Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

Anishchenko, Ivan; Kundrotas, Petras J.; Vakser, Ilya A.

doi:10.1016/j.bpj.2018.07.035

Cited by 29Protein Structure and DynamicsEnzyme Structure and FunctionRNA and protein synthesis mechanisms

Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

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Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A.·2018·Biophysical Journal

DOI 10.1016/j.bpj.2018.07.035PMC PMC6127504

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Abstract

The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.

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All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp973084f · Cited by 14,270 · see citing works
Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids
Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996) · Journal of the American Chemical Society · doi:10.1021/ja9621760 · Cited by 14,078 · see citing works
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,111 · see citing works
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931 · see citing works
Practical Methods of Optimization
Fletcher, R. (2000) · Wiley eBooks · doi:10.1002/9781118723203 · Cited by 7,649 · see citing works
A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force‐field parameter sets 53A5 and 53A6
Oostenbrink, C., Villa, A., Mark, A. E., & A, W. F. V. G. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20090 · Cited by 3,726 · see citing works
The Potts model
Wu, F. Y. (1982) · Reviews of Modern Physics · doi:10.1103/revmodphys.54.235 · Cited by 3,570 · see citing works
PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations
Dolinsky, T. J., Nielsen, J. E., McCammon, J. A., & Baker, N. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh381 · Cited by 3,485 · see citing works
Relative Importance for Linear Regression in <i>R</i> : The Package <b>relaimpo</b>
Grömping, U. (2006) · Journal of Statistical Software · doi:10.18637/jss.v017.i01 · Cited by 2,615 · see citing works
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · see citing works
Statistical potential for assessment and prediction of protein structures
Shen, M., & Šali, A. (2006) · Protein Science · doi:10.1110/ps.062416606 · Cited by 2,294 · see citing works
Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations
Guerois, R., Nielsen, J. E., & Serrano, L. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00442-4 · Cited by 1,728 · cited 1× · see citing works
“Thus, they are successfully applied to many problems, such as discrimination of the native structure from decoys (9,10), fold recognition (11), structure prediction (12), protein docking (13–15) and design (16,17), and prediction of protein stability and afﬁnity (18–20).”
Background
Weak pairwise correlations imply strongly correlated network states in a neural population
Schneidman, E., Berry, M. J., Segev, R., & Bialek, W. (2006) · Nature · doi:10.1038/nature04701 · Cited by 1,701 · see citing works
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666 · see citing works
Spin glasses and the statistical mechanics of protein folding.
Bryngelson, J. D., & Wolynes, P. G. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.21.7524 · Cited by 1,647 · see citing works
Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation
Miyazawa, S., & Jernigan, R. L. (1985) · Macromolecules · doi:10.1021/ma00145a039 · Cited by 1,523 · see citing works
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions
Simons, K. T., Kooperberg, C. L., Huang, E. S., & Baker, D. A. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0959 · Cited by 1,387 · see citing works
Improved prediction of protein side‐chain conformations with SCWRL4
Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22488 · Cited by 1,312 · see citing works
Markov Random Fields and Their Applications
Kindermann, R. P., & Snell, J. L. (1980) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/001 · Cited by 1,292 · see citing works
Hydrophobicity of Amino Acid Residues in Globular Proteins
Rose, G. D., Geselowitz, A., Lesser, G. J., Lee, R., & Zehfus, M. H. (1985) · Science · doi:10.1126/science.4023714 · Cited by 1,176 · see citing works
Introduction to bivariate and multivariate analysis
Sen, P., Lindeman, R., Merenda, P. F., & Gold, R. Z. (1981) · Journal of the American Statistical Association · doi:10.2307/2287559 · Cited by 1,155 · see citing works
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144 · see citing works
Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.
Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A., Aflalo, C., & Vakser, I. (1992) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.89.6.2195 · Cited by 1,143 · see citing works
Self-citation
LGA: a method for finding 3D similarities in protein structures
Zemla, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg571 · Cited by 1,069 · see citing works
Calculation of conformational ensembles from potentials of mena force
Sippl, M. J. (1990) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80269-4 · Cited by 1,064 · see citing works
WATER MEDIATION IN PROTEIN FOLDING AND MOLECULAR RECOGNITION
Levy, Y., & Onuchic, J. N. (2006) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.35.040405.102134 · Cited by 1,051 · see citing works
Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction
