1
|
Zhou HX, Pang X. Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation. Chem Rev 2018; 118:1691-1741. [PMID: 29319301 DOI: 10.1021/acs.chemrev.7b00305] [Citation(s) in RCA: 454] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
Collapse
Affiliation(s)
- Huan-Xiang Zhou
- Department of Chemistry and Department of Physics, University of Illinois at Chicago , Chicago, Illinois 60607, United States.,Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
| | - Xiaodong Pang
- Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
| |
Collapse
|
2
|
Abstract
We report the performance of our approaches for protein-protein docking and interface analysis in CAPRI rounds 20-26. At the core of our pipeline was the ZDOCK program for rigid-body protein-protein docking. We then reranked the ZDOCK predictions using the ZRANK or IRAD scoring functions, pruned and analyzed energy landscapes using clustering, and analyzed the docking results using our interface prediction approach RCF. When possible, we used biological information from the literature to apply constraints to the search space during or after the ZDOCK runs. For approximately half of the standard docking challenges we made at least one prediction that was acceptable or better. For the scoring challenges we made acceptable or better predictions for all but one target. This indicates that our scoring functions are generally able to select the correct binding mode.
Collapse
Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | | | | | | |
Collapse
|
3
|
Huang SY. Search strategies and evaluation in protein–protein docking: principles, advances and challenges. Drug Discov Today 2014; 19:1081-96. [DOI: 10.1016/j.drudis.2014.02.005] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Revised: 01/04/2014] [Accepted: 02/24/2014] [Indexed: 01/10/2023]
|
4
|
Esmaielbeiki R, Nebel JC. Scoring docking conformations using predicted protein interfaces. BMC Bioinformatics 2014; 15:171. [PMID: 24906633 PMCID: PMC4057934 DOI: 10.1186/1471-2105-15-171] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 05/29/2014] [Indexed: 12/22/2022] Open
Abstract
Background Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). Results First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. Conclusion Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.
Collapse
Affiliation(s)
- Reyhaneh Esmaielbeiki
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
| | | |
Collapse
|
5
|
Qin S, Zhou HX. Using the concept of transient complex for affinity predictions in CAPRI rounds 20-27 and beyond. Proteins 2013; 81:2229-36. [PMID: 23873496 PMCID: PMC3842397 DOI: 10.1002/prot.24366] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Revised: 06/26/2013] [Accepted: 06/29/2013] [Indexed: 11/10/2022]
Abstract
Predictions of protein-protein binders and binding affinities have traditionally focused on features pertaining to the native complexes. In developing a computational method for predicting protein-protein association rate constants, we introduced the concept of transient complex after mapping the interaction energy surface. The transient complex is located at the outer boundary of the bound-state energy well, having near-native separation and relative orientation between the subunits but not yet formed most of the short-range native interactions. We found that the width of the binding funnel and the electrostatic interaction energy of the transient complex are among the features predictive of binders and binding affinities. These ideas were very promising for the five affinity-related targets (T43-45, 55, and 56) of CAPRI rounds 20-27. For T43, we ranked the single crystallographic complex as number 1 and were one of only two groups that clearly identified that complex as a true binder; for T44, we ranked the only design with measurable binding affinity as number 4. For the nine docking targets, continuing on our success in previous CAPRI rounds, we produced 10 medium-quality models for T47 and acceptable models for T48 and T49. We conclude that the interaction energy landscape and the transient complex in particular will complement existing features in leading to better prediction of binding affinities.
Collapse
Affiliation(s)
- Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| |
Collapse
|
6
|
Li L, Huang Y, Xiao Y. How to use not-always-reliable binding site information in protein-protein docking prediction. PLoS One 2013; 8:e75936. [PMID: 24124522 PMCID: PMC3790831 DOI: 10.1371/journal.pone.0075936] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Accepted: 08/22/2013] [Indexed: 11/19/2022] Open
Abstract
In many protein-protein docking algorithms, binding site information is used to help predicting the protein complex structures. Using correct and accurate binding site information can increase protein-protein docking success rate significantly. On the other hand, using wrong binding sites information should lead to a failed prediction, or, at least decrease the success rate. Recently, various successful theoretical methods have been proposed to predict the binding sites of proteins. However, the predicted binding site information is not always reliable, sometimes wrong binding site information could be given. Hence there is a high risk to use the predicted binding site information in current docking algorithms. In this paper, a softly restricting method (SRM) is developed to solve this problem. By utilizing predicted binding site information in a proper way, the SRM algorithm is sensitive to the correct binding site information but insensitive to wrong information, which decreases the risk of using predicted binding site information. This SRM is tested on benchmark 3.0 using purely predicted binding site information. The result shows that when the predicted information is correct, SRM increases the success rate significantly; however, even if the predicted information is completely wrong, SRM only decreases success rate slightly, which indicates that the SRM is suitable for utilizing predicted binding site information.
