-Ling; Gao, Dong-Sheng; Wan, Wen-Yan; Yang, Xia; Li, Yong-Tao; Chang, Hong-Tao; Chen, Lu; Wang, Chuan-Qing; Zhao, Jun

2016-03-01

Previous research showed that a lectin from the mushroom Laetiporus sulphureus, designed LSL, bound to Sepharose and could be eluted by lactose. In this study, by taking advantage of the strong affinity of LSL-tag for Sepharose, we developed a single-step purification method for LSL-tagged fusion proteins. We utilized unmodified Sepharose-4B as a specific adsorbent and 0.2 M lactose solution as an elution buffer. Fusion proteins of LSL-tag and porcine circovirus capsid protein, designated LSL-Cap was recovered with purity of 90 ± 4%, and yield of 87 ± 3% from crude extract of recombinant Escherichia coli. To enable the remove of LSL-tag, tobacco etch virus (TEV) protease recognition sequence was placed downstream of LSL-tag in the expression vector, and LSL-tagged TEV protease, designated LSL-TEV, was also expressed in E. coli., and was recovered with purity of 82 ± 5%, and yield of 85 ± 2% from crude extract of recombinant E. coli. After digestion of LSL-tagged recombinant proteins with LSL-TEV, the LSL tag and LSL-TEV can be easily removed by passing the digested products through the Sepharose column. It is of worthy noting that the Sepharose can be reused after washing with PBS. The LSL affinity purification method enables rapid and inexpensive purification of LSL-tagged fusion proteins and scale-up production of native proteins. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification.

PubMed

Wang, LiQiang; Li, CuiFeng

2014-10-01

A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.

Computer-aided drug design for AMP-activated protein kinase activators.

PubMed

Wang, Zhanli; Huo, Jianxin; Sun, Lidan; Wang, Yongfu; Jin, Hongwei; Yu, Hui; Zhang, Liangren; Zhou, Lishe

2011-09-01

AMP-activated protein kinase (AMPK) is an important therapeutic target for the potential treatment of metabolic disorders, cardiovascular disease and cancer. Recently, various classes of compounds that activate AMPK by direct or indirect interactions have been reported. The importance of computer-aided drug design approaches in the search for potent activators of AMPK is now established, including structure-based design, ligand-based design, fragment-based design, as well as structural analysis. This review article highlights the computer-aided drug design approaches utilized to discover of activators targeting AMPK. The principles, advantages or limitation of the different methods are also being discussed together with examples of applications taken from the literatures.

Prion extraction methods: comparison of bead beating, ultrasonic disruption and repeated freeze-thaw methodologies for the recovery of functional renilla-prion fusion protein from bacteria

USDA-ARS?s Scientific Manuscript database

Molecular DNA technology allows for production of mammalian proteins in bacteria at sufficient quantities for downstream use and analysis. Variation in design and engineering of DNA expression vectors imparts selective alterations resulting in the generation of fusion proteins with intrinsic report...

Dietary Protein and Calcium Interact to Influence Calcium Retention: A Controlled Feeding Study

USDA-ARS?s Scientific Manuscript database

Objective: To test the effect of dietary protein on Ca (Ca) retention at low and high Ca intakes. Methods: In a randomized, controlled feeding study with a 2x2 factorial crossover design, healthy post-menopausal women (n=27), consumed either ~675 or ~1510 mg Ca/d, with both low and high protein (pro...

A critical analysis of computational protein design with sparse residue interaction graphs

PubMed Central

Georgiev, Ivelin S.

2017-01-01

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies. PMID:28358804

A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2013-11-07

Protein-Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal. In this paper, a sequence-based, fast, and adaptive PPI prediction method is introduced to assign two proteins to an interaction class (yes, no). First, in order to improve the presentation of the sequences, twelve physicochemical properties of amino acid have been used by different representation methods to transform the sequence of protein pairs into different feature vectors. Then, for speeding up the learning process and reducing the effect of noise PPI data, principal component analysis (PCA) is carried out as a proper feature extraction algorithm. Finally, a new and adaptive Learning Vector Quantization (LVQ) predictor is designed to deal with different models of datasets that are classified into balanced and imbalanced datasets. The accuracy of 93.88%, 90.03%, and 89.72% has been found on S. cerevisiae, H. pylori, and independent datasets, respectively. The results of various experiments indicate the efficiency and validity of the method. © 2013 Published by Elsevier Ltd.

A set of ligation-independent in vitro translation vectors for eukaryotic protein production.

PubMed

Bardóczy, Viola; Géczi, Viktória; Sawasaki, Tatsuya; Endo, Yaeta; Mészáros, Tamás

2008-03-27

The last decade has brought the renaissance of protein studies and accelerated the development of high-throughput methods in all aspects of proteomics. Presently, most protein synthesis systems exploit the capacity of living cells to translate proteins, but their application is limited by several factors. A more flexible alternative protein production method is the cell-free in vitro protein translation. Currently available in vitro translation systems are suitable for high-throughput robotic protein production, fulfilling the requirements of proteomics studies. Wheat germ extract based in vitro translation system is likely the most promising method, since numerous eukaryotic proteins can be cost-efficiently synthesized in their native folded form. Although currently available vectors for wheat embryo in vitro translation systems ensure high productivity, they do not meet the requirements of state-of-the-art proteomics. Target genes have to be inserted using restriction endonucleases and the plasmids do not encode cleavable affinity purification tags. We designed four ligation independent cloning (LIC) vectors for wheat germ extract based in vitro protein translation. In these constructs, the RNA transcription is driven by T7 or SP6 phage polymerase and two TEV protease cleavable affinity tags can be added to aid protein purification. To evaluate our improved vectors, a plant mitogen activated protein kinase was cloned in all four constructs. Purification of this eukaryotic protein kinase demonstrated that all constructs functioned as intended: insertion of PCR fragment by LIC worked efficiently, affinity purification of translated proteins by GST-Sepharose or MagneHis particles resulted in high purity kinase, and the affinity tags could efficiently be removed under different reaction conditions. Furthermore, high in vitro kinase activity testified of proper folding of the purified protein. Four newly designed in vitro translation vectors have been constructed which allow fast and parallel cloning and protein purification, thus representing useful molecular tools for high-throughput production of eukaryotic proteins.

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

NASA Astrophysics Data System (ADS)

Baumgartner, Matthew P.; Evans, David A.

2018-01-01

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

A method for fast energy estimation and visualization of protein-ligand interaction

NASA Astrophysics Data System (ADS)

Tomioka, Nobuo; Itai, Akiko; Iitaka, Yoichi

1987-10-01

A new computational and graphical method for facilitating ligand-protein docking studies is developed on a three-dimensional computer graphics display. Various physical and chemical properties inside the ligand binding pocket of a receptor protein, whose structure is elucidated by X-ray crystal analysis, are calculated on three-dimensional grid points and are stored in advance. By utilizing those tabulated data, it is possible to estimate the non-bonded and electrostatic interaction energy and the number of possible hydrogen bonds between protein and ligand molecules in real time during an interactive docking operation. The method also provides a comprehensive visualization of the local environment inside the binding pocket. With this method, it becomes easier to find a roughly stable geometry of ligand molecules, and one can therefore make a rapid survey of the binding capability of many drug candidates. The method will be useful for drug design as well as for the examination of protein-ligand interactions.

The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design.

PubMed

Schubert, Christian R; Stultz, Collin M

2009-08-01

Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.

Ab initio folding of proteins using all-atom discrete molecular dynamics

PubMed Central

Ding, Feng; Tsao, Douglas; Nie, Huifen; Dokholyan, Nikolay V.

2008-01-01

Summary Discrete molecular dynamics (DMD) is a rapid sampling method used in protein folding and aggregation studies. Until now, DMD was used to perform simulations of simplified protein models in conjunction with structure-based force fields. Here, we develop an all-atom protein model and a transferable force field featuring packing, solvation, and environment-dependent hydrogen bond interactions. Using the replica exchange method, we perform folding simulations of six small proteins (20–60 residues) with distinct native structures. In all cases, native or near-native states are reached in simulations. For three small proteins, multiple folding transitions are observed and the computationally-characterized thermodynamics are in quantitative agreement with experiments. The predictive power of all-atom DMD highlights the importance of environment-dependent hydrogen bond interactions in modeling protein folding. The developed approach can be used for accurate and rapid sampling of conformational spaces of proteins and protein-protein complexes, and applied to protein engineering and design of protein-protein interactions. PMID:18611374

Diversity in Genetic In Vivo Methods for Protein-Protein Interaction Studies: from the Yeast Two-Hybrid System to the Mammalian Split-Luciferase System

PubMed Central

Stynen, Bram; Tournu, Hélène; Tavernier, Jan

2012-01-01

Summary: The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays. PMID:22688816

Using Symmetry to Design Self-Assembling Protein Cages and Nanomaterials on the Mid-Nanometer Scale

NASA Astrophysics Data System (ADS)

Yeates, Todd

Self-assembling molecular structures having diverse cellular functions are widespread in nature. Some of the largest and most sophisticated types are built from many copies of the same or similar protein molecules arranged following principles of symmetry. A long-standing engineering goal has been to design novel protein molecules to self-assemble into geometrically specific structures similar to the extraordinary structures that have evolved in Nature. Practical routes to this goal have been developed by using ideas in symmetry to articulate the minimum design requirements for achieving various types of symmetric architectures, including cages, extended two-dimensional layers, and three-dimensional crystalline materials. The key requirement is that two distinct self-associating interfaces, each conferring one element of rotational symmetry, have to be engineered into the protein molecule (or molecules), following particular geometric specifications. The main principle is that combining two separate symmetry elements into a single molecular entity produces a molecule that necessarily assembles into an architecture dictated by a symmetry group that is the product of the two simpler contributing symmetries. Recent experiments have demonstrated success using a variety of symmetry-based strategies. Strategic variations are emerging that differ from each other with respect to biophysical features such as flexibility vs rigidity in the assembled structures, and with respect to design aspects such as whether the protein interfaces are inherited from natural oligomeric proteins or are designed de novo by advanced computational methods. The success of these strategies has been proven by determining crystal structures of several giant, self-assembling protein cages and clusters (10-25 nm in diameter), created by design. The ability to create sophisticated supramolecular structures from designed protein subunits opens the way to broad applications in synthetic biology and nanotechnology.

A Method for WD40 Repeat Detection and Secondary Structure Prediction

PubMed Central

Wang, Yang; Jiang, Fan; Zhuo, Zhu; Wu, Xian-Hui; Wu, Yun-Dong

2013-01-01

WD40-repeat proteins (WD40s), as one of the largest protein families in eukaryotes, play vital roles in assembling protein-protein/DNA/RNA complexes. WD40s fold into similar β-propeller structures despite diversified sequences. A program WDSP (WD40 repeat protein Structure Predictor) has been developed to accurately identify WD40 repeats and predict their secondary structures. The method is designed specifically for WD40 proteins by incorporating both local residue information and non-local family-specific structural features. It overcomes the problem of highly diversified protein sequences and variable loops. In addition, WDSP achieves a better prediction in identifying multiple WD40-domain proteins by taking the global combination of repeats into consideration. In secondary structure prediction, the average Q3 accuracy of WDSP in jack-knife test reaches 93.7%. A disease related protein LRRK2 was used as a representive example to demonstrate the structure prediction. PMID:23776530

Computational approaches for de novo design and redesign of metal-binding sites on proteins.

PubMed

Akcapinar, Gunseli Bayram; Sezerman, Osman Ugur

2017-04-28

Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature's own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox. © 2017 The Author(s).

Single step generation of protein arrays from DNA by cell-free expression and in situ immobilisation (PISA method).

PubMed

He, M; Taussig, M J

2001-08-01

We describe a format for production of protein arrays termed 'protein in situ array' (PISA). A PISA is rapidly generated in one step directly from PCR-generated DNA fragments by cell-free protein expression and in situ immobilisation at a surface. The template for expression is DNA encoding individual proteins or domains, which is produced by PCR using primers designed from information in DNA databases. Coupled transcription and translation is carried out on a surface to which the tagged protein adheres as soon as it is synthesised. Because proteins generated by cell-free synthesis are usually soluble and functional, this method can overcome problems of insolubility or degradation associated with bacterial expression of recombinant proteins. Moreover, the use of PCR-generated DNA enables rapid production of proteins or domains based on genome information alone and will be particularly useful where cloned material is not available. Here we show that human single-chain antibody fragments (three domain, V(H)/K form) and an enzyme (luciferase) can be functionally arrayed by the PISA method.

De novo design of the hydrophobic core of ubiquitin.

PubMed Central

Lazar, G. A.; Desjarlais, J. R.; Handel, T. M.

1997-01-01

We have previously reported the development and evaluation of a computational program to assist in the design of hydrophobic cores of proteins. In an effort to investigate the role of core packing in protein structure, we have used this program, referred to as Repacking of Cores (ROC), to design several variants of the protein ubiquitin. Nine ubiquitin variants containing from three to eight hydrophobic core mutations were constructed, purified, and characterized in terms of their stability and their ability to adopt a uniquely folded native-like conformation. In general, designed ubiquitin variants are more stable than control variants in which the hydrophobic core was chosen randomly. However, in contrast to previous results with 434 cro, all designs are destabilized relative to the wild-type (WT) protein. This raises the possibility that beta-sheet structures have more stringent packing requirements than alpha-helical proteins. A more striking observation is that all variants, including random controls, adopt fairly well-defined conformations, regardless of their stability. This result supports conclusions from the cro studies that non-core residues contribute significantly to the conformational uniqueness of these proteins while core packing largely affects protein stability and has less impact on the nature or uniqueness of the fold. Concurrent with the above work, we used stability data on the nine ubiquitin variants to evaluate and improve the predictive ability of our core packing algorithm. Additional versions of the program were generated that differ in potential function parameters and sampling of side chain conformers. Reasonable correlations between experimental and predicted stabilities suggest the program will be useful in future studies to design variants with stabilities closer to that of the native protein. Taken together, the present study provides further clarification of the role of specific packing interactions in protein structure and stability, and demonstrates the benefit of using systematic computational methods to predict core packing arrangements for the design of proteins. PMID:9194177

Improved packing of protein side chains with parallel ant colonies

PubMed Central

2014-01-01

Introduction The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains. Methods We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library. Results We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X1 and 77.11% of X12 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains. Conclusions This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms. PMID:25474164

Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.

PubMed

Gromiha, M Michael; Anoosha, P; Huang, Liang-Tsung

2016-01-01

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production.

PubMed

Oyarzún, Patricio; Kobe, Bostjan

2016-03-03

Novel vaccination approaches based on rational design of B- and T-cell epitopes - epitope-based vaccines - are making progress in the clinical trial pipeline. The epitope-focused recombinant protein-based malaria vaccine (termed RTS,S) is a next-generation approach that successfully reached phase-III trials, and will potentially become the first commercial vaccine against a human parasitic disease. Progress made on methods such as recombinant DNA technology, advanced cell-culture techniques, immunoinformatics and rational design of immunogens are driving the development of these novel concepts. Synthetic recombinant proteins comprising both B- and T-cell epitopes can be efficiently produced through modern biotechnology and bioprocessing methods, and can enable the induction of large repertoires of immune specificities. In particular, the inclusion of appropriate CD4+ T-cell epitopes is increasingly considered a key vaccine component to elicit robust immune responses, as suggested by results coming from HIV-1 clinical trials. In silico strategies for vaccine design are under active development to address genetic variation in pathogens and several broadly protective "universal" influenza and HIV-1 vaccines are currently at different stages of clinical trials. Other methods focus on improving population coverage in target populations by rationally considering specificity and prevalence of the HLA proteins, though a proof-of-concept in humans has not been demonstrated yet. Overall, we expect immunoinformatics and bioprocessing methods to become a central part of the next-generation epitope-based vaccine development and production process.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

A curated gluten protein sequence database to support development of proteomics methods for determination of gluten in gluten-free foods.

PubMed

Bromilow, Sophie; Gethings, Lee A; Buckley, Mike; Bromley, Mike; Shewry, Peter R; Langridge, James I; Clare Mills, E N

2017-06-23

The unique physiochemical properties of wheat gluten enable a diverse range of food products to be manufactured. However, gluten triggers coeliac disease, a condition which is treated using a gluten-free diet. Analytical methods are required to confirm if foods are gluten-free, but current immunoassay-based methods can unreliable and proteomic methods offer an alternative but require comprehensive and well annotated sequence databases which are lacking for gluten. A manually a curated database (GluPro V1.0) of gluten proteins, comprising 630 discrete unique full length protein sequences has been compiled. It is representative of the different types of gliadin and glutenin components found in gluten. An in silico comparison of their coeliac toxicity was undertaken by analysing the distribution of coeliac toxic motifs. This demonstrated that whilst the α-gliadin proteins contained more toxic motifs, these were distributed across all gluten protein sub-types. Comparison of annotations observed using a discovery proteomics dataset acquired using ion mobility MS/MS showed that more reliable identifications were obtained using the GluPro V1.0 database compared to the complete reviewed Viridiplantae database. This highlights the value of a curated sequence database specifically designed to support the proteomic workflows and the development of methods to detect and quantify gluten. We have constructed the first manually curated open-source wheat gluten protein sequence database (GluPro V1.0) in a FASTA format to support the application of proteomic methods for gluten protein detection and quantification. We have also analysed the manually verified sequences to give the first comprehensive overview of the distribution of sequences able to elicit a reaction in coeliac disease, the prevalent form of gluten intolerance. Provision of this database will improve the reliability of gluten protein identification by proteomic analysis, and aid the development of targeted mass spectrometry methods in line with Codex Alimentarius Commission requirements for foods designed to meet the needs of gluten intolerant individuals. Copyright © 2017. Published by Elsevier B.V.

CCBuilder: an interactive web-based tool for building, designing and assessing coiled-coil protein assemblies.

PubMed

Wood, Christopher W; Bruning, Marc; Ibarra, Amaurys Á; Bartlett, Gail J; Thomson, Andrew R; Sessions, Richard B; Brady, R Leo; Woolfson, Derek N

2014-11-01

The ability to accurately model protein structures at the atomistic level underpins efforts to understand protein folding, to engineer natural proteins predictably and to design proteins de novo. Homology-based methods are well established and produce impressive results. However, these are limited to structures presented by and resolved for natural proteins. Addressing this problem more widely and deriving truly ab initio models requires mathematical descriptions for protein folds; the means to decorate these with natural, engineered or de novo sequences; and methods to score the resulting models. We present CCBuilder, a web-based application that tackles the problem for a defined but large class of protein structure, the α-helical coiled coils. CCBuilder generates coiled-coil backbones, builds side chains onto these frameworks and provides a range of metrics to measure the quality of the models. Its straightforward graphical user interface provides broad functionality that allows users to build and assess models, in which helix geometry, coiled-coil architecture and topology and protein sequence can be varied rapidly. We demonstrate the utility of CCBuilder by assembling models for 653 coiled-coil structures from the PDB, which cover >96% of the known coiled-coil types, and by generating models for rarer and de novo coiled-coil structures. CCBuilder is freely available, without registration, at http://coiledcoils.chm.bris.ac.uk/app/cc_builder/. © The Author 2014. Published by Oxford University Press.

Protein mechanics: from single molecules to functional biomaterials.

PubMed

Li, Hongbin; Cao, Yi

2010-10-19

Elastomeric proteins act as the essential functional units in a wide variety of biomechanical machinery and serve as the basic building blocks for biological materials that exhibit superb mechanical properties. These proteins provide the desired elasticity, mechanical strength, resilience, and toughness within these materials. Understanding the mechanical properties of elastomeric protein-based biomaterials is a multiscale problem spanning from the atomistic/molecular level to the macroscopic level. Uncovering the design principles of individual elastomeric building blocks is critical both for the scientific understanding of multiscale mechanics of biomaterials and for the rational engineering of novel biomaterials with desirable mechanical properties. The development of single-molecule force spectroscopy techniques has provided methods for characterizing mechanical properties of elastomeric proteins one molecule at a time. Single-molecule atomic force microscopy (AFM) is uniquely suited to this purpose. Molecular dynamic simulations, protein engineering techniques, and single-molecule AFM study have collectively revealed tremendous insights into the molecular design of single elastomeric proteins, which can guide the design and engineering of elastomeric proteins with tailored mechanical properties. Researchers are focusing experimental efforts toward engineering artificial elastomeric proteins with mechanical properties that mimic or even surpass those of natural elastomeric proteins. In this Account, we summarize our recent experimental efforts to engineer novel artificial elastomeric proteins and develop general and rational methodologies to tune the nanomechanical properties of elastomeric proteins at the single-molecule level. We focus on general design principles used for enhancing the mechanical stability of proteins. These principles include the development of metal-chelation-based general methodology, strategies to control the unfolding hierarchy of multidomain elastomeric proteins, and the design of novel elastomeric proteins that exhibit stimuli-responsive mechanical properties. Moving forward, we are now exploring the use of these artificial elastomeric proteins as building blocks of protein-based biomaterials. Ultimately, we would like to rationally tailor mechanical properties of elastomeric protein-based materials by programming the molecular sequence, and thus nanomechanical properties, of elastomeric proteins at the single-molecule level. This step would help bridge the gap between single protein mechanics and material biomechanics, revealing how the mechanical properties of individual elastomeric proteins are translated into the properties of macroscopic materials.

Homology modeling a fast tool for drug discovery: current perspectives.

PubMed

Vyas, V K; Ukawala, R D; Ghate, M; Chintha, C

2012-01-01

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives

PubMed Central

Vyas, V. K.; Ukawala, R. D.; Ghate, M.; Chintha, C.

2012-01-01

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery. PMID:23204616

The application of quantum mechanics in structure-based drug design.

PubMed

Mucs, Daniel; Bryce, Richard A

2013-03-01

Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein-ligand recognition. In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein-ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein-ligand docking, protein-ligand binding affinities and ligand strain on binding. Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.

Sequence signatures of allosteric proteins towards rational design.

PubMed

Namboodiri, Saritha; Verma, Chandra; Dhar, Pawan K; Giuliani, Alessandro; Nair, Achuthsankar S

2010-12-01

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence-based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.

Exploring the protein folding free energy landscape: coupling replica exchange method with P3ME/RESPA algorithm.

PubMed

Zhou, Ruhong

2004-05-01

A highly parallel replica exchange method (REM) that couples with a newly developed molecular dynamics algorithm particle-particle particle-mesh Ewald (P3ME)/RESPA has been proposed for efficient sampling of protein folding free energy landscape. The algorithm is then applied to two separate protein systems, beta-hairpin and a designed protein Trp-cage. The all-atom OPLSAA force field with an explicit solvent model is used for both protein folding simulations. Up to 64 replicas of solvated protein systems are simulated in parallel over a wide range of temperatures. The combined trajectories in temperature and configurational space allow a replica to overcome free energy barriers present at low temperatures. These large scale simulations reveal detailed results on folding mechanisms, intermediate state structures, thermodynamic properties and the temperature dependences for both protein systems.

Materials-by-design: computation, synthesis, and characterization from atoms to structures

NASA Astrophysics Data System (ADS)

Yeo, Jingjie; Jung, Gang Seob; Martín-Martínez, Francisco J.; Ling, Shengjie; Gu, Grace X.; Qin, Zhao; Buehler, Markus J.

2018-05-01

In the 50 years that succeeded Richard Feynman’s exposition of the idea that there is ‘plenty of room at the bottom’ for manipulating individual atoms for the synthesis and manufacturing processing of materials, the materials-by-design paradigm is being developed gradually through synergistic integration of experimental material synthesis and characterization with predictive computational modeling and optimization. This paper reviews how this paradigm creates the possibility to develop materials according to specific, rational designs from the molecular to the macroscopic scale. We discuss promising techniques in experimental small-scale material synthesis and large-scale fabrication methods to manipulate atomistic or macroscale structures, which can be designed by computational modeling. These include recombinant protein technology to produce peptides and proteins with tailored sequences encoded by recombinant DNA, self-assembly processes induced by conformational transition of proteins, additive manufacturing for designing complex structures, and qualitative and quantitative characterization of materials at different length scales. We describe important material characterization techniques using numerous methods of spectroscopy and microscopy. We detail numerous multi-scale computational modeling techniques that complements these experimental techniques: DFT at the atomistic scale; fully atomistic and coarse-grain molecular dynamics at the molecular to mesoscale; continuum modeling at the macroscale. Additionally, we present case studies that utilize experimental and computational approaches in an integrated manner to broaden our understanding of the properties of two-dimensional materials and materials based on silk and silk-elastin-like proteins.

A coevolution analysis for identifying protein-protein interactions by Fourier transform.

PubMed

Yin, Changchuan; Yau, Stephen S-T

2017-01-01

Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI).

A coevolution analysis for identifying protein-protein interactions by Fourier transform

PubMed Central

Yin, Changchuan; Yau, Stephen S. -T.

2017-01-01

Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI). PMID:28430779

Nonlinear Optical Characterization of Membrane Protein Microcrystals and Nanocrystals.

PubMed

Newman, Justin A; Simpson, Garth J

2016-01-01

Nonlinear optical methods such as second harmonic generation (SHG) and two-photon excited UV fluorescence (TPE-UVF) imaging are promising approaches to address bottlenecks in the membrane protein structure determination pipeline. The general principles of SHG and TPE-UVF are discussed here along with instrument design considerations. Comparisons to conventional methods in high throughput crystallization condition screening and crystal quality assessment prior to X-ray diffraction are also discussed.

Protein thermal stability enhancement by designing salt bridges: a combined computational and experimental study.

PubMed

Lee, Chi-Wen; Wang, Hsiu-Jung; Hwang, Jenn-Kang; Tseng, Ching-Ping

2014-01-01

Protein thermal stability is an important factor considered in medical and industrial applications. Many structural characteristics related to protein thermal stability have been elucidated, and increasing salt bridges is considered as one of the most efficient strategies to increase protein thermal stability. However, the accurate simulation of salt bridges remains difficult. In this study, a novel method for salt-bridge design was proposed based on the statistical analysis of 10,556 surface salt bridges on 6,493 X-ray protein structures. These salt bridges were first categorized based on pairing residues, secondary structure locations, and Cα-Cα distances. Pairing preferences generalized from statistical analysis were used to construct a salt-bridge pair index and utilized in a weighted electrostatic attraction model to find the effective pairings for designing salt bridges. The model was also coupled with B-factor, weighted contact number, relative solvent accessibility, and conservation prescreening to determine the residues appropriate for the thermal adaptive design of salt bridges. According to our method, eight putative salt-bridges were designed on a mesophilic β-glucosidase and 24 variants were constructed to verify the predictions. Six putative salt-bridges leaded to the increase of the enzyme thermal stability. A significant increase in melting temperature of 8.8, 4.8, 3.7, 1.3, 1.2, and 0.7°C of the putative salt-bridges N437K-D49, E96R-D28, E96K-D28, S440K-E70, T231K-D388, and Q277E-D282 was detected, respectively. Reversing the polarity of T231K-D388 to T231D-D388K resulted in a further increase in melting temperatures by 3.6°C, which may be caused by the transformation of an intra-subunit electrostatic interaction into an inter-subunit one depending on the local environment. The combination of the thermostable variants (N437K, E96R, T231D and D388K) generated a melting temperature increase of 15.7°C. Thus, this study demonstrated a novel method for the thermal adaptive design of salt bridges through inference of suitable positions and substitutions.

Structure-based design of combinatorial mutagenesis libraries

PubMed Central

Verma, Deeptak; Grigoryan, Gevorg; Bailey-Kellogg, Chris

2015-01-01

The development of protein variants with improved properties (thermostability, binding affinity, catalytic activity, etc.) has greatly benefited from the application of high-throughput screens evaluating large, diverse combinatorial libraries. At the same time, since only a very limited portion of sequence space can be experimentally constructed and tested, an attractive possibility is to use computational protein design to focus libraries on a productive portion of the space. We present a general-purpose method, called “Structure-based Optimization of Combinatorial Mutagenesis” (SOCoM), which can optimize arbitrarily large combinatorial mutagenesis libraries directly based on structural energies of their constituents. SOCoM chooses both positions and substitutions, employing a combinatorial optimization framework based on library-averaged energy potentials in order to avoid explicitly modeling every variant in every possible library. In case study applications to green fluorescent protein, β-lactamase, and lipase A, SOCoM optimizes relatively small, focused libraries whose variants achieve energies comparable to or better than previous library design efforts, as well as larger libraries (previously not designable by structure-based methods) whose variants cover greater diversity while still maintaining substantially better energies than would be achieved by representative random library approaches. By allowing the creation of large-scale combinatorial libraries based on structural calculations, SOCoM promises to increase the scope of applicability of computational protein design and improve the hit rate of discovering beneficial variants. While designs presented here focus on variant stability (predicted by total energy), SOCoM can readily incorporate other structure-based assessments, such as the energy gap between alternative conformational or bound states. PMID:25611189

Domain selection combined with improved cloning strategy for high throughput expression of higher eukaryotic proteins

PubMed Central

Chen, Yunjia; Qiu, Shihong; Luan, Chi-Hao; Luo, Ming

2007-01-01

Background Expression of higher eukaryotic genes as soluble, stable recombinant proteins is still a bottleneck step in biochemical and structural studies of novel proteins today. Correct identification of stable domains/fragments within the open reading frame (ORF), combined with proper cloning strategies, can greatly enhance the success rate when higher eukaryotic proteins are expressed as these domains/fragments. Furthermore, a HTP cloning pipeline incorporated with bioinformatics domain/fragment selection methods will be beneficial to studies of structure and function genomics/proteomics. Results With bioinformatics tools, we developed a domain/domain boundary prediction (DDBP) method, which was trained by available experimental data. Combined with an improved cloning strategy, DDBP had been applied to 57 proteins from C. elegans. Expression and purification results showed there was a 10-fold increase in terms of obtaining purified proteins. Based on the DDBP method, the improved GATEWAY cloning strategy and a robotic platform, we constructed a high throughput (HTP) cloning pipeline, including PCR primer design, PCR, BP reaction, transformation, plating, colony picking and entry clones extraction, which have been successfully applied to 90 C. elegans genes, 88 Brucella genes, and 188 human genes. More than 97% of the targeted genes were obtained as entry clones. This pipeline has a modular design and can adopt different operations for a variety of cloning/expression strategies. Conclusion The DDBP method and improved cloning strategy were satisfactory. The cloning pipeline, combined with our recombinant protein HTP expression pipeline and the crystal screening robots, constitutes a complete platform for structure genomics/proteomics. This platform will increase the success rate of purification and crystallization dramatically and promote the further advancement of structure genomics/proteomics. PMID:17663785

Reproducing Crystal Binding Modes of Ligand Functional Groups using Site-Identification by Ligand Competitive Saturation (SILCS) Simulations

PubMed Central

Raman, E. Prabhu; Yu, Wenbo; Guvench, Olgun; MacKerell, Alexander D.

2011-01-01

The applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS), to identify regions on a protein surface with which different types of functional groups on low-molecular weight inhibitors interact is demonstrated. The method involves molecular dynamics (MD) simulations of a protein in an aqueous solution of chemically diverse small molecules from which probability distributions of fragments types, termed FragMaps, are obtained. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogen and oxygen atoms serve as probes for hydrogen bond donor and acceptor affinity, respectively. The method is tested using a set of 7 proteins for which crystal structures of complexes with several high affinity inhibitors are known. Good agreement is obtained between FragMaps and the positions of chemically similar functional groups in inhibitors as observed in the X-ray crystallographic structures. Quantitative capabilities of the SILCS approach are demonstrated by converting FragMaps to free energies, termed Grid Free Energies (GFE), and showing correlation between the GFE values and experimental binding affinities. For proteins for which ligand decoy sets are available, GFE values are shown to typically score the crystal conformation and conformations similar to it more favorable than decoys. Additionally, SILCS is tested for its ability to capture the subtle differences in ligand affinity across homologous proteins, information which may be of utility towards specificity-guided drug design. Taken together, our results show that SILCS can recapitulate the known location of functional groups of bound inhibitors for a number of proteins, suggesting that the method may be of utility for rational drug design. PMID:21456594

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

PubMed

Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2018-06-14

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

SeqRate: sequence-based protein folding type classification and rates prediction

PubMed Central

2010-01-01

Background Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines. Results We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs. Conclusions Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html. PMID:20438647

De novo design and engineering of functional metal and porphyrin-binding protein domains

NASA Astrophysics Data System (ADS)

Everson, Bernard H.

In this work, I describe an approach to the rational, iterative design and characterization of two functional cofactor-binding protein domains. First, a hybrid computational/experimental method was developed with the aim of algorithmically generating a suite of porphyrin-binding protein sequences with minimal mutual sequence information. This method was explored by generating libraries of sequences, which were then expressed and evaluated for function. One successful sequence is shown to bind a variety of porphyrin-like cofactors, and exhibits light- activated electron transfer in mixed hemin:chlorin e6 and hemin:Zn(II)-protoporphyrin IX complexes. These results imply that many sophisticated functions such as cofactor binding and electron transfer require only a very small number of residue positions in a protein sequence to be fixed. Net charge and hydrophobic content are important in determining protein solubility and stability. Accordingly, rational modifications were made to the aforementioned design procedure in order to improve its overall success rate. The effects of these modifications are explored using two `next-generation' sequence libraries, which were separately expressed and evaluated. Particular modifications to these design parameters are demonstrated to effectively double the purification success rate of the procedure. Finally, I describe the redesign of the artificial di-iron protein DF2 into CDM13, a single chain di-Manganese four-helix bundle. CDM13 acts as a functional model of natural manganese catalase, exhibiting a kcat of 0.08s-1 under steady-state conditions. The bound manganese cofactors have a reduction potential of +805 mV vs NHE, which is too high for efficient dismutation of hydrogen peroxide. These results indicate that as a high-potential manganese complex, CDM13 may represent a promising first step toward a polypeptide model of the Oxygen Evolving Complex of the photosynthetic enzyme Photosystem II.

Targeted Quantitation of Proteins by Mass Spectrometry

PubMed Central

2013-01-01

Quantitative measurement of proteins is one of the most fundamental analytical tasks in a biochemistry laboratory, but widely used immunochemical methods often have limited specificity and high measurement variation. In this review, we discuss applications of multiple-reaction monitoring (MRM) mass spectrometry, which allows sensitive, precise quantitative analyses of peptides and the proteins from which they are derived. Systematic development of MRM assays is permitted by databases of peptide mass spectra and sequences, software tools for analysis design and data analysis, and rapid evolution of tandem mass spectrometer technology. Key advantages of MRM assays are the ability to target specific peptide sequences, including variants and modified forms, and the capacity for multiplexing that allows analysis of dozens to hundreds of peptides. Different quantitative standardization methods provide options that balance precision, sensitivity, and assay cost. Targeted protein quantitation by MRM and related mass spectrometry methods can advance biochemistry by transforming approaches to protein measurement. PMID:23517332

Targeted quantitation of proteins by mass spectrometry.

PubMed

Liebler, Daniel C; Zimmerman, Lisa J

2013-06-04

Quantitative measurement of proteins is one of the most fundamental analytical tasks in a biochemistry laboratory, but widely used immunochemical methods often have limited specificity and high measurement variation. In this review, we discuss applications of multiple-reaction monitoring (MRM) mass spectrometry, which allows sensitive, precise quantitative analyses of peptides and the proteins from which they are derived. Systematic development of MRM assays is permitted by databases of peptide mass spectra and sequences, software tools for analysis design and data analysis, and rapid evolution of tandem mass spectrometer technology. Key advantages of MRM assays are the ability to target specific peptide sequences, including variants and modified forms, and the capacity for multiplexing that allows analysis of dozens to hundreds of peptides. Different quantitative standardization methods provide options that balance precision, sensitivity, and assay cost. Targeted protein quantitation by MRM and related mass spectrometry methods can advance biochemistry by transforming approaches to protein measurement.

Effects of sugar-sweetened beverages on plasma acylation stimulating protein, leptin, and adiponectin: Relationships with metabolic outcomes

USDA-ARS?s Scientific Manuscript database

OBJECTIVE: The effects of fructose and glucose consumption on plasma acylation stimulating protein (ASP), adiponectin, and leptin concentrations relative to energy intake, body weight, adiposity, circulating triglycerides, and insulin sensitivity were determined. DESIGN AND METHODS: Thirty two over...

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

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods. PMID:18452616

Genetically modified proteins: functional improvement and chimeragenesis

PubMed Central

Balabanova, Larissa; Golotin, Vasily; Podvolotskaya, Anna; Rasskazov, Valery

2015-01-01

This review focuses on the emerging role of site-specific mutagenesis and chimeragenesis for the functional improvement of proteins in areas where traditional protein engineering methods have been extensively used and practically exhausted. The novel path for the creation of the novel proteins has been created on the farther development of the new structure and sequence optimization algorithms for generating and designing the accurate structure models in result of x-ray crystallography studies of a lot of proteins and their mutant forms. Artificial genetic modifications aim to expand nature's repertoire of biomolecules. One of the most exciting potential results of mutagenesis or chimeragenesis finding could be design of effective diagnostics, bio-therapeutics and biocatalysts. A sampling of recent examples is listed below for the in vivo and in vitro genetically improvement of various binding protein and enzyme functions, with references for more in-depth study provided for the reader's benefit. PMID:26211369

Prediction of physical protein protein interactions

NASA Astrophysics Data System (ADS)

Szilágyi, András; Grimm, Vera; Arakaki, Adrián K.; Skolnick, Jeffrey

2005-06-01

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

Study and selection of in vivo protein interactions by coupling bimolecular fluorescence complementation and flow cytometry.