Zhou, H., & Zhou, Y. (2002) · Protein Science · doi:10.1110/ps.0217002 · Cited by 902 · cited 2× · see citing works
“Thus, they are successfully applied to many problems, such as discrimination of the native structure from decoys (9,10), fold recognition (11), structure prediction (12), protein docking (13–15) and design (16,17), and prediction of protein stability and afﬁnity (18–20).”
Background
Native protein sequences are close to optimal for their structures
Kuhlman, B. A., & Baker, D. A. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.19.10383 · Cited by 814 · see citing works
History of the Lenz-Ising Model
Brush, S. G. (1967) · Reviews of Modern Physics · doi:10.1103/revmodphys.39.883 · Cited by 739 · see citing works
Critical assessment of methods of protein structure prediction (CASP) — round x
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24452 · Cited by 673 · see citing works
Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661 · see citing works
A large‐scale experiment to assess protein structure prediction methods
Moult, J., Pedersen, J. T., Judson, R., & Fidelis, K. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340230303 · Cited by 555 · see citing works
Determination of atomic desolvation energies from the structures of crystallized proteins
Zhang, C., Vasmatzis, G., Cornette, J. L., & DeLisi, C. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0859 · Cited by 536 · see citing works
ZRANK: Reranking protein docking predictions with an optimized energy function
Pierce, B., & Weng, Z. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21373 · Cited by 468 · see citing works
An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction 1 1Edited by F. Cohen
Samudrala, R., & Moult, J. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1479 · Cited by 446 · see citing works
Effective energy functions for protein structure prediction
Lazaridis, T. (2000) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(00)00063-4 · Cited by 445 · see citing works
Assessment of blind predictions of protein–protein interactions: Current status of docking methods
Mendez, R., Leplae, R., Maria, L. D., & Wodak, S. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10393 · Cited by 438 · see citing works
Medium- and Long-Range Interaction Parameters between Amino Acids for Predicting Three-Dimensional Structures of Proteins
Tanaka, S., & Scheraga, H. A. (1976) · Macromolecules · doi:10.1021/ma60054a013 · Cited by 437 · see citing works
Statistical Potentials Extracted From Protein Structures: How Accurate Are They?
Thomas, P. D., & Dill, K. A. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0175 · Cited by 425 · see citing works
Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2
Vreven, T., Moal, I. H., Vangone, A., Pierce, B. G., Kastritis, P. L., Torchala, M., Chaleil, R., Jiménez-García, B., Bates, P. A., Fernandez-Recio, J., et al. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.07.016 · Cited by 424 · see citing works
Energy Functions that Discriminate X-ray and Near-native Folds from Well-constructed Decoys
Park, B. H., & Levitt, M. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0256 · Cited by 410 · see citing works
Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0
Dehouck, Y., Grosfils, A., Folch, B., Gilis, D., Bogaerts, P., & Rooman, M. J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp445 · Cited by 409 · see citing works
ProTherm, version 4.0: thermodynamic database for proteins and mutants
Bava, K. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh082 · Cited by 367 · see citing works
PISCES: recent improvements to a PDB sequence culling server
Wang, G., & Dunbrack, R. L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki402 · Cited by 355 · see citing works
Contact potential that recognizes the correct folding of globular proteins
Maiorov, V. N., & Grippen, G. M. (1992) · Journal of Molecular Biology · doi:10.1016/0022-2836(92)90228-c · Cited by 346 · see citing works
Statistical potentials for fold assessment
Melo, F., Sánchez, R., & Šali, A. (2002) · Protein Science · doi:10.1002/pro.110430 · Cited by 345 · see citing works
Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes
Betancourt, M. R., & Thirumalai, D. (1999) · Protein Science · doi:10.1110/ps.8.2.361 · Cited by 345 · see citing works
Protein Folding Theory: From Lattice to All-Atom Models
Mirny, L., & Shakhnovich, E. (2001) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.30.1.361 · Cited by 341 · see citing works
Learning generative models for protein fold families
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316 · see citing works
Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns
Lezon, T. R., Banavar, J. R., Cieplak, M., Maritan, A., & Fedoroff, N. V. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609152103 · Cited by 270 · see citing works
GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction
Zhou, H., & Skolnick, J. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.09.012 · Cited by 270 · see citing works
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1
Figliuzzi, M., Jacquier, H., Schug, A. H., Tenaillon, O., & Weigt, M. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv211 · Cited by 263 · see citing works
An iterative method for extracting energy-like quantities from protein structures.