Collapse
Affiliation(s)
- Lin Li
- Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, South Carolina, United States of America
| | - Yanzhao Huang
- Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail: (YH); (YX)
| | - Yi Xiao
- Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail: (YH); (YX)
| |
Collapse
|
7
|
Qin S, Zhou HX. PI 2PE: A Suite of Web Servers for Predictions Ranging From Protein Structure to Binding Kinetics. Biophys Rev 2012; 5:41-46. [PMID: 23526172 DOI: 10.1007/s12551-012-0086-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
PI2PE (http://pipe.sc.fsu.edu) is a suite of four web servers for predicting a variety of folding- and binding-related properties of proteins. These include the solvent accessibility of amino acids upon protein folding, the amino acids forming the interfaces of protein-protein and protein-nucleic acid complexes, and the binding rate constants of these complexes. Three of the servers debuted in 2007, and have garnered ~2,500 unique users and finished over 30,000 jobs. The functionalities of these servers are now enhanced, and a new sever, for predicting the binding rate constants, is added. Together, these web servers form a pipeline from protein sequence to tertiary structure, then to quaternary structure, and finally to binding kinetics.
Collapse
Affiliation(s)
- Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
| | | |
Collapse
|
8
|
Qin S, Zhou HX. Structural models of protein-DNA complexes based on interface prediction and docking. Curr Protein Pept Sci 2012; 12:531-9. [PMID: 21787304 DOI: 10.2174/138920311796957694] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 04/01/2011] [Accepted: 05/04/2011] [Indexed: 11/22/2022]
Abstract
Protein-DNA interactions are the physical basis of gene expression and DNA modification. Structural models that reveal these interactions are essential for their understanding. As only a limited number of structures for protein-DNA complexes have been determined by experimental methods, computation methods provide a potential way to fill the need. We have developed the DISPLAR method to predict DNA binding sites on proteins. Predicted binding sites have been used to assist the building of structural models by docking, either by guiding the docking or by selecting near-native candidates from the docked poses. Here we applied the DISPLAR method to predict the DNA binding sites for 20 DNA-binding proteins, which have had their DNA binding sites characterized by NMR chemical shift perturbation. For two of these proteins, the structures of their complexes with DNA have also been determined. With the help of the DISPLAR predictions, we built structural models for these two complexes. Evaluations of both the DNA binding sites for 20 proteins and the structural models of the two protein-DNA complexes against experimental results demonstrate the significant promise of our model-building approach.
Collapse
Affiliation(s)
- Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | | |
Collapse
|
9
|
Park SH, Hansen B. Prediction of Protein-Protein Interaction Sites Based on 3D Surface Patches Using SVM. ACTA ACUST UNITED AC 2012. [DOI: 10.3745/kipstd.2012.19d.1.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
10
|
Qiu Z, Wang X. Prediction of protein-protein interaction sites using patch-based residue characterization. J Theor Biol 2011; 293:143-50. [PMID: 22037062 DOI: 10.1016/j.jtbi.2011.10.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2011] [Revised: 09/13/2011] [Accepted: 10/15/2011] [Indexed: 10/15/2022]
Abstract
Identifying protein-protein interaction sites provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Using a patch-based model for residue characterization, we trained random forest classifiers for residue-based interface prediction, which was followed by a clustering procedure to produce patches for patch-based interface prediction. For residue-based interface prediction, our method achieves a specificity rate of 0.7 and a sensitivity rate of 0.78. For patch-based interface prediction, a success rate of 0.80 is achieved. Based on same datasets, we also compare it with several published methods. The results show that our method is a successful predictor for residue-based and patch-based interface prediction.
Collapse
Affiliation(s)
- Zhijun Qiu
- The State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology, 2 Ling-Gong Road, Dalian 116024, China
| | | |
Collapse
|
11
|
Bao L, Huang Q, Chang L, Sun Q, Zhou J, Lu H. Cloning and Characterization of Two β-Glucosidase/Xylosidase Enzymes from Yak Rumen Metagenome. Appl Biochem Biotechnol 2011; 166:72-86. [PMID: 22020745 DOI: 10.1007/s12010-011-9405-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2011] [Accepted: 10/04/2011] [Indexed: 10/16/2022]
|
12
|
Bound water at protein-protein interfaces: partners, roles and hydrophobic bubbles as a conserved motif. PLoS One 2011; 6:e24712. [PMID: 21961043 PMCID: PMC3178540 DOI: 10.1371/journal.pone.0024712] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2011] [Accepted: 08/17/2011] [Indexed: 12/18/2022] Open
Abstract
Background There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region. Methodology A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules. Results The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins' interaction (−0.46 kcal mol−1), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable (+0.03 kcal mol−1). Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Å2 and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions.