PubMed

Morell, Montse; Espargaro, Alba; Aviles, Francesc Xavier; Ventura, Salvador

2008-01-01

We present a high-throughput approach to study weak protein-protein interactions by coupling bimolecular fluorescent complementation (BiFC) to flow cytometry (FC). In BiFC, the interaction partners (bait and prey) are fused to two rationally designed fragments of a fluorescent protein, which recovers its function upon the binding of the interacting proteins. For weak protein-protein interactions, the detected fluorescence is proportional to the interaction strength, thereby allowing in vivo discrimination between closely related binders with different affinity for the bait protein. FC provides a method for high-speed multiparametric data acquisition and analysis; the assay is simple, thousands of cells can be analyzed in seconds and, if required, selected using fluorescence-activated cell sorting (FACS). The combination of both methods (BiFC-FC) provides a technically straightforward, fast and highly sensitive method to validate weak protein interactions and to screen and identify optimal ligands in biologically synthesized libraries. Once plasmids encoding the protein fusions have been obtained, the evaluation of a specific interaction, the generation of a library and selection of active partners using BiFC-FC can be accomplished in 5 weeks.

Predicting permanent and transient protein-protein interfaces.

PubMed

La, David; Kong, Misun; Hoffman, William; Choi, Youn Im; Kihara, Daisuke

2013-05-01

Protein-protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs. Copyright © 2013 Wiley Periodicals, Inc.

A Colorimetric Microplate Assay for DNA-Binding Activity of His-Tagged MutS Protein.

PubMed

Banasik, Michał; Sachadyn, Paweł

2016-09-01

A simple microplate method was designed for rapid testing DNA-binding activity of proteins. The principle of the assay involves binding of tested DNA by his-tagged protein immobilized on a nickel-coated ELISA plate, following colorimetric detection of biotinylated DNA with avidin conjugated to horseradish peroxidase. The method was used to compare DNA mismatch binding activities of MutS proteins from three bacterial species. The assay required relatively low amounts of tested protein (approximately 0.5-10 pmol) and DNA (0.1-10 pmol) and a relatively short time of analysis (up to 60 min). The method is very simple to apply and convenient to test different buffer conditions of DNA-protein binding. Sensitive colorimetric detection enables naked eye observations and quantitation with an ELISA reader. The performance of the assay, which we believe is a distinguishing trait of the method, is based on two strong and specific molecular interactions: binding of a his-tagged protein to a nickel-coated microplate and binding of biotinylated DNA to avidin. In the reported experiments, the solution was used to optimize the conditions for DNA mismatch binding by MutS protein; however, the approach could be implemented to test nucleic acids interactions with any protein of interest.

Fluorescent labeling of tetracysteine-tagged proteins in intact cells.

PubMed

Hoffmann, Carsten; Gaietta, Guido; Zürn, Alexander; Adams, Stephen R; Terrillon, Sonia; Ellisman, Mark H; Tsien, Roger Y; Lohse, Martin J

2010-09-01

In this paper, we provide a general protocol for labeling proteins with the membrane-permeant fluorogenic biarsenical dye fluorescein arsenical hairpin binder-ethanedithiol (FlAsH-EDT₂). Generation of the tetracysteine-tagged protein construct by itself is not described, as this is a protein-specific process. This method allows site-selective labeling of proteins in living cells and has been applied to a wide variety of proteins and biological problems. We provide here a generally applicable labeling procedure and discuss the problems that can occur as well as general considerations that must be taken into account when designing and implementing the procedure. The method can even be applied to proteins with expression below 1 pmol mg⁻¹ of protein, such as G protein-coupled receptors, and it can be used to study the intracellular localization of proteins as well as functional interactions in fluorescence resonance energy transfer experiments. The labeling procedure using FlAsH-EDT₂ as described takes 2-3 h, depending on the number of samples to be processed.

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.

PubMed

Liu, Bin; Wang, Xiaolong; Lin, Lei; Dong, Qiwen; Wang, Xuan

2008-12-01

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences. In this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods. The method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.

Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction.

PubMed

Cang, Zixuan; Wei, Guo-Wei

2018-02-01

Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology-based machine-learning paradigm outperforms other existing methods in protein-ligand binding affinity predictions. ESPH reveals protein-ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein-ligand hydrophobic interactions are extended to 40Å  away from the binding site, which has a significant ramification to drug and protein design. Copyright © 2017 John Wiley & Sons, Ltd.

Engineered Proteins: Redox Properties and Their Applications

PubMed Central

Prabhulkar, Shradha; Tian, Hui; Wang, Xiaotang; Zhu, Jun-Jie

2012-01-01

Abstract Oxidoreductases and metalloproteins, representing more than one third of all known proteins, serve as significant catalysts for numerous biological processes that involve electron transfers such as photosynthesis, respiration, metabolism, and molecular signaling. The functional properties of the oxidoreductases/metalloproteins are determined by the nature of their redox centers. Protein engineering is a powerful approach that is used to incorporate biological and abiological redox cofactors as well as novel enzymes and redox proteins with predictable structures and desirable functions for important biological and chemical applications. The methods of protein engineering, mainly rational design, directed evolution, protein surface modifications, and domain shuffling, have allowed the creation and study of a number of redox proteins. This review presents a selection of engineered redox proteins achieved through these methods, resulting in a manipulation in redox potentials, an increase in electron-transfer efficiency, and an expansion of native proteins by de novo design. Such engineered/modified redox proteins with desired properties have led to a broad spectrum of practical applications, ranging from biosensors, biofuel cells, to pharmaceuticals and hybrid catalysis. Glucose biosensors are one of the most successful products in enzyme electrochemistry, with reconstituted glucose oxidase achieving effective electrical communication with the sensor electrode; direct electron-transfer-type biofuel cells are developed to avoid thermodynamic loss and mediator leakage; and fusion proteins of P450s and redox partners make the biocatalytic generation of drug metabolites possible. In summary, this review includes the properties and applications of the engineered redox proteins as well as their significance and great potential in the exploration of bioelectrochemical sensing devices. Antioxid. Redox Signal. 17, 1796–1822. PMID:22435347

Impact of computational structure-based methods on drug discovery.

PubMed

Reynolds, Charles H

2014-01-01

Structure-based drug design has become an indispensible tool in drug discovery. The emergence of structure-based design is due to gains in structural biology that have provided exponential growth in the number of protein crystal structures, new computational algorithms and approaches for modeling protein-ligand interactions, and the tremendous growth of raw computer power in the last 30 years. Computer modeling and simulation have made major contributions to the discovery of many groundbreaking drugs in recent years. Examples are presented that highlight the evolution of computational structure-based design methodology, and the impact of that methodology on drug discovery.

Recent progress in making protein microarray through BioLP

NASA Astrophysics Data System (ADS)

Yang, Rusong; Wei, Lian; Feng, Ying; Li, Xiujian; Zhou, Quan

2017-02-01

Biological laser printing (BioLP) is a promising biomaterial printing technique. It has the advantage of high resolution, high bioactivity, high printing frequency and small transported liquid amount. In this paper, a set of BioLP device is design and made, and protein microarrays are printed by this device. It's found that both laser intensity and fluid layer thickness have an influence on the microarrays acquired. Besides, two kinds of the fluid layer coating methods are compared, and the results show that blade coating method is better than well-coating method in BioLP. A microarray of 0.76pL protein microarray and a "NUDT" patterned microarray are printed to testify the printing ability of BioLP.

GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution

PubMed Central

Kinjo, Akira R.; Nakamura, Haruki

2012-01-01

Comparison and classification of protein structures are fundamental means to understand protein functions. Due to the computational difficulty and the ever-increasing amount of structural data, however, it is in general not feasible to perform exhaustive all-against-all structure comparisons necessary for comprehensive classifications. To efficiently handle such situations, we have previously proposed a method, now called GIRAF. We herein describe further improvements in the GIRAF protein structure search and alignment method. The GIRAF method achieves extremely efficient search of similar structures of ligand binding sites of proteins by exploiting database indexing of structural features of local coordinate frames. In addition, it produces refined atom-wise alignments by iterative applications of the Hungarian method to the bipartite graph defined for a pair of superimposed structures. By combining the refined alignments based on different local coordinate frames, it is made possible to align structures involving domain movements. We provide detailed accounts for the database design, the search and alignment algorithms as well as some benchmark results. PMID:27493524

Protein Quantification by Elemental Mass Spectrometry: An Experiment for Graduate Students

ERIC Educational Resources Information Center

Schwarz, Gunnar; Ickert, Stefanie; Wegner, Nina; Nehring, Andreas; Beck, Sebastian; Tiemann, Ruediger; Linscheid, Michael W.

2014-01-01

A multiday laboratory experiment was designed to integrate inductively coupled plasma-mass spectrometry (ICP-MS) in the context of protein quantification into an advanced practical course in analytical and environmental chemistry. Graduate students were familiar with the analytical methods employed, whereas the combination of bioanalytical assays…

Hydrophobicity – Shake Flasks, Protein Folding and Drug Discovery

PubMed Central

Sarkar, Aurijit; Kellogg, Glen E.

2009-01-01

Hydrophobic interactions are some of the most important interactions in nature. They are the primary driving force in a number of phenomena. This is mostly an entropic effect and can account for a number of biophysical events such as protein-protein or protein-ligand binding that are of immense importance in drug design. The earliest studies on this phenomenon can be dated back to the end of the 19th century when Meyer and Overton independently correlated the hydrophobic nature of gases to their anesthetic potency. Since then, significant progress has been made in this realm of science. This review briefly traces the history of hydrophobicity research along with the theoretical estimation of partition coefficients. Finally, the application of hydrophobicity estimation methods in the field of drug design and protein folding is discussed. PMID:19929828

Protein construct storage: Bayesian variable selection and prediction with mixtures.

PubMed

Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

Surface energetics and protein-protein interactions: analysis and mechanistic implications

PubMed Central

Peri, Claudio; Morra, Giulia; Colombo, Giorgio

2016-01-01

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions. PMID:27050828

Interaction Entropy: A New Paradigm for Highly Efficient and Reliable Computation of Protein-Ligand Binding Free Energy.

PubMed

Duan, Lili; Liu, Xiao; Zhang, John Z H

2016-05-04

Efficient and reliable calculation of protein-ligand binding free energy is a grand challenge in computational biology and is of critical importance in drug design and many other molecular recognition problems. The main challenge lies in the calculation of entropic contribution to protein-ligand binding or interaction systems. In this report, we present a new interaction entropy method which is theoretically rigorous, computationally efficient, and numerically reliable for calculating entropic contribution to free energy in protein-ligand binding and other interaction processes. Drastically different from the widely employed but extremely expensive normal mode method for calculating entropy change in protein-ligand binding, the new method calculates the entropic component (interaction entropy or -TΔS) of the binding free energy directly from molecular dynamics simulation without any extra computational cost. Extensive study of over a dozen randomly selected protein-ligand binding systems demonstrated that this interaction entropy method is both computationally efficient and numerically reliable and is vastly superior to the standard normal mode approach. This interaction entropy paradigm introduces a novel and intuitive conceptual understanding of the entropic effect in protein-ligand binding and other general interaction systems as well as a practical method for highly efficient calculation of this effect.

The application of SSADM to modelling the logical structure of proteins.

PubMed

Saldanha, J; Eccles, J

1991-10-01

A logical design that describes the overall structure of proteins, together with a more detailed design describing secondary and some supersecondary structures, has been constructed using the computer-aided software engineering (CASE) tool, Auto-mate. Auto-mate embodies the philosophy of the Structured Systems Analysis and Design Method (SSADM) which enables the logical design of computer systems. Our design will facilitate the building of large information systems, such as databases and knowledgebases in the field of protein structure, by the derivation of system requirements from our logical model prior to producing the final physical system. In addition, the study has highlighted the ease of employing SSADM as a formalism in which to conduct the transferral of concepts from an expert into a design for a knowledge-based system that can be implemented on a computer (the knowledge-engineering exercise). It has been demonstrated how SSADM techniques may be extended for the purpose of modelling the constituent Prolog rules. This facilitates the integration of the logical system design model with the derived knowledge-based system.

Membrane Protein Structure Determination Using Crystallography and Lipidic Mesophases - Recent Advances and Successes

PubMed Central

Caffrey, Martin; Li, Dianfan; Dukkipati, Abhiram

2012-01-01

The crystal structure of the β2-adrenergic receptor in complex with an agonist and its cognate G protein has just recently been solved. It is now possible to explore in molecular detail the means by which this paradigmatic transmembrane receptor binds agonist, communicates the impulse or signalling event across the membrane and sets in motion a series of G protein-directed intracellular responses. The structure was determined using crystals of the ternary complex grown in a rationally designed lipidic mesophase by the so-called in meso method. The method is proving to be particularly useful in the G protein-coupled receptor field where the structures of thirteen distinct receptor types have been solved in the past five years. In addition to receptors, the method has proven useful with a wide variety of integral membrane protein classes that include bacterial and eukaryotic rhodopsins, a light harvesting complex II (LHII), photosynthetic reaction centers, cytochrome oxidases, β-barrels, an exchanger, and an integral membrane peptide. This attests to the versatility and range of the method and supports the view that the in meso method should be included in the arsenal of the serious membrane structural biologist. For this to happen however, the reluctance in adopting it attributable, in part, to the anticipated difficulties associated with handling the sticky, viscous cubic mesophase in which crystals grow must be overcome. Harvesting and collecting diffraction data with the mesophase-grown crystals is also viewed with some trepidation. It is acknowledged that there are challenges associated with the method. Over the years, we have endeavored to establish how the method works at a molecular level and to make it user-friendly. To these ends, tools for handling the mesophase in the pico- to nano-liter volume range have been developed for highly efficient crystallization screening in manual and robotic modes. Methods have been implemented for evaluating the functional activity of membrane proteins reconstituted into the bilayer of the cubic phase as a prelude to crystallogenesis. Glass crystallization plates have been built that provide unparalleled optical quality and sensitivity to nascent crystals. Lipid and precipitant screens have been designed for a more rational approach to crystallogenesis such that the method can now be applied to an even wider variety of membrane protein types. In this Current Topics article, these assorted advances are outlined along with a summary of the membrane proteins that have yielded to the method. The prospects for and the challenges that must be overcome to further develop the method are described. PMID:22783824

Improved packing of protein side chains with parallel ant colonies.

PubMed

Quan, Lijun; Lü, Qiang; Li, Haiou; Xia, Xiaoyan; Wu, Hongjie

2014-01-01

The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains. We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library. We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X1 and 77.11% of X12 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains. This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms.

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

PubMed Central

Ponomarenko, Julia V; Bourne, Philip E

2007-01-01

Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. Results Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. Conclusion It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found. PMID:17910770

ASSESSING AND COMBINING RELIABILITY OF PROTEIN INTERACTION SOURCES

PubMed Central

LEACH, SONIA; GABOW, AARON; HUNTER, LAWRENCE; GOLDBERG, DEBRA S.

2008-01-01

Integrating diverse sources of interaction information to create protein networks requires strategies sensitive to differences in accuracy and coverage of each source. Previous integration approaches calculate reliabilities of protein interaction information sources based on congruity to a designated ‘gold standard.’ In this paper, we provide a comparison of the two most popular existing approaches and propose a novel alternative for assessing reliabilities which does not require a gold standard. We identify a new method for combining the resultant reliabilities and compare it against an existing method. Further, we propose an extrinsic approach to evaluation of reliability estimates, considering their influence on the downstream tasks of inferring protein function and learning regulatory networks from expression data. Results using this evaluation method show 1) our method for reliability estimation is an attractive alternative to those requiring a gold standard and 2) the new method for combining reliabilities is less sensitive to noise in reliability assignments than the similar existing technique. PMID:17990508

A synergistic approach to protein crystallization: Combination of a fixed-arm carrier with surface entropy reduction

PubMed Central

Moon, Andrea F; Mueller, Geoffrey A; Zhong, Xuejun; Pedersen, Lars C

2010-01-01

Protein crystallographers are often confronted with recalcitrant proteins not readily crystallizable, or which crystallize in problematic forms. A variety of techniques have been used to surmount such obstacles: crystallization using carrier proteins or antibody complexes, chemical modification, surface entropy reduction, proteolytic digestion, and additive screening. Here we present a synergistic approach for successful crystallization of proteins that do not form diffraction quality crystals using conventional methods. This approach combines favorable aspects of carrier-driven crystallization with surface entropy reduction. We have generated a series of maltose binding protein (MBP) fusion constructs containing different surface mutations designed to reduce surface entropy and encourage crystal lattice formation. The MBP advantageously increases protein expression and solubility, and provides a streamlined purification protocol. Using this technique, we have successfully solved the structures of three unrelated proteins that were previously unattainable. This crystallization technique represents a valuable rescue strategy for protein structure solution when conventional methods fail. PMID:20196072

Specific and Non-Specific Protein Association in Solution: Computation of Solvent Effects and Prediction of First-Encounter Modes for Efficient Configurational Bias Monte Carlo Simulations

PubMed Central

Cardone, Antonio; Pant, Harish; Hassan, Sergio A.

2013-01-01

Weak and ultra-weak protein-protein association play a role in molecular recognition, and can drive spontaneous self-assembly and aggregation. Such interactions are difficult to detect experimentally, and are a challenge to the force field and sampling technique. A method is proposed to identify low-population protein-protein binding modes in aqueous solution. The method is designed to identify preferential first-encounter complexes from which the final complex(es) at equilibrium evolves. A continuum model is used to represent the effects of the solvent, which accounts for short- and long-range effects of water exclusion and for liquid-structure forces at protein/liquid interfaces. These effects control the behavior of proteins in close proximity and are optimized based on binding enthalpy data and simulations. An algorithm is described to construct a biasing function for self-adaptive configurational-bias Monte Carlo of a set of interacting proteins. The function allows mixing large and local changes in the spatial distribution of proteins, thereby enhancing sampling of relevant microstates. The method is applied to three binary systems. Generalization to multiprotein complexes is discussed. PMID:24044772

Incorporation of native antibodies and Fc-fusion proteins on DNA nanostructures via a modular conjugation strategy† †Electronic supplementary information (ESI) available: Experimental methods, DNA origami design, DNA sequences, and additional experimental data. See DOI: 10.1039/c7cc04178k

PubMed Central

Rosier, Bas J. H. M.; Cremers, Glenn A. O.; Engelen, Wouter; Merkx, Maarten; Brunsveld, Luc

2017-01-01

A photocrosslinkable protein G variant was used as an adapter protein to covalently and site-specifically conjugate an antibody and an Fc-fusion protein to an oligonucleotide. This modular approach enables straightforward decoration of DNA nanostructures with complex native proteins while retaining their innate binding affinity, allowing precise control over the nanoscale spatial organization of such proteins for in vitro and in vivo biomedical applications. PMID:28617516

Blind test of physics-based prediction of protein structures.

PubMed

Shell, M Scott; Ozkan, S Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A

2009-02-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences.

Blind Test of Physics-Based Prediction of Protein Structures

PubMed Central

Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

2009-01-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

A Novel Cassette Method for Probe Evaluation in the Designed Biochips

PubMed Central

Zinkevich, Vitaly; Sapojnikova, Nelly; Mitchell, Julian; Kartvelishvili, Tamar; Asatiani, Nino; Alkhalil, Samia; Bogdarina, Irina; Al-Humam, Abdulmohsen A.

2014-01-01

A critical step in biochip design is the selection of probes with identical hybridisation characteristics. In this article we describe a novel method for evaluating DNA hybridisation probes, allowing the fine-tuning of biochips, that uses cassettes with multiple probes. Each cassette contains probes in equimolar proportions so that their hybridisation performance can be assessed in a single reaction. The model used to demonstrate this method was a series of probes developed to detect TORCH pathogens. DNA probes were designed for Toxoplasma gondii, Chlamidia trachomatis, Rubella, Cytomegalovirus, and Herpes virus and these were used to construct the DNA cassettes. Five cassettes were constructed to detect TORCH pathogens using a variety of genes coding for membrane proteins, viral matrix protein, an early expressed viral protein, viral DNA polymerase and the repetitive gene B1 of Toxoplasma gondii. All of these probes, except that for the B1 gene, exhibited similar profiles under the same hybridisation conditions. The failure of the B1 gene probe to hybridise was not due to a position effect, and this indicated that the probe was unsuitable for inclusion in the biochip. The redesigned probe for the B1 gene exhibited identical hybridisation properties to the other probes, suitable for inclusion in a biochip. PMID:24897111

Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

PubMed

Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

2018-02-20

Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

Revealing Atomic-Level Mechanisms of Protein Allostery with Molecular Dynamics Simulations.

PubMed

Hertig, Samuel; Latorraca, Naomi R; Dror, Ron O

2016-06-01

Molecular dynamics (MD) simulations have become a powerful and popular method for the study of protein allostery, the widespread phenomenon in which a stimulus at one site on a protein influences the properties of another site on the protein. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric binding sites and the determination of the mechanistic basis for allostery. These results can provide a foundation for applications including rational drug design and protein engineering. Here, we provide an introduction to the investigation of protein allostery using molecular dynamics simulation. We emphasize the importance of designing simulations that include appropriate perturbations to the molecular system, such as the addition or removal of ligands or the application of mechanical force. We also demonstrate how the bidirectional nature of allostery-the fact that the two sites involved influence one another in a symmetrical manner-can facilitate such investigations. Through a series of case studies, we illustrate how these concepts have been used to reveal the structural basis for allostery in several proteins and protein complexes of biological and pharmaceutical interest.

Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery.

PubMed

Basith, Shaherin; Lee, Yoonji; Choi, Sun

2018-01-01

Unraveling the mystery of protein allostery has been one of the greatest challenges in both structural and computational biology. However, recent advances in computational methods, particularly molecular dynamics (MD) simulations, have led to its utility as a powerful and popular tool for the study of protein allostery. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric hot spots and the determination of the mechanistic basis for allostery. These structural and dynamic studies can provide a foundation for a wide range of applications, including rational drug design and protein engineering. In our laboratory, the use of MD simulations and network analysis assisted in the elucidation of the allosteric hotspots and intracellular signal transduction of G protein-coupled receptors (GPCRs), primarily on one of the adenosine receptor subtypes, A 2A adenosine receptor (A 2A AR). In this chapter, we describe a method for calculating the map of allosteric signal flow in different GPCR conformational states and illustrate how these concepts have been utilized in understanding the mechanism of GPCR allostery. These structural studies will provide valuable insights into the allosteric and orthosteric modulations that would be of great help to design novel drugs targeting GPCRs in pathological states.

Modeling backbone flexibility to achieve sequence diversity: The design of novel alpha-helical ligands for Bcl-xL

PubMed Central

Fu, Xiaoran; Apgar, James R.; Keating, Amy E.

2007-01-01

Computational protein design can be used to select sequences that are compatible with a fixed-backbone template. This strategy has been used in numerous instances to engineer novel proteins. However, the fixed-backbone assumption severely restricts the sequence space that is accessible via design. For challenging problems, such as the design of functional proteins, this may not be acceptable. In this paper, we present a method for introducing backbone flexibility into protein design calculations and apply it to the design of diverse helical BH3 ligands that bind to the anti-apoptotic protein Bcl-xL, a member of the Bcl-2 protein family. We demonstrate how normal mode analysis can be used to sample different BH3 backbones, and show that this leads to a larger and more diverse set of low-energy solutions than can be achieved using a native high-resolution Bcl-xL complex crystal structure as a template. We tested several of the designed solutions experimentally and found that this approach worked well when normal mode calculations were used to deform a native BH3 helix structure, but less well when they were used to deform an idealized helix. A subsequent round of design and testing identified a likely source of the problem as inadequate sampling of the helix pitch. In all, we tested seventeen designed BH3 peptide sequences, including several point mutants. Of these, eight bound well to Bcl-xL and four others showed weak but detectable binding. The successful designs showed a diversity of sequences that would have been difficult or impossible to achieve using only a fixed backbone. Thus, introducing backbone flexibility via normal mode analysis effectively broadened the set of sequences identified by computational design, and provided insight into positions important for binding Bcl-xL. PMID:17597151

In search of new lead compounds for trypanosomiasis drug design: A protein structure-based linked-fragment approach

NASA Astrophysics Data System (ADS)

Verlinde, Christophe L. M. J.; Rudenko, Gabrielle; Hol, Wim G. J.

1992-04-01

A modular method for pursuing structure-based inhibitor design in the framework of a design cycle is presented. The approach entails four stages: (1) a design pathway is defined in the three-dimensional structure of a target protein; (2) this pathway is divided into subregions; (3) complementary building blocks, also called fragments, are designed in each subregion; complementarity is defined in terms of shape, hydrophobicity, hydrogen bond properties and electrostatics; and (4) fragments from different subregions are linked into potential lead compounds. Stages (3) and (4) are qualitatively guided by force-field calculations. In addition, the designed fragments serve as entries for retrieving existing compounds from chemical databases. This linked-fragment approach has been applied in the design of potentially selective inhibitors of triosephosphate isomerase from Trypanosoma brucei, the causative agent of sleeping sickness.

Self-assembled bionanostructures: proteins following the lead of DNA nanostructures

PubMed Central

2014-01-01

Natural polymers are able to self-assemble into versatile nanostructures based on the information encoded into their primary structure. The structural richness of biopolymer-based nanostructures depends on the information content of building blocks and the available biological machinery to assemble and decode polymers with a defined sequence. Natural polypeptides comprise 20 amino acids with very different properties in comparison to only 4 structurally similar nucleotides, building elements of nucleic acids. Nevertheless the ease of synthesizing polynucleotides with selected sequence and the ability to encode the nanostructural assembly based on the two specific nucleotide pairs underlay the development of techniques to self-assemble almost any selected three-dimensional nanostructure from polynucleotides. Despite more complex design rules, peptides were successfully used to assemble symmetric nanostructures, such as fibrils and spheres. While earlier designed protein-based nanostructures used linked natural oligomerizing domains, recent design of new oligomerizing interaction surfaces and introduction of the platform for topologically designed protein fold may enable polypeptide-based design to follow the track of DNA nanostructures. The advantages of protein-based nanostructures, such as the functional versatility and cost effective and sustainable production methods provide strong incentive for further development in this direction. PMID:24491139

Comparison of Tobacco Host Cell Protein Removal Methods by Blanching Intact Plants or by Heat Treatment of Extracts.

PubMed

Buyel, Johannes F; Hubbuch, Jürgen; Fischer, Rainer

2016-08-08

Plants not only provide food, feed and raw materials for humans, but have also been developed as an economical production system for biopharmaceutical proteins, such as antibodies, vaccine candidates and enzymes. These must be purified from the plant biomass but chromatography steps are hindered by the high concentrations of host cell proteins (HCPs) in plant extracts. However, most HCPs irreversibly aggregate at temperatures above 60 °C facilitating subsequent purification of the target protein. Here, three methods are presented to achieve the heat precipitation of tobacco HCPs in either intact leaves or extracts. The blanching of intact leaves can easily be incorporated into existing processes but may have a negative impact on subsequent filtration steps. The opposite is true for heat precipitation of leaf extracts in a stirred vessel, which can improve the performance of downstream operations albeit with major changes in process equipment design, such as homogenizer geometry. Finally, a heat exchanger setup is well characterized in terms of heat transfer conditions and easy to scale, but cleaning can be difficult and there may be a negative impact on filter capacity. The design-of-experiments approach can be used to identify the most relevant process parameters affecting HCP removal and product recovery. This facilitates the application of each method in other expression platforms and the identification of the most suitable method for a given purification strategy.

Method for Developing Optical Sensors Using a Synthetic Dye-Fluorescent Protein FRET Pair and Computational Modeling and Assessment.

PubMed

Mitchell, Joshua A; Zhang, William H; Herde, Michel K; Henneberger, Christian; Janovjak, Harald; O'Mara, Megan L; Jackson, Colin J

2017-01-01

Biosensors that exploit Förster resonance energy transfer (FRET) can be used to visualize biological and physiological processes and are capable of providing detailed information in both spatial and temporal dimensions. In a FRET-based biosensor, substrate binding is associated with a change in the relative positions of two fluorophores, leading to a change in FRET efficiency that may be observed in the fluorescence spectrum. As a result, their design requires a ligand-binding protein that exhibits a conformational change upon binding. However, not all ligand-binding proteins produce responsive sensors upon conjugation to fluorescent proteins or dyes, and identifying the optimum locations for the fluorophores often involves labor-intensive iterative design or high-throughput screening. Combining the genetic fusion of a fluorescent protein to the ligand-binding protein with site-specific covalent attachment of a fluorescent dye can allow fine control over the positions of the two fluorophores, allowing the construction of very sensitive sensors. This relies upon the accurate prediction of the locations of the two fluorophores in bound and unbound states. In this chapter, we describe a method for computational identification of dye-attachment sites that allows the use of cysteine modification to attach synthetic dyes that can be paired with a fluorescent protein for the purposes of creating FRET sensors.

Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain

ERIC Educational Resources Information Center

Hannagan, Thomas; Grainger, Jonathan

2012-01-01

It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jakel, Scholkopf, & Wichmann, 2009). We point out that "String kernels," initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal…

Fluorescence quantum yield measurements of fluorescent proteins: a laboratory experiment for a biochemistry or molecular biophysics laboratory course.

PubMed

Wall, Kathryn P; Dillon, Rebecca; Knowles, Michelle K

2015-01-01

Fluorescent proteins are commonly used in cell biology to assess where proteins are within a cell as a function of time and provide insight into intracellular protein function. However, the usefulness of a fluorescent protein depends directly on the quantum yield. The quantum yield relates the efficiency at which a fluorescent molecule converts absorbed photons into emitted photons and it is necessary to know for assessing what fluorescent protein is the most appropriate for a particular application. In this work, we have designed an upper-level, biochemistry laboratory experiment where students measure the fluorescence quantum yields of fluorescent proteins relative to a standard organic dye. Four fluorescent protein variants, enhanced cyan fluorescent protein (ECFP), enhanced green fluorescent protein (EGFP), mCitrine, and mCherry, were used, however the methods described are useful for the characterization of any fluorescent protein or could be expanded to fluorescent quantum yield measurements of organic dye molecules. The laboratory is designed as a guided inquiry project and takes two, 4 hr laboratory periods. During the first day students design the experiment by selecting the excitation wavelength, choosing the standard, and determining the concentration needed for the quantum yield experiment that takes place in the second laboratory period. Overall, this laboratory provides students with a guided inquiry learning experience and introduces concepts of fluorescence biophysics into a biochemistry laboratory curriculum. © 2014 The International Union of Biochemistry and Molecular Biology.

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Prediction of Protein Configurational Entropy (Popcoen).

PubMed

Goethe, Martin; Gleixner, Jan; Fita, Ignacio; Rubi, J Miguel

2018-03-13

A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

Accurate de novo design of hyperstable constrained peptides

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

Bhardwaj, Gaurav; Mulligan, Vikram Khipple; Bahl, Christopher D.

Covalently-crosslinked peptides present attractive opportunities for developing new therapeutics. Lying between small molecule and protein therapeutics in size, natural crosslinked peptides play critical roles in signaling, virulence and immunity. Engineering novel peptides with precise control over their three-dimensional structures is a significant challenge. Here we describe the development of computational methods for de novo design of conformationally-restricted peptides, and the use of these methods to design hyperstable disulfide-stabilized miniproteins, heterochiral peptides, and N-C cyclic peptides. Experimentally-determined X-ray and NMR structures for 12 of the designs are nearly identical to the computational models. The computational design methods and stable scaffolds providemore » the basis for a new generation of peptide-based drugs.« less

Crystallization of bFGF-DNA Aptamer Complexes Using a Sparse Matrix Designed for Protein-Nucleic Acid Complexes

NASA Technical Reports Server (NTRS)

Cannone, Jaime J.; Barnes, Cindy L.; Achari, Aniruddha; Kundrot, Craig E.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

The Sparse Matrix approach for obtaining lead crystallization conditions has proven to be very fruitful for the crystallization of proteins and nucleic acids. Here we report a Sparse Matrix developed specifically for the crystallization of protein-DNA complexes. This method is rapid and economical, typically requiring 2.5 mg of complex to test 48 conditions. The method was originally developed to crystallize basic fibroblast growth factor (bFGF) complexed with DNA sequences identified through in vitro selection, or SELEX, methods. Two DNA aptamers that bind with approximately nanomolar affinity and inhibit the angiogenic properties of bFGF were selected for co-crystallization. The Sparse Matrix produced lead crystallization conditions for both bFGF-DNA complexes.

Ensemble Generation and the Influence of Protein Flexibility on Geometric Tunnel Prediction in Cytochrome P450 Enzymes

PubMed Central

Kingsley, Laura J.; Lill, Markus A.

2014-01-01

Computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology and has proven useful in fields such as protein engineering and drug design. Geometric tunnel prediction programs, such as Caver3.0 and MolAxis, are computationally efficient methods to identify potential ligand entry and egress routes in proteins. Although many geometric tunnel programs are designed to accommodate a single input structure, the increasingly recognized importance of protein flexibility in tunnel formation and behavior has led to the more widespread use of protein ensembles in tunnel prediction. However, there has not yet been an attempt to directly investigate the influence of ensemble size and composition on geometric tunnel prediction. In this study, we compared tunnels found in a single crystal structure to ensembles of various sizes generated using different methods on both the apo and holo forms of cytochrome P450 enzymes CYP119, CYP2C9, and CYP3A4. Several protein structure clustering methods were tested in an attempt to generate smaller ensembles that were capable of reproducing the data from larger ensembles. Ultimately, we found that by including members from both the apo and holo data sets, we could produce ensembles containing less than 15 members that were comparable to apo or holo ensembles containing over 100 members. Furthermore, we found that, in the absence of either apo or holo crystal structure data, pseudo-apo or –holo ensembles (e.g. adding ligand to apo protein throughout MD simulations) could be used to resemble the structural ensembles of the corresponding apo and holo ensembles, respectively. Our findings not only further highlight the importance of including protein flexibility in geometric tunnel prediction, but also suggest that smaller ensembles can be as capable as larger ensembles at capturing many of the protein motions important for tunnel prediction at a lower computational cost. PMID:24956479

Atomic decomposition of the protein solvation free energy and its application to amyloid-beta protein in water

NASA Astrophysics Data System (ADS)

Chong, Song-Ho; Ham, Sihyun

2011-07-01

We report the development of an atomic decomposition method of the protein solvation free energy in water, which ascribes global change in the solvation free energy to local changes in protein conformation as well as in hydration structure. So far, empirical decomposition analyses based on simple continuum solvation models have prevailed in the study of protein-protein interactions, protein-ligand interactions, as well as in developing scoring functions for computer-aided drug design. However, the use of continuum solvation model suffers serious drawbacks since it yields the protein free energy landscape which is quite different from that of the explicit solvent model and since it does not properly account for the non-polar hydrophobic effects which play a crucial role in biological processes in water. Herein, we develop an exact and general decomposition method of the solvation free energy that overcomes these hindrances. We then apply this method to elucidate the molecular origin for the solvation free energy change upon the conformational transitions of 42-residue amyloid-beta protein (Aβ42) in water, whose aggregation has been implicated as a primary cause of Alzheimer's disease. We address why Aβ42 protein exhibits a great propensity to aggregate when transferred from organic phase to aqueous phase.

Probabilistic models for capturing more physicochemical properties on protein-protein interface.

PubMed

Guo, Fei; Li, Shuai Cheng; Du, Pufeng; Wang, Lusheng

2014-06-23

Protein-protein interactions play a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. It is of great interest to understand how proteins interact with each other. The general approach is to explore all possible poses and identify near-native ones with the energy function. The key issue here is to design an effective energy function, based on various physicochemical properties. In this paper, we first identify two new features, the coupled dihedral angles on the interfaces and the geometrical information on π-π interactions. We study these two features through statistical methods: a mixture of bivariate von Mises distributions is used to model the correlation of the coupled dihedral angles, while a mixture of bivariate normal distributions is used to model the orientation of the aromatic rings on π-π interactions. Using 6438 complexes, we parametrize the joint distribution of each new feature. Then, we propose a novel method to construct the energy function for protein-protein interface prediction, which includes the new features as well as the existing energy items such as dDFIRE energy, side-chain energy, atom contact energy, and amino acid energy. Experiments show that our method outperforms the state-of-the-art methods, ZRANK and ClusPro. We use the CAPRI evaluation criteria, Irmsd value, and Fnat value. On Benchmark v4.0, our method has an average Irmsd value of 3.39 Å and Fnat value of 62%, which improves upon the average Irmsd value of 3.89 Å and Fnat value of 49% for ZRANK, and the average Irmsd value of 3.99 Å and Fnat value of 46% for ClusPro. On the CAPRI targets, our method has an average Irmsd value of 3.56 Å and Fnat value of 42%, which improves upon the average Irmsd value of 4.27 Å and Fnat value of 39% for ZRANK, the average Irmsd value of 5.15 Å and Fnat value of 30% for ClusPro.