Thomas, P. D., & Dill, K. A. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.21.11628 · Cited by 254 · see citing works
Protein docking for low-resolution structures
Vakser, I. A. (1995) · Protein Engineering Design and Selection · doi:10.1093/protein/8.4.371 · Cited by 246 · see citing works
Self-citation
Specific interactions for ab initio folding of protein terminal regions with secondary structures
Yang, Y., & Zhou, Y. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21968 · Cited by 245 · see citing works
A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction
Zhang, J., & Zhang, Y. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015386 · Cited by 237 · see citing works
Critical assessment of methods of protein structure prediction: Progress and new directions in round XI
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25064 · Cited by 226 · see citing works
Nature of Driving Force for Protein Folding: A Result From Analyzing the Statistical Potential
Li, H., Tang, C., & Wingreen, N. S. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.765 · Cited by 223 · see citing works
Protein tertiary structure recognition using optimized Hamiltonians with local interactions.
Goldstein, R. A., Luthey-Schulten, Z. A., & Wolynes, P. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.19.9029 · Cited by 217 · see citing works
Decoys ‘R’ Us: A database of incorrect conformations to improve protein structure prediction
Samudrala, R., & Levitt, M. (2000) · Protein Science · doi:10.1110/ps.9.7.1399 · Cited by 207 · see citing works
Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods
Cocco, S., Leibler, S., & Monasson, R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906705106 · Cited by 192 · see citing works
OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing.
Lu, M., Dousis, A., & Ma, J.-P. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.11.033 · Cited by 179 · see citing works
Inverse Ising Inference Using All the Data
Aurell, E., & Ekeberg, M. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.090201 · Cited by 162 · see citing works
DARS (Decoys As the Reference State) potentials for protein-protein docking.
Chuang, G., Kozakov, D., Brenke, R., Comeau, S. R., & Vajda, S. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.135814 · Cited by 162 · see citing works
Statistical potentials extracted from protein structures: Are these meaningful potentials?
Ben-Naim, A. Y. (1997) · The Journal of Chemical Physics · doi:10.1063/1.474725 · Cited by 150 · see citing works
Potential energy functions for protein design
Boas, F. E., & Harbury, P. B. (2007) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2007.03.006 · Cited by 143 · see citing works
In quest of an empirical potential for protein structure prediction
Skolnick, J. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.02.004 · Cited by 142 · see citing works
Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all‐atom statistical energy functions
Yang, Y., & Zhou, Y. (2008) · Protein Science · doi:10.1110/ps.033480.107 · Cited by 137 · see citing works
Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness
Levy, R. M., Haldane, A., & Flynn, W. F. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.11.004 · Cited by 134 · see citing works
Atomic contact vectors in protein‐protein recognition
Mintseris, J., & Weng, Z. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10432 · Cited by 126 · see citing works
Large‐scale prediction of protein geometry and stability changes for arbitrary single point mutations
Bordner, A. J., & Abagyan, R. A. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20185 · Cited by 120 · see citing works
Development of novel statistical potentials for protein fold recognition
Buchete, N., Straub, J., & Thirumalai, D. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.03.002 · Cited by 119 · see citing works
The scoring of poses in protein-protein docking: current capabilities and future directions
Moal, I. H., Torchala, M., Bates, P. A., & Fernández‐Recio, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-286 · Cited by 116 · see citing works
Stochastic Differential Systems, Stochastic Control Theory and Applications
Fleming, W. H., & Lions, P. (1988) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4613-8762-6 · Cited by 101 · see citing works
New statistical potential for quality assessment of protein models and a survey of energy functions
Rykunov, D., & Fiser, A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-128 · Cited by 89 · see citing works
Integrating atom‐based and residue‐based scoring functions for protein–protein docking
Vreven, T., Hwang, H., & Weng, Z. (2011) · Protein Science · doi:10.1002/pro.687 · Cited by 84 · see citing works
Dockground: A comprehensive data resource for modeling of protein complexes
Kundrotas, P. J., Anishchenko, I. V., Dauzhenka, T. A., Kotthoff, I., Mnevets, D., Copeland, M. M., & Vakser, I. A. (2017) · Protein Science · doi:10.1002/pro.3295 · Cited by 82 · see citing works
Self-citation
Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes
Shekhar, K., Ruberman, C., Ferguson, A. L., Barton, J. P., Mehran Kardar, A., & Chakraborty, A. K. (2013) · Physical Review E · doi:10.1103/physreve.88.062705 · Cited by 78 · see citing works
Inferring ideal amino acid interaction forms from statistical protein contact potentials
