Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction

Zhou, Hongyi; Zhou, Yaoqi

doi:10.1110/ps.0217002

Cited by 902Protein Structure and DynamicsEnzyme Structure and FunctionComputational Drug Discovery Methods

Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction

Open access

Zhou, Hongyi, Zhou, Yaoqi·2002·Protein Science

DOI 10.1110/ps.0217002PMC PMC2373736

Open this paper in CoChat — free account Open PDF

Abstract

The distance-dependent structure-derived potentials developed so far all employed a reference state that can be characterized as a residue (atom)-averaged state. Here, we establish a new reference state called the distance-scaled, finite ideal-gas reference (DFIRE) state. The reference state is used to construct a residue-specific all-atom potential of mean force from a database of 1011 nonhomologous (less than 30% homology) protein structures with resolution less than 2 A. The new all-atom potential recognizes more native proteins from 32 multiple decoy sets, and raises an average Z-score by 1.4 units more than two previously developed, residue-specific, all-atom knowledge-based potentials. When only backbone and C(beta) atoms are used in scoring, the performance of the DFIRE-based potential, although is worse than that of the all-atom version, is comparable to those of the previously developed potentials on the all-atom level. In addition, the DFIRE-based all-atom potential provides the most accurate prediction of the stabilities of 895 mutants among three knowledge-based all-atom potentials. Comparison with several physical-based potentials is made.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1110/ps.0217002