Protein-Ligand Interaction Detection with a Novel Method of Transient Induced Molecular Electronic Spectroscopy (TIMES): Experimental and Theoretical Studies.

PubMed

Zhang, Tiantian; Wei, Tao; Han, Yuanyuan; Ma, Heng; Samieegohar, Mohammadreza; Chen, Ping-Wei; Lian, Ian; Lo, Yu-Hwa

2016-11-23

Protein-ligand interaction detection without disturbances (e.g., surface immobilization, fluorescent labeling, and crystallization) presents a key question in protein chemistry and drug discovery. The emergent technology of transient induced molecular electronic spectroscopy (TIMES), which incorporates a unique design of microfluidic platform and integrated sensing electrodes, is designed to operate in a label-free and immobilization-free manner to provide crucial information for protein-ligand interactions in relevant physiological conditions. Through experiments and theoretical simulations, we demonstrate that the TIMES technique actually detects protein-ligand binding through signals generated by surface electric polarization. The accuracy and sensitivity of experiments were demonstrated by precise measurements of dissociation constant of lysozyme and N -acetyl-d-glucosamine (NAG) ligand and its trimer, NAG 3 . Computational fluid dynamics (CFD) computation is performed to demonstrate that the surface's electric polarization signal originates from the induced image charges during the transition state of surface mass transport, which is governed by the overall effects of protein concentration, hydraulic forces, and surface fouling due to protein adsorption. Hybrid atomistic molecular dynamics (MD) simulations and free energy computation show that ligand binding affects lysozyme structure and stability, producing different adsorption orientation and surface polarization to give the characteristic TIMES signals. Although the current work is focused on protein-ligand interactions, the TIMES method is a general technique that can be applied to study signals from reactions between many kinds of molecules.

An Approach to the Design of a Particulate System for Oral Protein Delivery .II. Preparation and Stability Study of rhGH-Loaded Microspheres in Simulated Gastrointestinal Fluids

PubMed Central

Nafissi Varcheh, Nastaran; Aboofazeli, Reza

2011-01-01

The delivery of therapeutic proteins has gained momentum with development of biotechnology. However, large molecular weight, hydrophilic nature and susceptibility to harsh environment of gastrointestinal tract (GIT) resulted in low absorption. The main objective of this work was the design of a particulate system for oral delivery of recombinant human growth hormone (rhGH) on the basis of particle uptake mechanism in GIT. Biodegradable protein-loaded microspheres were prepared using Resomers (RG207, RG756 and RG505) by double emulsion methods. Aqueous solution of protein and freshly prepared rhGH-zinc complex were used for loading process. Various analytical methods, including fluorescence spectroscopy, SDS-PAGE electrophoresis and reversed-phase chromatography, were set up for the quantification and qualification of rhGH before and after the formulation and fabrication procedures. At the optimum conditions, microspheres were mostly below 10 μm with relatively high protein loading (> 50%). Obtained data showed that the stability of protein did not change during the formulation and microencapsulation processes. Results also showed that the encapsulation process in the presence of zinc caused no detectable change in the protein chemical stability. In-vitro stability study of microspheres in different simulated GI media indicated that the entrapped protein was physically stable. Less than 20% of rhGH was released from the microspheres incubated in both simulated stomach and intestine fluids for 3 and 6 h, respectively. PMID:24250342

Detecting protein-protein interactions using Renilla luciferase fusion proteins.

PubMed

Burbelo, Peter D; Kisailus, Adam E; Peck, Jeremy W

2002-11-01

We have developed a novel system designated the luciferase assay for protein detection (LAPD) to study protein-protein interactions. This method involves two protein fusions, a soluble reporter fusion and a fusion for immobilizing the target protein. The soluble reporter is an N-terminal Renilla luciferase fusion protein that exhibits high Renilla luciferase activity. Crude cleared lysates from transfected Cos1 cells that express the Renilla luciferase fusion protein can be used in binding assays with immobilized target proteins. Following incubation and washing, target-bound Renilla luciferase fusion proteins produce light from the coelenterazine substrate, indicating an interaction between the two proteins of interest. As proof of the principle, we reproduced known, transient protein-protein interactions between the Cdc42 GTPase and its effector proteins. GTPase Renilla fusion proteins produced in Cos1 cells were tested with immobilized recombinant GST-N-WASP and CEP5 effector proteins. Using this assay, we could detect specific interactions of Cdc42 with these effector proteins in approximately 50 min. The specificity of these interactions was demonstrated by showing that they were GTPase-specific and GTP-dependent and not seen with other unrelated target proteins. These results suggest that the LAPD method, which is both rapid and sensitive, may have research and practical applications.

New strategy for protein interactions and application to structure-based drug design

NASA Astrophysics Data System (ADS)

Zou, Xiaoqin

One of the greatest challenges in computational biophysics is to predict interactions between biological molecules, which play critical roles in biological processes and rational design of therapeutic drugs. Biomolecular interactions involve delicate interplay between multiple interactions, including electrostatic interactions, van der Waals interactions, solvent effect, and conformational entropic effect. Accurate determination of these complex and subtle interactions is challenging. Moreover, a biological molecule such as a protein usually consists of thousands of atoms, and thus occupies a huge conformational space. The large degrees of freedom pose further challenges for accurate prediction of biomolecular interactions. Here, I will present our development of physics-based theory and computational modeling on protein interactions with other molecules. The major strategy is to extract microscopic energetics from the information embedded in the experimentally-determined structures of protein complexes. I will also present applications of the methods to structure-based therapeutic design. Supported by NSF CAREER Award DBI-0953839, NIH R01GM109980, and the American Heart Association (Midwest Affiliate) [13GRNT16990076].

HIV-1 Protease Function and Structure Studies with the Simplicial Neighborhood Analysis of Protein Packing (SNAPP) Method

PubMed Central

Zhang, Shuxing; Kaplan, Andrew H.; Tropsha, Alexander

2009-01-01

The Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method was used to predict the effect of mutagenesis on the enzymatic activity of the HIV-1 protease (HIVP). SNAPP relies on a four-body statistical scoring function derived from the analysis of spatially nearest neighbor residue compositional preferences in a diverse and representative subset of protein structures from the Protein Data Bank. The method was applied to the analysis of HIVP mutants with residue substitutions in the hydrophobic core as well as at the interface between the two protease monomers. Both wild type and tethered structures were employed in the calculations. We obtained a strong correlation, with R2 as high as 0.96, between ΔSNAPP score (i.e., the difference in SNAPP scores between wild type and mutant proteins) and the protease catalytic activity for tethered structures. A weaker but significant correlation was also obtained for non-tethered structures as well. Our analysis identified residues both in the hydrophobic core and at the dimeric interface (DI) that are very important for the protease function. This study demonstrates a potential utility of the SNAPP method for rational design of mutagenesis studies and protein engineering. PMID:18498108

Mapping of ligand-binding cavities in proteins.

PubMed

Andersson, C David; Chen, Brian Y; Linusson, Anna

2010-05-01

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs. 2009 Wiley-Liss, Inc.

Mapping of Ligand-Binding Cavities in Proteins

PubMed Central

Andersson, C. David; Chen, Brian Y.; Linusson, Anna

2010-01-01

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterise and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity and charge). This approach can provide valuable information on the similarities, and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterisation and mapping of “orphan structures”, selection of protein structures for docking studies in structure-based design and identification of proteins for selectivity screens in drug design programs. PMID:20034113

Isotachophoresis-Based Surface Immunoassay.

PubMed

Paratore, Federico; Zeidman Kalman, Tal; Rosenfeld, Tally; Kaigala, Govind V; Bercovici, Moran

2017-07-18

In the absence of amplification methods for proteins, the immune-detection of low-abundance proteins using antibodies is fundamentally limited by binding kinetic rates. Here, we present a new class of surface-based immunoassays in which protein-antibody reaction is accelerated by isotachophoresis (ITP). We demonstrate the use of ITP to preconcentrate and deliver target proteins to a surface decorated with specific antibodies, where effective utilization of the focused sample is achieved by modulating the driving electric field (stop-and-diffuse ITP mode) or applying a counter flow that opposes the ITP motion (counterflow ITP mode). Using enhanced green fluorescent protein (EGFP) as a model protein, we carry out an experimental optimization of the ITP-based immunoassay and demonstrate a 1300-fold improvement in limit of detection compared to a standard immunoassay, in a 6 min protein-antibody reaction. We discuss the design of buffer chemistries for other protein systems and, in concert with experiments, provide full analytical solutions for the two operation modes, elucidating the interplay between reaction, diffusion, and accumulation time scales and enabling the prediction and design of future immunoassays.

Recent mass spectrometry-based techniques and considerations for disulfide bond characterization in proteins.

PubMed

Lakbub, Jude C; Shipman, Joshua T; Desaire, Heather

2018-04-01

Disulfide bonds are important structural moieties of proteins: they ensure proper folding, provide stability, and ensure proper function. With the increasing use of proteins for biotherapeutics, particularly monoclonal antibodies, which are highly disulfide bonded, it is now important to confirm the correct disulfide bond connectivity and to verify the presence, or absence, of disulfide bond variants in the protein therapeutics. These studies help to ensure safety and efficacy. Hence, disulfide bonds are among the critical quality attributes of proteins that have to be monitored closely during the development of biotherapeutics. However, disulfide bond analysis is challenging because of the complexity of the biomolecules. Mass spectrometry (MS) has been the go-to analytical tool for the characterization of such complex biomolecules, and several methods have been reported to meet the challenging task of mapping disulfide bonds in proteins. In this review, we describe the relevant, recent MS-based techniques and provide important considerations needed for efficient disulfide bond analysis in proteins. The review focuses on methods for proper sample preparation, fragmentation techniques for disulfide bond analysis, recent disulfide bond mapping methods based on the fragmentation techniques, and automated algorithms designed for rapid analysis of disulfide bonds from liquid chromatography-MS/MS data. Researchers involved in method development for protein characterization can use the information herein to facilitate development of new MS-based methods for protein disulfide bond analysis. In addition, individuals characterizing biotherapeutics, especially by disulfide bond mapping in antibodies, can use this review to choose the best strategies for disulfide bond assignment of their biologic products. Graphical Abstract This review, describing characterization methods for disulfide bonds in proteins, focuses on three critical components: sample preparation, mass spectrometry data, and software tools.

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

NASA Astrophysics Data System (ADS)

Lerner, Michael G.; Meagher, Kristin L.; Carlson, Heather A.

2008-10-01

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.

A combination of spin diffusion methods for the determination of protein-ligand complex structural ensembles.

PubMed

Pilger, Jens; Mazur, Adam; Monecke, Peter; Schreuder, Herman; Elshorst, Bettina; Bartoschek, Stefan; Langer, Thomas; Schiffer, Alexander; Krimm, Isabelle; Wegstroth, Melanie; Lee, Donghan; Hessler, Gerhard; Wendt, K-Ulrich; Becker, Stefan; Griesinger, Christian

2015-05-26

Structure-based drug design (SBDD) is a powerful and widely used approach to optimize affinity of drug candidates. With the recently introduced INPHARMA method, the binding mode of small molecules to their protein target can be characterized even if no spectroscopic information about the protein is known. Here, we show that the combination of the spin-diffusion-based NMR methods INPHARMA, trNOE, and STD results in an accurate scoring function for docking modes and therefore determination of protein-ligand complex structures. Applications are shown on the model system protein kinase A and the drug targets glycogen phosphorylase and soluble epoxide hydrolase (sEH). Multiplexing of several ligands improves the reliability of the scoring function further. The new score allows in the case of sEH detecting two binding modes of the ligand in its binding site, which was corroborated by X-ray analysis. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cancer cell profiling by barcoding allows multiplexed protein analysis in fine-needle aspirates.

PubMed

Ullal, Adeeti V; Peterson, Vanessa; Agasti, Sarit S; Tuang, Suan; Juric, Dejan; Castro, Cesar M; Weissleder, Ralph

2014-01-15

Immunohistochemistry-based clinical diagnoses require invasive core biopsies and use a limited number of protein stains to identify and classify cancers. We introduce a technology that allows analysis of hundreds of proteins from minimally invasive fine-needle aspirates (FNAs), which contain much smaller numbers of cells than core biopsies. The method capitalizes on DNA-barcoded antibody sensing, where barcodes can be photocleaved and digitally detected without any amplification steps. After extensive benchmarking in cell lines, this method showed high reproducibility and achieved single-cell sensitivity. We used this approach to profile ~90 proteins in cells from FNAs and subsequently map patient heterogeneity at the protein level. Additionally, we demonstrate how the method could be used as a clinical tool to identify pathway responses to molecularly targeted drugs and to predict drug response in patient samples. This technique combines specificity with ease of use to offer a new tool for understanding human cancers and designing future clinical trials.

Cancer cell profiling by barcoding allows multiplexed protein analysis in fine needle aspirates

PubMed Central

Ullal, Adeeti V.; Peterson, Vanessa; Agasti, Sarit S.; Tuang, Suan; Juric, Dejan; Castro, Cesar M.; Weissleder, Ralph

2014-01-01

Immunohistochemistry-based clinical diagnoses require invasive core biopsies and use a limited number of protein stains to identify and classify cancers. Here, we introduce a technology that allows analysis of hundreds of proteins from minimally invasive fine needle aspirates (FNA), which contain much smaller numbers of cells than core biopsies. The method capitalizes on DNA-barcoded antibody sensing where barcodes can be photo-cleaved and digitally detected without any amplification steps. Following extensive benchmarking in cell lines, this method showed high reproducibility and achieved single cell sensitivity. We used this approach to profile ~90 proteins in cells from FNAs and subsequently map patient heterogeneity at the protein level. Additionally, we demonstrate how the method could be used as a clinical tool to identify pathway responses to molecularly targeted drugs and to predict drug response in patient samples. This technique combines specificity with ease of use to offer a new tool for understanding human cancers and designing future clinical trials. PMID:24431113

Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods

PubMed Central

2015-01-01

Background Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology. Methods In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins. Results We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy. Conclusions Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy. PMID:26680552

Rational assembly of nanoparticle superlattices with designed lattice symmetries

DOEpatents

Gang, Oleg; Lu, Fang; Tagawa, Miho

2017-09-05

A method for lattice design via multivalent linkers (LDML) is disclosed that introduces a rationally designed symmetry of connections between particles in order to achieve control over the morphology of their assembly. The method affords the inclusion of different programmable interactions within one linker that allow an assembly of different types of particles. The designed symmetry of connections is preferably provided utilizing DNA encoding. The linkers may include fabricated "patchy" particles, DNA scaffold constructs and Y-shaped DNA linkers, anisotropic particles, which are preferably functionalized with DNA, multimeric protein-DNA complexes, and particles with finite numbers of DNA linkers.

Deep learning methods for protein torsion angle prediction.

PubMed

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

PubMed

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

2014-04-15

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

Medicinal Chemistry Projects Requiring Imaginative Structure-Based Drug Design Methods.

PubMed

Moitessier, Nicolas; Pottel, Joshua; Therrien, Eric; Englebienne, Pablo; Liu, Zhaomin; Tomberg, Anna; Corbeil, Christopher R

2016-09-20

Computational methods for docking small molecules to proteins are prominent in drug discovery. There are hundreds, if not thousands, of documented examples-and several pertinent cases within our research program. Fifteen years ago, our first docking-guided drug design project yielded nanomolar metalloproteinase inhibitors and illustrated the potential of structure-based drug design. Subsequent applications of docking programs to the design of integrin antagonists, BACE-1 inhibitors, and aminoglycosides binding to bacterial RNA demonstrated that available docking programs needed significant improvement. At that time, docking programs primarily considered flexible ligands and rigid proteins. We demonstrated that accounting for protein flexibility, employing displaceable water molecules, and using ligand-based pharmacophores improved the docking accuracy of existing methods-enabling the design of bioactive molecules. The success prompted the development of our own program, Fitted, implementing all of these aspects. The primary motivation has always been to respond to the needs of drug design studies; the majority of the concepts behind the evolution of Fitted are rooted in medicinal chemistry projects and collaborations. Several examples follow: (1) Searching for HDAC inhibitors led us to develop methods considering drug-zinc coordination and its effect on the pKa of surrounding residues. (2) Targeting covalent prolyl oligopeptidase (POP) inhibitors prompted an update to Fitted to identify reactive groups and form bonds with a given residue (e.g., a catalytic residue) when the geometry allows it. Fitted-the first fully automated covalent docking program-was successfully applied to the discovery of four new classes of covalent POP inhibitors. As a result, efficient stereoselective syntheses of a few screening hits were prioritized rather than synthesizing large chemical libraries-yielding nanomolar inhibitors. (3) In order to study the metabolism of POP inhibitors by cytochrome P450 enzymes (CYPs)-for toxicology studies-the program Impacts was derived from Fitted and helped us to reveal a complex metabolism with unforeseen stereocenter isomerizations. These efforts, combined with those of other docking software developers, have strengthened our understanding of the complex drug-protein binding process while providing the medicinal chemistry community with useful tools that have led to drug discoveries. In this Account, we describe our contributions over the past 15 years-within their historical context-to the design of drug candidates, including BACE-1 inhibitors, POP covalent inhibitors, G-quadruplex binders, and aminoglycosides binding to nucleic acids. We also remark the necessary developments of docking programs, specifically Fitted, that enabled structure-based design to flourish and yielded multiple fruitful, rational medicinal chemistry campaigns.

Applications of cell-free protein synthesis in synthetic biology: Interfacing bio-machinery with synthetic environments.

PubMed

Lee, Kyung-Ho; Kim, Dong-Myung

2013-11-01

Synthetic biology is built on the synthesis, engineering, and assembly of biological parts. Proteins are the first components considered for the construction of systems with designed biological functions because proteins carry out most of the biological functions and chemical reactions inside cells. Protein synthesis is considered to comprise the most basic levels of the hierarchical structure of synthetic biology. Cell-free protein synthesis has emerged as a powerful technology that can potentially transform the concept of bioprocesses. With the ability to harness the synthetic power of biology without many of the constraints of cell-based systems, cell-free protein synthesis enables the rapid creation of protein molecules from diverse sources of genetic information. Cell-free protein synthesis is virtually free from the intrinsic constraints of cell-based methods and offers greater flexibility in system design and manipulability of biological synthetic machinery. Among its potential applications, cell-free protein synthesis can be combined with various man-made devices for rapid functional analysis of genomic sequences. This review covers recent efforts to integrate cell-free protein synthesis with various reaction devices and analytical platforms. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Structure-based approach to the prediction of disulfide bonds in proteins.

PubMed

Salam, Noeris K; Adzhigirey, Matvey; Sherman, Woody; Pearlman, David A

2014-10-01

Protein engineering remains an area of growing importance in pharmaceutical and biotechnology research. Stabilization of a folded protein conformation is a frequent goal in projects that deal with affinity optimization, enzyme design, protein construct design, and reducing the size of functional proteins. Indeed, it can be desirable to assess and improve protein stability in order to avoid liabilities such as aggregation, degradation, and immunogenic response that may arise during development. One way to stabilize a protein is through the introduction of disulfide bonds. Here, we describe a method to predict pairs of protein residues that can be mutated to form a disulfide bond. We combine a physics-based approach that incorporates implicit solvent molecular mechanics with a knowledge-based approach. We first assign relative weights to the terms that comprise our scoring function using a genetic algorithm applied to a set of 75 wild-type structures that each contains a disulfide bond. The method is then tested on a separate set of 13 engineered proteins comprising 15 artificial stabilizing disulfides introduced via site-directed mutagenesis. We find that the native disulfide in the wild-type proteins is scored well, on average (within the top 6% of the reasonable pairs of residues that could form a disulfide bond) while 6 out of the 15 artificial stabilizing disulfides scored within the top 13% of ranked predictions. Overall, this suggests that the physics-based approach presented here can be useful for triaging possible pairs of mutations for disulfide bond formation to improve protein stability. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Protein backbone engineering as a strategy to advance foldamers toward the frontier of protein-like tertiary structure.

PubMed

Reinert, Zachary E; Horne, W Seth

2014-11-28

A variety of non-biological structural motifs have been incorporated into the backbone of natural protein sequences. In parallel work, diverse unnatural oligomers of de novo design (termed "foldamers") have been developed that fold in defined ways. In this Perspective article, we survey foundational studies on protein backbone engineering, with a focus on alterations made in the context of complex tertiary folds. We go on to summarize recent work illustrating the potential promise of these methods to provide a general framework for the construction of foldamer mimics of protein tertiary structures.

Identifying and reducing error in cluster-expansion approximations of protein energies.

PubMed

Hahn, Seungsoo; Ashenberg, Orr; Grigoryan, Gevorg; Keating, Amy E

2010-12-01

Protein design involves searching a vast space for sequences that are compatible with a defined structure. This can pose significant computational challenges. Cluster expansion is a technique that can accelerate the evaluation of protein energies by generating a simple functional relationship between sequence and energy. The method consists of several steps. First, for a given protein structure, a training set of sequences with known energies is generated. Next, this training set is used to expand energy as a function of clusters consisting of single residues, residue pairs, and higher order terms, if required. The accuracy of the sequence-based expansion is monitored and improved using cross-validation testing and iterative inclusion of additional clusters. As a trade-off for evaluation speed, the cluster-expansion approximation causes prediction errors, which can be reduced by including more training sequences, including higher order terms in the expansion, and/or reducing the sequence space described by the cluster expansion. This article analyzes the sources of error and introduces a method whereby accuracy can be improved by judiciously reducing the described sequence space. The method is applied to describe the sequence-stability relationship for several protein structures: coiled-coil dimers and trimers, a PDZ domain, and T4 lysozyme as examples with computationally derived energies, and SH3 domains in amphiphysin-1 and endophilin-1 as examples where the expanded pseudo-energies are obtained from experiments. Our open-source software package Cluster Expansion Version 1.0 allows users to expand their own energy function of interest and thereby apply cluster expansion to custom problems in protein design. © 2010 Wiley Periodicals, Inc.

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Trends in the Design and Development of Specific Aptamers Against Peptides and Proteins.

PubMed

Tabarzad, Maryam; Jafari, Marzieh

2016-04-01

Aptamers are single stranded oligonucleotides, comparable to monoclonal antibodies (mAbs) in selectivity and affinity and have significant strategic properties in design, development and applications more than mAbs. Ease of design and development, simple chemical modification and the attachment of functional groups, easily handling and more adaptability with analytical methods, small size and adaptation with nanostructures are the valuable characteristics of aptamers in comparison to large protein based ligands. Among a broad range of targets that their specific aptamers developed, proteins and peptides have significant position according to the number of related studies performed so far. Since proteins control many of important physiological and pathological incidents in the living organisms, particularly human beings and because of the benefits of aptamers in clinical and analytical applications, aptamer related technologies in the field of proteins and peptides are under progress, exclusively. Currently, there is only one FDA approved therapeutic aptamer in the pharmaceutical market, which is specific to vascular endothelial growth factor and is prescribed for age related macular degenerative disease. Additionally, there are several aptamers in the different phases of clinical trials. Almost all of these aptamers are specific to clinically important peptide or protein targets. In addition, the application of protein specific aptamers in the design and development of targeted drug delivery systems and diagnostic biosensors is another interesting field of aptamer technology. In this review, significant efforts related to development and applications of aptamer technologies in proteins and peptides sciences were considered to emphasis on the importance of aptamers in medicinal and clinical applications.

MPAI (mass probes aided ionization) method for total analysis of biomolecules by mass spectrometry.

PubMed

Honda, Aki; Hayashi, Shinichiro; Hifumi, Hiroki; Honma, Yuya; Tanji, Noriyuki; Iwasawa, Naoko; Suzuki, Yoshio; Suzuki, Koji

2007-01-01

We have designed and synthesized various mass probes, which enable us to effectively ionize various molecules to be detected with mass spectrometry. We call the ionization method using mass probes the "MPAI (mass probes aided ionization)" method. We aim at the sensitive detection of various biological molecules, and also the detection of bio-molecules by a single mass spectrometry serially without changing the mechanical settings. Here, we review mass probes for small molecules with various functional groups and mass probes for proteins. Further, we introduce newly developed mass probes for proteins for highly sensitive detection.

Impact of Profiling Technologies in the Understanding of Recombinant Protein Production

NASA Astrophysics Data System (ADS)

Vijayendran, Chandran; Flaschel, Erwin

Since expression profiling methods have been available in a high throughput fashion, the implication of these technologies in the field of biotechnology has increased dramatically. Microarray technology is one such unique and efficient methodology for simultaneous exploration of expression levels of numerous genes. Likewise, two-dimensional gel electrophoresis or multidimensional liquid chromatography coupled with mass spectrometry are extensively utilised for studying expression levels of numerous proteins. In the field of biotechnology these highly parallel analytical methods have paved the way to study and understand various biological phenomena depending on expression patterns. The next phenomenological level is represented by the metabolome and the (metabolic) fluxome. However, this chapter reviews gene and protein profiling and their impact on understanding recombinant protein production. We focus on the computational methods utilised for the analyses of data obtained from these profiling technologies as well as prominent results focusing on recombinant protein expression with Escherichia coli. Owing to the knowledge accumulated with respect to cellular signals triggered during recombinant protein production, this field is on the way to design strategies for developing improved processes. Both gene and protein profiling have exhibited a handful of functional categories to concentrate on in order to identify target genes and proteins, respectively, involved in the signalling network with major impact on recombinant protein production.

RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins.

PubMed

Walia, Rasna R; Xue, Li C; Wilkins, Katherine; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2014-01-01

Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.

Characterization of Protein-Excipient Microheterogeneity in Biopharmaceutical Solid-State Formulations by Confocal Fluorescence Microscopy.

PubMed

Koshari, Stijn H S; Ross, Jean L; Nayak, Purnendu K; Zarraga, Isidro E; Rajagopal, Karthikan; Wagner, Norman J; Lenhoff, Abraham M

2017-02-06

Protein-stabilizer microheterogeneity is believed to influence long-term protein stability in solid-state biopharmaceutical formulations and its characterization is therefore essential for the rational design of stable formulations. However, the spatial distribution of the protein and the stabilizer in a solid-state formulation is, in general, difficult to characterize because of the lack of a functional, simple, and reliable characterization technique. We demonstrate the use of confocal fluorescence microscopy with fluorescently labeled monoclonal antibodies (mAbs) and antibody fragments (Fabs) to directly visualize three-dimensional particle morphologies and protein distributions in dried biopharmaceutical formulations, without restrictions on processing conditions or the need for extensive data analysis. While industrially relevant lyophilization procedures of a model IgG1 mAb generally lead to uniform protein-excipient distribution, the method shows that specific spray-drying conditions lead to distinct protein-excipient segregation. Therefore, this method can enable more definitive optimization of formulation conditions than has previously been possible.

Stability of halophilic proteins: from dipeptide attributes to discrimination classifier.

PubMed

Zhang, Guangya; Huihua, Ge; Yi, Lin

2013-02-01

To investigate the molecular features responsible for protein halophilicity is of great significance for understanding the structure basis of protein halo-stability and would help to develop a practical strategy for designing halophilic proteins. In this work, we have systematically analyzed the dipeptide composition of the halophilic and non-halophilic protein sequences. We observed the halophilic proteins contained more DA, RA, AD, RR, AP, DD, PD, EA, VG and DV at the expense of LK, IL, II, IA, KK, IS, KA, GK, RK and AI. We identified some macromolecular signatures of halo-adaptation, and thought the dipeptide composition might contain more information than amino acid composition. Based on the dipeptide composition, we have developed a machine learning method for classifying halophilic and non-halophilic proteins for the first time. The accuracy of our method for the training dataset was 100.0%, and for the 10-fold cross-validation was 93.1%. We also discussed the influence of some specific dipeptides on prediction accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Rapid regulation of nuclear proteins by rapamycin-induced translocation in fission yeast

PubMed Central

Ding, Lin; Laor, Dana; Weisman, Ronit; Forsburg, Susan L

2014-01-01

Genetic analysis of protein function requires a rapid means of inactivating the gene under study. Typically this exploits temperature sensitive mutations, or promoter shut-off techniques. We report the adaptation to Schizosaccharomyces pombe of the Anchor Away technique, originally designed in budding yeast (Haruki et al., 2008a). This method relies on a rapamycin-mediated interaction between the FRB and FKBP12 binding domains, to relocalize nuclear proteins of interest to the cytoplasm. We demonstrate a rapid nuclear depletion of abundant proteins as proof-of-principle. PMID:24733494

A hybrid network-based method for the detection of disease-related genes

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

2018-02-01

Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

Design and Implementation of Scientific Software Components to Enable Multiscale Modeling: The Effective Fragment Potential (QM/EFP) Method

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

Gaenko, Alexander; Windus, Theresa L.; Sosonkina, Masha

2012-10-19

The design and development of scientific software components to provide an interface to the effective fragment potential (EFP) methods are reported. Multiscale modeling of physical and chemical phenomena demands the merging of software packages developed by research groups in significantly different fields. Componentization offers an efficient way to realize new high performance scientific methods by combining the best models available in different software packages without a need for package readaptation after the initial componentization is complete. The EFP method is an efficient electronic structure theory based model potential that is suitable for predictive modeling of intermolecular interactions in large molecularmore » systems, such as liquids, proteins, atmospheric aerosols, and nanoparticles, with an accuracy that is comparable to that of correlated ab initio methods. The developed components make the EFP functionality accessible for any scientific component-aware software package. The performance of the component is demonstrated on a protein interaction model, and its accuracy is compared with results obtained with coupled cluster methods.« less

RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite.

PubMed

Fleishman, Sarel J; Leaver-Fay, Andrew; Corn, Jacob E; Strauch, Eva-Maria; Khare, Sagar D; Koga, Nobuyasu; Ashworth, Justin; Murphy, Paul; Richter, Florian; Lemmon, Gordon; Meiler, Jens; Baker, David

2011-01-01

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.

Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

PubMed

Zhang, Guangya; Ge, Huihua

2013-10-01

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Fluorescent labeling of tetracysteine-tagged proteins in intact cells

PubMed Central

Hoffmann, Carsten; Gaietta, Guido; Zürn, Alexander; Adams, Stephen R; Terrillon, Sonia; Ellisman, Mark H; Tsien, Roger Y; Lohse, Martin J

2011-01-01

In this paper, we provide a general protocol for labeling proteins with the membrane-permeant fluorogenic biarsenical dye fluorescein arsenical hairpin binder–ethanedithiol (FlAsH-EDT2). Generation of the tetracysteine-tagged protein construct by itself is not described, as this is a protein-specific process. This method allows site-selective labeling of proteins in living cells and has been applied to a wide variety of proteins and biological problems. We provide here a generally applicable labeling procedure and discuss the problems that can occur as well as general considerations that must be taken into account when designing and implementing the procedure. The method can even be applied to proteins with expression below 1 pmol mg−1 of protein, such as G protein–coupled receptors, and it can be used to study the intracellular localization of proteins as well as functional interactions in fluorescence resonance energy transfer experiments. The labeling procedure using FlAsH-EDT2 as described takes 2–3 h, depending on the number of samples to be processed. PMID:20885379

Effective Design of Multifunctional Peptides by Combining Compatible Functions

PubMed Central

Diener, Christian; Garza Ramos Martínez, Georgina; Moreno Blas, Daniel; Castillo González, David A.; Corzo, Gerardo; Castro-Obregon, Susana; Del Rio, Gabriel

2016-01-01

Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf. PMID:27096600

Smarter Drugs: How Protein Crystallography Revolutionizes Drug Design

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

Smith, Clyde

2005-04-26

According to Smith, protein crystallography allows scientists to design drugs in a much more efficient way than the standard methods traditionally used by large drug companies, which can cost close to a billion dollars and take 10 to 15 years. 'A lot of the work can be compressed down,' Smith said. Protein crystallography enables researchers to learn the structure of molecules involved in disease and health. Seeing the loops, folds and placement of atoms in anything from a virus to a healthy cell membrane gives important information about how these things work - and how to encourage, sidestep or stopmore » their functions. Drug design can be much faster when the relationship between structure and function tells you what area of a molecule to target. Smith will use a timeline to illustrate the traditional methods of drug development and the new ways it can be done now. 'It is very exciting work. There have been some failures, but many successes too.' A new drug to combat the flu was developed in a year or so. Smith will tell us how. He will also highlight drugs developed to combat HIV, Tuberculosis, hypertension and Anthrax.« less

EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.

PubMed

Choi, Yoonjoo; Verma, Deeptak; Griswold, Karl E; Bailey-Kellogg, Chris

2017-01-01

Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates.

Elucidating the design principles of photosynthetic electron-transfer proteins by site-directed spin labeling EPR spectroscopy.

PubMed

Ishara Silva, K; Jagannathan, Bharat; Golbeck, John H; Lakshmi, K V

2016-05-01

Site-directed spin labeling electron paramagnetic resonance (SDSL EPR) spectroscopy is a powerful tool to determine solvent accessibility, side-chain dynamics, and inter-spin distances at specific sites in biological macromolecules. This information provides important insights into the structure and dynamics of both natural and designed proteins and protein complexes. Here, we discuss the application of SDSL EPR spectroscopy in probing the charge-transfer cofactors in photosynthetic reaction centers (RC) such as photosystem I (PSI) and the bacterial reaction center (bRC). Photosynthetic RCs are large multi-subunit proteins (molecular weight≥300 kDa) that perform light-driven charge transfer reactions in photosynthesis. These reactions are carried out by cofactors that are paramagnetic in one of their oxidation states. This renders the RCs unsuitable for conventional nuclear magnetic resonance spectroscopy investigations. However, the presence of native paramagnetic centers and the ability to covalently attach site-directed spin labels in RCs makes them ideally suited for the application of SDSL EPR spectroscopy. The paramagnetic centers serve as probes of conformational changes, dynamics of subunit assembly, and the relative motion of cofactors and peptide subunits. In this review, we describe novel applications of SDSL EPR spectroscopy for elucidating the effects of local structure and dynamics on the electron-transfer cofactors of photosynthetic RCs. Because SDSL EPR Spectroscopy is uniquely suited to provide dynamic information on protein motion, it is a particularly useful method in the engineering and analysis of designed electron transfer proteins and protein networks. This article is part of a Special Issue entitled Biodesign for Bioenergetics--the design and engineering of electronic transfer cofactors, proteins and protein networks, edited by Ronald L. Koder and J.L. Ross Anderson. Copyright © 2016. Published by Elsevier B.V.

EpiSweep: Computationally-driven Reengineering of Therapeutic Proteins to Reduce immunogenicity while Maintaining Function

PubMed Central

Choi, Yoonjoo; Verma, Deeptak; Griswold, Karl E.; Bailey-Kellogg, Chris

2016-01-01

Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics renders them subject to immune surveillance within the patient’s body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity. To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure- based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates. PMID:27914063

Nonlinear optical methods for the analysis of protein nanocrystals and biological tissues

NASA Astrophysics Data System (ADS)

Dow, Ximeng You

Structural biology underpins rational drug design and fundamental understanding of protein function. X-ray diffraction (XRD) has been the golden standard for solving for high-resolution protein structure. Second harmonic generation (SHG) microscopy has been developed by the Simpson lab as a sensitive, crystal-specific detection method for the identification of protein crystal and help optimize the crystallization condition. Protein nanocrystals has been widely used for structure determination of membrane proteins in serial femtosecond nanocrystallography. In this thesis work, novel nonlinear optical methods were developed to address the challenges associated with the detection and characterization of protein nanocrystals. SHG-correlation spectroscopy (SHG-CS) was developed to take advantage of the diffusing motion and retrieve the size distribution and crystal quality of the nanocrystals. Polarization-dependent SHG imaging technique was developed to measure the relative orientation as well as the internal structure of the sample. Two photon- excited fluorescence has been used in the Simpson lab as a complementary measurement besides the inherent SHG signal from the crystals. A novel instrumentation development was also introduced in this thesis work to greatly improve the speed of fluorescence lifetime imaging (FLIM).

Empirical Estimation of Local Dielectric Constants: Toward Atomistic Design of Collagen Mimetic Peptides

PubMed Central

Pike, Douglas H.; Nanda, Vikas

2017-01-01

One of the key challenges in modeling protein energetics is the treatment of solvent interactions. This is particularly important in the case of peptides, where much of the molecule is highly exposed to solvent due to its small size. In this study, we develop an empirical method for estimating the local dielectric constant based on an additive model of atomic polarizabilities. Calculated values match reported apparent dielectric constants for a series of Staphylococcus aureus nuclease mutants. Calculated constants are used to determine screening effects on Coulombic interactions and to determine solvation contributions based on a modified Generalized Born model. These terms are incorporated into the protein modeling platform protCAD, and benchmarked on a data set of collagen mimetic peptides for which experimentally determined stabilities are available. Computing local dielectric constants using atomistic protein models and the assumption of additive atomic polarizabilities is a rapid and potentially useful method for improving electrostatics and solvation calculations that can be applied in the computational design of peptides. PMID:25784456

Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry.