Collapse
|
13
|
Abstract
In CAPRI rounds 13-19, we submitted models that are of acceptable or higher quality for 6 of the total of 13 targets. This success builds on our record in previous CAPRI rounds. The docking problem can be divided into two steps. In the first, translational/rotational and conformational space is searched to generate a pool of docked poses; the success of this search step is measured by whether near-native poses are included in the pool. In the second step, the pool is selected for near-native poses. In our previous assessment of CAPRI results, we suggested that the search problem is largely solved; a remaining problem is to select near-native poses. Our work in these new rounds of CAPRI was guided by this assessment. To solve the selection problem, we used an assortment of criteria on the interfaces of candidate poses. In one extreme, represented by T29, with very little known interface information, our criterion for top models was based on interface prediction. Poses in which the predicted interface residues occurred in interfaces were selected. Our model 1 for T29 was of medium quality. In the other extreme, represented by T40, with reliably known interface information, our selection was solely based on such information. Nine of the ten models submitted for T40 were of high (3 models), medium (4 models), and acceptable (2 models) quality. Our strategy of mixing predicted and known interface information appears to be widely applicable for the selection of near-native poses.
Collapse
Affiliation(s)
- Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
| | | |
Collapse
|
14
|
Gong X, Wang P, Yang F, Chang S, Liu B, He H, Cao L, Xu X, Li C, Chen W, Wang C. Protein-protein docking with binding site patch prediction and network-based terms enhanced combinatorial scoring. Proteins 2010; 78:3150-5. [DOI: 10.1002/prot.22831] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
15
|
Janin J. Protein–protein docking tested in blind predictions: the CAPRI experiment. MOLECULAR BIOSYSTEMS 2010; 6:2351-62. [DOI: 10.1039/c005060c] [Citation(s) in RCA: 132] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
16
|
Abstract
The quaternary structure (QS) of a protein is determined by measuring its molecular weight in solution. The data have to be extracted from the literature, and they may be missing even for proteins that have a crystal structure reported in the Protein Data Bank (PDB). The PDB and other databases derived from it report QS information that either was obtained from the depositors or is based on an analysis of the contacts between polypeptide chains in the crystal, and this frequently differs from the QS determined in solution.The QS of a protein can be predicted from its sequence using either homology or threading methods. However, a majority of the proteins with less than 30% sequence identity have different QSs. A model of the QS can also be derived by docking the subunits when their 3D structure is independently known, but the model is likely to be incorrect if large conformation changes take place when the oligomer assembles.
Collapse
Affiliation(s)
- Anne Poupon
- Yeast Structural Genomics, IBBMC UMR 8619 CNRS, Université Paris-Sud, Orsay, France
| | | |
Collapse
|
17
|
Li N, Sun Z, Jiang F. Prediction of protein-protein binding site by using core interface residue and support vector machine. BMC Bioinformatics 2008; 9:553. [PMID: 19102736 PMCID: PMC2627892 DOI: 10.1186/1471-2105-9-553] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2008] [Accepted: 12/22/2008] [Indexed: 12/04/2022] Open
Abstract
Background The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods. Results In this work, we describe a binding site prediction method by designing a new residue neighbour profile and by selecting only the core-interface residues for SVM training. The residue neighbour profile includes both the sequential and the spatial neighbour residues of an interface residue, which is a more complete description of the physical and chemical characteristics surrounding the interface residue. The concept of core interface is applied in selecting the interface residues for training the SVM models, which is shown to result in better discrimination between the core interface and other residues. The best SVM model trained was tested on a test set of 50 randomly selected proteins. The sensitivity, specificity, and MCC for the prediction of the core interface residues were 60.6%, 53.4%, and 0.243, respectively. Our prediction results on this test set were compared with other three binding site prediction methods and found to perform better. Furthermore, our method was tested on the 101 unbound proteins from the protein-protein interaction benchmark v2.0. The sensitivity, specificity, and MCC of this test were 57.5%, 32.5%, and 0.168, respectively. Conclusion By improving both the descriptions of the interface residues and their surrounding environment and the training strategy, better SVM models were obtained and shown to outperform previous methods. Our tests on the unbound protein structures suggest further improvement is possible.
Collapse
Affiliation(s)
- Nan Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, PR China.
| | | | | |
Collapse
|
18
|
Zhou HX, Qin S, Tjong H. Modeling Protein–Protein and Protein–Nucleic Acid Interactions: Structure, Thermodynamics, and Kinetics. ANNUAL REPORTS IN COMPUTATIONAL CHEMISTRY 2008. [DOI: 10.1016/s1574-1400(08)00004-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|