Pokarowski, P., Kloczkowski, A., Jernigan, R. L., et al. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20380 · Cited by 78 · see citing works
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized
Hamelryck, T., Borg, M., Paluszewski, M., Paulsen, J., Frellsen, J., Andreetta, C., Boomsma, W., Bottaro, S., & Ferkinghoff‐Borg, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013714 · Cited by 75 · see citing works
Tertiary and quaternary conformational changes in aspartate transcarbamylase: a normal mode study
Thomas, A. M., Hinsen, K., Field, M. J., & Pérahia, D. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990101)34:1 · Cited by 64 · see citing works
Low-resolution structural modeling of protein interactome
Vakser, I. A. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.12.003 · Cited by 62 · see citing works
Effects of amino acid composition, finite size of proteins, and sparse statistics on distance‐dependent statistical pair potentials
Rykunov, D., & Fiser, A. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21279 · Cited by 61 · see citing works
Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease
Flynn, W. F., Haldane, A., Torbett, B. E., & Levy, R. M. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx095 · Cited by 55 · see citing works
DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking
Liu, S., & Vakser, I. A. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-280 · Cited by 52 · see citing works
Self-citation
A simple and efficient statistical potential for scoring ensembles of protein structures
Cossio, P., Granata, D., Laio, A., Seno, F., & Trovato, A. (2012) · Scientific Reports · doi:10.1038/srep00351 · Cited by 48 · see citing works
Consistency of Maximum Likelihood and Pseudo-Likelihood Estimators for Gibbs Distributions
Gidas, B. (1988) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4613-8762-6_10 · Cited by 43 · see citing works
A maximum likelihood framework for protein design
Kleinman, C. L., Rodrigue, N., Bonnard, C., et al. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-326 · Cited by 32 · see citing works
Developing optimal non-linear scoring function for protein design
Hu, C., Li, X., & Liang, J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth369 · Cited by 31 · see citing works
Modeling complexes of modeled proteins
Anishchenko, I. V., Kundrotas, P. J., & Vakser, I. A. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25183 · Cited by 26 · see citing works
Self-citation
Artefacts and biases affecting the evaluation of scoring functions on decoy sets for protein structure prediction
Handl, J., Knowles, J. D., & Lovell, S. C. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp150 · Cited by 22 · see citing works
Bayesian approach to inverse statistical mechanics
Habeck, M. (2014) · Physical Review E · doi:10.1103/physreve.89.052113 · Cited by 20 · see citing works
Modeling CAPRI targets 110‐120 by template‐based and free docking using contact potential and combined scoring function
Kundrotas, P. J., Anishchenko, I. V., Badal, V. D., Das, M., Dauzhenka, T. A., & Vakser, I. A. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25380 · Cited by 20 · see citing works
Self-citation
ProPairs: A Data Set for Protein–Protein Docking
Krull, F., Korff, G., Elghobashi‐Meinhardt, N., & Knapp, E. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00082 · Cited by 16 · see citing works
Developing optimal nonlinear scoring function for protein designPreprint
Hu, C., Li, X., & Liang, J. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0407040 · Cited by 6 · see citing works
Structure of Methods
Fletcher, R. (2000) · doi:10.1002/9781118723203.ch2 · Cited by 4 · see citing works
Fast optimization of statistical potentials for structurally constrained phylogenetic models
Bonnard, C., Kleinman, C. L., Rodrigue, N., et al. (2009) · BMC Evolutionary Biology · doi:10.1186/1471-2148-9-227 · Cited by 2 · see citing works

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Authors: Anishchenko, I., Kundrotas, P. J., & Vakser, I. A.
Affiliations: University of Kansas (US)
Corresponding author: Kundrotas, Petras J.
Vakser, Ilya A.
ORCID: Anishchenko, Ivan
Kundrotas, Petras J.
Vakser, Ilya A.
Venue: Biophysical Journal
Publisher: Elsevier BV
Published: 2018-09
Volume: 115
Issue: 5
Pages: 809-821
ISSN: 0006-3495 (ISSN-L)
DOI: 10.1016/j.bpj.2018.07.035
PMC: PMC6127504
OpenAlex: W2886522305
Cited by: 29
References: 97
Citations by year: 2026: 3 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 8 · 2021: 9 · 2020: 2 · 2019: 3 · 2017: 1
FWCI: 1.25
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 368 d)
Locations: Biophysical Journal (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (R01GM074255)
National Science Foundation (DBI1262621, DBI1565107)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords: contact potential difference, protein structure prediction, Potts model, knowledge-based potentials, protein complexes, protein docking, protein-protein binding affinity, residue-residue contacts, decoy discrimination
MeSH terms: Models, Molecular, Protein Conformation, Proteins, Thermodynamics, Likelihood Functions, Point Mutation, Protein Stability

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Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

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