PubMed

Pandini, Alessandro; Fraccalvieri, Domenico; Bonati, Laura

2013-01-01

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry.

Blind predictions of protein interfaces by docking calculations in CAPRI.

PubMed

Lensink, Marc F; Wodak, Shoshana J

2010-11-15

Reliable prediction of the amino acid residues involved in protein-protein interfaces can provide valuable insight into protein function, and inform mutagenesis studies, and drug design applications. A fast-growing number of methods are being proposed for predicting protein interfaces, using structural information, energetic criteria, or sequence conservation or by integrating multiple criteria and approaches. Overall however, their performance remains limited, especially when applied to nonobligate protein complexes, where the individual components are also stable on their own. Here, we evaluate interface predictions derived from protein-protein docking calculations. To this end we measure the overlap between the interfaces in models of protein complexes submitted by 76 participants in CAPRI (Critical Assessment of Predicted Interactions) and those of 46 observed interfaces in 20 CAPRI targets corresponding to nonobligate complexes. Our evaluation considers multiple models for each target interface, submitted by different participants, using a variety of docking methods. Although this results in a substantial variability in the prediction performance across participants and targets, clear trends emerge. Docking methods that perform best in our evaluation predict interfaces with average recall and precision levels of about 60%, for a small majority (60%) of the analyzed interfaces. These levels are significantly higher than those obtained for nonobligate complexes by most extant interface prediction methods. We find furthermore that a sizable fraction (24%) of the interfaces in models ranked as incorrect in the CAPRI assessment are actually correctly predicted (recall and precision ≥50%), and that these models contribute to 70% of the correct docking-based interface predictions overall. Our analysis proves that docking methods are much more successful in identifying interfaces than in predicting complexes, and suggests that these methods have an excellent potential of addressing the interface prediction challenge. © 2010 Wiley-Liss, Inc.

The synergistic use of computation, chemistry and biology to discover novel peptide-based drugs: the time is right.

PubMed

Audie, J; Boyd, C

2010-01-01

The case for peptide-based drugs is compelling. Due to their chemical, physical and conformational diversity, and relatively unproblematic toxicity and immunogenicity, peptides represent excellent starting material for drug discovery. Nature has solved many physiological and pharmacological problems through the use of peptides, polypeptides and proteins. If nature could solve such a diversity of challenging biological problems through the use of peptides, it seems reasonable to infer that human ingenuity will prove even more successful. And this, indeed, appears to be the case, as a number of scientific and methodological advances are making peptides and peptide-based compounds ever more promising pharmacological agents. Chief among these advances are powerful chemical and biological screening technologies for lead identification and optimization, methods for enhancing peptide in vivo stability, bioavailability and cell-permeability, and new delivery technologies. Other advances include the development and experimental validation of robust computational methods for peptide lead identification and optimization. Finally, scientific analysis, biology and chemistry indicate the prospect of designing relatively small peptides to therapeutically modulate so-called 'undruggable' protein-protein interactions. Taken together a clear picture is emerging: through the synergistic use of the scientific imagination and the computational, chemical and biological methods that are currently available, effective peptide therapeutics for novel targets can be designed that surpass even the proven peptidic designs of nature.

A graphic method for identification of novel glioma related genes.

PubMed

Gao, Yu-Fei; Shu, Yang; Yang, Lei; He, Yi-Chun; Li, Li-Peng; Huang, GuaHua; Li, Hai-Peng; Jiang, Yang

2014-01-01

Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.

A scalable and accurate method for classifying protein-ligand binding geometries using a MapReduce approach.

PubMed

Estrada, T; Zhang, B; Cicotti, P; Armen, R S; Taufer, M

2012-07-01

We present a scalable and accurate method for classifying protein-ligand binding geometries in molecular docking. Our method is a three-step process: the first step encodes the geometry of a three-dimensional (3D) ligand conformation into a single 3D point in the space; the second step builds an octree by assigning an octant identifier to every single point in the space under consideration; and the third step performs an octree-based clustering on the reduced conformation space and identifies the most dense octant. We adapt our method for MapReduce and implement it in Hadoop. The load-balancing, fault-tolerance, and scalability in MapReduce allow screening of very large conformation spaces not approachable with traditional clustering methods. We analyze results for docking trials for 23 protein-ligand complexes for HIV protease, 21 protein-ligand complexes for Trypsin, and 12 protein-ligand complexes for P38alpha kinase. We also analyze cross docking trials for 24 ligands, each docking into 24 protein conformations of the HIV protease, and receptor ensemble docking trials for 24 ligands, each docking in a pool of HIV protease receptors. Our method demonstrates significant improvement over energy-only scoring for the accurate identification of native ligand geometries in all these docking assessments. The advantages of our clustering approach make it attractive for complex applications in real-world drug design efforts. We demonstrate that our method is particularly useful for clustering docking results using a minimal ensemble of representative protein conformational states (receptor ensemble docking), which is now a common strategy to address protein flexibility in molecular docking. Copyright © 2012 Elsevier Ltd. All rights reserved.

Design rules for biomolecular adhesion: lessons from force measurements.

PubMed

Leckband, Deborah

2010-01-01

Cell adhesion to matrix, other cells, or pathogens plays a pivotal role in many processes in biomolecular engineering. Early macroscopic methods of quantifying adhesion led to the development of quantitative models of cell adhesion and migration. The more recent use of sensitive probes to quantify the forces that alter or manipulate adhesion proteins has revealed much greater functional diversity than was apparent from population average measurements of cell adhesion. This review highlights theoretical and experimental methods that identified force-dependent molecular properties that are central to the biological activity of adhesion proteins. Experimental and theoretical methods emphasized in this review include the surface force apparatus, atomic force microscopy, and vesicle-based probes. Specific examples given illustrate how these tools have revealed unique properties of adhesion proteins and their structural origins.

Detection and Alignment of 3D Domain Swapping Proteins Using Angle-Distance Image-Based Secondary Structural Matching Techniques

PubMed Central

Wang, Hsin-Wei; Hsu, Yen-Chu; Hwang, Jenn-Kang; Lyu, Ping-Chiang; Pai, Tun-Wen; Tang, Chuan Yi

2010-01-01

This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had “opened” their “closed” structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimer's and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8Å for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications. PMID:20976204

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. (c) 2007 Wiley-Liss, Inc.

Benchmarking protein classification algorithms via supervised cross-validation.

PubMed

Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

2008-04-24

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

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

NASA Astrophysics Data System (ADS)

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

2002-12-01

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

Estimating structure quality trends in the Protein Data Bank by equivalent resolution.

PubMed

Bagaria, Anurag; Jaravine, Victor; Güntert, Peter

2013-10-01

The quality of protein structures obtained by different experimental and ab-initio calculation methods varies considerably. The methods have been evolving over time by improving both experimental designs and computational techniques, and since the primary aim of these developments is the procurement of reliable and high-quality data, better techniques resulted on average in an evolution toward higher quality structures in the Protein Data Bank (PDB). Each method leaves a specific quantitative and qualitative "trace" in the PDB entry. Certain information relevant to one method (e.g. dynamics for NMR) may be lacking for another method. Furthermore, some standard measures of quality for one method cannot be calculated for other experimental methods, e.g. crystal resolution or NMR bundle RMSD. Consequently, structures are classified in the PDB by the method used. Here we introduce a method to estimate a measure of equivalent X-ray resolution (e-resolution), expressed in units of Å, to assess the quality of any type of monomeric, single-chain protein structure, irrespective of the experimental structure determination method. We showed and compared the trends in the quality of structures in the Protein Data Bank over the last two decades for five different experimental techniques, excluding theoretical structure predictions. We observed that as new methods are introduced, they undergo a rapid method development evolution: within several years the e-resolution score becomes similar for structures obtained from the five methods and they improve from initially poor performance to acceptable quality, comparable with previously established methods, the performance of which is essentially stable. Copyright © 2013 Elsevier Ltd. All rights reserved.

Phage display for the discovery of hydroxyapatite-associated peptides.

PubMed

Jin, Hyo-Eon; Chung, Woo-Jae; Lee, Seung-Wuk

2013-01-01

In nature, proteins play a critical role in the biomineralization process. Understanding how different peptide or protein sequences selectively interact with the target crystal is of great importance. Identifying such protein structures is one of the critical steps in verifying the molecular mechanisms of biomineralization. One of the promising ways to obtain such information for a particular crystal surface is to screen combinatorial peptide libraries in a high-throughput manner. Among the many combinatorial library screening procedures, phage display is a powerful method to isolate such proteins and peptides. In this chapter, we will describe our established methods to perform phage display with inorganic crystal surfaces. Specifically, we will use hydroxyapatite as a model system for discovery of apatite-associated proteins in bone or tooth biomineralization studies. This model approach can be generalized to other desired crystal surfaces using the same experimental design principles with a little modification of the procedures. © 2013 Elsevier Inc. All rights reserved.

The sweet tooth of biopharmaceuticals: importance of recombinant protein glycosylation analysis.

PubMed

Lingg, Nico; Zhang, Peiqing; Song, Zhiwei; Bardor, Muriel

2012-12-01

Biopharmaceuticals currently represent the fastest growing sector of the pharmaceutical industry, mainly driven by a rapid expansion in the manufacture of recombinant protein-based drugs. Glycosylation is the most prominent post-translational modification occurring on these protein drugs. It constitutes one of the critical quality attributes that requires thorough analysis for optimal efficacy and safety. This review examines the functional importance of glycosylation of recombinant protein drugs, illustrated using three examples of protein biopharmaceuticals: IgG antibodies, erythropoietin and glucocerebrosidase. Current analytical methods are reviewed as solutions for qualitative and quantitative measurements of glycosylation to monitor quality target product profiles of recombinant glycoprotein drugs. Finally, we propose a framework for designing the quality target product profile of recombinant glycoproteins and planning workflow for glycosylation analysis with the selection of available analytical methods and tools. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparative Investigation of Normal Modes and Molecular Dynamics of Hepatitis C NS5B Protein

NASA Astrophysics Data System (ADS)

Asafi, M. S.; Yildirim, A.; Tekpinar, M.

2016-04-01

Understanding dynamics of proteins has many practical implications in terms of finding a cure for many protein related diseases. Normal mode analysis and molecular dynamics methods are widely used physics-based computational methods for investigating dynamics of proteins. In this work, we studied dynamics of Hepatitis C NS5B protein with molecular dynamics and normal mode analysis. Principal components obtained from a 100 nanoseconds molecular dynamics simulation show good overlaps with normal modes calculated with a coarse-grained elastic network model. Coarse-grained normal mode analysis takes at least an order of magnitude shorter time. Encouraged by this good overlaps and short computation times, we analyzed further low frequency normal modes of Hepatitis C NS5B. Motion directions and average spatial fluctuations have been analyzed in detail. Finally, biological implications of these motions in drug design efforts against Hepatitis C infections have been elaborated.

Isolation and quantification of Quillaja saponaria Molina saponins and lipids in iscom-matrix and iscoms.

PubMed

Behboudi, S; Morein, B; Rönnberg, B

1995-12-01

In the iscom, multiple copies of antigen are attached by hydrophobic interaction to a matrix which is built up by Quillaja triterpenoid saponins and lipids. Thus, the iscom presents antigen in multimeric form in a small particle with a built-in adjuvant resulting in a highly immunogenic antigen formulation. We have designed a chloroform-methanol-water extraction procedure to isolate the triterpenoid saponins and lipids incorporated into iscom-matrix and iscoms. The triterpenoids in the triterpenoid phase were quantitated using orcinol sulfuric acid detecting their carbohydrate chains and by HPLC. The cholesterol and phosphatidylcholine in the lipid phase were quantitated by HPLC and a commercial colorimetric method for the cholesterol. The quantitative methods showed an almost total separation and recovery of triterpenoids and lipids in their respective phases, while protein was detected in all phases after extraction. The protein content was determined by the method of Lowry and by amino acid analysis. Amino acid analysis was shown to be the reliable method of the two to quantitate proteins in iscoms. In conclusion, simple, reproducible and efficient procedures have been designed to isolate and quantitate the triterpenoids and lipids added for preparation of iscom-matrix and iscoms. The procedures described should also be useful to adequately define constituents in prospective vaccines.

Protein fold recognition using geometric kernel data fusion.

PubMed

Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

2014-07-01

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

A Novel Method to Predict Highly Expressed Genes Based on Radius Clustering and Relative Synonymous Codon Usage.

PubMed

Tran, Tuan-Anh; Vo, Nam Tri; Nguyen, Hoang Duc; Pham, Bao The

2015-12-01

Recombinant proteins play an important role in many aspects of life and have generated a huge income, notably in the industrial enzyme business. A gene is introduced into a vector and expressed in a host organism-for example, E. coli-to obtain a high productivity of target protein. However, transferred genes from particular organisms are not usually compatible with the host's expression system because of various reasons, for example, codon usage bias, GC content, repetitive sequences, and secondary structure. The solution is developing programs to optimize for designing a nucleotide sequence whose origin is from peptide sequences using properties of highly expressed genes (HEGs) of the host organism. Existing data of HEGs determined by practical and computer-based methods do not satisfy for qualifying and quantifying. Therefore, the demand for developing a new HEG prediction method is critical. We proposed a new method for predicting HEGs and criteria to evaluate gene optimization. Codon usage bias was weighted by amplifying the difference between HEGs and non-highly expressed genes (non-HEGs). The number of predicted HEGs is 5% of the genome. In comparison with Puigbò's method, the result is twice as good as Puigbò's one, in kernel ratio and kernel sensitivity. Concerning transcription/translation factor proteins (TF), the proposed method gives low TF sensitivity, while Puigbò's method gives moderate one. In summary, the results indicated that the proposed method can be a good optional applying method to predict optimized genes for particular organisms, and we generated an HEG database for further researches in gene design.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

PubMed

Shazman, Shula; Elber, Gershon; Mandel-Gutfreund, Yael

2011-09-01

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

The property distance index PD predicts peptides that cross-react with IgE antibodies

PubMed Central

Ivanciuc, Ovidiu; Midoro-Horiuti, Terumi; Schein, Catherine H.; Xie, Liping; Hillman, Gilbert R.; Goldblum, Randall M.; Braun, Werner

2009-01-01

Similarities in the sequence and structure of allergens can explain clinically observed cross-reactivities. Distinguishing sequences that bind IgE in patient sera can be used to identify potentially allergenic protein sequences and aid in the design of hypo-allergenic proteins. The property distance index PD, incorporated in our Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/), may identify potentially cross-reactive segments of proteins, based on their similarity to known IgE epitopes. We sought to obtain experimental validation of the PD index as a quantitative predictor of IgE cross-reactivity, by designing peptide variants with predetermined PD scores relative to three linear IgE epitopes of Jun a 1, the dominant allergen from mountain cedar pollen. For each of the three epitopes, 60 peptides were designed with increasing PD values (decreasing physicochemical similarity) to the starting sequence. The peptides synthesized on a derivatized cellulose membrane were probed with sera from patients who were allergic to Jun a 1, and the experimental data were interpreted with a PD classification method. Peptides with low PD values relative to a given epitope were more likely to bind IgE from the sera than were those with PD values larger than 6. Control sequences, with PD values between 18 and 20 to all the three epitopes, did not bind patient IgE, thus validating our procedure for identifying negative control peptides. The PD index is a statistically validated method to detect discrete regions of proteins that have a high probability of cross-reacting with IgE from allergic patients. PMID:18950868

Microplate-Based Method for High-Throughput Screening (HTS) of Chromatographic Conditions Studies for Recombinant Protein Purification.

PubMed

Carvalho, Rimenys J; Cruz, Thayana A

2018-01-01

High-throughput screening (HTS) systems have emerged as important tools to provide fast and low cost evaluation of several conditions at once since it requires small quantities of material and sample volumes. These characteristics are extremely valuable for experiments with large number of variables enabling the application of design of experiments (DoE) strategies or simple experimental planning approaches. Once, the capacity of HTS systems to mimic chromatographic purification steps was established, several studies were performed successfully including scale down purification. Here, we propose a method for studying different purification conditions that can be used for any recombinant protein, including complex and glycosylated proteins, using low binding filter microplates.

Development of a Dehalogenase-Based Protein Fusion Tag Capable of Rapid, Selective and Covalent Attachment to Customizable Ligands

PubMed Central

Encell, Lance P; Friedman Ohana, Rachel; Zimmerman, Kris; Otto, Paul; Vidugiris, Gediminas; Wood, Monika G; Los, Georgyi V; McDougall, Mark G; Zimprich, Chad; Karassina, Natasha; Learish, Randall D; Hurst, Robin; Hartnett, James; Wheeler, Sarah; Stecha, Pete; English, Jami; Zhao, Kate; Mendez, Jacqui; Benink, Hélène A; Murphy, Nancy; Daniels, Danette L; Slater, Michael R; Urh, Marjeta; Darzins, Aldis; Klaubert, Dieter H; Bulleit, Robert F; Wood, Keith V

2012-01-01

Our fundamental understanding of proteins and their biological significance has been enhanced by genetic fusion tags, as they provide a convenient method for introducing unique properties to proteins so that they can be examinedin isolation. Commonly used tags satisfy many of the requirements for applications relating to the detection and isolation of proteins from complex samples. However, their utility at low concentration becomes compromised if the binding affinity for a detection or capture reagent is not adequate to produce a stable interaction. Here, we describe HaloTag® (HT7), a genetic fusion tag based on a modified haloalkane dehalogenase designed and engineered to overcome the limitation of affinity tags by forming a high affinity, covalent attachment to a binding ligand. HT7 and its ligand have additional desirable features. The tag is relatively small, monomeric, and structurally compatible with fusion partners, while the ligand is specific, chemically simple, and amenable to modular synthetic design. Taken together, the design features and molecular evolution of HT7 have resulted in a superior alternative to common tags for the overexpression, detection, and isolation of target proteins. PMID:23248739

High yield bacterial expression, purification and characterisation of bioactive Human Tousled-like Kinase 1B involved in cancer.

PubMed

Bhoir, Siddhant; Shaik, Althaf; Thiruvenkatam, Vijay; Kirubakaran, Sivapriya

2018-03-19

Human Tousled-like kinases (TLKs) are highly conserved serine/threonine protein kinases responsible for cell proliferation, DNA repair, and genome surveillance. Their possible involvement in cancer via efficient DNA repair mechanisms have made them clinically relevant molecular targets for anticancer therapy. Innovative approaches in chemical biology have played a key role in validating the importance of kinases as molecular targets. However, the detailed understanding of the protein structure and the mechanisms of protein-drug interaction through biochemical and biophysical techniques demands a method for the production of an active protein of exceptional stability and purity on a large scale. We have designed a bacterial expression system to express and purify biologically active, wild-type Human Tousled-like Kinase 1B (hTLK1B) by co-expression with the protein phosphatase from bacteriophage λ. We have obtained remarkably high amounts of the soluble and homogeneously dephosphorylated form of biologically active hTLK1B with our unique, custom-built vector design strategy. The recombinant hTLK1B can be used for the structural studies and may further facilitate the development of new TLK inhibitors for anti-cancer therapy using a structure-based drug design approach.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

PubMed

Diller, David J; Swanson, Jon; Bayden, Alexander S; Jarosinski, Mark; Audie, Joseph

2015-01-01

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

PubMed Central

Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

2016-01-01

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

A 1-year lifestyle intervention for weight loss in individuals with type 2 diabetes reduces high C-reactive protein levels and identifies metabolic predictors of change

USDA-ARS?s Scientific Manuscript database

OBJECTIVE: We examined whether a 1-year intensive lifestyle intervention (ILI) for weight loss reduced elevated high-sensitivity C-reactive protein (hs-CRP) levels in obese individuals with diabetes and identified metabolic and fitness predictors of hs-CRP change. RESEARCH DESIGN AND METHODS: Look A...

Ligand Binding Site Detection by Local Structure Alignment and Its Performance Complementarity

PubMed Central

Lee, Hui Sun; Im, Wonpil

2013-01-01

Accurate determination of potential ligand binding sites (BS) is a key step for protein function characterization and structure-based drug design. Despite promising results of template-based BS prediction methods using global structure alignment (GSA), there is a room to improve the performance by properly incorporating local structure alignment (LSA) because BS are local structures and often similar for proteins with dissimilar global folds. We present a template-based ligand BS prediction method using G-LoSA, our LSA tool. A large benchmark set validation shows that G-LoSA predicts drug-like ligands’ positions in single-chain protein targets more precisely than TM-align, a GSA-based method, while the overall success rate of TM-align is better. G-LoSA is particularly efficient for accurate detection of local structures conserved across proteins with diverse global topologies. Recognizing the performance complementarity of G-LoSA to TM-align and a non-template geometry-based method, fpocket, a robust consensus scoring method, CMCS-BSP (Complementary Methods and Consensus Scoring for ligand Binding Site Prediction), is developed and shows improvement on prediction accuracy. The G-LoSA source code is freely available at http://im.bioinformatics.ku.edu/GLoSA. PMID:23957286

Conditions that allow for effective transfer of membrane proteins onto nitrocellulose membrane in Western blots.

PubMed

Abeyrathne, Priyanka D; Lam, Joseph S

2007-04-01

A major hurdle in characterizing bacterial membrane proteins by Western blotting is the ineffectiveness of transferring these proteins from sodium dodecyl sulfate -- polyacrylamide gel electrophoresis (SDS-PAGE) gel onto nitrocellulose membrane, using standard Western blot buffers and electrophoretic conditions. In this study, we compared a number of modified Western blotting buffers and arrived at a composition designated as the SDS-PAGE-Urea Lysis buffer. The use of this buffer and specific conditions allowed the reproducible transfer of highly hydrophobic bacterial membrane proteins with 2-12 transmembrane-spanning segments as well as soluble proteins onto nitrocellulose membranes. This method should be broadly applicable for immunochemical studies of other membrane proteins.

Proteolysis targeting peptide (PROTAP) strategy for protein ubiquitination and degradation.

PubMed

Zheng, Jing; Tan, Chunyan; Xue, Pengcheng; Cao, Jiakun; Liu, Feng; Tan, Ying; Jiang, Yuyang

2016-02-19

Ubiquitination proteasome pathway (UPP) is the most important and selective way to degrade proteins in vivo. Here, a novel proteolysis targeting peptide (PROTAP) strategy, composed of a target protein binding peptide, a linker and a ubiquitin E3 ligase recognition peptide, was designed to recruit both target protein and E3 ligase and then induce polyubiquitination and degradation of the target protein through UPP. In our study, the PROTAP strategy was proved to be a general method with high specificity using Bcl-xL protein as model target in vitro and in cells, which indicates that the strategy has great potential for in vivo application. Copyright © 2016 Elsevier Inc. All rights reserved.

Chemical Posttranslational Modification with Designed Rhodium(II) Catalysts.

PubMed

Martin, S C; Minus, M B; Ball, Z T

2016-01-01

Natural enzymes use molecular recognition to perform exquisitely selective transformations on nucleic acids, proteins, and natural products. Rhodium(II) catalysts mimic this selectivity, using molecular recognition to allow selective modification of proteins with a variety of functionalized diazo reagents. The rhodium catalysts and the diazo reactivity have been successfully applied to a variety of protein folds, the chemistry succeeds in complex environments such as cell lysate, and a simple protein blot method accurately assesses modification efficiency. The studies with rhodium catalysts provide a new tool to study and probe protein-binding events, as well as a new synthetic approach to protein conjugates for medical, biochemical, or materials applications. © 2016 Elsevier Inc. All rights reserved.

Characterization of extended channel bioreactors for continuous-flow protein production

DOE PAGES

Timm, Andrea C.; Shankles, Peter G.; Foster, Carmen M.; ...

2015-10-02

In this paper, protein based therapeutics are an important class of drugs, used to treat a variety of medical conditions including cancer and autoimmune diseases. Requiring continuous cold storage, and having a limited shelf life, the ability to produce such therapeutics at the point-of-care would open up new opportunities in distributing medicines and treating patients in more remote locations. Here, the authors describe the first steps in the development of a microfluidic platform that can be used for point-of-care protein synthesis. While biologic medicines, including therapeutic proteins, are commonly produced using recombinant deoxyribonucleic acid (DNA) technology in large batch cellmore » cultures, the system developed here utilizes cell-free protein synthesis (CFPS) technology. CFPS is a scalable technology that uses cell extracts containing the biological machinery required for transcription and translation and combines those extracts with DNA, encoding a specific gene, and the additional metabolites required to produce proteins in vitro. While CFPS reactions are typically performed in batch or fed-batch reactions, a well-engineered reaction scheme may improve both the rate of protein production and the economic efficiency of protein synthesis reactions, as well as enable a more streamlined method for subsequent purification of the protein product—all necessary requirements for point-of-care protein synthesis. In this work, the authors describe a new bioreactor design capable of continuous production of protein using cell-free protein synthesis. The bioreactors were designed with three inlets to separate reactive components prior to on-chip mixing, which lead into a long, narrow, serpentine channel. These multiscale, serpentine channel bioreactors were designed to take advantage of microscale diffusion distances across narrow channels in reactors containing enough volume to produce a therapeutic dose of protein, and open the possibility of performing these reactions continuously and in line with downstream purification modules. Here, the authors demonstrate the capability to produce protein over time with continuous-flow reactions and examine basic design features and operation specifications fundamental to continuous microfluidic protein synthesis.« less

Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.

PubMed

Li, Minghui; Goncearenco, Alexander; Panchenko, Anna R

2017-01-01

In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.

New trends and affinity tag designs for recombinant protein purification.

PubMed

Wood, David W

2014-06-01

Engineered purification tags can facilitate very efficient purification of recombinant proteins, resulting in high yields and purities in a few standard steps. Over the years, many different purification tags have been developed, including short peptides, epitopes, folded protein domains, non-chromatographic tags and more recently, compound multifunctional tags with optimized capabilities. Although classic proteases are still primarily used to remove the tags from target proteins, new self-cleaving methods are gaining traction as a highly convenient alternative. In this review, we discuss some of these emerging trends, and examine their potential impacts and remaining challenges in recombinant protein research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nitrosative Stress in the Nervous System: Guidelines for Designing Experimental Strategies to Study Protein S-Nitrosylation.

PubMed

Nakamura, Tomohiro; Lipton, Stuart A

2016-03-01

Reactive nitrogen species, such as nitric oxide (NO), exert their biological activity in large part through post-translational modification of cysteine residues, forming S-nitrosothiols. This chemical reaction proceeds via a process that we and our colleagues have termed protein S-nitrosylation. Under conditions of normal NO production, S-nitrosylation regulates the activity of many normal proteins. However, in degenerative conditions characterized by nitrosative stress, increased levels of NO lead to aberrant S-nitrosylation that contributes to the pathology of the disease. Thus, S-nitrosylation has been implicated in a wide range of cellular mechanisms, including mitochondrial function, proteostasis, transcriptional regulation, synaptic activity, and cell survival. In recent years, the research area of protein S-nitrosylation has become prominent due to improvements in the detection systems as well as the demonstration that protein S-nitrosylation plays a critical role in the pathogenesis of neurodegenerative and other neurological disorders. To further promote our understanding of how protein S-nitrosylation affects cellular systems, guidelines for the design and conduct of research on S-nitrosylated (or SNO-)proteins would be highly desirable, especially for those newly entering the field. In this review article, we provide a strategic overview of designing experimental approaches to study protein S-nitrosylation. We specifically focus on methods that can provide critical data to demonstrate that an S-nitrosylated protein plays a (patho-)physiologically-relevant role in a biological process. Hence, the implementation of the approaches described herein will contribute to further advancement of the study of S-nitrosylated proteins, not only in neuroscience but also in other research fields.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2016-09-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Characterizing the Hot Spots Involved in RON-MSPβ Complex Formation Using In Silico Alanine Scanning Mutagenesis and Molecular Dynamics Simulation

PubMed Central

Zarei, Omid; Hamzeh-Mivehroud, Maryam; Benvenuti, Silvia; Ustun-Alkan, Fulya; Dastmalchi, Siavoush

2017-01-01

Purpose: Implication of protein-protein interactions (PPIs) in development of many diseases such as cancer makes them attractive for therapeutic intervention and rational drug design. RON (Recepteur d’Origine Nantais) tyrosine kinase receptor has gained considerable attention as promising target in cancer therapy. The activation of RON via its ligand, macrophage stimulation protein (MSP) is the most common mechanism of activation for this receptor. The aim of the current study was to perform in silico alanine scanning mutagenesis and to calculate binding energy for prediction of hot spots in protein-protein interface between RON and MSPβ chain (MSPβ). Methods: In this work the residues at the interface of RON-MSPβ complex were mutated to alanine and then molecular dynamics simulation was used to calculate binding free energy. Results: The results revealed that Gln193, Arg220, Glu287, Pro288, Glu289, and His424 residues from RON and Arg521, His528, Ser565, Glu658, and Arg683 from MSPβ may play important roles in protein-protein interaction between RON and MSP. Conclusion: Identification of these RON hot spots is important in designing anti-RON drugs when the aim is to disrupt RON-MSP interaction. In the same way, the acquired information regarding the critical amino acids of MSPβ can be used in the process of rational drug design for developing MSP antagonizing agents, the development of novel MSP mimicking peptides where inhibition of RON activation is required, and the design of experimental site directed mutagenesis studies. PMID:28507948

Statistical approaches to maximize recombinant protein expression in Escherichia coli: a general review.

PubMed

Papaneophytou, Christos P; Kontopidis, George

2014-02-01

The supply of many valuable proteins that have potential clinical or industrial use is often limited by their low natural availability. With the modern advances in genomics, proteomics and bioinformatics, the number of proteins being produced using recombinant techniques is exponentially increasing and seems to guarantee an unlimited supply of recombinant proteins. The demand of recombinant proteins has increased as more applications in several fields become a commercial reality. Escherichia coli (E. coli) is the most widely used expression system for the production of recombinant proteins for structural and functional studies. However, producing soluble proteins in E. coli is still a major bottleneck for structural biology projects. One of the most challenging steps in any structural biology project is predicting which protein or protein fragment will express solubly and purify for crystallographic studies. The production of soluble and active proteins is influenced by several factors including expression host, fusion tag, induction temperature and time. Statistical designed experiments are gaining success in the production of recombinant protein because they provide information on variable interactions that escape the "one-factor-at-a-time" method. Here, we review the most important factors affecting the production of recombinant proteins in a soluble form. Moreover, we provide information about how the statistical design experiments can increase protein yield and purity as well as find conditions for crystal growth. Copyright © 2013 Elsevier Inc. All rights reserved.

Protein engineering for metabolic engineering: current and next-generation tools

PubMed Central

Marcheschi, Ryan J.; Gronenberg, Luisa S.; Liao, James C.

2014-01-01

Protein engineering in the context of metabolic engineering is increasingly important to the field of industrial biotechnology. As the demand for biologically-produced food, fuels, chemicals, food additives, and pharmaceuticals continues to grow, the ability to design and modify proteins to accomplish new functions will be required to meet the high productivity demands for the metabolism of engineered organisms. This article reviews advances of selecting, modeling, and engineering proteins to improve or alter their activity. Some of the methods have only recently been developed for general use and are just beginning to find greater application in the metabolic engineering community. We also discuss methods of generating random and targeted diversity in proteins to generate mutant libraries for analysis. Recent uses of these techniques to alter cofactor use, produce non-natural amino acids, alcohols, and carboxylic acids, and alter organism phenotypes are presented and discussed as examples of the successful engineering of proteins for metabolic engineering purposes. PMID:23589443

Hyperspectral imaging to monitor simultaneously multiple protein subtypes and live track their spatial dynamics: a new platform to screen drugs for CNS diseases

NASA Astrophysics Data System (ADS)

Labrecque, S.; Sylvestre, J.-P.; Marcet, S.; Mangiarini, F.; Verhaegen, M.; De Koninck, P.; Blais-Ouellette, S.

2015-03-01

In the past decade, the efficacy of existing therapies and the discovery of innovative treatments for Central Nervous System (CNS) diseases have been limited by the lack of appropriate methods to investigate complex molecular processes at the synaptic level. In order to better understand the fundamental mechanisms that regulate diseases of the CNS, a fast fluorescence hyperspectral imaging platform was designed to track simultaneously various neurotransmitter receptors trafficking in and out of synapses. With this hyperspectral imaging platform, it was possible to image simultaneously five different synaptic proteins, including subtypes of glutamate receptors (mGluR, NMDAR, AMPAR), postsynaptic density proteins, and signaling proteins. This new imaging platform allows fast simultaneous acquisitions of at least five fluorescent markers in living neurons with a high spatial resolution. This technique provides an effective method to observe several synaptic proteins at the same time, thus study how drugs for CNS impact the spatial dynamics of these proteins.

Protein crystal nucleation in pores.

PubMed

Nanev, Christo N; Saridakis, Emmanuel; Chayen, Naomi E

2017-01-16

The most powerful method for protein structure determination is X-ray crystallography which relies on the availability of high quality crystals. Obtaining protein crystals is a major bottleneck, and inducing their nucleation is of crucial importance in this field. An effective method to form crystals is to introduce nucleation-inducing heterologous materials into the crystallization solution. Porous materials are exceptionally effective at inducing nucleation. It is shown here that a combined diffusion-adsorption effect can increase protein concentration inside pores, which enables crystal nucleation even under conditions where heterogeneous nucleation on flat surfaces is absent. Provided the pore is sufficiently narrow, protein molecules approach its walls and adsorb more frequently than they can escape. The decrease in the nucleation energy barrier is calculated, exhibiting its quantitative dependence on the confinement space and the energy of interaction with the pore walls. These results provide a detailed explanation of the effectiveness of porous materials for nucleation of protein crystals, and will be useful for optimal design of such materials.

Protein engineering for metabolic engineering: current and next-generation tools.

PubMed

Marcheschi, Ryan J; Gronenberg, Luisa S; Liao, James C

2013-05-01

Protein engineering in the context of metabolic engineering is increasingly important to the field of industrial biotechnology. As the demand for biologically produced food, fuels, chemicals, food additives, and pharmaceuticals continues to grow, the ability to design and modify proteins to accomplish new functions will be required to meet the high productivity demands for the metabolism of engineered organisms. We review advances in selecting, modeling, and engineering proteins to improve or alter their activity. Some of the methods have only recently been developed for general use and are just beginning to find greater application in the metabolic engineering community. We also discuss methods of generating random and targeted diversity in proteins to generate mutant libraries for analysis. Recent uses of these techniques to alter cofactor use; produce non-natural amino acids, alcohols, and carboxylic acids; and alter organism phenotypes are presented and discussed as examples of the successful engineering of proteins for metabolic engineering purposes. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Structure-based energetics of protein interfaces guide Foot-and-Mouth Disease virus vaccine design

PubMed Central

Scott, Katherine; Burman, Alison; Loureiro, Silvia; Ren, Jingshan; Porta, Claudine; Ginn, Helen M.; Jackson, Terry; Perez-Martin, Eva; Siebert, C. Alistair; Paul, Guntram; Huiskonen, Juha T.; Jones, Ian M.; Esnouf, Robert M.; Fry, Elizabeth E.; Maree, Francois F.; Charleston, Bryan; Stuart, David I.

2018-01-01

Summary Virus capsids are primed for disassembly yet capsid integrity is key to generating a protective immune response. Here we devise a computational method to assess relative stability of protein-protein interfaces and use it to design improved candidate vaccines for two of the least stable, but globally important, serotypes of Foot-and-Mouth Disease virus (FMDV), O and SAT2. FMDV capsids comprise identical pentameric protein subunits held together by tenuous non-covalent interactions, and are often unstable. Chemically inactivated or recombinant empty capsids, which could form the basis of future vaccines, are even less stable than live virus. We use a novel restrained molecular dynamics strategy, to rank mutations predicted to strengthen the pentamer interfaces to produce stabilized capsids. Structural analyses and stability assays confirmed the predictions, and vaccinated animals generated improved neutralising antibody responses to stabilised particles over parental viruses and wild-type capsids. PMID:26389739

The 'retro-design' concept for novel kinase inhibitors.

PubMed

Müller, Gerhard; Sennhenn, Peter C; Woodcock, Timothy; Neumann, Lars

2010-07-01

Protein kinases are among the most attractive therapeutic targets for a broad range of diseases. This feature review highlights and classifies the main design principles employed to generate active and selective kinase inhibitors. In particular, emphasis is focused on a fragment-based lead-generation approach, which constitutes a novel design method for developing type II kinase inhibitors with distinct binding kinetic attributes. This 'retro-design' strategy relies on a customized fragment library, and contrasts the traditional approach used in the design of type II inhibitors.

Characterization of glycoprotein biopharmaceutical products by Caliper LC90 CE-SDS gel technology.

PubMed

Chen, Grace; Ha, Sha; Rustandi, Richard R

2013-01-01

Over the last decade, science has greatly improved in the area of protein sizing and characterization. Efficient high-throughput methods are now available to substitute for the traditional labor-intensive SDS-PAGE methods, which alternatively take days to analyze a very limited number of samples. Currently, PerkinElmer(®) (Caliper) has designed an automated chip-based fluorescence detection method capable of analyzing proteins in minutes with sensitivity similar to standard SDS-PAGE. Here, we describe the use and implementation of this technology to characterize and screen a large number of formulations of target glycoproteins in the 14-200 kDa molecular weight range.

Codon Optimizing for Increased Membrane Protein Production: A Minimalist Approach.

PubMed

Mirzadeh, Kiavash; Toddo, Stephen; Nørholm, Morten H H; Daley, Daniel O

2016-01-01

Reengineering a gene with synonymous codons is a popular approach for increasing production levels of recombinant proteins. Here we present a minimalist alternative to this method, which samples synonymous codons only at the second and third positions rather than the entire coding sequence. As demonstrated with two membrane-embedded transporters in Escherichia coli, the method was more effective than optimizing the entire coding sequence. The method we present is PCR based and requires three simple steps: (1) the design of two PCR primers, one of which is degenerate; (2) the amplification of a mini-library by PCR; and (3) screening for high-expressing clones.

Accurate de novo design of hyperstable constrained peptides

PubMed Central

Bhardwaj, Gaurav; Mulligan, Vikram Khipple; Bahl, Christopher D.; Gilmore, Jason M.; Harvey, Peta J.; Cheneval, Olivier; Buchko, Garry W.; Pulavarti, Surya V.S.R.K.; Kaas, Quentin; Eletsky, Alexander; Huang, Po-Ssu; Johnsen, William A.; Greisen, Per; Rocklin, Gabriel J.; Song, Yifan; Linsky, Thomas W.; Watkins, Andrew; Rettie, Stephen A.; Xu, Xianzhong; Carter, Lauren P.; Bonneau, Richard; Olson, James M.; Coutsias, Evangelos; Correnti, Colin E.; Szyperski, Thomas; Craik, David J.; Baker, David

2016-01-01

Summary Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for de novo design of conformationally-restricted peptides, and the use of these methods to design 15–50 residue disulfide-crosslinked and heterochiral N-C backbone-cyclized peptides. These peptides are exceptionally stable to thermal and chemical denaturation, and twelve experimentally-determined X-ray and NMR structures are nearly identical to the computational models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs. PMID:27626386

A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

PubMed

Dehzangi, Abdollah; Paliwal, Kuldip; Sharma, Alok; Dehzangi, Omid; Sattar, Abdul

2013-01-01

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.

A rapid, generally applicable method to engineer zinc fingers illustrated by targeting the HIV-1 promoter.

PubMed

Isalan, M; Klug, A; Choo, Y

2001-07-01

DNA-binding domains with predetermined sequence specificity are engineered by selection of zinc finger modules using phage display, allowing the construction of customized transcription factors. Despite remarkable progress in this field, the available protein-engineering methods are deficient in many respects, thus hampering the applicability of the technique. Here we present a rapid and convenient method that can be used to design zinc finger proteins against a variety of DNA-binding sites. This is based on a pair of pre-made zinc finger phage-display libraries, which are used in parallel to select two DNA-binding domains each of which recognizes given 5 base pair sequences, and whose products are recombined to produce a single protein that recognizes a composite (9 base pair) site of predefined sequence. Engineering using this system can be completed in less than two weeks and yields proteins that bind sequence-specifically to DNA with Kd values in the nanomolar range. To illustrate the technique, we have selected seven different proteins to bind various regions of the human immunodeficiency virus 1 (HIV-1) promoter.

KISS for STRAP: user extensions for a protein alignment editor.

PubMed

Gille, Christoph; Lorenzen, Stephan; Michalsky, Elke; Frömmel, Cornelius

2003-12-12

The Structural Alignment Program STRAP is a comfortable comprehensive editor and analyzing tool for protein alignments. A wide range of functions related to protein sequences and protein structures are accessible with an intuitive graphical interface. Recent features include mapping of mutations and polymorphisms onto structures and production of high quality figures for publication. Here we address the general problem of multi-purpose program packages to keep up with the rapid development of bioinformatical methods and the demand for specific program functions. STRAP was remade implementing a novel design which aims at Keeping Interfaces in STRAP Simple (KISS). KISS renders STRAP extendable to bio-scientists as well as to bio-informaticians. Scientists with basic computer skills are capable of implementing statistical methods or embedding existing bioinformatical tools in STRAP themselves. For bio-informaticians STRAP may serve as an environment for rapid prototyping and testing of complex algorithms such as automatic alignment algorithms or phylogenetic methods. Further, STRAP can be applied as an interactive web applet to present data related to a particular protein family and as a teaching tool. JAVA-1.4 or higher. http://www.charite.de/bioinf/strap/

Peptide/protein-polymer conjugates: synthetic strategies and design concepts.

PubMed

Gauthier, Marc A; Klok, Harm-Anton

2008-06-21

This feature article provides a compilation of tools available for preparing well-defined peptide/protein-polymer conjugates, which are defined as hybrid constructs combining (i) a defined number of peptide/protein segments with uniform chain lengths and defined monomer sequences (primary structure) with (ii) a defined number of synthetic polymer chains. The first section describes methods for post-translational, or direct, introduction of chemoselective handles onto natural or synthetic peptides/proteins. Addressed topics include the residue- and/or site-specific modification of peptides/proteins at Arg, Asp, Cys, Gln, Glu, Gly, His, Lys, Met, Phe, Ser, Thr, Trp, Tyr and Val residues and methods for producing peptides/proteins containing non-canonical amino acids by peptide synthesis and protein engineering. In the second section, methods for introducing chemoselective groups onto the side-chain or chain-end of synthetic polymers produced by radical, anionic, cationic, metathesis and ring-opening polymerization are described. The final section discusses convergent and divergent strategies for covalently assembling polymers and peptides/proteins. An overview of the use of chemoselective reactions such as Heck, Sonogashira and Suzuki coupling, Diels-Alder cycloaddition, Click chemistry, Staudinger ligation, Michael's addition, reductive alkylation and oxime/hydrazone chemistry for the convergent synthesis of peptide/protein-polymer conjugates is given. Divergent approaches for preparing peptide/protein-polymer conjugates which are discussed include peptide synthesis from synthetic polymer supports, polymerization from peptide/protein macroinitiators or chain transfer agents and the polymerization of peptide side-chain monomers.

Multicolor microcontact printing of proteins on nanoporous surface for patterned immunoassay

NASA Astrophysics Data System (ADS)

Ng, Elaine; Gopal, Ashwini; Hoshino, Kazunori; Zhang, Xiaojing

2011-07-01

The large scale patterning of therapeutic proteins is a key to the efficient design, characterization, and production of biologics for cost effective, high throughput, and point-of-care detection and analysis system. We demonstrate an efficient method for protein deposition and adsorption on nanoporous silica substrates in specific patterns using a method called "micro-contact printing". Multiple color-tagged proteins can be printed through sequential application of such micro-patterning technique. Two groups of experiments were performed. In the first group, the protein stamp was aligned precisely with the printing sites, where the stamp was applied multiple times. Optimal conditions were identified for protein transfer and adsorption using the pore size of 4 nm and thickness of 30 nm porous silica thin film. In the second group, we demonstrate the patterning of two-color rabbit immunoglobin labeled with fluorescein isothiocyanate and tetramethyl rhodamine iso-thiocyanate on porous silica substrates that have a pore size 4 nm, porosity 57% and thickness of the porous layer 30 nm. A pair of protein stamps, with corresponding alignment markings and coupled patterns, were aligned and used to produce a two-colored stamp pattern of proteins on porous silica. Different colored proteins can be applied to exemplify the diverse protein composition within a sample. This method of multicolor microcontact printing can be used to perform a fluorescence-based patterned enzyme-linked immunosorbent assay to detect the presence of various proteins within a sample.

Detection of Protein Interactions in T3S Systems Using Yeast Two-Hybrid Analysis.

PubMed

Nilles, Matthew L

2017-01-01

Two-hybrid systems, sometimes termed interaction traps, are genetic systems designed to find and analyze interactions between proteins. The most common systems are yeast based (commonly Saccharomyces cerevisae) and rely on the functional reconstitution of the GAL4 transcriptional activator. Reporter genes, such as the lacZ gene of Escherichia coli (encodes β-galactosidase), are placed under GAL4-dependent transcriptional control to provide quick and reliable detection of protein interactions. In this method the use of a yeast-based two-hybrid system is described to study protein interactions between components of type III secretion systems.

Electrochemical Aptamer Scaffold Biosensors for Detection of Botulism and Ricin Proteins.

PubMed

Daniel, Jessica; Fetter, Lisa; Jett, Susan; Rowland, Teisha J; Bonham, Andrew J

2017-01-01

Electrochemical DNA (E-DNA) biosensors enable the detection and quantification of a variety of molecular targets, including oligonucleotides, small molecules, heavy metals, antibodies, and proteins. Here we describe the design, electrode preparation and sensor attachment, and voltammetry conditions needed to generate and perform measurements using E-DNA biosensors against two protein targets, the biological toxins ricin and botulinum neurotoxin. This method can be applied to generate E-DNA biosensors for the detection of many other protein targets, with potential advantages over other systems including sensitive detection limits typically in the nanomolar range, real-time monitoring, and reusable biosensors.

Discrete Molecular Dynamics Can Predict Helical Prestructured Motifs in Disordered Proteins

PubMed Central

Han, Kyou-Hoon; Dokholyan, Nikolay V.; Tompa, Péter; Kalmár, Lajos; Hegedűs, Tamás

2014-01-01

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available. PMID:24763499

PL-PatchSurfer: a novel molecular local surface-based method for exploring protein-ligand interactions.

PubMed

Hu, Bingjie; Zhu, Xiaolei; Monroe, Lyman; Bures, Mark G; Kihara, Daisuke

2014-08-27

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets.

PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions

PubMed Central

Hu, Bingjie; Zhu, Xiaolei; Monroe, Lyman; Bures, Mark G.; Kihara, Daisuke

2014-01-01

Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets. PMID:25167137

Predicting "Hot" and "Warm" Spots for Fragment Binding.

PubMed

Rathi, Prakash Chandra; Ludlow, R Frederick; Hall, Richard J; Murray, Christopher W; Mortenson, Paul N; Verdonk, Marcel L

2017-05-11

Computational fragment mapping methods aim to predict hotspots on protein surfaces where small fragments will bind. Such methods are popular for druggability assessment as well as structure-based design. However, to date researchers developing or using such tools have had no clear way of assessing the performance of these methods. Here, we introduce the first diverse, high quality validation set for computational fragment mapping. The set contains 52 diverse examples of fragment binding "hot" and "warm" spots from the Protein Data Bank (PDB). Additionally, we describe PLImap, a novel protocol for fragment mapping based on the Protein-Ligand Informatics force field (PLIff). We evaluate PLImap against the new fragment mapping test set, and compare its performance to that of simple shape-based algorithms and fragment docking using GOLD. PLImap is made publicly available from https://bitbucket.org/AstexUK/pli .

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

PubMed

Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael

2017-06-15

Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Influence of protein fold stability on immunogenicity and its implications for vaccine design

PubMed Central

Scheiblhofer, Sandra; Laimer, Josef; Machado, Yoan; Weiss, Richard; Thalhamer, Josef

2017-01-01

ABSTRACT Introduction: In modern vaccinology and immunotherapy, recombinant proteins more and more replace whole organisms to induce protective or curative immune responses. Structural stability of proteins is of crucial importance for efficient presentation of antigenic peptides on MHC, which plays a decisive role for triggering strong immune reactions. Areas covered: In this review, we discuss structural stability as a key factor for modulating the potency of recombinant vaccines and its importance for antigen proteolysis, presentation, and stimulation of B and T cells. Moreover, the impact of fold stability on downstream events determining the differentiation of T cells into effector cells is reviewed. We summarize studies investigating the impact of protein fold stability on the outcome of the immune response and provide an overview on computational methods to estimate the effects of point mutations on protein stability. Expert commentary: Based on this information, the rational design of up-to-date vaccines is discussed. A model for predicting immunogenicity of proteins based on their conformational stability at different pH values is proposed. PMID:28290225

Computational approaches for drug discovery.

PubMed

Hung, Che-Lun; Chen, Chi-Chun

2014-09-01

Cellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand-receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure-affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs. © 2014 Wiley Periodicals, Inc.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

A Single-Chain Photoswitchable CRISPR-Cas9 Architecture for Light-Inducible Gene Editing and Transcription.

PubMed

Zhou, Xin X; Zou, Xinzhi; Chung, Hokyung K; Gao, Yuchen; Liu, Yanxia; Qi, Lei S; Lin, Michael Z

2018-02-16

Optical control of CRISPR-Cas9-derived proteins would be useful for restricting gene editing or transcriptional regulation to desired times and places. Optical control of Cas9 functions has been achieved with photouncageable unnatural amino acids or by using light-induced protein interactions to reconstitute Cas9-mediated functions from two polypeptides. However, these methods have only been applied to one Cas9 species and have not been used for optical control of different perturbations at two genes. Here, we use photodissociable dimeric fluorescent protein domains to engineer single-chain photoswitchable Cas9 (ps-Cas9) proteins in which the DNA-binding cleft is occluded at baseline and opened upon illumination. This design successfully controlled different species and functional variants of Cas9, mediated transcriptional activation more robustly than previous optogenetic methods, and enabled light-induced transcription of one gene and editing of another in the same cells. Thus, a single-chain photoswitchable architecture provides a general method to control a variety of Cas9-mediated functions.

Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

PubMed Central

2011-01-01

Background Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van Der Waals volume, pK (pK-C, pK-N, pK-COOH and pK-a(RCOOH)), etc. Conclusions The proposed approach Auto-IDPCPs would help designers to investigate informative physicochemical and biochemical properties by considering both prediction accuracy and analysis of binding mechanism simultaneously. The approach Auto-IDPCPs can be also applicable to predict and analyze other protein functions from sequences. PMID:21342579

Synergistic Integration of Experimental and Simulation Approaches for the de Novo Design of Silk-Based Materials.

PubMed

Huang, Wenwen; Ebrahimi, Davoud; Dinjaski, Nina; Tarakanova, Anna; Buehler, Markus J; Wong, Joyce Y; Kaplan, David L

2017-04-18

Tailored biomaterials with tunable functional properties are crucial for a variety of task-specific applications ranging from healthcare to sustainable, novel bio-nanodevices. To generate polymeric materials with predictive functional outcomes, exploiting designs from nature while morphing them toward non-natural systems offers an important strategy. Silks are Nature's building blocks and are produced by arthropods for a variety of uses that are essential for their survival. Due to the genetic control of encoded protein sequence, mechanical properties, biocompatibility, and biodegradability, silk proteins have been selected as prototype models to emulate for the tunable designs of biomaterial systems. The bottom up strategy of material design opens important opportunities to create predictive functional outcomes, following the exquisite polymeric templates inspired by silks. Recombinant DNA technology provides a systematic approach to recapitulate, vary, and evaluate the core structure peptide motifs in silks and then biosynthesize silk-based polymers by design. Post-biosynthesis processing allows for another dimension of material design by controlled or assisted assembly. Multiscale modeling, from the theoretical prospective, provides strategies to explore interactions at different length scales, leading to selective material properties. Synergy among experimental and modeling approaches can provide new and more rapid insights into the most appropriate structure-function relationships to pursue while also furthering our understanding in terms of the range of silk-based systems that can be generated. This approach utilizes nature as a blueprint for initial polymer designs with useful functions (e.g., silk fibers) but also employs modeling-guided experiments to expand the initial polymer designs into new domains of functional materials that do not exist in nature. The overall path to these new functional outcomes is greatly accelerated via the integration of modeling with experiment. In this Account, we summarize recent advances in understanding and functionalization of silk-based protein systems, with a focus on the integration of simulation and experiment for biopolymer design. Spider silk was selected as an exemplary protein to address the fundamental challenges in polymer designs, including specific insights into the role of molecular weight, hydrophobic/hydrophilic partitioning, and shear stress for silk fiber formation. To expand current silk designs toward biointerfaces and stimuli responsive materials, peptide modules from other natural proteins were added to silk designs to introduce new functions, exploiting the modular nature of silk proteins and fibrous proteins in general. The integrated approaches explored suggest that protein folding, silk volume fraction, and protein amino acid sequence changes (e.g., mutations) are critical factors for functional biomaterial designs. In summary, the integrated modeling-experimental approach described in this Account suggests a more rationally directed and more rapid method for the design of polymeric materials. It is expected that this combined use of experimental and computational approaches has a broad applicability not only for silk-based systems, but also for other polymer and composite materials.

Validation of quantitative and qualitative methods for detecting allergenic ingredients in processed foods in Japan.

PubMed

Sakai, Shinobu; Adachi, Reiko; Akiyama, Hiroshi; Teshima, Reiko

2013-06-19

A labeling system for food allergenic ingredients was established in Japan in April 2002. To monitor the labeling, the Japanese government announced official methods for detecting allergens in processed foods in November 2002. The official methods consist of quantitative screening tests using enzyme-linked immunosorbent assays (ELISAs) and qualitative confirmation tests using Western blotting or polymerase chain reactions (PCR). In addition, the Japanese government designated 10 μg protein/g food (the corresponding allergenic ingredient soluble protein weight/food weight), determined by ELISA, as the labeling threshold. To standardize the official methods, the criteria for the validation protocol were described in the official guidelines. This paper, which was presented at the Advances in Food Allergen Detection Symposium, ACS National Meeting and Expo, San Diego, CA, Spring 2012, describes the validation protocol outlined in the official Japanese guidelines, the results of interlaboratory studies for the quantitative detection method (ELISA for crustacean proteins) and the qualitative detection method (PCR for shrimp and crab DNAs), and the reliability of the detection methods.

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

An approach to crystallizing proteins by metal-mediated synthetic symmetrization

PubMed Central

Laganowsky, Arthur; Zhao, Minglei; Soriaga, Angela B; Sawaya, Michael R; Cascio, Duilio; Yeates, Todd O

2011-01-01

Combining the concepts of synthetic symmetrization with the approach of engineering metal-binding sites, we have developed a new crystallization methodology termed metal-mediated synthetic symmetrization. In this method, pairs of histidine or cysteine mutations are introduced on the surface of target proteins, generating crystal lattice contacts or oligomeric assemblies upon coordination with metal. Metal-mediated synthetic symmetrization greatly expands the packing and oligomeric assembly possibilities of target proteins, thereby increasing the chances of growing diffraction-quality crystals. To demonstrate this method, we designed various T4 lysozyme (T4L) and maltose-binding protein (MBP) mutants and cocrystallized them with one of three metal ions: copper (Cu2+), nickel (Ni2+), or zinc (Zn2+). The approach resulted in 16 new crystal structures—eight for T4L and eight for MBP—displaying a variety of oligomeric assemblies and packing modes, representing in total 13 new and distinct crystal forms for these proteins. We discuss the potential utility of the method for crystallizing target proteins of unknown structure by engineering in pairs of histidine or cysteine residues. As an alternate strategy, we propose that the varied crystallization-prone forms of T4L or MBP engineered in this work could be used as crystallization chaperones, by fusing them genetically to target proteins of interest. PMID:21898649

Integrated Controlling System and Unified Database for High Throughput Protein Crystallography Experiments

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

Gaponov, Yu.A.; Igarashi, N.; Hiraki, M.

2004-05-12

An integrated controlling system and a unified database for high throughput protein crystallography experiments have been developed. Main features of protein crystallography experiments (purification, crystallization, crystal harvesting, data collection, data processing) were integrated into the software under development. All information necessary to perform protein crystallography experiments is stored (except raw X-ray data that are stored in a central data server) in a MySQL relational database. The database contains four mutually linked hierarchical trees describing protein crystals, data collection of protein crystal and experimental data processing. A database editor was designed and developed. The editor supports basic database functions to view,more » create, modify and delete user records in the database. Two search engines were realized: direct search of necessary information in the database and object oriented search. The system is based on TCP/IP secure UNIX sockets with four predefined sending and receiving behaviors, which support communications between all connected servers and clients with remote control functions (creating and modifying data for experimental conditions, data acquisition, viewing experimental data, and performing data processing). Two secure login schemes were designed and developed: a direct method (using the developed Linux clients with secure connection) and an indirect method (using the secure SSL connection using secure X11 support from any operating system with X-terminal and SSH support). A part of the system has been implemented on a new MAD beam line, NW12, at the Photon Factory Advanced Ring for general user experiments.« less

Role of substrate dynamics in protein prenylation reactions.

PubMed

Chakravorty, Dhruva K; Merz, Kenneth M

2015-02-17

CONSPECTUS: The role dynamics plays in proteins is of intense contemporary interest. Fundamental insights into how dynamics affects reactivity and product distributions will facilitate the design of novel catalysts that can produce high quality compounds that can be employed, for example, as fuels and life saving drugs. We have used molecular dynamics (MD) methods and combined quantum mechanical/molecular mechanical (QM/MM) methods to study a series of proteins either whose substrates are too far away from the catalytic center or whose experimentally resolved substrate binding modes cannot explain the observed product distribution. In particular, we describe studies of farnesyl transferase (FTase) where the farnesyl pyrophosphate (FPP) substrate is ∼8 Å from the zinc-bound peptide in the active site of FTase. Using MD and QM/MM studies, we explain how the FPP substrate spans the gulf between it and the active site, and we have elucidated the nature of the transition state (TS) and offered an alternate explanation of experimentally observed kinetic isotope effects (KIEs). Our second story focuses on the nature of substrate dynamics in the aromatic prenyltransferase (APTase) protein NphB and how substrate dynamics affects the observed product distribution. Through the examples chosen we show the power of MD and QM/MM methods to provide unique insights into how protein substrate dynamics affects catalytic efficiency. We also illustrate how complex these reactions are and highlight the challenges faced when attempting to design de novo catalysts. While the methods used in our previous studies provided useful insights, several clear challenges still remain. In particular, we have utilized a semiempirical QM model (self-consistent charge density functional tight binding, SCC-DFTB) in our QM/MM studies since the problems we were addressing required extensive sampling. For the problems illustrated, this approach performed admirably (we estimate for these systems an uncertainty of ∼2 kcal/mol), but it is still a semiempirical model, and studies of this type would benefit greatly from more accurate ab initio or DFT models. However, the challenge with these methods is to reach the level of sampling needed to study systems where large conformational changes happen in the many nanoseconds to microsecond time regimes. Hence, how to couple expensive and accurate QM methods with sophisticated sampling algorithms is an important future challenge especially when large-scale studies of catalyst design become of interest. The use of MD and QM/MM models to elucidate enzyme catalytic pathways and to design novel catalytic agents is in its infancy but shows tremendous promise. While this Account summarizes where we have been, we also discuss briefly future directions that improve our fundamental ability to understand enzyme catalysis.

Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model.

PubMed

Casey, F P; Baird, D; Feng, Q; Gutenkunst, R N; Waterfall, J J; Myers, C R; Brown, K S; Cerione, R A; Sethna, J P

2007-05-01

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.

Recent advances in automated protein design and its future challenges.

PubMed

Setiawan, Dani; Brender, Jeffrey; Zhang, Yang

2018-04-25

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

In silico fragment-based drug design.

PubMed

Konteatis, Zenon D

2010-11-01

In silico fragment-based drug design (FBDD) is a relatively new approach inspired by the success of the biophysical fragment-based drug discovery field. Here, we review the progress made by this approach in the last decade and showcase how it complements and expands the capabilities of biophysical FBDD and structure-based drug design to generate diverse, efficient drug candidates. Advancements in several areas of research that have enabled the development of in silico FBDD and some applications in drug discovery projects are reviewed. The reader is introduced to various computational methods that are used for in silico FBDD, the fragment library composition for this technique, special applications used to identify binding sites on the surface of proteins and how to assess the druggability of these sites. In addition, the reader will gain insight into the proper application of this approach from examples of successful programs. In silico FBDD captures a much larger chemical space than high-throughput screening and biophysical FBDD increasing the probability of developing more diverse, patentable and efficient molecules that can become oral drugs. The application of in silico FBDD holds great promise for historically challenging targets such as protein-protein interactions. Future advances in force fields, scoring functions and automated methods for determining synthetic accessibility will all aid in delivering more successes with in silico FBDD.

A Step Closer to Membrane Protein Multiplexed Nanoarrays Using Biotin-Doped Polypyrrole

PubMed Central

2015-01-01

Whether for fundamental biological research or for diagnostic and drug discovery applications, protein micro- and nanoarrays are attractive technologies because of their low sample consumption, high-throughput, and multiplexing capabilities. However, the arraying platforms developed so far are still not able to handle membrane proteins, and specific methods to selectively immobilize these hydrophobic and fragile molecules are needed to understand their function and structural complexity. Here we integrate two technologies, electropolymerization and amphipols, to demonstrate the electrically addressable functionalization of micro- and nanosurfaces with membrane proteins. Gold surfaces are selectively modified by electrogeneration of a polymeric film in the presence of biotin, where avidin conjugates can then be selectively immobilized. The method is successfully applied to the preparation of protein-multiplexed arrays by sequential electropolymerization and biomolecular functionalization steps. The surface density of the proteins bound to the electrodes can be easily tuned by adjusting the amount of biotin deposited during electropolymerization. Amphipols are specially designed amphipathic polymers that provide a straightforward method to stabilize and add functionalities to membrane proteins. Exploiting the strong affinity of biotin for streptavidin, we anchor distinct membrane proteins onto different electrodes via a biotin-tagged amphipol. Antibody-recognition events demonstrate that the proteins are stably immobilized and that the electrodeposition of polypyrrole films bearing biotin units is compatible with the protein-binding activity. Since polypyrrole films show good conductivity properties, the platform described here is particularly well suited to prepare electronically transduced bionanosensors. PMID:24476392

Bioluminescence Resonance Energy Transfer System for Measuring Dynamic Protein-Protein Interactions in Bacteria

PubMed Central

Cui, Boyu; Wang, Yao; Song, Yunhong; Wang, Tietao; Li, Changfu; Wei, Yahong

2014-01-01

ABSTRACT Protein-protein interactions are important for virtually every biological process, and a number of elegant approaches have been designed to detect and evaluate such interactions. However, few of these methods allow the detection of dynamic and real-time protein-protein interactions in bacteria. Here we describe a bioluminescence resonance energy transfer (BRET) system based on the bacterial luciferase LuxAB. We found that enhanced yellow fluorescent protein (eYFP) accepts the emission from LuxAB and emits yellow fluorescence. Importantly, BRET occurred when LuxAB and eYFP were fused, respectively, to the interacting protein pair FlgM and FliA. Furthermore, we observed sirolimus (i.e., rapamycin)-inducible interactions between FRB and FKBP12 and a dose-dependent abolishment of such interactions by FK506, the ligand of FKBP12. Using this system, we showed that osmotic stress or low pH efficiently induced multimerization of the regulatory protein OmpR and that the multimerization induced by low pH can be reversed by a neutralizing agent, further indicating the usefulness of this system in the measurement of dynamic interactions. This method can be adapted to analyze dynamic protein-protein interactions and the importance of such interactions in bacterial processes such as development and pathogenicity. PMID:24846380

Non-interacting surface solvation and dynamics in protein-protein interactions.

PubMed

Visscher, Koen M; Kastritis, Panagiotis L; Bonvin, Alexandre M J J

2015-03-01

Protein-protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure-based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein-protein binding, even for simple lock-and-key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein-protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein-protein surfaces. We compare properties of the interface, rim, and non-interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non-interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non-interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein-protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein-protein complexes from unintended protein-protein interactions. © 2014 Wiley Periodicals, Inc.

Rhodium(II) metallopeptide catalyst design enables fine control in selective functionalization of natural SH3 domains.

PubMed

Vohidov, Farrukh; Coughlin, Jane M; Ball, Zachary T

2015-04-07

Chemically modified proteins are increasingly important for use in fundamental biophysical studies, chemical biology, therapeutic protein development, and biomaterials. However, chemical methods typically produce heterogeneous labeling and cannot approach the exquisite selectivity of enzymatic reactions. While bioengineered methods are sometimes an option, selective reactions of natural proteins remain an unsolved problem. Here we show that rhodium(II) metallopeptides combine molecular recognition with promiscuous catalytic activity to allow covalent decoration of natural SH3 domains, depending on choice of catalyst but independent of the specific residue present. A metallopeptide catalyst succeeds in modifying a single SH3-containing kinase at endogenous concentrations in prostate cancer (PC-3) cell lysate. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach

NASA Astrophysics Data System (ADS)

Wang, Yu; Guo, Yanzhi; Kuang, Qifan; Pu, Xuemei; Ji, Yue; Zhang, Zhihang; Li, Menglong

2015-04-01

The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein-ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction. Feature processing and compression was respectively implemented for different protein family datasets, which indicates that different features contribute to different models, so individual representation for each protein family is necessary. Three family-specific models were constructed for three important protein target families of HIV-1 protease, trypsin and carbonic anhydrase respectively. As a comparison, two generic models including diverse protein families were also built. The evaluation results show that models on family-specific datasets have the superior performance to those on the generic datasets and the Pearson and Spearman correlation coefficients ( R p and Rs) on the test sets are 0.740, 0.874, 0.735 and 0.697, 0.853, 0.723 for HIV-1 protease, trypsin and carbonic anhydrase respectively. Comparisons with the other methods further demonstrate that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family.

Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence

PubMed Central

Bernardes, Juliana; Zaverucha, Gerson; Vaquero, Catherine; Carbone, Alessandra

2016-01-01

Traditional protein annotation methods describe known domains with probabilistic models representing consensus among homologous domain sequences. However, when relevant signals become too weak to be identified by a global consensus, attempts for annotation fail. Here we address the fundamental question of domain identification for highly divergent proteins. By using high performance computing, we demonstrate that the limits of state-of-the-art annotation methods can be bypassed. We design a new strategy based on the observation that many structural and functional protein constraints are not globally conserved through all species but might be locally conserved in separate clades. We propose a novel exploitation of the large amount of data available: 1. for each known protein domain, several probabilistic clade-centered models are constructed from a large and differentiated panel of homologous sequences, 2. a decision-making protocol combines outcomes obtained from multiple models, 3. a multi-criteria optimization algorithm finds the most likely protein architecture. The method is evaluated for domain and architecture prediction over several datasets and statistical testing hypotheses. Its performance is compared against HMMScan and HHblits, two widely used search methods based on sequence-profile and profile-profile comparison. Due to their closeness to actual protein sequences, clade-centered models are shown to be more specific and functionally predictive than the broadly used consensus models. Based on them, we improved annotation of Plasmodium falciparum protein sequences on a scale not previously possible. We successfully predict at least one domain for 72% of P. falciparum proteins against 63% achieved previously, corresponding to 30% of improvement over the total number of Pfam domain predictions on the whole genome. The method is applicable to any genome and opens new avenues to tackle evolutionary questions such as the reconstruction of ancient domain duplications, the reconstruction of the history of protein architectures, and the estimation of protein domain age. Website and software: http://www.lcqb.upmc.fr/CLADE. PMID:27472895

NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins.

PubMed

Borguesan, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

2017-03-01

The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for example, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at http://sbcb.inf.ufrgs.br/nias .

Computational prediction of protein hot spot residues.

PubMed

Morrow, John Kenneth; Zhang, Shuxing

2012-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces

PubMed Central

Shazman, Shula; Elber, Gershon; Mandel-Gutfreund, Yael

2011-01-01

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design. PMID:21693557

Computational design of cyclic peptides for the customized oriented immobilization of globular proteins.

PubMed

Soler, Miguel A; Rodriguez, Alex; Russo, Anna; Adedeji, Abimbola Feyisara; Dongmo Foumthuim, Cedrix J; Cantarutti, Cristina; Ambrosetti, Elena; Casalis, Loredana; Corazza, Alessandra; Scoles, Giacinto; Marasco, Daniela; Laio, Alessandro; Fortuna, Sara

2017-01-25

The oriented immobilization of proteins, key for the development of novel responsive biomaterials, relies on the availability of effective probes. These are generally provided by standard approaches based on in vivo maturation and in vitro selection of antibodies and/or aptamers. These techniques can suffer technical problems when a non-immunogenic epitope needs to be targeted. Here we propose a strategy to circumvent this issue by in silico design. In our method molecular binders, in the form of cyclic peptides, are computationally evolved by stochastically exploring their sequence and structure space to identify high-affinity peptides for a chosen epitope of a target globular protein: here a solvent-exposed site of β2-microglobulin (β2m). Designed sequences were screened by explicit solvent molecular dynamics simulations (MD) followed by experimental validation. Five candidates gave dose-response surface plasmon resonance signals with dissociation constants in the micromolar range. One of them was further analyzed by means of isothermal titration calorimetry, nuclear magnetic resonance, and 250 ns of MD. Atomic-force microscopy imaging showed that this peptide is able to immobilize β2m on a gold surface. In short, we have shown by a variety of experimental techniques that it is possible to capture a protein through an epitope of choice by computational design.

Statistical Coupling Analysis-Guided Library Design for the Discovery of Mutant Luciferases.

PubMed

Liu, Mira D; Warner, Elliot A; Morrissey, Charlotte E; Fick, Caitlyn W; Wu, Taia S; Ornelas, Marya Y; Ochoa, Gabriela V; Zhang, Brendan S; Rathbun, Colin M; Porterfield, William B; Prescher, Jennifer A; Leconte, Aaron M

2018-02-06

Directed evolution has proven to be an invaluable tool for protein engineering; however, there is still a need for developing new approaches to continue to improve the efficiency and efficacy of these methods. Here, we demonstrate a new method for library design that applies a previously developed bioinformatic method, Statistical Coupling Analysis (SCA). SCA uses homologous enzymes to identify amino acid positions that are mutable and functionally important and engage in synergistic interactions between amino acids. We use SCA to guide a library of the protein luciferase and demonstrate that, in a single round of selection, we can identify luciferase mutants with several valuable properties. Specifically, we identify luciferase mutants that possess both red-shifted emission spectra and improved stability relative to those of the wild-type enzyme. We also identify luciferase mutants that possess a >50-fold change in specificity for modified luciferins. To understand the mutational origin of these improved mutants, we demonstrate the role of mutations at N229, S239, and G246 in altered function. These studies show that SCA can be used to guide library design and rapidly identify synergistic amino acid mutations from a small library.

Microwave Tissue Soldering for Immediate Wound Closure

NASA Technical Reports Server (NTRS)

Arndt, G. Dickey; Ngo, Phong H.; Phan, Chau T.; Byerly, Diane; Dusl, John; Sognier, Marguerite A.; Carl, James

2011-01-01

A novel approach for the immediate sealing of traumatic wounds is under development. A portable microwave generator and handheld antenna are used to seal wounds, binding the edges of the wound together using a biodegradable protein sealant or solder. This method could be used for repairing wounds in emergency settings by restoring the wound surface to its original strength within minutes. This technique could also be utilized for surgical purposes involving solid visceral organs (i.e., liver, spleen, and kidney) that currently do not respond well to ordinary surgical procedures. A miniaturized microwave generator and a handheld antenna are used to deliver microwave energy to the protein solder, which is applied to the wound. The antenna can be of several alternative designs optimized for placement either in contact with or in proximity to the protein solder covering the wound. In either case, optimization of the design includes the matching of impedances to maximize the energy delivered to the protein solder and wound at a chosen frequency. For certain applications, an antenna could be designed that would emit power only when it is in direct contact with the wound. The optimum frequency or frequencies for a specific application would depend on the required depth of penetration of the microwave energy. In fact, a computational simulation for each specific application could be performed, which would then match the characteristics of the antenna with the protein solder and tissue to best effect wound closure. An additional area of interest with potential benefit that remains to be validated is whether microwave energy can effectively kill bacteria in and around the wound. Thus, this may be an efficient method for simultaneously sterilizing and closing wounds.

A comprehensive and scalable database search system for metaproteomics.

PubMed

Chatterjee, Sandip; Stupp, Gregory S; Park, Sung Kyu Robin; Ducom, Jean-Christophe; Yates, John R; Su, Andrew I; Wolan, Dennis W

2016-08-16

Mass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations. Our approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed "Blazmass") to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy and allowing for a more in-depth characterization of the functional landscape of the samples. The combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomic search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

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

PubMed Central

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

2016-01-01

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

Protein structural similarity search by Ramachandran codes

PubMed Central

Lo, Wei-Cheng; Huang, Po-Jung; Chang, Chih-Hung; Lyu, Ping-Chiang

2007-01-01

Background Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. Results We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms. Conclusion As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era. PMID:17716377

Targeting the Allosteric Site of Oncoprotein BCR-ABL as an Alternative Strategy for Effective Target Protein Degradation.

PubMed

Shimokawa, Kenichiro; Shibata, Norihito; Sameshima, Tomoya; Miyamoto, Naoki; Ujikawa, Osamu; Nara, Hiroshi; Ohoka, Nobumichi; Hattori, Takayuki; Cho, Nobuo; Naito, Mikihiko

2017-10-12

Protein degradation technology based on hybrid small molecules is an emerging drug modality that has significant potential in drug discovery and as a unique method of post-translational protein knockdown in the field of chemical biology. Here, we report the first example of a novel and potent protein degradation inducer that binds to an allosteric site of the oncogenic BCR-ABL protein. BCR-ABL allosteric ligands were incorporated into the SNIPER (Specific and Nongenetic inhibitor of apoptosis protein [IAP]-dependent Protein Erasers) platform, and a series of in vitro biological assays of binding affinity, target protein modulation, signal transduction, and growth inhibition were carried out. One of the designed compounds, 6 (SNIPER(ABL)-062), showed desirable binding affinities against ABL1, cIAP1/2, and XIAP and consequently caused potent BCR-ABL degradation.

Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles.

PubMed

Davey, James A; Chica, Roberto A

2014-05-01

Multistate computational protein design (MSD) with backbone ensembles approximating conformational flexibility can predict higher quality sequences than single-state design with a single fixed backbone. However, it is currently unclear what characteristics of backbone ensembles are required for the accurate prediction of protein sequence stability. In this study, we aimed to improve the accuracy of protein stability predictions made with MSD by using a variety of backbone ensembles to recapitulate the experimentally measured stability of 85 Streptococcal protein G domain β1 sequences. Ensembles tested here include an NMR ensemble as well as those generated by molecular dynamics (MD) simulations, by Backrub motions, and by PertMin, a new method that we developed involving the perturbation of atomic coordinates followed by energy minimization. MSD with the PertMin ensembles resulted in the most accurate predictions by providing the highest number of stable sequences in the top 25, and by correctly binning sequences as stable or unstable with the highest success rate (≈90%) and the lowest number of false positives. The performance of PertMin ensembles is due to the fact that their members closely resemble the input crystal structure and have low potential energy. Conversely, the NMR ensemble as well as those generated by MD simulations at 500 or 1000 K reduced prediction accuracy due to their low structural similarity to the crystal structure. The ensembles tested herein thus represent on- or off-target models of the native protein fold and could be used in future studies to design for desired properties other than stability. Copyright © 2013 Wiley Periodicals, Inc.

Accelerating large-scale protein structure alignments with graphics processing units

PubMed Central

2012-01-01

Background Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons. Findings We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH. Conclusions ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU. PMID:22357132

DLocalMotif: a discriminative approach for discovering local motifs in protein sequences.

PubMed

Mehdi, Ahmed M; Sehgal, Muhammad Shoaib B; Kobe, Bostjan; Bailey, Timothy L; Bodén, Mikael

2013-01-01

Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences. DLocalMotif combines three scoring functions, measuring degrees of motif over-representation, entropy and spatial confinement, specifically designed to discriminatively exploit the availability of negative data. The method is shown to outperform current methods that use only a subset of these motif characteristics. We apply the method to several biological datasets. The analysis of peroxisomal targeting signals uncovers several novel motifs that occur immediately upstream of the dominant peroxisomal targeting signal-1 signal. The analysis of proline-tyrosine nuclear localization signals uncovers multiple novel motifs that overlap with C2H2 zinc finger domains. We also evaluate the method on classical nuclear localization signals and endoplasmic reticulum retention signals and find that DLocalMotif successfully recovers biologically relevant sequence properties. http://bioinf.scmb.uq.edu.au/dlocalmotif/

GneimoSim: A Modular Internal Coordinates Molecular Dynamics Simulation Package

PubMed Central

Larsen, Adrien B.; Wagner, Jeffrey R.; Kandel, Saugat; Salomon-Ferrer, Romelia; Vaidehi, Nagarajan; Jain, Abhinandan

2014-01-01

The Generalized Newton Euler Inverse Mass Operator (GNEIMO) method is an advanced method for internal coordinates molecular dynamics (ICMD). GNEIMO includes several theoretical and algorithmic advancements that address longstanding challenges with ICMD simulations. In this paper we describe the GneimoSim ICMD software package that implements the GNEIMO method. We believe that GneimoSim is the first software package to include advanced features such as the equipartition principle derived for internal coordinates, and a method for including the Fixman potential to eliminate systematic statistical biases introduced by the use of hard constraints. Moreover, by design, GneimoSim is extensible and can be easily interfaced with third party force field packages for ICMD simulations. Currently, GneimoSim includes interfaces to LAMMPS, OpenMM, Rosetta force field calculation packages. The availability of a comprehensive Python interface to the underlying C++ classes and their methods provides a powerful and versatile mechanism for users to develop simulation scripts to configure the simulation and control the simulation flow. GneimoSim has been used extensively for studying the dynamics of protein structures, refinement of protein homology models, and for simulating large scale protein conformational changes with enhanced sampling methods. GneimoSim is not limited to proteins and can also be used for the simulation of polymeric materials. PMID:25263538

GneimoSim: a modular internal coordinates molecular dynamics simulation package.

PubMed

Larsen, Adrien B; Wagner, Jeffrey R; Kandel, Saugat; Salomon-Ferrer, Romelia; Vaidehi, Nagarajan; Jain, Abhinandan

2014-12-05

The generalized Newton-Euler inverse mass operator (GNEIMO) method is an advanced method for internal coordinates molecular dynamics (ICMD). GNEIMO includes several theoretical and algorithmic advancements that address longstanding challenges with ICMD simulations. In this article, we describe the GneimoSim ICMD software package that implements the GNEIMO method. We believe that GneimoSim is the first software package to include advanced features such as the equipartition principle derived for internal coordinates, and a method for including the Fixman potential to eliminate systematic statistical biases introduced by the use of hard constraints. Moreover, by design, GneimoSim is extensible and can be easily interfaced with third party force field packages for ICMD simulations. Currently, GneimoSim includes interfaces to LAMMPS, OpenMM, and Rosetta force field calculation packages. The availability of a comprehensive Python interface to the underlying C++ classes and their methods provides a powerful and versatile mechanism for users to develop simulation scripts to configure the simulation and control the simulation flow. GneimoSim has been used extensively for studying the dynamics of protein structures, refinement of protein homology models, and for simulating large scale protein conformational changes with enhanced sampling methods. GneimoSim is not limited to proteins and can also be used for the simulation of polymeric materials. © 2014 Wiley Periodicals, Inc.

Targeted inhibition of oncogenic miR-21 maturation with designed RNA-binding proteins

PubMed Central

Chen, Yu; Yang, Fan; Zubovic, Lorena; Pavelitz, Tom; Yang, Wen; Godin, Katherine; Walker, Matthew; Zheng, Suxin; Macchi, Paolo; Varani, Gabriele

2016-01-01

The RNA Recognition Motif (RRM) is the largest family of eukaryotic RNA-binding proteins. Engineered RRMs with new specificity would provide valuable tools and an exacting test of our understanding of specificity. We have achieved the first successful re-design of the specificity of an RRM using rational methods and demonstrated re-targeting of activity in cells. We engineered the conserved RRM of human Rbfox proteins to specifically bind to the terminal loop of miR-21 precursor with high affinity and inhibit its processing by Drosha and Dicer. We further engineered Giardia Dicer by replacing its PAZ domain with the designed RRM. The reprogrammed enzyme degrades pre-miR-21 specifically in vitro and suppresses mature miR-21 levels in cells, which results in increased expression of PDCD4 and significantly decreased viability for cancer cells. The results demonstrate the feasibility of engineering the sequence-specificity of RRMs and of using this ubiquitous platform for diverse biological applications. PMID:27428511

Sandia Simple Particle Tracking (Sandia SPT) v. 1.0

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

Anthony, Stephen M.

2015-06-15

Sandia SPT is designed as software to accompany a book chapter being published a methods chapter which provides an introduction on how to label and track individual proteins. The Sandia Simple Particle Tracking code uses techniques common to the image processing community, where its value is that it facilitates implementing the methods described in the book chapter by providing the necessary open-source code. The code performs single particle spot detection (or segmentation and localization) followed by tracking (or connecting the detected particles into trajectories). The book chapter, which along with the headers in each file, constitutes the documentation for themore » code is: Anthony, S.M.; Carroll-Portillo, A.; Timlon, J.A., Dynamics and Interactions of Individual Proteins in the Membrane of Living Cells. In Anup K. Singh (Ed.) Single Cell Protein Analysis Methods in Molecular Biology. Springer« less

Matrix Assisted Ionization Vacuum (MAIV), a New Ionization Method for Biological Materials Analysis Using Mass Spectrometry*

PubMed Central

Inutan, Ellen D.; Trimpin, Sarah

2013-01-01

The introduction of electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) for the mass spectrometric analysis of peptides and proteins had a dramatic impact on biological science. We now report that a wide variety of compounds, including peptides, proteins, and protein complexes, are transported directly from a solid-state small molecule matrix to gas-phase ions when placed into the vacuum of a mass spectrometer without the use of high voltage, a laser, or added heat. This ionization process produces ions having charge states similar to ESI, making the method applicable for high performance mass spectrometers designed for atmospheric pressure ionization. We demonstrate highly sensitive ionization using intermediate pressure MALDI and modified ESI sources. This matrix and vacuum assisted soft ionization method is suitable for the direct surface analysis of biological materials, including tissue, via mass spectrometry. PMID:23242551

Supported Lipid Bilayer Technology for the Study of Cellular Interfaces

PubMed Central

Crites, Travis J.; Maddox, Michael; Padhan, Kartika; Muller, James; Eigsti, Calvin; Varma, Rajat

2015-01-01

Glass-supported lipid bilayers presenting freely diffusing proteins have served as a powerful tool for studying cell-cell interfaces, in particular, T cell–antigen presenting cell (APC) interactions, using optical microscopy. Here we expand upon existing protocols and describe the preparation of liposomes by an extrusion method, and describe how this system can be used to study immune synapse formation by Jurkat cells. We also present a method for forming such lipid bilayers on silica beads for the study of signaling responses by population methods, such as western blotting, flow cytometry, and gene-expression analysis. Finally, we describe how to design and prepare transmembrane-anchored protein-laden liposomes, following expression in suspension CHO (CHOs) cells, a mammalian expression system alternative to insect and bacterial cell lines, which do not produce mammalian glycosylation patterns. Such transmembrane-anchored proteins may have many novel applications in cell biology and immunology. PMID:26331983

A novel feature extraction scheme with ensemble coding for protein-protein interaction prediction.

PubMed

Du, Xiuquan; Cheng, Jiaxing; Zheng, Tingting; Duan, Zheng; Qian, Fulan

2014-07-18

Protein-protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp.

Protein and Antibody Engineering by Phage Display

PubMed Central

Frei, J.C.; Lai, J.R.

2017-01-01

Phage display is an in vitro selection technique that allows for the rapid isolation of proteins with desired properties including increased affinity, specificity, stability, and new enzymatic activity. The power of phage display relies on the phenotype-to-genotype linkage of the protein of interest displayed on the phage surface with the encoding DNA packaged within the phage particle, which allows for selective enrichment of library pools and high-throughput screening of resulting clones. As an in vitro method, the conditions of the binding selection can be tightly controlled. Due to the high-throughput nature, rapidity, and ease of use, phage display is an excellent technological platform for engineering antibody or proteins with enhanced properties. Here, we describe methods for synthesis, selection, and screening of phage libraries with particular emphasis on designing humanizing antibody libraries and combinatorial scanning mutagenesis libraries. We conclude with a brief section on troubleshooting for all stages of the phage display process. PMID:27586328

Proteins as templates for complex synthetic metalloclusters: towards biologically programmed heterogeneous catalysis

PubMed Central

Fehl, Charlie

2016-01-01

Despite nature’s prevalent use of metals as prosthetics to adapt or enhance the behaviour of proteins, our ability to programme such architectural organization remains underdeveloped. Multi-metal clusters buried in proteins underpin the most remarkable chemical transformations in nature, but we are not yet in a position to fully mimic or exploit such systems. With the advent of copious, relevant structural information, judicious mechanistic studies and the use of accessible computational methods in protein design coupled with new synthetic methods for building biomacromolecules, we can envisage a ‘new dawn’ that will allow us to build de novo metalloenzymes that move beyond mono-metal centres. In particular, we highlight the need for systems that approach the multi-centred clusters that have evolved to couple electron shuttling with catalysis. Such hybrids may be viewed as exciting mid-points between homogeneous and heterogeneous catalysts which also exploit the primary benefits of biocatalysis. PMID:27279776

Redox Proteomics of Protein-bound Methionine Oxidation*

PubMed Central

Ghesquière, Bart; Jonckheere, Veronique; Colaert, Niklaas; Van Durme, Joost; Timmerman, Evy; Goethals, Marc; Schymkowitz, Joost; Rousseau, Frederic; Vandekerckhove, Joël; Gevaert, Kris

2011-01-01

We here present a new method to measure the degree of protein-bound methionine sulfoxide formation at a proteome-wide scale. In human Jurkat cells that were stressed with hydrogen peroxide, over 2000 oxidation-sensitive methionines in more than 1600 different proteins were mapped and their extent of oxidation was quantified. Meta-analysis of the sequences surrounding the oxidized methionine residues revealed a high preference for neighboring polar residues. Using synthetic methionine sulfoxide containing peptides designed according to the observed sequence preferences in the oxidized Jurkat proteome, we discovered that the substrate specificity of the cellular methionine sulfoxide reductases is a major determinant for the steady-state of methionine oxidation. This was supported by a structural modeling of the MsrA catalytic center. Finally, we applied our method onto a serum proteome from a mouse sepsis model and identified 35 in vivo methionine oxidation events in 27 different proteins. PMID:21406390

Optical system for the Protein Crystallisation Diagnostics Facility (PCDF) on board the ISS

NASA Astrophysics Data System (ADS)

Joannes, Luc; Dupont, Olivier; Dewandel, Jean-Luc; Ligot, Renaud; Algrain, Hervé

2004-06-01

The Protein Crystallisation Diagnostic Facility (PCDF) is a multi-user facility to study the protein crystallisation under the conditions of micro-gravity onboard the International Space Station (ISS) Columbus facility. Large size protein crystals will growth under reduced gravity in thermally controlled reactors. A combination of diagnostic tools like video system, microscope, interferometer, and light scattering device shall help to understand the growth phenomena. Common methods of protein crystallisation shall be performed in PCDF: Dialysis where the protein solution and the salt solution are separated by a semi-permeable membrane. Extended Length Dialysis Batch where the saturation to get crystals is achieved by changing the concentration of the protein in the sample liquid. The overall ESA project is leaded by EADS Space Transportation, Friedrichshafen, Germany. Lambda-X is responsible for the Optical System (OS), with Verhaert Design and Development as sub-contractor for the mechanical design. The OS includes different compact parts: Original illumination systems based on LEDs of difference colours; Quantitative Mach-Zehnder interferometers to measure the concentration distribution around crystals; Imaging assemblies to visualize the protein volume with different field of views. The paper concentrates on the description of each part, and in particular on the imaging assembly which allow switching from one field of view to another by passive elements only.

Prediction and analysis of beta-turns in proteins by support vector machine.

PubMed

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2003-01-01

Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

Accelerating Biomedical Research in Designing Diagnostic Assays, Drugs, and Vaccines

DTIC Science & Technology

2010-10-01

biodefense. For example, USAMRIID researchers are using Dovis to initiate drug discovery efforts against the ricin A-chain toxin and the Ebola virus...in host cell invasion and bacterial toxin production). Traditional experimental methods to determine the functions of proteins encoded in genomic...readily modeled. A second study involved determining the pro- tein structure of VP24, the smallest protein in the Ebola and Marburg virus genomes.9

Gold nanoparticles-based electrochemical method for the detection of protein kinase with a peptide-like inhibitor as the bioreceptor

PubMed Central

Sun, Kai; Chang, Yong; Zhou, Binbin; Wang, Xiaojin; Liu, Lin

2017-01-01

This article presents a general method for the detection of protein kinase with a peptide-like kinase inhibitor as the bioreceptor, and it was done by converting gold nanoparticles (AuNPs)-based colorimetric assay into sensitive electrochemical analysis. In the colorimetric assay, the kinase-specific aptameric peptide triggered the aggregation of AuNPs in solution. However, the specific binding of peptide to the target protein (kinase) inhibited its ability to trigger the assembly of AuNPs. In the electrochemical analysis, peptides immobilized on a gold electrode and presented as solution triggered together the in situ formation of AuNPs-based network architecture on the electrode surface. Nevertheless, the formation of peptide–kinase complex on the electrode surface made the peptide-triggered AuNPs assembly difficult. Electrochemical impedance spectroscopy was used to measure the change in surface property in the binding events. When a ferrocene-labeled peptide (Fc-peptide) was used in this design, the network of AuNPs/Fc-peptide produced a good voltammetric signal. The competitive assay allowed for the detection of protein kinase A with a detection limit of 20 mU/mL. This work should be valuable for designing novel optical or electronic biosensors and likely lead to many detection applications. PMID:28331314

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery.

PubMed

Pérot, Stéphanie; Sperandio, Olivier; Miteva, Maria A; Camproux, Anne-Claude; Villoutreix, Bruno O

2010-08-01

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

Optimal network alignment with graphlet degree vectors.

PubMed

Milenković, Tijana; Ng, Weng Leong; Hayes, Wayne; Przulj, Natasa

2010-06-30

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

Characterization and prediction of residues determining protein functional specificity.

PubMed

Capra, John A; Singh, Mona

2008-07-01

Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular functional specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolutionary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/. Supplementary data are available at Bioinformatics online.

Fabrication of Protein Microparticles and Microcapsules with Biomolecular Tools

NASA Astrophysics Data System (ADS)

Cheung, Kwan Yee; Lai, Kwok Kei; Mak, Wing Cheung

2018-05-01

Microparticles have attracted much attention for medical, analytical and biological applications. Calcium carbonate (CaCO3) templating method with the advantages of having narrow size distribution, controlled morphology and good biocompatibility that has been widely used for the synthesis of various protein-based microparticles. Despite CaCO3 template is biocompatible, most of the conventional methods to create stable protein microparticles are mainly driven by chemical crosslink reagents which may induce potential harmful effect and remains undesirable especially for biomedical or clinical applications. In this article, we demonstrate the fabrication of protein microparticles and microcapsules with an innovative method using biomolecular tools such as enzymes and affinity molecules to trigger the assembling of protein molecules within a porous CaCO3 template followed by a template removal step. We demonstrated the enzyme-assisted fabrication of collagen microparticles triggered by transglutaminase, as well as the affinity-assisted fabrication of BSA-biotin avidin microcapsules triggered by biotin-avidin affinity interaction, respectively. Based on the different protein assemble mechanisms, the collagen microparticles appeared as a solid-structured particles, while the BSA-biotin avidin microcapsules appeared as hollow-structured morphology. The fabrication procedures are simple and robust that allows producing protein microparticles or microcapsules under mild conditions at physiological pH and temperature. In addition, the microparticle morphologies, protein compositions and the assemble mechanisms were studied. Our technology provides a facile approach to design and fabricate protein microparticles and microcapsules that are useful in the area of biomaterials, pharmaceuticals and analytical chemistry.

Highly abundant defense proteins in human sweat as revealed by targeted proteomics and label-free quantification mass spectrometry.

PubMed

Csősz, É; Emri, G; Kalló, G; Tsaprailis, G; Tőzsér, J

2015-10-01

The healthy human skin with its effective antimicrobial defense system forms an efficient barrier against invading pathogens. There is evidence suggesting that the composition of this chemical barrier varies between diseases, making the easily collected sweat an ideal candidate for biomarker discoveries. Our aim was to provide information about the normal composition of the sweat, and to study the chemical barrier found at the surface of skin. Sweat samples from healthy individuals were collected during sauna bathing, and the global protein panel was analysed by label-free mass spectrometry. SRM-based targeted proteomic methods were designed and stable isotope labelled reference peptides were used for method validation. Ninety-five sweat proteins were identified, 20 of them were novel proteins. It was shown that dermcidin is the most abundant sweat protein, and along with apolipoprotein D, clusterin, prolactin-inducible protein and serum albumin, they make up 91% of secreted sweat proteins. The roles of these highly abundant proteins were reviewed; all of which have protective functions, highlighting the importance of sweat glands in composing the first line of innate immune defense system, and maintaining the epidermal barrier integrity. Our findings with regard to the proteins forming the chemical barrier of the skin as determined by label-free quantification and targeted proteomics methods are in accordance with previous studies, and can be further used as a starting point for non-invasive sweat biomarker research. © 2015 European Academy of Dermatology and Venereology.

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation.

PubMed

Giollo, Manuel; Martin, Alberto J M; Walsh, Ian; Ferrari, Carlo; Tosatto, Silvio C E

2014-01-01

The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes. Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/. NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

Computational Prediction of Hot Spot Residues

PubMed Central

Morrow, John Kenneth; Zhang, Shuxing

2013-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues. PMID:22316154

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

PubMed

Abramyan, Tigran M; Snyder, James A; Thyparambil, Aby A; Stuart, Steven J; Latour, Robert A

2016-08-05

Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Grid-based Molecular Footprint Comparison Method for Docking and De Novo Design: Application to HIVgp41

PubMed Central

Mukherjee, Sudipto; Rizzo, Robert C.

2014-01-01

Scoring functions are a critically important component of computer-aided screening methods for the identification of lead compounds during early stages of drug discovery. Here, we present a new multi-grid implementation of the footprint similarity (FPS) scoring function that was recently developed in our laboratory which has proven useful for identification of compounds which bind to a protein on a per-residue basis in a way that resembles a known reference. The grid-based FPS method is much faster than its Cartesian-space counterpart which makes it computationally tractable for on-the-fly docking, virtual screening, or de novo design. In this work, we establish that: (i) relatively few grids can be used to accurately approximate Cartesian space footprint similarity, (ii) the method yields improved success over the standard DOCK energy function for pose identification across a large test set of experimental co-crystal structures, for crossdocking, and for database enrichment, and (iii) grid-based FPS scoring can be used to tailor construction of new molecules to have specific properties, as demonstrated in a series of test cases targeting the viral protein HIVgp41. The method will be made available in the program DOCK6. PMID:23436713

High-throughput methods for electron crystallography.

PubMed

Stokes, David L; Ubarretxena-Belandia, Iban; Gonen, Tamir; Engel, Andreas

2013-01-01

Membrane proteins play a tremendously important role in cell physiology and serve as a target for an increasing number of drugs. Structural information is key to understanding their function and for developing new strategies for combating disease. However, the complex physical chemistry associated with membrane proteins has made them more difficult to study than their soluble cousins. Electron crystallography has historically been a successful method for solving membrane protein structures and has the advantage of providing a native lipid environment for these proteins. Specifically, when membrane proteins form two-dimensional arrays within a lipid bilayer, electron microscopy can be used to collect images and diffraction and the corresponding data can be combined to produce a three-dimensional reconstruction, which under favorable conditions can extend to atomic resolution. Like X-ray crystallography, the quality of the structures are very much dependent on the order and size of the crystals. However, unlike X-ray crystallography, high-throughput methods for screening crystallization trials for electron crystallography are not in general use. In this chapter, we describe two alternative methods for high-throughput screening of membrane protein crystallization within the lipid bilayer. The first method relies on the conventional use of dialysis for removing detergent and thus reconstituting the bilayer; an array of dialysis wells in the standard 96-well format allows the use of a liquid-handling robot and greatly increases throughput. The second method relies on titration of cyclodextrin as a chelating agent for detergent; a specialized pipetting robot has been designed not only to add cyclodextrin in a systematic way, but to use light scattering to monitor the reconstitution process. In addition, the use of liquid-handling robots for making negatively stained grids and methods for automatically imaging samples in the electron microscope are described.

The Structure and Function of Non-Collagenous Bone Proteins

NASA Technical Reports Server (NTRS)

Hook, Magnus

1997-01-01

The long-term goal for this program is to determine the structural and functional relationships of bone proteins and proteins that interact with bone. This information will used to design useful pharmacological compounds that will have a beneficial effect in osteoporotic patients and in the osteoporotic-like effects experienced on long duration space missions. The first phase of this program, funded under a cooperative research agreement with NASA through the Texas Medical Center, aimed to develop powerful recombinant expression systems and purification methods for production of large amounts of target proteins. Proteins expressed in sufficient'amount and purity would be characterized by a variety of structural methods, and made available for crystallization studies. In order to increase the likelihood of crystallization and subsequent high resolution solution of structures, we undertook to develop expression of normal and mutant forms of proteins by bacterial and mammalian cells. In addition to the main goals of this program, we would also be able to provide reagents for other related studies, including development of anti-fibrotic and anti-metastatic therapeutics.

Cloning-free template DNA preparation for cell-free protein synthesis via two-step PCR using versatile primer designs with short 3'-UTR.

PubMed

Nomoto, Mika; Tada, Yasuomi

2018-01-01

Cell-free protein synthesis (CFPS) systems largely retain the endogenous translation machinery of the host organism, making them highly applicable for proteomics analysis of diverse biological processes. However, laborious and time-consuming cloning procedures hinder progress with CFPS systems. Herein, we report the development of a rapid and efficient two-step polymerase chain reaction (PCR) method to prepare linear DNA templates for a wheat germ CFPS system. We developed a novel, effective short 3'-untranslated region (3'-UTR) sequence that facilitates translation. Application of the short 3'-UTR to two-step PCR enabled the generation of various transcription templates from the same plasmid, including fusion proteins with N- or C-terminal tags, and truncated proteins. Our method supports the cloning-free expression of target proteins using an mRNA pool from biological material. The established system is a highly versatile platform for in vitro protein synthesis using wheat germ CFPS. © 2017 Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.

Use of conserved key amino acid positions to morph protein folds.

PubMed

Reddy, Boojala V B; Li, Wilfred W; Bourne, Philip E

2002-07-15

By using three-dimensional (3D) structure alignments and a previously published method to determine Conserved Key Amino Acid Positions (CKAAPs) we propose a theoretical method to design mutations that can be used to morph the protein folds. The original Paracelsus challenge, met by several groups, called for the engineering of a stable but different structure by modifying less than 50% of the amino acid residues. We have used the sequences from the Protein Data Bank (PDB) identifiers 1ROP, and 2CRO, which were previously used in the Paracelsus challenge by those groups, and suggest mutation to CKAAPs to morph the protein fold. The total number of mutations suggested is less than 40% of the starting sequence theoretically improving the challenge results. From secondary structure prediction experiments of the proposed mutant sequence structures, we observe that each of the suggested mutant protein sequences likely folds to a different, non-native potentially stable target structure. These results are an early indicator that analyses using structure alignments leading to CKAAPs of a given structure are of value in protein engineering experiments. Copyright 2002 Wiley Periodicals, Inc.

Dengue virus NS2 and NS4: Minor proteins, mammoth roles.

PubMed

Gopala Reddy, Sindhoora Bhargavi; Chin, Wei-Xin; Shivananju, Nanjunda Swamy

2018-04-17

Despite the ever-increasing global incidence of dengue fever, there are no specific chemotherapy regimens for its treatment. Structural studies on dengue virus (DENV) proteins have revealed potential drug targets. Major DENV proteins such as the envelope protein and non-structural (NS) proteins 3 and 5 have been extensively investigated in antiviral studies, but with limited success in vitro. However, the minor NS proteins NS2 and NS4 have remained relatively underreported. Emerging evidence indicating their indispensable roles in virus propagation and host immunomodulation should encourage us to target these proteins for drug discovery. This review covers current knowledge on DENV NS2 and NS4 proteins from structural and functional perspectives and assesses their potential as targets for antiviral design. Antiviral targets in NS2A include surface-exposed transmembrane regions involved in pathogenesis, while those in NS2B include protease-binding sites in a conserved hydrophilic domain. Ideal drug targets in NS4A include helix α4 and the PEPEKQR sequence, which are essential for NS4A-2K cleavage and NS4A-NS4B association, respectively. In NS4B, the cytoplasmic loop connecting helices α5 and α7 is an attractive target for antiviral design owing to its role in dimerization and NS4B-NS3 interaction. Findings implicating NS2A, NS2B, and NS4A in membrane-modulation and viroporin-like activities indicate an opportunity to target these proteins by disrupting their association with membrane lipids. Despite the lack of 3D structural data, recent topological findings and progress in structure-prediction methods should be sufficient impetus for targeting NS2 and NS4 for drug design. Copyright © 2018 Elsevier Inc. All rights reserved.

Fluorescence Live Cell Imaging

PubMed Central

Ettinger, Andreas

2014-01-01

Fluorescence microscopy of live cells has become an integral part of modern cell biology. Fluorescent protein tags, live cell dyes, and other methods to fluorescently label proteins of interest provide a range of tools to investigate virtually any cellular process under the microscope. The two main experimental challenges in collecting meaningful live cell microscopy data are to minimize photodamage while retaining a useful signal-to-noise ratio, and to provide a suitable environment for cells or tissues to replicate physiological cell dynamics. This chapter aims to give a general overview on microscope design choices critical for fluorescence live cell imaging that apply to most fluorescence microscopy modalities, and on environmental control with a focus on mammalian tissue culture cells. In addition, we provide guidance on how to design and evaluate fluorescent protein constructs by spinning disk confocal microscopy. PMID:24974023

Protein design to understand peptide ligand recognition by tetratricopeptide repeat proteins.

PubMed

Cortajarena, Aitziber L; Kajander, Tommi; Pan, Weilan; Cocco, Melanie J; Regan, Lynne

2004-04-01

Protein design aims to understand the fundamentals of protein structure by creating novel proteins with pre-specified folds. An equally important goal is to understand protein function by creating novel proteins with pre-specified activities. Here we describe the design and characterization of a tetratricopeptide (TPR) protein, which binds to the C-terminal peptide of the eukaryotic chaperone Hsp90. The design emphasizes the importance of both direct, short-range protein-peptide interactions and of long-range electrostatic optimization. We demonstrate that the designed protein binds specifically to the desired peptide and discriminates between it and the similar C-terminal peptide of Hsp70.

CyClus: a fast, comprehensive cylindrical interface approximation clustering/reranking method for rigid-body protein-protein docking decoys.

PubMed

Omori, Satoshi; Kitao, Akio

2013-06-01

We propose a fast clustering and reranking method, CyClus, for protein-protein docking decoys. This method enables comprehensive clustering of whole decoys generated by rigid-body docking using cylindrical approximation of the protein-proteininterface and hierarchical clustering procedures. We demonstrate the clustering and reranking of 54,000 decoy structures generated by ZDOCK for each complex within a few minutes. After parameter tuning for the test set in ZDOCK benchmark 2.0 with the ZDOCK and ZRANK scoring functions, blind tests for the incremental data in ZDOCK benchmark 3.0 and 4.0 were conducted. CyClus successfully generated smaller subsets of decoys containing near-native decoys. For example, the number of decoys required to create subsets containing near-native decoys with 80% probability was reduced from 22% to 50% of the number required in the original ZDOCK. Although specific ZDOCK and ZRANK results were demonstrated, the CyClus algorithm was designed to be more general and can be applied to a wide range of decoys and scoring functions by adjusting just two parameters, p and T. CyClus results were also compared to those from ClusPro. Copyright © 2013 Wiley Periodicals, Inc.

A Structural Perspective on the Modulation of Protein-Protein Interactions with Small Molecules.

PubMed

Demirel, Habibe Cansu; Dogan, Tunca; Tuncbag, Nurcan

2018-05-31

Protein-protein interactions (PPIs) are the key components in many cellular processes including signaling pathways, enzymatic reactions and epigenetic regulation. Abnormal interactions of some proteins may be pathogenic and cause various disorders including cancer and neurodegenerative diseases. Although inhibiting PPIs with small molecules is a challenging task, it gained an increasing interest because of its strong potential for drug discovery and design. The knowledge of the interface as well as the structural and chemical characteristics of the PPIs and their roles in the cellular pathways are necessary for a rational design of small molecules to modulate PPIs. In this study, we review the recent progress in the field and detail the physicochemical properties of PPIs including binding hot spots with a focus on structural methods. Then, we review recent approaches for structural prediction of PPIs. Finally, we revisit the concept of targeting PPIs in a systems biology perspective and we refer to the non-structural approaches, usually employed when the structural information is not present. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Proof of principle for epitope-focused vaccine design

PubMed Central

Correia, Bruno E.; Bates, John T.; Loomis, Rebecca J.; Baneyx, Gretchen; Carrico, Christopher; Jardine, Joseph G.; Rupert, Peter; Correnti, Colin; Kalyuzhniy, Oleksandr; Vittal, Vinayak; Connell, Mary J.; Stevens, Eric; Schroeter, Alexandria; Chen, Man; MacPherson, Skye; Serra, Andreia M.; Adachi, Yumiko; Holmes, Margaret A.; Li, Yuxing; Klevit, Rachel E.; Graham, Barney S.; Wyatt, Richard T.; Baker, David; Strong, Roland K.; Crowe, James E.; Johnson, Philip R.; Schief, William R.

2014-01-01

Summary Vaccines prevent infectious disease largely by inducing protective neutralizing antibodies against vulnerable epitopes. Multiple major pathogens have resisted traditional vaccine development, although vulnerable epitopes targeted by neutralizing antibodies have been identified for several such cases. Hence, new vaccine design methods to induce epitope-specific neutralizing antibodies are needed. Here we show, with a neutralization epitope from respiratory syncytial virus (RSV), that computational protein design can generate small, thermally and conformationally stable protein scaffolds that accurately mimic the viral epitope structure and induce potent neutralizing antibodies. These scaffolds represent promising leads for research and development of a human RSV vaccine needed to protect infants, young children and the elderly. More generally, the results provide proof of principle for epitope-focused and scaffold-based vaccine design, and encourage the evaluation and further development of these strategies for a variety of other vaccine targets including antigenically highly variable pathogens such as HIV and influenza. PMID:24499818

Proof of principle for epitope-focused vaccine design

NASA Astrophysics Data System (ADS)

Correia, Bruno E.; Bates, John T.; Loomis, Rebecca J.; Baneyx, Gretchen; Carrico, Chris; Jardine, Joseph G.; Rupert, Peter; Correnti, Colin; Kalyuzhniy, Oleksandr; Vittal, Vinayak; Connell, Mary J.; Stevens, Eric; Schroeter, Alexandria; Chen, Man; MacPherson, Skye; Serra, Andreia M.; Adachi, Yumiko; Holmes, Margaret A.; Li, Yuxing; Klevit, Rachel E.; Graham, Barney S.; Wyatt, Richard T.; Baker, David; Strong, Roland K.; Crowe, James E.; Johnson, Philip R.; Schief, William R.

2014-03-01

Vaccines prevent infectious disease largely by inducing protective neutralizing antibodies against vulnerable epitopes. Several major pathogens have resisted traditional vaccine development, although vulnerable epitopes targeted by neutralizing antibodies have been identified for several such cases. Hence, new vaccine design methods to induce epitope-specific neutralizing antibodies are needed. Here we show, with a neutralization epitope from respiratory syncytial virus, that computational protein design can generate small, thermally and conformationally stable protein scaffolds that accurately mimic the viral epitope structure and induce potent neutralizing antibodies. These scaffolds represent promising leads for the research and development of a human respiratory syncytial virus vaccine needed to protect infants, young children and the elderly. More generally, the results provide proof of principle for epitope-focused and scaffold-based vaccine design, and encourage the evaluation and further development of these strategies for a variety of other vaccine targets, including antigenically highly variable pathogens such as human immunodeficiency virus and influenza.

Protocols for efficient simulations of long-time protein dynamics using coarse-grained CABS model.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2014-01-01

Coarse-grained (CG) modeling is a well-acknowledged simulation approach for getting insight into long-time scale protein folding events at reasonable computational cost. Depending on the design of a CG model, the simulation protocols vary from highly case-specific-requiring user-defined assumptions about the folding scenario-to more sophisticated blind prediction methods for which only a protein sequence is required. Here we describe the framework protocol for the simulations of long-term dynamics of globular proteins, with the use of the CABS CG protein model and sequence data. The simulations can start from a random or a selected (e.g., native) structure. The described protocol has been validated using experimental data for protein folding model systems-the prediction results agreed well with the experimental results.

Protein-Polymer Conjugates: Synthetic Approaches by Controlled Radical Polymerizations & Interesting Applications

PubMed Central

Grover, Gregory N.; Maynard, Heather D.

2011-01-01

Protein-polymer conjugates are of interest to researchers in diverse fields. Attachment of polymers to proteins results in improved pharmacokinetics, which is important in medicine. From an engineering standpoint, conjugates are exciting because they exhibit properties of both the biomolecules and synthetic polymers. This allows the activity of the protein to be altered or tuned, a key aspect in therapeutic design, anchoring conjugates to surfaces, and utilizing these materials for supramolecular self-assembly. Thus, there is broad interest in straightforward synthetic methods to make protein-polymer conjugates. Controlled radical polymerization (CRP) techniques have emerged as excellent strategies to make conjugates because the resulting polymers have narrow molecular weight distributions, targeted molecular weights, and attach to specific sites on proteins. Herein, recent advances in the synthesis and application of protein-polymer conjugates by CRP are highlighted. PMID:21071260

Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

PubMed

Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

2016-05-18

Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and related documents of PUCPI are available at: http://admis.fudan.edu.cn/projects/pucpi.html.

Optimal protein library design using recombination or point mutations based on sequence-based scoring functions.

PubMed

Pantazes, Robert J; Saraf, Manish C; Maranas, Costas D

2007-08-01

In this paper, we introduce and test two new sequence-based protein scoring systems (i.e. S1, S2) for assessing the likelihood that a given protein hybrid will be functional. By binning together amino acids with similar properties (i.e. volume, hydrophobicity and charge) the scoring systems S1 and S2 allow for the quantification of the severity of mismatched interactions in the hybrids. The S2 scoring system is found to be able to significantly functionally enrich a cytochrome P450 library over other scoring methods. Given this scoring base, we subsequently constructed two separate optimization formulations (i.e. OPTCOMB and OPTOLIGO) for optimally designing protein combinatorial libraries involving recombination or mutations, respectively. Notably, two separate versions of OPTCOMB are generated (i.e. model M1, M2) with the latter allowing for position-dependent parental fragment skipping. Computational benchmarking results demonstrate the efficacy of models OPTCOMB and OPTOLIGO to generate high scoring libraries of a prespecified size.

Application of multi-factorial design of experiments to successfully optimize immunoassays for robust measurements of therapeutic proteins.

PubMed

Ray, Chad A; Patel, Vimal; Shih, Judy; Macaraeg, Chris; Wu, Yuling; Thway, Theingi; Ma, Mark; Lee, Jean W; Desilva, Binodh

2009-02-20

Developing a process that generates robust immunoassays that can be used to support studies with tight timelines is a common challenge for bioanalytical laboratories. Design of experiments (DOEs) is a tool that has been used by many industries for the purpose of optimizing processes. The approach is capable of identifying critical factors and their interactions with a minimal number of experiments. The challenge for implementing this tool in the bioanalytical laboratory is to develop a user-friendly approach that scientists can understand and apply. We have successfully addressed these challenges by eliminating the screening design, introducing automation, and applying a simple mathematical approach for the output parameter. A modified central composite design (CCD) was applied to three ligand binding assays. The intra-plate factors selected were coating, detection antibody concentration, and streptavidin-HRP concentrations. The inter-plate factors included incubation times for each step. The objective was to maximize the logS/B (S/B) of the low standard to the blank. The maximum desirable conditions were determined using JMP 7.0. To verify the validity of the predictions, the logS/B prediction was compared against the observed logS/B during pre-study validation experiments. The three assays were optimized using the multi-factorial DOE. The total error for all three methods was less than 20% which indicated method robustness. DOE identified interactions in one of the methods. The model predictions for logS/B were within 25% of the observed pre-study validation values for all methods tested. The comparison between the CCD and hybrid screening design yielded comparable parameter estimates. The user-friendly design enables effective application of multi-factorial DOE to optimize ligand binding assays for therapeutic proteins. The approach allows for identification of interactions between factors, consistency in optimal parameter determination, and reduced method development time.

MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions.

PubMed

Li, Minghui; Simonetti, Franco L; Goncearenco, Alexander; Panchenko, Anna R

2016-07-08

Proteins engage in highly selective interactions with their macromolecular partners. Sequence variants that alter protein binding affinity may cause significant perturbations or complete abolishment of function, potentially leading to diseases. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design. MutaBind is available at http://www.ncbi.nlm.nih.gov/projects/mutabind/. Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Membrane proteins from the cyanobacterium Synechocystis sp. PCC 6803 interacting with thioredoxin.

PubMed

Mata-Cabana, Alejandro; Florencio, Francisco J; Lindahl, Marika

2007-11-01

Cysteine dithiol/disulphide exchange forms the molecular basis for regulation of a wide variety of enzymatic activities and for transduction of cellular signals. Thus, the search for proteins with reactive, accessible cysteines is expected to contribute to the unravelling of new molecular mechanisms for enzyme regulation and signal transduction. Several methods have been designed for this purpose taking advantage of the interactions between thioredoxins and their protein substrates. Thioredoxins comprise a family of redox-active enzymes, which catalyse reduction of protein disulphides and sulphenic acids. Due to the inherent practical difficulties associated with studies of membrane proteins these have been largely overlooked in the many proteomic studies of thioredoxin-interacting proteins. In the present work, we have developed a procedure to isolate membrane proteins interacting with thioredoxin by binding in situ to a monocysteinic His-tagged thioredoxin added directly to the intact membranes. Following fractionation and solubilisation of the membranes, thioredoxin target proteins were isolated by Ni-affinity chromatography and 2-DE SDS-PAGE under nonreducing/reducing conditions. Applying this method to total membranes, including thylakoid and plasma membranes, from the cyanobacterium Synechocystis sp. PCC 6803 we have identified 50 thioredoxin-interacting proteins. Among the 38 newly identified thioredoxin targets are the ATP-binding subunits of several transporters and members of the AAA-family of ATPases.

Blocking the RecA activity and SOS-response in bacteria with a short α-helical peptide.

PubMed

Yakimov, Alexander; Pobegalov, Georgii; Bakhlanova, Irina; Khodorkovskii, Mikhail; Petukhov, Michael; Baitin, Dmitry

2017-09-19

The RecX protein, a very active natural RecA protein inhibitor, can completely disassemble RecA filaments at nanomolar concentrations that are two to three orders of magnitude lower than that of RecA protein. Based on the structure of RecX protein complex with the presynaptic RecA filament, we designed a short first in class α-helical peptide that both inhibits RecA protein activities in vitro and blocks the bacterial SOS-response in vivo. The peptide was designed using SEQOPT, a novel method for global sequence optimization of protein α-helices. SEQOPT produces artificial peptide sequences containing only 20 natural amino acids with the maximum possible conformational stability at a given pH, ionic strength, temperature, peptide solubility. It also accounts for restrictions due to known amino acid residues involved in stabilization of protein complexes under consideration. The results indicate that a few key intermolecular interactions inside the RecA protein presynaptic complex are enough to reproduce the main features of the RecX protein mechanism of action. Since the SOS-response provides a major mechanism of bacterial adaptation to antibiotics, these results open new ways for the development of antibiotic co-therapy that would not cause bacterial resistance. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Cloning strategy for producing brush-forming protein-based polymers.

PubMed

Henderson, Douglas B; Davis, Richey M; Ducker, William A; Van Cott, Kevin E

2005-01-01

Brush-forming polymers are being used in a variety of applications, and by using recombinant DNA technology, there exists the potential to produce protein-based polymers that incorporate unique structures and functions in these brush layers. Despite this potential, production of protein-based brush-forming polymers is not routinely performed. For the design and production of new protein-based polymers with optimal brush-forming properties, it would be desirable to have a cloning strategy that allows an iterative approach wherein the protein based-polymer product can be produced and evaluated, and then if necessary, it can be sequentially modified in a controlled manner to obtain optimal surface density and brush extension. In this work, we report on the development of a cloning strategy intended for the production of protein-based brush-forming polymers. This strategy is based on the assembly of modules of DNA that encode for blocks of protein-based polymers into a commercially available expression vector; there is no need for custom-modified vectors and no need for intermediate cloning vectors. Additionally, because the design of new protein-based biopolymers can be an iterative process, our method enables sequential modification of a protein-based polymer product. With at least 21 bacterial expression vectors and 11 yeast expression vectors compatible with this strategy, there are a number of options available for production of protein-based polymers. It is our intent that this strategy will aid in advancing the production of protein-based brush-forming polymers.

Proteomics of buccal squamous cell carcinoma: the involvement of multiple pathways in tumorigenesis.

PubMed

Chen, Jia; He, Qing-Yu; Yuen, Anthony Po-Wing; Chiu, Jeng-Fu

2004-08-01

Squamous cell carcinoma (SCC) of the buccal mucosa is an aggressive oral cancer. It mainly occurs in Central and Southeast Asia, and is closely related to the practice of tobacco smoking and betel squid chewing. The high recurrence and low survival rates of buccal SCC require our continued efforts to understand the pathogenesis of the disease for designing better therapeutic strategies. We used proteomic technology to analyze buccal SCC tissues aiming at identifying tumor-associated proteins for the utilization as biomarkers or molecular targets. With the exception of alpha B-crystallin being substantially reduced, a number of proteins were found to be significantly over-expressed in cancer tissues. These increased proteins included glycolytic enzymes, heat-shock proteins, tumor antigens, cytoskeleton proteins, enzymes involved in detoxification and anti-oxidation systems, and proteins involved in mitochondrial and intracellular signaling pathways. These extensive protein variations indicate that multiple cellular pathways were involved in the process of tumorigenesis, and suggest that multiple protein molecules should be simultaneously targeted as an effective strategy to counter the disease. At least, SCC antigen, G protein, glutathione S-transferase, manganese superoxide dismutase, annexins, voltage-dependent anion channel, cyclophilin A, stratifin and galectin 7 are candidates for targeted proteins. The present findings also demonstrated that rich protein information can be produced by means of proteomic analysis for a better understanding of the oncogenesis and pathogenesis in a global way, which in turn is a basis for the rational designs of diagnostic and therapeutic methods.

Application of linker technique to trap transiently interacting protein complexes for structural studies

PubMed Central

Reddy Chichili, Vishnu Priyanka; Kumar, Veerendra; Sivaraman, J.

2016-01-01

Protein-protein interactions are key events controlling several biological processes. We have developed and employed a method to trap transiently interacting protein complexes for structural studies using glycine-rich linkers to fuse interacting partners, one of which is unstructured. Initial steps involve isothermal titration calorimetry to identify the minimum binding region of the unstructured protein in its interaction with its stable binding partner. This is followed by computational analysis to identify the approximate site of the interaction and to design an appropriate linker length. Subsequently, fused constructs are generated and characterized using size exclusion chromatography and dynamic light scattering experiments. The structure of the chimeric protein is then solved by crystallization, and validated both in vitro and in vivo by substituting key interacting residues of the full length, unlinked proteins with alanine. This protocol offers the opportunity to study crucial and currently unattainable transient protein interactions involved in various biological processes. PMID:26985443

Crystallization of PTP Domains.

PubMed

Levy, Colin; Adams, James; Tabernero, Lydia

2016-01-01

Protein crystallography is the most powerful method to obtain atomic resolution information on the three-dimensional structure of proteins. An essential step towards determining the crystallographic structure of a protein is to produce good quality crystals from a concentrated sample of purified protein. These crystals are then used to obtain X-ray diffraction data necessary to determine the 3D structure by direct phasing or molecular replacement if the model of a homologous protein is available. Here, we describe the main approaches and techniques to obtain suitable crystals for X-ray diffraction. We include tools and guidance on how to evaluate and design the protein construct, how to prepare Se-methionine derivatized protein, how to assess the stability and quality of the sample, and how to crystallize and prepare crystals for diffraction experiments. While general strategies for protein crystallization are summarized, specific examples of the application of these strategies to the crystallization of PTP domains are discussed.

Recent advances in the applications of ionic liquids in protein stability and activity: a review.

PubMed

Patel, Rajan; Kumari, Meena; Khan, Abbul Bashar

2014-04-01

Room temperatures ionic liquids are considered as miraculous solvents for biological system. Due to their inimitable properties and large variety of applications, they have been widely used in enzyme catalysis and protein stability and separation. The related information present in the current review is helpful to the researchers working in the field of biotechnology and biochemistry to design or choose an ionic liquid that can serve as a noble and selective solvent for any particular enzymatic reaction, protein preservation and other protein based applications. We have extensively analyzed the methods used for studying the protein-IL interaction which is useful in providing information about structural and conformational dynamics of protein. This can be helpful to develop and understanding about the effect of ionic liquids on stability and activity of proteins. In addition, the affect of physico-chemical properties of ionic liquids, viz. hydrogen bond capacity and hydrophobicity on protein stability are discussed.

Luciferase Protein Complementation Assays for Bioluminescence Imaging of Cells and Mice

PubMed Central

Luker, Gary D.; Luker, Kathryn E.

2015-01-01

Summary Protein fragment complementation assays (PCAs) with luciferase reporters currently are the preferred method for detecting and quantifying protein-protein interactions in living animals. At the most basic level, PCAs involve fusion of two proteins of interest to enzymatically inactive fragments of luciferase. Upon association of the proteins of interest, the luciferase fragments are capable of reconstituting enzymatic activity to generate luminescence in vivo. In addition to bi-molecular luciferase PCAs, unimolecular biosensors for hormones, kinases, and proteases also have been developed using target peptides inserted between inactive luciferase fragments. Luciferase PCAs offer unprecedented opportunities to quantify dynamics of protein-protein interactions in intact cells and living animals, but successful use of luciferase PCAs in cells and mice involves careful consideration of many technical factors. This chapter discusses the design of luciferase PCAs appropriate for animal imaging, including construction of reporters, incorporation of reporters into cells and mice, imaging techniques, and data analysis. PMID:21153371

Predicting stability of alpha-helical, orthogonal-bundle proteins on surfaces

NASA Astrophysics Data System (ADS)

Wei, Shuai; Knotts, Thomas A.

2010-09-01

The interaction of proteins with surfaces is a key phenomenon in many applications, but current understanding of the biophysics involved is lacking. At present, rational design of such emerging technologies is difficult as no methods or theories exist that correctly predict how surfaces influence protein behavior. Using molecular simulation and a coarse-grain model, this study illustrates for the first time that stability of proteins on surfaces can be correlated with tertiary structural elements for alpha-helical, orthogonal-bundle proteins. Results show that several factors contribute to stability on surfaces including the nature of the loop region where the tether is placed and the ability of the protein to freely rotate on the surface. A thermodynamic analysis demonstrates that surfaces stabilize proteins entropically and that any destabilization is an enthalpic effect. Moreover, the entropic effects are concentrated on the unfolded state of the protein while the ethalpic effects are focused on the folded state.

DAnTE: a statistical tool for quantitative analysis of –omics data

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

Polpitiya, Ashoka D.; Qian, Weijun; Jaitly, Navdeep

2008-05-03

DAnTE (Data Analysis Tool Extension) is a statistical tool designed to address challenges unique to quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide to protein rollup methods, an extensive array of plotting functions, and a comprehensive ANOVA scheme that can handle unbalanced data and random effects. The Graphical User Interface (GUI) is designed to be very intuitive and user friendly.

Collecting Protein Biomarkers in Breath Using Electret Filters: A Preliminary Method on New Technical Model and Human Study.

PubMed

Li, Wang; Pi, Xitian; Qiao, Panpan; Liu, Hongying

2016-01-01

Biomarkers in exhaled breath are useful for respiratory disease diagnosis in human volunteers. Conventional methods that collect non-volatile biomarkers, however, necessitate an extensive dilution and sanitation processes that lowers collection efficiencies and convenience of use. Electret filter emerged in recent decade to collect virus biomarkers in exhaled breath given its simplicity and effectiveness. To investigate the capability of electret filters to collect protein biomarkers, a model that consists of an atomizer that produces protein aerosol and an electret filter that collects albumin and carcinoembryonic antigen-a typical biomarker in lung cancer development- from the atomizer is developed. A device using electret filter as the collecting medium is designed to collect human albumin from exhaled breath of 6 volunteers. Comparison of the collecting ability between the electret filter method and other 2 reported methods is finally performed based on the amounts of albumin collected from human exhaled breath. In conclusion, a decreasing collection efficiency ranging from 17.6% to 2.3% for atomized albumin aerosol and 42% to 12.5% for atomized carcinoembryonic antigen particles is found; moreover, an optimum volume of sampling human exhaled breath ranging from 100 L to 200 L is also observed; finally, the self-designed collecting device shows a significantly better performance in collecting albumin from human exhaled breath than the exhaled breath condensate method (p<0.05) but is not significantly more effective than reported 3-stage impactor method (p>0.05). In summary, electret filters are potential in collecting non-volatile biomarkers in human exhaled breath not only because it was simpler, cheaper and easier to use than traditional methods but also for its better collecting performance.

Designing and defining dynamic protein cage nanoassemblies in solution

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

Lai, Y. -T.; Hura, G. L.; Dyer, K. N.

Central challenges in the design of large and dynamic macromolecular assemblies for synthetic biology lie in developing effective methods for testing design strategies and their outcomes, including comprehensive assessments of solution behavior. Here, we created and validated an advanced design of a 600-kDa protein homododecamer that self-assembles into a symmetric tetrahedral cage. The monomeric unit is composed of a trimerizing apex-forming domain genetically linked to an edge-forming dimerizing domain. Enhancing the crystallographic results, high-throughput small-angle x-ray scattering (SAXS) comprehensively contrasted our modifications under diverse solution conditions. To generate a phase diagram associating structure and assembly, we developed force plots thatmore » measure dissimilarity among multiple SAXS data sets. These new tools, which provided effective feedback on experimental constructs relative to design, have general applicability in analyzing the solution behavior of heterogeneous nanosystems and have been made available as a web-based application. Specifically, our results probed the influence of solution conditions and symmetry on stability and structural adaptability, identifying the dimeric interface as the weak point in the assembly. Force plots comparing SAXS data sets further reveal more complex and controllable behavior in solution than captured by our crystal structures. Lastly, these methods for objectively and comprehensively comparing SAXS profiles for systems critically affected by solvent conditions and structural heterogeneity provide an enabling technology for advancing the design and bioengineering of nanoscale biological materials.« less

Designing and defining dynamic protein cage nanoassemblies in solution

DOE PAGES

Lai, Y. -T.; Hura, G. L.; Dyer, K. N.; ...

2016-12-14

Central challenges in the design of large and dynamic macromolecular assemblies for synthetic biology lie in developing effective methods for testing design strategies and their outcomes, including comprehensive assessments of solution behavior. Here, we created and validated an advanced design of a 600-kDa protein homododecamer that self-assembles into a symmetric tetrahedral cage. The monomeric unit is composed of a trimerizing apex-forming domain genetically linked to an edge-forming dimerizing domain. Enhancing the crystallographic results, high-throughput small-angle x-ray scattering (SAXS) comprehensively contrasted our modifications under diverse solution conditions. To generate a phase diagram associating structure and assembly, we developed force plots thatmore » measure dissimilarity among multiple SAXS data sets. These new tools, which provided effective feedback on experimental constructs relative to design, have general applicability in analyzing the solution behavior of heterogeneous nanosystems and have been made available as a web-based application. Specifically, our results probed the influence of solution conditions and symmetry on stability and structural adaptability, identifying the dimeric interface as the weak point in the assembly. Force plots comparing SAXS data sets further reveal more complex and controllable behavior in solution than captured by our crystal structures. Lastly, these methods for objectively and comprehensively comparing SAXS profiles for systems critically affected by solvent conditions and structural heterogeneity provide an enabling technology for advancing the design and bioengineering of nanoscale biological materials.« less

Development of an Enhanced Metaproteomic Approach for Deepening the Microbiome Characterization of the Human Infant Gut

PubMed Central

2015-01-01

The establishment of early life microbiota in the human infant gut is highly variable and plays a crucial role in host nutrient availability/uptake and maturation of immunity. Although high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the acquisition of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants. This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification. This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories. PMID:25350865

Sulfenic acid chemistry, detection and cellular lifetime☆

PubMed Central

Gupta, Vinayak; Carroll, Kate S.

2014-01-01

Background Reactive oxygen species-mediated cysteine sulfenic acid modification has emerged as an important regulatory mechanism in cell signaling. The stability of sulfenic acid in proteins is dictated by the local microenvironment and ability of antioxidants to reduce this modification. Several techniques for detecting this cysteine modification have been developed, including direct and in situ methods. Scope of review This review presents a historical discussion of sulfenic acid chemistry and highlights key examples of this modification in proteins. A comprehensive survey of available detection techniques with advantages and limitations is discussed. Finally, issues pertaining to rates of sulfenic acid formation, reduction, and chemical trapping methods are also covered. Major conclusions Early chemical models of sulfenic acid yielded important insights into the unique reactivity of this species. Subsequent pioneering studies led to the characterization of sulfenic acid formation in proteins. In parallel, the discovery of oxidant-mediated cell signaling pathways and pathological oxidative stress has led to significant interest in methods to detect these modifications. Advanced methods allow for direct chemical trapping of protein sulfenic acids directly in cells and tissues. At the same time, many sulfenic acids are short-lived and the reactivity of current probes must be improved to sample these species, while at the same time, preserving their chemical selectivity. Inhibitors with binding scaffolds can be rationally designed to target sulfenic acid modifications in specific proteins. General significance Ever increasing roles for protein sulfenic acids have been uncovered in physiology and pathology. A more complete understanding of sulfenic acid-mediated regulatory mechanisms will continue to require rigorous and new chemical insights. This article is part of a Special Issue entitled Current methods to study reactive oxygen species - pros and cons and biophysics of membrane proteins. Guest Editor: Christine Winterbourn. PMID:23748139

Computational protein design-the next generation tool to expand synthetic biology applications.

PubMed

Gainza-Cirauqui, Pablo; Correia, Bruno Emanuel

2018-05-02

One powerful approach to engineer synthetic biology pathways is the assembly of proteins sourced from one or more natural organisms. However, synthetic pathways often require custom functions or biophysical properties not displayed by natural proteins, limitations that could be overcome through modern protein engineering techniques. Structure-based computational protein design is a powerful tool to engineer new functional capabilities in proteins, and it is beginning to have a profound impact in synthetic biology. Here, we review efforts to increase the capabilities of synthetic biology using computational protein design. We focus primarily on computationally designed proteins not only validated in vitro, but also shown to modulate different activities in living cells. Efforts made to validate computational designs in cells can illustrate both the challenges and opportunities in the intersection of protein design and synthetic biology. We also highlight protein design approaches, which although not validated as conveyors of new cellular function in situ, may have rapid and innovative applications in synthetic biology. We foresee that in the near-future, computational protein design will vastly expand the functional capabilities of synthetic cells. Copyright © 2018. Published by Elsevier Ltd.

Design, Assembly, and Characterization of TALE-Based Transcriptional Activators and Repressors.

PubMed

Thakore, Pratiksha I; Gersbach, Charles A

2016-01-01

Transcription activator-like effectors (TALEs) are modular DNA-binding proteins that can be fused to a variety of effector domains to regulate the epigenome. Nucleotide recognition by TALE monomers follows a simple cipher, making this a powerful and versatile method to activate or repress gene expression. Described here are methods to design, assemble, and test TALE transcription factors (TALE-TFs) for control of endogenous gene expression. In this protocol, TALE arrays are constructed by Golden Gate cloning and tested for activity by transfection and quantitative RT-PCR. These methods for engineering TALE-TFs are useful for studies in reverse genetics and genomics, synthetic biology, and gene therapy.

Increased Depth and Breadth of Plasma Protein Quantitation via Two-Dimensional Liquid Chromatography/Multiple Reaction Monitoring-Mass Spectrometry with Labeled Peptide Standards.

PubMed

Percy, Andrew J; Yang, Juncong; Chambers, Andrew G; Borchers, Christoph H

2016-01-01

Absolute quantitative strategies are emerging as a powerful and preferable means of deriving concentrations in biological samples for systems biology applications. Method development is driven by the need to establish new-and validate current-protein biomarkers of high-to-low abundance for clinical utility. In this chapter, we describe a methodology involving two-dimensional (2D) reversed-phase liquid chromatography (RPLC), operated under alkaline and acidic pH conditions, combined with multiple reaction monitoring (MRM)-mass spectrometry (MS) (also called selected reaction monitoring (SRM)-MS) and a complex mixture of stable isotope-labeled standard (SIS) peptides, to quantify a broad and diverse panel of 253 proteins in human blood plasma. The quantitation range spans 8 orders of magnitude-from 15 mg/mL (for vitamin D-binding protein) to 450 pg/mL (for protein S100-B)-and includes 31 low-abundance proteins (defined as being <10 ng/mL) of potential disease relevance. The method is designed to assess candidates at the discovery and/or verification phases of the biomarker pipeline and can be adapted to examine smaller or alternate panels of proteins for higher sample throughput. Also detailed here is the application of our recently developed software tool-Qualis-SIS-for protein quantitation (via regression analysis of standard curves) and quality assessment of the resulting data. Overall, this chapter provides the blueprint for the replication of this quantitative proteomic method by proteomic scientists of all skill levels.

pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC.

PubMed

Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen

2017-08-22

One of the fundamental goals in cellular biochemistry is to identify the functions of proteins in the context of compartments that organize them in the cellular environment. To realize this, it is indispensable to develop an automated method for fast and accurate identification of the subcellular locations of uncharacterized proteins. The current study is focused on plant protein subcellular location prediction based on the sequence information alone. Although considerable efforts have been made in this regard, the problem is far from being solved yet. Most of the existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions. This kind of multiplex protein is particularly important for both basic research and drug design. Using the multi-label theory, we present a new predictor called "pLoc-mPlant" by extracting the optimal GO (Gene Ontology) information into the Chou's general PseAAC (Pseudo Amino Acid Composition). Rigorous cross-validation on the same stringent benchmark dataset indicated that the proposed pLoc-mPlant predictor is remarkably superior to iLoc-Plant, the state-of-the-art method for predicting plant protein subcellular localization. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at , by which users can easily get their desired results without the need to go through the complicated mathematics involved.

iTRAQ Quantitative Proteomic Comparison of Metastatic and Non-Metastatic Uveal Melanoma Tumors

PubMed Central

Crabb, John W.; Hu, Bo; Crabb, John S.; Triozzi, Pierre; Saunthararajah, Yogen; Singh, Arun D.

2015-01-01

Background Uveal melanoma is the most common malignancy of the adult eye. The overall mortality rate is high because this aggressive cancer often metastasizes before ophthalmic diagnosis. Quantitative proteomic analysis of primary metastasizing and non-metastasizing tumors was pursued for insights into mechanisms and biomarkers of uveal melanoma metastasis. Methods Eight metastatic and 7 non-metastatic human primary uveal melanoma tumors were analyzed by LC MS/MS iTRAQ technology with Bruch’s membrane/choroid complex from normal postmortem eyes as control tissue. Tryptic peptides from tumor and control proteins were labeled with iTRAQ tags, fractionated by cation exchange chromatography, and analyzed by LC MS/MS. Protein identification utilized the Mascot search engine and the human Uni-Prot/Swiss-Protein database with false discovery ≤ 1%; protein quantitation utilized the Mascot weighted average method. Proteins designated differentially expressed exhibited quantitative differences (p ≤ 0.05, t-test) in a training set of five metastatic and five non-metastatic tumors. Logistic regression models developed from the training set were used to classify the metastatic status of five independent tumors. Results Of 1644 proteins identified and quantified in 5 metastatic and 5 non-metastatic tumors, 12 proteins were found uniquely in ≥ 3 metastatic tumors, 28 were found significantly elevated and 30 significantly decreased only in metastatic tumors, and 31 were designated differentially expressed between metastatic and non-metastatic tumors. Logistic regression modeling of differentially expressed collagen alpha-3(VI) and heat shock protein beta-1 allowed correct prediction of metastasis status for each of five independent tumor specimens. Conclusions The present data provide new clues to molecular differences in metastatic and non-metastatic uveal melanoma tumors. While sample size is limited and validation required, the results support collagen alpha-3(VI) and heat shock protein beta-1 as candidate biomarkers of uveal melanoma metastasis and establish a quantitative proteomic database for uveal melanoma primary tumors. PMID:26305875

Genome-Level Determination of Plasmodium falciparum Blood-Stage Targets of Malarial Clinical Immunity in the Peruvian Amazon

PubMed Central

Torres, Katherine J.; Castrillon, Carlos E.; Moss, Eli L.; Saito, Mayuko; Tenorio, Roy; Molina, Douglas M.; Davies, Huw; Neafsey, Daniel E.; Felgner, Philip; Vinetz, Joseph M.; Gamboa, Dionicia

2015-01-01

Background. Persons with blood-stage Plasmodium falciparum parasitemia in the absence of symptoms are considered to be clinically immune. We hypothesized that asymptomatic subjects with P. falciparum parasitemia would differentially recognize a subset of P. falciparum proteins on a genomic scale. Methods and Findings. Compared with symptomatic subjects, sera from clinically immune, asymptomatically infected individuals differentially recognized 51 P. falciparum proteins, including the established vaccine candidate PfMSP1. Novel, hitherto unstudied hypothetical proteins and other proteins not previously recognized as potential vaccine candidates were also differentially recognized. Genes encoding the proteins differentially recognized by the Peruvian clinically immune individuals exhibited a significant enrichment of nonsynonymous nucleotide variation, an observation consistent with these genes undergoing immune selection. Conclusions. A limited set of P. falciparum protein antigens was associated with the development of naturally acquired clinical immunity in the low-transmission setting of the Peruvian Amazon. These results imply that, even in a low-transmission setting, an asexual blood-stage vaccine designed to reduce clinical malaria symptoms will likely need to contain large numbers of often-polymorphic proteins, a finding at odds with many current efforts in the design of vaccines against asexual blood-stage P. falciparum. PMID:25381370

Modeling the assembly order of multimeric heteroprotein complexes

PubMed Central

Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Shin, Woong-Hee

2018-01-01

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes. PMID:29329283

Modeling the assembly order of multimeric heteroprotein complexes.

PubMed

Peterson, Lenna X; Togawa, Yoichiro; Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Roy, Amitava; Shin, Woong-Hee; Kihara, Daisuke

2018-01-01

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.

In silico platform for predicting and initiating β-turns in a protein at desired locations.

PubMed

Singh, Harinder; Singh, Sandeep; Raghava, Gajendra P S

2015-05-01

Numerous studies have been performed for analysis and prediction of β-turns in a protein. This study focuses on analyzing, predicting, and designing of β-turns to understand the preference of amino acids in β-turn formation. We analyzed around 20,000 PDB chains to understand the preference of residues or pair of residues at different positions in β-turns. Based on the results, a propensity-based method has been developed for predicting β-turns with an accuracy of 82%. We introduced a new approach entitled "Turn level prediction method," which predicts the complete β-turn rather than focusing on the residues in a β-turn. Finally, we developed BetaTPred3, a Random forest based method for predicting β-turns by utilizing various features of four residues present in β-turns. The BetaTPred3 achieved an accuracy of 79% with 0.51 MCC that is comparable or better than existing methods on BT426 dataset. Additionally, models were developed to predict β-turn types with better performance than other methods available in the literature. In order to improve the quality of prediction of turns, we developed prediction models on a large and latest dataset of 6376 nonredundant protein chains. Based on this study, a web server has been developed for prediction of β-turns and their types in proteins. This web server also predicts minimum number of mutations required to initiate or break a β-turn in a protein at specified location of a protein. © 2015 Wiley Periodicals, Inc.

hBfl-1/hNOXA Interaction Studies Provide New Insights on the Role of Bfl-1 in Cancer Cell Resistance and for the Design of Novel Anticancer Agents

PubMed Central

2016-01-01

Upregulation of antiapoptotic Bcl-2 proteins in certain tumors confers cancer cell resistance to chemotherapy or radiations. Members of the antiapoptotic Bcl-2 proteins, including Bcl-2, Mcl-1, Bcl-xL, Bcl-w, and Bfl-1, inhibit apoptosis by selectively binding to conserved α-helical regions, named BH3 domains, of pro-apoptotic proteins such as Bim, tBid, Bad, or NOXA. Five antiapoptotic proteins have been identified that interact with various selectivity with BH3 containing pro-apoptotic counterparts. Cancer cells present various and variable levels of these proteins, making the design of effective apoptosis based therapeutics challenging. Recently, BH3 profiling was introduced as a method to classify cancer cells based on their ability to resist apoptosis following exposure to selected BH3 peptides. However, these studies were based on binding affinities measured with model BH3 peptides and Bcl-2-proteins taken from mouse sequences. While the majority of these interactions are conserved between mice and humans, we found surprisingly that human NOXA binds to human Bfl-1 potently and covalently via conserved Cys residues, with over 2 orders of magnitude increased affinity over hMcl-1. Our data suggest that some assumptions of the original BH3 profiling need to be revisited and that perhaps further targeting efforts should be redirected toward Bfl-1, for which no suitable specific inhibitors or pharmacological tools have been reported. In this regard, we also describe the initial design and characterizations of novel covalent BH3-based agents that potently target Bfl-1. These molecules could provide a novel platform on which to design effective Bfl-1 targeting therapeutics. PMID:28026162

Plasmonic Thermal Decomposition/Digestion of Proteins: A Rapid On-Surface Protein Digestion Technique for Mass Spectrometry Imaging.

PubMed

Zhou, Rong; Basile, Franco

2017-09-05

A method based on plasmon surface resonance absorption and heating was developed to perform a rapid on-surface protein thermal decomposition and digestion suitable for imaging mass spectrometry (MS) and/or profiling. This photothermal process or plasmonic thermal decomposition/digestion (plasmonic-TDD) method incorporates a continuous wave (CW) laser excitation and gold nanoparticles (Au-NPs) to induce known thermal decomposition reactions that cleave peptides and proteins specifically at the C-terminus of aspartic acid and at the N-terminus of cysteine. These thermal decomposition reactions are induced by heating a solid protein sample to temperatures between 200 and 270 °C for a short period of time (10-50 s per 200 μm segment) and are reagentless and solventless, and thus are devoid of sample product delocalization. In the plasmonic-TDD setup the sample is coated with Au-NPs and irradiated with 532 nm laser radiation to induce thermoplasmonic heating and bring about site-specific thermal decomposition on solid peptide/protein samples. In this manner the Au-NPs act as nanoheaters that result in a highly localized thermal decomposition and digestion of the protein sample that is independent of the absorption properties of the protein, making the method universally applicable to all types of proteinaceous samples (e.g., tissues or protein arrays). Several experimental variables were optimized to maximize product yield, and they include heating time, laser intensity, size of Au-NPs, and surface coverage of Au-NPs. Using optimized parameters, proof-of-principle experiments confirmed the ability of the plasmonic-TDD method to induce both C-cleavage and D-cleavage on several peptide standards and the protein lysozyme by detecting their thermal decomposition products with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The high spatial specificity of the plasmonic-TDD method was demonstrated by using a mask to digest designated sections of the sample surface with the heating laser and MALDI-MS imaging to map the resulting products. The solventless nature of the plasmonic-TDD method enabled the nonenzymatic on-surface digestion of proteins to proceed with undetectable delocalization of the resulting products from their precursor protein location. The advantages of this novel plasmonic-TDD method include short reaction times (<30 s/200 μm), compatibility with MALDI, universal sample compatibility, high spatial specificity, and localization of the digestion products. These advantages point to potential applications of this method for on-tissue protein digestion and MS-imaging/profiling for the identification of proteins, high-fidelity MS imaging of high molecular weight (>30 kDa) proteins, and the rapid analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples.

Structure-based functional annotation of putative conserved proteins having lyase activity from Haemophilus influenzae.

PubMed

Shahbaaz, Mohd; Ahmad, Faizan; Imtaiyaz Hassan, Md

2015-06-01

Haemophilus influenzae is a small pleomorphic Gram-negative bacteria which causes several chronic diseases, including bacteremia, meningitis, cellulitis, epiglottitis, septic arthritis, pneumonia, and empyema. Here we extensively analyzed the sequenced genome of H. influenzae strain Rd KW20 using protein family databases, protein structure prediction, pathways and genome context methods to assign a precise function to proteins whose functions are unknown. These proteins are termed as hypothetical proteins (HPs), for which no experimental information is available. Function prediction of these proteins would surely be supportive to precisely understand the biochemical pathways and mechanism of pathogenesis of Haemophilus influenzae. During the extensive analysis of H. influenzae genome, we found the presence of eight HPs showing lyase activity. Subsequently, we modeled and analyzed three-dimensional structure of all these HPs to determine their functions more precisely. We found these HPs possess cystathionine-β-synthase, cyclase, carboxymuconolactone decarboxylase, pseudouridine synthase A and C, D-tagatose-1,6-bisphosphate aldolase and aminodeoxychorismate lyase-like features, indicating their corresponding functions in the H. influenzae. Lyases are actively involved in the regulation of biosynthesis of various hormones, metabolic pathways, signal transduction, and DNA repair. Lyases are also considered as a key player for various biological processes. These enzymes are critically essential for the survival and pathogenesis of H. influenzae and, therefore, these enzymes may be considered as a potential target for structure-based rational drug design. Our structure-function relationship analysis will be useful to search and design potential lead molecules based on the structure of these lyases, for drug design and discovery.

Quantitative structure-activity relationship: promising advances in drug discovery platforms.

PubMed

Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

2015-12-01

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

Computational smart polymer design based on elastin protein mutability.

PubMed

Tarakanova, Anna; Huang, Wenwen; Weiss, Anthony S; Kaplan, David L; Buehler, Markus J

2017-05-01

Soluble elastin-like peptides (ELPs) can be engineered into a range of physical forms, from hydrogels and scaffolds to fibers and artificial tissues, finding numerous applications in medicine and engineering as "smart polymers". Elastin-like peptides are attractive candidates as a platform for novel biomaterial design because they exhibit a highly tunable response spectrum, with reversible phase transition capabilities. Here, we report the design of the first virtual library of elastin-like protein models using methods for enhanced sampling to study the effect of peptide chemistry, chain length, and salt concentration on the structural transitions of ELPs, exposing associated molecular mechanisms. We describe the behavior of the local molecular structure under increasing temperatures and the effect of peptide interactions with nearest hydration shell water molecules on peptide mobility and propensity to exhibit structural transitions. Shifts in the magnitude of structural transitions at the single-molecule scale are explained from the perspective of peptide-ion-water interactions in a library of four unique elastin-like peptide systems. Predictions of structural transitions are subsequently validated in experiment. This library is a valuable resource for recombinant protein design and synthesis as it elucidates mechanisms at the single-molecule level, paving a feedback path between simulation and experiment for smart material designs, with applications in biomedicine and diagnostic devices. Copyright © 2017. Published by Elsevier Ltd.

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions.

PubMed

Du, Q S; Ma, Y; Xie, N Z; Huang, R B

2014-01-01

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

Prediction of Protein-Protein Interaction Sites Using Electrostatic Desolvation Profiles

PubMed Central

Fiorucci, Sébastien; Zacharias, Martin

2010-01-01

Abstract Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces. PMID:20441756

Quantitative Investigation of Protein-Nucleic Acid Interactions by Biosensor Surface Plasmon Resonance.

PubMed

Wang, Shuo; Poon, Gregory M K; Wilson, W David

2015-01-01

Biosensor-surface plasmon resonance (SPR) technology has emerged as a powerful label-free approach for the study of nucleic acid interactions in real time. The method provides simultaneous equilibrium and kinetic characterization for biomolecular interactions with low sample requirements and without the need for external probes. A detailed and practical guide for protein-DNA interaction analyses using biosensor-SPR methods is presented. Details of SPR technology and basic fundamentals are described with recommendations on the preparation of the SPR instrument, sensor chips and samples, experimental design, quantitative and qualitative data analyses and presentation. A specific example of the interaction of a transcription factor with DNA is provided with results evaluated by both kinetic and steady-state SPR methods.

Measuring binding of protein to gel-bound ligands using magnetic levitation.

PubMed

Shapiro, Nathan D; Mirica, Katherine A; Soh, Siowling; Phillips, Scott T; Taran, Olga; Mace, Charles R; Shevkoplyas, Sergey S; Whitesides, George M

2012-03-28

This paper describes the use of magnetic levitation (MagLev) to measure the association of proteins and ligands. The method starts with diamagnetic gel beads that are functionalized covalently with small molecules (putative ligands). Binding of protein to the ligands within the bead causes a change in the density of the bead. When these beads are suspended in a paramagnetic aqueous buffer and placed between the poles of two NbFeB magnets with like poles facing, the changes in the density of the bead on binding of protein result in changes in the levitation height of the bead that can be used to quantify the amount of protein bound. This paper uses a reaction-diffusion model to examine the physical principles that determine the values of rate and equilibrium constants measured by this system, using the well-defined model system of carbonic anhydrase and aryl sulfonamides. By tuning the experimental protocol, the method is capable of quantifying either the concentration of protein in a solution, or the binding affinities of a protein to several resin-bound small molecules simultaneously. Since this method requires no electricity and only a single piece of inexpensive equipment, it may find use in situations where portability and low cost are important, such as in bioanalysis in resource-limited settings, point-of-care diagnosis, veterinary medicine, and plant pathology. It still has several practical disadvantages. Most notably, the method requires relatively long assay times and cannot be applied to large proteins (>70 kDa), including antibodies. The design and synthesis of beads with improved characteristics (e.g., larger pore size) has the potential to resolve these problems.

Measuring Binding of Protein to Gel-Bound Ligands Using Magnetic Levitation

PubMed Central

Shapiro, Nathan D.; Mirica, Katherine A.; Soh, Siowling; Phillips, Scott T.; Taran, Olga; Mace, Charles R.; Shevkoplyas, Sergey S.; Whitesides, George M.

2012-01-01

This paper describes the use of magnetic levitation (MagLev) to measure the association of proteins and ligands. The method starts with diamagnetic gel beads that are functionalized covalently with small molecules (putative ligands). Binding of protein to the ligands within the bead causes a change in the density of the bead. When these beads are suspended in a paramagnetic aqueous buffer and placed between the poles of two NbFeB magnets with like poles facing, the changes in the density of the bead on binding of protein result in changes in the levitation height of the bead that can be used to quantify the amount of protein bound. This paper uses a reaction-diffusion model to examine the physical principles that determine the values of rate and equilibrium constants measured by this system, using the well-defined model system of carbonic anhydrase and aryl sulfonamides. By tuning the experimental protocol, the method is capable of quantifying either the concentration of protein in a solution, or the binding affinities of a protein to several resin-bound small molecules simultaneously. Since this method requires no electricity and only a single piece of inexpensive equipment, it may find use in situations where portability and low cost are important, such as in bioanalysis in resource-limited settings, point-of-care diagnosis, veterinary medicine, and plant pathology. It still has several practical disadvantages. Most notably, the method requires relatively long assay times and cannot be applied to large proteins (> 70 kDa), including antibodies. The design and synthesis of beads with improved characteristics (e.g., larger pore size) has the potential to resolve these problems. PMID:22364170

Compositions and methods for improved protein production

DOEpatents

Bodie, Elizabeth A [San Carlos, CA; Kim, Steve [San Francisco, CA

2012-07-10

The present invention relates to the identification of novel nucleic acid sequences, designated herein as 7p, 8k, 7E, 9G, 8Q and 203, in a host cell which effect protein production. The present invention also provides host cells having a mutation or deletion of part or all of the gene encoding 7p, 8k, 7E, 9G, 8Q and 203, which are presented in FIG. 1, and are SEQ ID NOS.: 1-6, respectively. The present invention also provides host cells further comprising a nucleic acid encoding a desired heterologous protein such as an enzyme.

Direct measurements of protein-stabilized gold nanoparticle interactions.

PubMed

Eichmann, Shannon L; Bevan, Michael A

2010-09-21

We report integrated video and total internal reflection microscopy measurements of protein stabilized 110 nm Au nanoparticles confined in 280 nm gaps in physiological media. Measured potential energy profiles display quantitative agreement with Brownian dynamic simulations that include hydrodynamic interactions and camera exposure time and noise effects. Our results demonstrate agreement between measured nonspecific van der Waals and adsorbed protein interactions with theoretical potentials. Confined, lateral nanoparticle diffusivity measurements also display excellent agreement with predictions. These findings provide a basis to interrogate specific biomacromolecular interactions in similar experimental configurations and to design future improved measurement methods.

Compositions and methods for improved protein production

DOEpatents

Bodie, Elizabeth A.; Kim, Steve Sungjin

2014-06-03

The present invention relates to the identification of novel nucleic acid sequences, designated herein as 7p, 8k, 7E, 9G, 8Q and 203, in a host cell which effect protein production. The present invention also provides host cells having a mutation or deletion of part or all of the gene encoding 7p, 8k, 7E, 9G, 8Q and 203, which are presented in FIG. 1, and are SEQ ID NOS.: 1-6, respectively. The present invention also provides host cells further comprising a nucleic acid encoding a desired heterologous protein such as an enzyme.

Understand protein functions by comparing the similarity of local structural environments.

PubMed

Chen, Jiawen; Xie, Zhong-Ru; Wu, Yinghao

2017-02-01

The three-dimensional structures of proteins play an essential role in regulating binding between proteins and their partners, offering a direct relationship between structures and functions of proteins. It is widely accepted that the function of a protein can be determined if its structure is similar to other proteins whose functions are known. However, it is also observed that proteins with similar global structures do not necessarily correspond to the same function, while proteins with very different folds can share similar functions. This indicates that function similarity is originated from the local structural information of proteins instead of their global shapes. We assume that proteins with similar local environments prefer binding to similar types of molecular targets. In order to testify this assumption, we designed a new structural indicator to define the similarity of local environment between residues in different proteins. This indicator was further used to calculate the probability that a given residue binds to a specific type of structural neighbors, including DNA, RNA, small molecules and proteins. After applying the method to a large-scale non-redundant database of proteins, we show that the positive signal of binding probability calculated from the local structural indicator is statistically meaningful. In summary, our studies suggested that the local environment of residues in a protein is a good indicator to recognize specific binding partners of the protein. The new method could be a potential addition to a suite of existing template-based approaches for protein function prediction. Copyright © 2016 Elsevier B.V. All rights reserved.

Modularity in protein structures: study on all-alpha proteins.

PubMed

Khan, Taushif; Ghosh, Indira

2015-01-01

Modularity is known as one of the most important features of protein's robust and efficient design. The architecture and topology of proteins play a vital role by providing necessary robust scaffolds to support organism's growth and survival in constant evolutionary pressure. These complex biomolecules can be represented by several layers of modular architecture, but it is pivotal to understand and explore the smallest biologically relevant structural component. In the present study, we have developed a component-based method, using protein's secondary structures and their arrangements (i.e. patterns) in order to investigate its structural space. Our result on all-alpha protein shows that the known structural space is highly populated with limited set of structural patterns. We have also noticed that these frequently observed structural patterns are present as modules or "building blocks" in large proteins (i.e. higher secondary structure content). From structural descriptor analysis, observed patterns are found to be within similar deviation; however, frequent patterns are found to be distinctly occurring in diverse functions e.g. in enzymatic classes and reactions. In this study, we are introducing a simple approach to explore protein structural space using combinatorial- and graph-based geometry methods, which can be used to describe modularity in protein structures. Moreover, analysis indicates that protein function seems to be the driving force that shapes the known structure space.

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.

Interaction betwen Lead and Bone Protein to Affect Bone Calcium Level Using UV-Vis Spectroscopy

NASA Astrophysics Data System (ADS)

Noor, Z.; Azharuddin, A.; Aflanie, I.; Kania, N.; Suhartono, E.

2018-05-01

This present study aim to evaluate the interactions between lead (Pb) and with bone protein by UV-Vis approach. In addition, this prsent study also aim to investigate the effect of Pb on bone calcium (Ca) level. The present study was a true experimental study design to examine the impact of Pb exposure in bone of male rats (Rattus novergicus). The study involved 5 groups, P1 was the control group, while the other (P2-P5) were the case group with exposure of Pb in different concentration within 4 weeks. At the end of the exposure, the interaction between Pb and protein was determined using UV-Vis spectrophotometric method, and the Ca level was determined using permanganometric method. The results shows that that there is an interaction between Pb and bone protein. The result also shows that the value of the binding constant of Protein-Pb is 32.71. It means Pb have an high affinity to bind with bone protein, which promote a further reaction to induced the release of bone Ca from the bone protein. In conclusion, this present study found an obvious relationship between Pb and bone protein which promote a further reaction to increase the releasing of bone calcium.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

PubMed

Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

2015-01-01

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

SPIRE: Systematic protein investigative research environment.

PubMed

Kolker, Eugene; Higdon, Roger; Morgan, Phil; Sedensky, Margaret; Welch, Dean; Bauman, Andrew; Stewart, Elizabeth; Haynes, Winston; Broomall, William; Kolker, Natali

2011-12-10

The SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is on usability and integration of the best analytic tools. SPIRE provides an easy to use web-interface and generates results in both interactive and simple data formats. In contrast to run-based approaches, SPIRE conducts the analysis based on the experimental design. It employs novel methods to generate false discovery rates and local false discovery rates (FDR, LFDR) and integrates the best and complementary open-source search and data analysis methods. The SPIRE approach of integrating X!Tandem, OMSSA and SpectraST can produce an increase in protein IDs (52-88%) over current combinations of scoring and single search engines while also providing accurate multi-faceted error estimation. One of SPIRE's primary assets is combining the results with data on protein function, pathways and protein expression from model organisms. We demonstrate some of SPIRE's capabilities by analyzing mitochondrial proteins from the wild type and 3 mutants of C. elegans. SPIRE also connects results to publically available proteomics data through its Model Organism Protein Expression Database (MOPED). SPIRE can also provide analysis and annotation for user supplied protein ID and expression data. Copyright © 2011. Published by Elsevier B.V.

AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures

PubMed Central

Zambrano, Rafael; Jamroz, Michal; Szczasiuk, Agata; Pujols, Jordi; Kmiecik, Sebastian; Ventura, Salvador

2015-01-01

Protein aggregation underlies an increasing number of disorders and constitutes a major bottleneck in the development of therapeutic proteins. Our present understanding on the molecular determinants of protein aggregation has crystalized in a series of predictive algorithms to identify aggregation-prone sites. A majority of these methods rely only on sequence. Therefore, they find difficulties to predict the aggregation properties of folded globular proteins, where aggregation-prone sites are often not contiguous in sequence or buried inside the native structure. The AGGRESCAN3D (A3D) server overcomes these limitations by taking into account the protein structure and the experimental aggregation propensity scale from the well-established AGGRESCAN method. Using the A3D server, the identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. Additionally, A3D server enables to take into account the dynamic fluctuations of protein structure in solution, which may influence aggregation propensity. This is possible in A3D Dynamic Mode that exploits the CABS-flex approach for the fast simulations of flexibility of globular proteins. The A3D server can be accessed at http://biocomp.chem.uw.edu.pl/A3D/. PMID:25883144

Salt bridges: geometrically specific, designable interactions.

PubMed

Donald, Jason E; Kulp, Daniel W; DeGrado, William F

2011-03-01

Salt bridges occur frequently in proteins, providing conformational specificity and contributing to molecular recognition and catalysis. We present a comprehensive analysis of these interactions in protein structures by surveying a large database of protein structures. Salt bridges between Asp or Glu and His, Arg, or Lys display extremely well-defined geometric preferences. Several previously observed preferences are confirmed, and others that were previously unrecognized are discovered. Salt bridges are explored for their preferences for different separations in sequence and in space, geometric preferences within proteins and at protein-protein interfaces, co-operativity in networked salt bridges, inclusion within metal-binding sites, preference for acidic electrons, apparent conformational side chain entropy reduction on formation, and degree of burial. Salt bridges occur far more frequently between residues at close than distant sequence separations, but, at close distances, there remain strong preferences for salt bridges at specific separations. Specific types of complex salt bridges, involving three or more members, are also discovered. As we observe a strong relationship between the propensity to form a salt bridge and the placement of salt-bridging residues in protein sequences, we discuss the role that salt bridges might play in kinetically influencing protein folding and thermodynamically stabilizing the native conformation. We also develop a quantitative method to select appropriate crystal structure resolution and B-factor cutoffs. Detailed knowledge of these geometric and sequence dependences should aid de novo design and prediction algorithms. Copyright © 2010 Wiley-Liss, Inc.

Solid-phase extraction method for the isolation of plant thionins from European mistletoe, wheat and barley using zirconium silicate embedded in poly(styrene-co-divinylbenzene) hollow-monoliths.

PubMed

Hussain, Shah; Güzel, Yüksel; Schönbichler, Stefan A; Rainer, Matthias; Huck, Christian W; Bonn, Günther K

2013-09-01

Thionins are cysteine-rich, biologically active small (∼5 kDa) and basic proteins occurring ubiquitously in the plant kingdom. This study describes an efficient solid-phase extraction (SPE) method for the selective isolation of these pharmacologically active proteins. Hollow-monolithic extraction tips based on poly(styrene-co-divinylbenzene) with embedded zirconium silicate nano-powder were designed, which showed an excellent selectivity for sulphur-rich proteins owing to strong co-ordination between zirconium and the sulphur atoms from the thiol-group of cysteine. The sorbent provides a combination of strong hydrophobic and electrostatic interactions which may help in targeted separation of certain classes of proteins in a complex mixture based upon the binding strength of different proteins. European mistletoe, wheat and barley samples were used for selective isolation of viscotoxins, purothionins and hordothionins, respectively. The enriched fractions were subjected to analysis by matrix-assisted laser desorption/ionisation-time-of-flight mass spectrometer to prove the selectivity of the SPE method towards thionins. For peptide mass-fingerprint analysis, tryptic digests of SPE eluates were examined. Reversed-phase high-performance liquid chromatography hyphenated to diode-array detection was employed for the purification of individual isoforms. The developed method was found to be highly specific for the isolation and purification of thionins.

Peptide-based protein capture agents with high affinity, selectivity, and stability as antibody replacements in biodetection assays

NASA Astrophysics Data System (ADS)

Coppock, Matthew B.; Farrow, Blake; Warner, Candice; Finch, Amethist S.; Lai, Bert; Sarkes, Deborah A.; Heath, James R.; Stratis-Cullum, Dimitra

2014-05-01

Current biodetection assays that employ monoclonal antibodies as primary capture agents exhibit limited fieldability, shelf life, and performance due to batch-to-batch production variability and restricted thermal stability. In order to improve upon the detection of biological threats in fieldable assays and systems for the Army, we are investigating protein catalyzed capture (PCC) agents as drop-in replacements for the existing antibody technology through iterative in situ click chemistry. The PCC agent oligopeptides are developed against known protein epitopes and can be mass produced using robotic methods. In this work, a PCC agent under development will be discussed. The performance, including affinity, selectivity, and stability of the capture agent technology, is analyzed by immunoprecipitation, western blotting, and ELISA experiments. The oligopeptide demonstrates superb selectivity coupled with high affinity through multi-ligand design, and improved thermal, chemical, and biochemical stability due to non-natural amino acid PCC agent design.

Design of Protein-Coated Carbon Nanotubes Loaded with Hydrophobic Drugs through Sacrificial Templating of Mesoporous Silica Shells.

PubMed

Fiegel, Vincent; Harlepp, Sebastien; Begin-Colin, Sylvie; Begin, Dominique; Mertz, Damien

2018-03-26

One key challenge in the fields of nanomedicine and tissue engineering is the design of theranostic nanoplatforms able to monitor their therapeutic effect by imaging. Among current developed nano-objects, carbon nanotubes (CNTs) were found suitable to combine imaging, photothermal therapy, and to be loaded with hydrophobic drugs. However, a main problem is their resulting low hydrophilicity. To face this problem, an innovative method is developed here, which consists in loading the surface of carbon nanotubes (CNTs) with drugs followed by a protein coating around them. The originality of this method relies on first covering CNTs with a sacrificial template mesoporous silica (MS) shell grafted with isobutyramide (IBAM) binders on which a protein nanofilm is strongly adhered through IBAM-mediated physical cross-linking. This concept is first demonstrated without drugs, and is further improved with the suitable loading of hydrophobic drugs, curcumin (CUR) and camptothecin (CPT), which are retained between the CNTs and human serum albumin (HSA) layer. Such novel nanocomposites with favorable photothermal properties are very promising for theranostic systems, drug delivery, and phototherapy applications. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling and design of light powered biomimicry micropump utilizing transporter proteins

NASA Astrophysics Data System (ADS)

Liu, Jin; Sze, Tsun-Kay Jackie; Dutta, Prashanta

2014-11-01

The creation of compact micropumps to provide steady flow has been an on-going challenge in the field of microfluidics. We present a mathematical model for a micropump utilizing Bacteriorhodopsin and sugar transporter proteins. This micropump utilizes transporter proteins as method to drive fluid flow by converting light energy into chemical potential. The fluid flow through a microchannel is simulated using the Nernst-Planck, Navier-Stokes, and continuity equations. Numerical results show that the micropump is capable of generating usable pressure. Designing parameters influencing the performance of the micropump are investigated including membrane fraction, lipid proton permeability, illumination, and channel height. The results show that there is a substantial membrane fraction region at which fluid flow is maximized. The use of lipids with low membrane proton permeability allows illumination to be used as a method to turn the pump on and off. This capability allows the micropump to be activated and shut off remotely without bulky support equipment. This modeling work provides new insights on mechanisms potentially useful for fluidic pumping in self-sustained bio-mimic microfluidic pumps. This work is supported in part by the National Science Fundation Grant CBET-1250107.

Software-supported USER cloning strategies for site-directed mutagenesis and DNA assembly.

PubMed

Genee, Hans Jasper; Bonde, Mads Tvillinggaard; Bagger, Frederik Otzen; Jespersen, Jakob Berg; Sommer, Morten O A; Wernersson, Rasmus; Olsen, Lars Rønn

2015-03-20

USER cloning is a fast and versatile method for engineering of plasmid DNA. We have developed a user friendly Web server tool that automates the design of optimal PCR primers for several distinct USER cloning-based applications. Our Web server, named AMUSER (Automated DNA Modifications with USER cloning), facilitates DNA assembly and introduction of virtually any type of site-directed mutagenesis by designing optimal PCR primers for the desired genetic changes. To demonstrate the utility, we designed primers for a simultaneous two-position site-directed mutagenesis of green fluorescent protein (GFP) to yellow fluorescent protein (YFP), which in a single step reaction resulted in a 94% cloning efficiency. AMUSER also supports degenerate nucleotide primers, single insert combinatorial assembly, and flexible parameters for PCR amplification. AMUSER is freely available online at http://www.cbs.dtu.dk/services/AMUSER/.

Computer-Aided Drug Design Methods.

PubMed

Yu, Wenbo; MacKerell, Alexander D

2017-01-01

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

Target-specific NMR detection of protein-ligand interactions with antibody-relayed 15N-group selective STD.

PubMed

Hetényi, Anasztázia; Hegedűs, Zsófia; Fajka-Boja, Roberta; Monostori, Éva; Kövér, Katalin E; Martinek, Tamás A

2016-12-01

Fragment-based drug design has been successfully applied to challenging targets where the detection of the weak protein-ligand interactions is a key element. 1 H saturation transfer difference (STD) NMR spectroscopy is a powerful technique for this work but it requires pure homogeneous proteins as targets. Monoclonal antibody (mAb)-relayed 15 N-GS STD spectroscopy has been developed to resolve the problem of protein mixtures and impure proteins. A 15 N-labelled target-specific mAb is selectively irradiated and the saturation is relayed through the target to the ligand. Tests on the anti-Gal-1 mAb/Gal-1/lactose system showed that the approach is experimentally feasible in a reasonable time frame. This method allows detection and identification of binding molecules directly from a protein mixture in a multicomponent system.

Rational Protein Engineering Guided by Deep Mutational Scanning

PubMed Central

Shin, HyeonSeok; Cho, Byung-Kwan

2015-01-01

Sequence–function relationship in a protein is commonly determined by the three-dimensional protein structure followed by various biochemical experiments. However, with the explosive increase in the number of genome sequences, facilitated by recent advances in sequencing technology, the gap between protein sequences available and three-dimensional structures is rapidly widening. A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing. Coupled with a highly efficient screening system, this approach assesses the phenotypic changes made by the substitution of each amino acid sequence that constitutes a protein. Such an informational resource provides the functional role of each amino acid sequence, thereby providing sufficient rationale for selecting target residues for protein engineering. Here, we discuss the current applications of deep mutational scanning and consider experimental design. PMID:26404267

A synthetic biology approach to the development of transcriptional regulatory models and custom enhancer design☆,☆☆

PubMed Central

Martinez, Carlos A.; Barr, Kenneth; Kim, Ah-Ram; Reinitz, John

2013-01-01

Synthetic biology offers novel opportunities for elucidating transcriptional regulatory mechanisms and enhancer logic. Complex cis-regulatory sequences—like the ones driving expression of the Drosophila even-skipped gene—have proven difficult to design from existing knowledge, presumably due to the large number of protein-protein interactions needed to drive the correct expression patterns of genes in multicellular organisms. This work discusses two novel computational methods for the custom design of enhancers that employ a sophisticated, empirically validated transcriptional model, optimization algorithms, and synthetic biology. These synthetic elements have both utilitarian and academic value, including improving existing regulatory models as well as evolutionary questions. The first method involves the use of simulated annealing to explore the sequence space for synthetic enhancers whose expression output fit a given search criterion. The second method uses a novel optimization algorithm to find functionally accessible pathways between two enhancer sequences. These paths describe a set of mutations wherein the predicted expression pattern does not significantly vary at any point along the path. Both methods rely on a predictive mathematical framework that maps the enhancer sequence space to functional output. PMID:23732772

Global analysis of protein folding using massively parallel design, synthesis and testing

PubMed Central

Rocklin, Gabriel J.; Chidyausiku, Tamuka M.; Goreshnik, Inna; Ford, Alex; Houliston, Scott; Lemak, Alexander; Carter, Lauren; Ravichandran, Rashmi; Mulligan, Vikram K.; Chevalier, Aaron; Arrowsmith, Cheryl H.; Baker, David

2017-01-01

Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Though these forces are “encoded” in the thousands of known protein structures, “decoding” them is challenging due to the complexity of natural proteins that have evolved for function, not stability. Here we combine computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for over 15,000 de novo designed miniproteins, 1,000 natural proteins, 10,000 point-mutants, and 30,000 negative control sequences, identifying over 2,500 new stable designed proteins in four basic folds. This scale—three orders of magnitude greater than that of previous studies of design or folding—enabled us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment, and promises to transform computational protein design into a data-driven science. PMID:28706065

Acyl donors for native chemical ligation.

PubMed

Yan, Bingjia; Shi, Weiwei; Ye, Linzhi; Liu, Lei

2018-04-11

Native chemical ligation (NCL) has become one of the most important methods in chemical syntheses of proteins. Recently, in order to expand its scope, considerable effort has been devoted to tuning the C-terminal acyl donor thioesters used in NCL. This article reviews the recent advances in the design of C-terminal acyl donors, their precursors and surrogates, and highlights some noteworthy progress that may lead the future direction of protein chemical synthesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dual-primer self-generation SERS signal amplification assay for PDGF-BB using label-free aptamer.

PubMed

Ye, SuJuan; Zhai, XiaoMo; Wu, YanYing; Kuang, ShaoPing

2016-05-15

Highly sensitive detection of proteins, especially those associated with cancers, is essential to biomedical research as well as clinical diagnosis. In this work, a simple and novel one-two-three signal amplification surface-enhanced Raman scattering (SERS) method for the detection of protein is fabricated by using label-free aptamer and dual-primer self-generation. Platelet-derived growth factor B-chain (PDGF-BB) is selected as the model protein. The one-two-three cascade DNA amplification means one target-aptamer binding event, two hairpin DNA switches and three DNA amplification reactions. This strategy possesses some remarkable features compared to conventional signal amplification methods: (i) A smart probe including a label-free aptamer is fabricated, for suitable hybridization without hindering the affinity of the aptamer toward its target. (ii) Using the unique structure switch of the aptamer and cooperator, a one-two-three working mode is developed to amplify the SERS signal. The amplification efficiency is enhanced. Given the unique and attractive characteristics, a simple and universal strategy is designed to accomplish ultrasensitive detection of proteins. The detection limit of PDGF-BB via SERS detection is 0.42 pM, with the linear range from 1.0×10(-12)M to 10(-8)M. It is potentially universal because the aptamer can be easily designed for biomolecules whose aptamers undergo similar conformational changes. Copyright © 2015 Elsevier B.V. All rights reserved.

Injectable nanocomposite cryogels for versatile protein drug delivery.

PubMed

Koshy, Sandeep T; Zhang, David K Y; Grolman, Joshua M; Stafford, Alexander G; Mooney, David J

2018-01-01

Sustained, localized protein delivery can enhance the safety and activity of protein drugs in diverse disease settings. While hydrogel systems are widely studied as vehicles for protein delivery, they often suffer from rapid release of encapsulated cargo, leading to a narrow duration of therapy, and protein cargo can be denatured by incompatibility with the hydrogel crosslinking chemistry. In this work, we describe injectable nanocomposite hydrogels that are capable of sustained, bioactive, release of a variety of encapsulated proteins. Injectable and porous cryogels were formed by bio-orthogonal crosslinking of alginate using tetrazine-norbornene coupling. To provide sustained release from these hydrogels, protein cargo was pre-adsorbed to charged Laponite nanoparticles that were incorporated within the walls of the cryogels. The presence of Laponite particles substantially hindered the release of a number of proteins that otherwise showed burst release from these hydrogels. By modifying the Laponite content within the hydrogels, the kinetics of protein release could be precisely tuned. This versatile strategy to control protein release simplifies the design of hydrogel drug delivery systems. Here we present an injectable nanocomposite hydrogel for simple and versatile controlled release of therapeutic proteins. Protein release from hydrogels often requires first entrapping the protein in particles and embedding these particles within the hydrogel to allow controlled protein release. This pre-encapsulation process can be cumbersome, can damage the protein's activity, and must be optimized for each protein of interest. The strategy presented in this work simply premixes the protein with charged nanoparticles that bind strongly with the protein. These protein-laden particles are then placed within a hydrogel and slowly release the protein into the surrounding environment. Using this method, tunable release from an injectable hydrogel can be achieved for a variety of proteins. This strategy greatly simplifies the design of hydrogel systems for therapeutic protein release applications. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Accuracy of Protein Embedding Potentials: An Analysis in Terms of Electrostatic Potentials.

PubMed

Olsen, Jógvan Magnus Haugaard; List, Nanna Holmgaard; Kristensen, Kasper; Kongsted, Jacob

2015-04-14

Quantum-mechanical embedding methods have in recent years gained significant interest and may now be applied to predict a wide range of molecular properties calculated at different levels of theory. To reach a high level of accuracy in embedding methods, both the electronic structure model of the active region and the embedding potential need to be of sufficiently high quality. In fact, failures in quantum mechanics/molecular mechanics (QM/MM)-based embedding methods have often been associated with the QM/MM methodology itself; however, in many cases the reason for such failures is due to the use of an inaccurate embedding potential. In this paper, we investigate in detail the quality of the electronic component of embedding potentials designed for calculations on protein biostructures. We show that very accurate explicitly polarizable embedding potentials may be efficiently designed using fragmentation strategies combined with single-fragment ab initio calculations. In fact, due to the self-interaction error in Kohn-Sham density functional theory (KS-DFT), use of large full-structure quantum-mechanical calculations based on conventional (hybrid) functionals leads to less accurate embedding potentials than fragment-based approaches. We also find that standard protein force fields yield poor embedding potentials, and it is therefore not advisable to use such force fields in general QM/MM-type calculations of molecular properties other than energies and structures.

Freeze drying formulation using microscale and design of experiment approaches: a case study using granulocyte colony-stimulating factor.

PubMed

Grant, Yitzchak; Matejtschuk, Paul; Bird, Christopher; Wadhwa, Meenu; Dalby, Paul A

2012-04-01

The lyophilization of proteins in microplates, to assess and optimise formulations rapidly, has been applied for the first time to a therapeutic protein and, in particular, one that requires a cell-based biological assay, in order to demonstrate the broader usefulness of the approach. Factorial design of experiment methods were combined with lyophilization in microplates to identify optimum formulations that stabilised granulocyte colony-stimulating factor during freeze drying. An initial screen rapidly identified key excipients and potential interactions, which was then followed by a central composite face designed optimisation experiment. Human serum albumin and Tween 20 had significant effects on maintaining protein stability. As previously, the optimum formulation was then freeze-dried in stoppered vials to verify that the microscale data is relevant to pilot scales. However, to validate the approach further, the selected formulation was also assessed for solid-state shelf-life through the use of accelerated stability studies. This approach allows for a high-throughput assessment of excipient options early on in product development, while also reducing costs in terms of time and quantity of materials required.

Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles

PubMed Central

2013-01-01

The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein–protein interaction prediction and design methods. PMID:24250278

Initial investigation of dietitian perception of plant-based protein quality.

PubMed

Hughes, Glenna J; Kress, Kathleen S; Armbrecht, Eric S; Mukherjea, Ratna; Mattfeldt-Beman, Mildred

2014-07-01

Interest in plant-based diets is increasing, evidenced by scientific and regulatory recommendations, including Dietary Guidelines for Americans. Dietitians provide guidance in dietary protein selection but little is known about how familiar dietitians are with the quality of plant versus animal proteins or methods for measuring protein quality. Likewise, there is a need to explore their beliefs related to dietary recommendations. The aim of this study was to assess dietitians' perceptions of plant-based protein quality and to determine if these are affected by demographic factors such as age and dietary practice group (DPG) membership. This was a cross-sectional design using an online survey. The survey was sent to all members of the Missouri Dietetic Association. All completed surveys (136) were analyzed. The main outcome measures were responses to belief and knowledge questions about the protein quality of plant-based diets, along with demographic information including age and DPG membership. Descriptive statistics and frequencies were determined, and chi-square analysis was used to determine the associations between belief and knowledge responses and demographic characteristics. Responses to belief statements suggested a high level of support for plant-based diets. No associations were found between any of the belief questions and demographic factors. A majority of respondents were not familiar with protein quality determination methods that are currently recognized by global regulatory and advisory agencies. Potential barriers identified in shifting to a more plant-based diet were lack of interest and perceived difficulty. Knowledge among dietitians of plant-based protein quality in general, and methods of protein quality measurement more specifically, needs to be addressed to enhance their knowledge base for making dietary protein recommendations. Two potential avenues for training are university curricula and continuing education opportunities provided to practitioners who provide dietary advice.

Combining NMR and Molecular Dynamics Studies for Insights into the Allostery of Small GTPase–Protein Interactions

PubMed Central

Zhang, Liqun; Bouguet-Bonnet, Sabine; Buck, Matthias

2014-01-01

Combinations of experimentally derived data from nuclear magnetic resonance spectroscopy and analyses of molecular dynamics trajectories increasingly allow us to obtain a detailed description of the molecular mechanisms by which proteins function in signal transduction. This chapter provides an introduction into these two methodologies, illustrated by example of a small GTPase–effector interaction. It is increasingly becoming clear that new insights are provided by the combination of experimental and computational methods. Understanding the structural and protein dynamical contributions to allostery will be useful for the engineering of new binding interfaces and protein functions, as well as for the design/in silico screening of chemical agents that can manipulate the function of small GTPase–protein interactions in diseases such as cancer. PMID:22052494

Development of an innovative immunoassay for CP4EPSPS and Cry1AB genetically modified protein detection and quantification.

PubMed

Ermolli, M; Prospero, A; Balla, B; Querci, M; Mazzeo, A; Van Den Eede, G

2006-09-01

An innovative immunoassay, called enzyme-linked immunoabsorbant assay (ELISA) Reverse, based on a new conformation of the solid phase, was developed. The solid support was expressly designed to be immersed directly in liquid samples to detect the presence of protein targets. Its application is proposed in those cases where a large number of samples have to be screened simultaneously or when the simultaneous detection of different proteins is required. As a first application, a quantitative immunoassay for Cry1AB protein in genetically modified maize was optimized. The method was tested using genetically modified organism concentrations from 0.1 to 2.0%. The limit of detection and limit of quantitation of the method were determined as 0.0056 and 0.0168 (expressed as the percentage of genetically modified organisms content), respectively. A qualitative multiplex assay to assess the presence of two genetically modified proteins simultaneously was also established for the case of the Cry1AB and the CP4EPSPS (5-enolpyruvylshikimate-3-phosphate synthase) present in genetically modified maize and soy, respectively.

IRaPPA: Information retrieval based integration of biophysical models for protein assembly selection

PubMed Central

Moal, Iain H.; Barradas-Bautista, Didier; Jiménez-García, Brian; Torchala, Mieczyslaw; van der Velde, Arjan; Vreven, Thom; Weng, Zhiping; Bates, Paul A.; Fernández-Recio, Juan

2018-01-01

Motivation In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability IRaPPA has been implemented in the SwarmDock server (http://bmm.crick.ac.uk/~SwarmDock/), pyDock server (http://life.bsc.es/pid/pydockrescoring/) and ZDOCK server (http://zdock.umassmed.edu/), with code available on request. PMID:28200016

Exploiting translational coupling for the selection of cells producing toxic recombinant proteins from expression vectors.

PubMed

Tagliavia, Marcello; Cuttitta, Angela

2016-01-01

High rates of plasmid instability are associated with the use of some expression vectors in Escherichia coli, resulting in the loss of recombinant protein expression. This is due to sequence alterations in vector promoter elements caused by the background expression of the cloned gene, which leads to the selection of fast-growing, plasmid-containing cells that do not express the target protein. This phenomenon, which is worsened when expressing toxic proteins, results in preparations containing very little or no recombinant protein, or even in clone loss; however, no methods to prevent loss of recombinant protein expression are currently available. We have exploited the phenomenon of translational coupling, a mechanism of prokaryotic gene expression regulation, in order to select cells containing plasmids still able to express recombinant proteins. Here we designed an expression vector in which the cloned gene and selection marker are co-expressed. Our approach allowed for the selection of the recombinant protein-expressing cells and proved effective even for clones encoding toxic proteins.

Overview of the main methods used to combine proteins with nanosystems: absorption, bioconjugation, and encapsulation

PubMed Central

Di Marco, Mariagrazia; Shamsuddin, Shaharum; Razak, Khairunisak Abdul; Aziz, Azlan Abdul; Devaux, Corinne; Borghi, Elsa; Levy, Laurent; Sadun, Claudia

2010-01-01

The latest development of protein engineering allows the production of proteins having desired properties and large potential markets, but the clinical advances of therapeutical proteins are still limited by their fragility. Nanotechnology could provide optimal vectors able to protect from degradation therapeutical biomolecules such as proteins, enzymes or specific polypeptides. On the other hand, some proteins can be also used as active ligands to help nanoparticles loaded with chemotherapeutic or other drugs to reach particular sites in the body. The aim of this review is to provide an overall picture of the general aspects of the most successful approaches used to combine proteins with nanosystems. This combination is mainly achieved by absorption, bioconjugation and encapsulation. Interactions of nanoparticles with biomolecules and caveats related to protein denaturation are also pointed out. A clear understanding of nanoparticle-protein interactions could make possible the design of precise and versatile hybrid nanosystems. This could further allow control of their pharmacokinetics as well as activity, and safety. PMID:20161986