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The six benchmark datasets have been widely used to test the predictive power of PPI prediction methods.
“These datasets have been widely used to test the predictive power of the PPI prediction methods [12, 13, 27–29, 29–35].”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [28]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
CHARMM: A program for macromolecular energy, minimization, and dynamics calculations
Brooks, B., Bruccoleri, R., Olafson, B. D., States, D., Swaminathan, S., & Karplus, M. (1983) · Journal of Computational Chemistry · doi:10.1002/jcc.540040211 · Cited by 13,736 · see citing works
Assessment of protein models with three-dimensional profiles
Lüthy, R., Bowie, J., & Eisenberg, D. (1992) · Nature · doi:10.1038/356083a0 · Cited by 3,588 · see citing works
An all atom force field for simulations of proteins and nucleic acids
Weiner, S. J., Kollman, P. A., Nguyen, D. T., & Case, D. A. (1986) · Journal of Computational Chemistry · doi:10.1002/jcc.540070216 · Cited by 3,440 · see citing works
Recognition of errors in three‐dimensional structures of proteins
Sippl, M. J. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340170404 · Cited by 2,156 · 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 · 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 1,588 · see citing works
The GROMOS Biomolecular Simulation Program Package
Scott, W. R. P., Hünenberger, P. H., Tironi, I. G., Mark, A. E., Billeter, S. R., Fennen, J., Torda, A. E., Huber, T., Krüger, P., & A, W. F. V. G. (1999) · The Journal of Physical Chemistry A · doi:10.1021/jp984217f · Cited by 1,489 · 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
Pathways to a Protein Folding Intermediate Observed in a 1-Microsecond Simulation in Aqueous Solution
Duan, Y., & Kollman, P. A. (1998) · Science · doi:10.1126/science.282.5389.740 · Cited by 1,341 · see citing works
Simulation of activation free energies in molecular systems
Neria, E., Fischer, S., & Karplus, M. (1996) · The Journal of Chemical Physics · doi:10.1063/1.472061 · Cited by 1,325 · see citing works
A new approach to protein fold recognition
Jones, D. T., Taylort, W. R., & Thornton, J. M. (1992) · Nature · doi:10.1038/358086a0 · Cited by 1,221 · see citing works
Topological and energetic factors: what determines the structural details of the transition state ensemble and "en-route" intermediates for protein folding? An investigation for small globular proteins.
Clementi, C., Nymeyer, H., & Onuchic, J. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3693 · Cited by 1,203 · 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
Selection of representative protein data sets
Hobohm, U., Scharf, M. E., Schneider, R., & Sander, C. (1992) · Protein Science · doi:10.1002/pro.5560010313 · Cited by 818 · 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
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
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
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
An empirical energy function for threading protein sequence through the folding motif
Bryant, S. H., & Lawrence, C. E. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340160110 · Cited by 400 · see citing works
A Course in Statistical Mechanics
Friedman, H. L. (1985) · Medical Entomology and Zoology · Cited by 394 · see citing works
Generalized Born Model Based on a Surface Integral Formulation
Ghosh, A., Rapp, C. S., & Friesner, R. A. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp982533o · Cited by 386 · 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
Identification of native protein folds amongst a large number of incorrect models
Hendlich, M., Lackner, P., Weitckus, S., Flöckner, H., Froschauer, R., Gottsbacher, K., Casari, G., & Sippl, M. J. (1990) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80068-3 · Cited by 296 · see citing works
A distance‐dependent atomic knowledge‐based potential for improved protein structure selection
Lu, H. J., & Skolnick, J. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.1087 · Cited by 290 · see citing works
Discrimination of the native from misfolded protein models with an energy function including implicit solvation
Lazaridis, T., & Karplus, M. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.2685 · Cited by 269 · see citing works
MONSSTER: a method for folding globular proteins with a small number of distance restraints
Skolnick, J., Koliński, A., & Ortíz, Á. R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0720 · Cited by 258 · see citing works
An algorithm for determining the conformation of polypeptide segments in proteins by systematic search
Moult, J., & James, M. N. G. (1986) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340010207 · Cited by 258 · see citing works
Discriminating between homodimeric and monomeric proteins in the crystalline state
Ponstingl, H., Henrick, K., & Thornton, J. M. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20001001)41:1 · Cited by 226 · see citing works
Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence
Gilis, D., & Rooman, M. J. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1237 · Cited by 221 · see citing works
Reduced representation model of protein structure prediction: Statistical potential and genetic algorithms
Sun, S. (1993) · Protein Science · doi:10.1002/pro.5560020508 · Cited by 208 · see citing works
Structure-derived hydrophobic potential
Casari, G., & Sippl, M. J. (1992) · Journal of Molecular Biology · doi:10.1016/0022-2836(92)90556-y · Cited by 166 · see citing works
Ab initio construction of protein tertiary structures using a hierarchical approach
Xia, Y., Huang, E. S., Levitt, M., & Samudrala, R. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3835 · Cited by 162 · see citing works
Recent improvements in prediction of protein structure by global optimization of a potential energy function
Pillardy, J., Czaplewski, C. R., Liwo, A., Lee, J., Ripoll, D. R., Kaźmierkiewicz, R., Ołdziej, S., Wedemeyer, W. J., Gibson, K. D., Arnautova, Y. A., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.041609598 · Cited by 159 · see citing works
Stability Changes upon Mutation of Solvent- accessible Residues in Proteins Evaluated by Database-derived Potentials
Gilis, D., & Rooman, M. J. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0226 · Cited by 157 · see citing works
Fast and simple monte carlo algorithm for side chain optimization in proteins: Application to model building by homology
Holm, L., & Sander, C. (1992) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340140208 · Cited by 153 · see citing works
Proline cis-trans isomers in calbindin D9k observed by X-ray crystallography
Svensson, L. A., Thulin, E., & Forsén, S. (1992) · Journal of Molecular Biology · doi:10.1016/0022-2836(92)90976-q · Cited by 147 · see citing works
Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: Application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K
Lee, J., Liwo, A., & Scheraga, H. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.5.2025 · Cited by 147 · see citing works
Protein folding simulations with genetic algorithms and a detailed molecular description
Pedersen, J. T., & Moult, J. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1010 · Cited by 141 · see citing works
A critical assessment of comparative molecular modeling of tertiary structures of proteins*
Mosimann, S. C., Meleshko, R. A., & James, M. N. G. (1995) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340230305 · Cited by 139 · see citing works
Comparison of systematic search and database methods for constructing segments of protein structure
Fidelis, K., Stern, P. S., Bacon, D., & Moult, J. (1994) · "Protein Engineering, Design and Selection" · doi:10.1093/protein/7.8.953 · Cited by 126 · cited 3× · see citing works
“Another multiple decoy set is loops (Moult and James 1986; Fidelis et al. 1994) from http://prostar.”
Background
A preference-based free-energy parameterization of enzyme-inhibitor binding. Applications to HIV-1-protease inhibitor design
Wallqvist, A., Jernigan, R. L., & Covell, D. G. (1995) · Protein Science · doi:10.1002/pro.5560040923 · Cited by 126 · see citing works
Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all‐atom force field and the surface generalized born solvent model
Felts, A. K., Gallicchio, E., Wallqvist, A., & Levy, R. M. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10171 · Cited by 124 · see citing works
Comparison of database potentials and molecular mechanics force fields
Moult, J. (1997) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(97)80025-5 · Cited by 122 · see citing works
Knowledge-based validation of protein structure coordinates derived by X-ray crystallography and NMR spectroscopy
MacArthur, M. W., Laskowski, R. A., & Thornton, J. M. (1994) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(94)90172-4 · Cited by 114 · see citing works
Solvent models for protein–ligand binding: Comparison of implicit solvent poisson and surface generalized born models with explicit solvent simulations
Zhang, L. Y., Gallicchio, E., Friesner, R. A., & Levy, R. M. (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.1031 · Cited by 114 · see citing works
Identifying native‐like protein structures using physics‐based potentials
Dominy, B. N., & Charles L. Brooks (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.10018 · Cited by 103 · see citing works
Prediction of loop geometries using a generalized born model of solvation effects
Rapp, C. S., & Friesner, R. A. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990501)35:2 · Cited by 103 · see citing works
Ranking potential binding peptides to MHC molecules by a computational threading approach
Altuvia, Y., Schueler‐Furman, O., & Margalit, H. (1995) · Journal of Molecular Biology · doi:10.1006/jmbi.1995.0293 · Cited by 97 · see citing works
Free energy determinants of tertiary structure and the evaluation of protein models
Petrey, D. S., & Honig, B. H. (2000) · Protein Science · doi:10.1110/ps.9.11.2181 · Cited by 95 · see citing works
Designing potential energy functions for protein folding
Hao, M., & SCHERAGAt, H. A. (1999) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(99)80026-8 · Cited by 85 · see citing works
Are membrane proteins ?inside-out? proteins?
Stevens, T. J., & Arkin, I. T. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990701)36:1 · Cited by 77 · see citing works
Evaluation of atomic level mean force potentials via inverse folding and inverse refinement of protein structures: atomic burial position and pairwise non-bonded interactions
DeBolt, S. E., & Skolnick, J. (1996) · Protein Engineering Design and Selection · doi:10.1093/protein/9.8.637 · Cited by 61 · see citing works
Molecular dynamics study of the structure and dynamics of a protein molecule in a crystalline ionic environment, Streptomyces griseus protease A
Avbelj, F., Moult, J., Kitson, D., James, M. N. G., & Hagler, A. T. (1990) · Biochemistry · doi:10.1021/bi00489a023 · Cited by 59 · see citing works
The introduction of strain and its effects on the structure and stability of T4 lysozyme
Liu, R., Baase, W. A., & Matthews, B. W. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3300 · Cited by 50 · see citing works
Functional aspects of the heme bound hemophore HasA by structural analysis of various crystal forms
Arnoux, P., Haser, R., Izadi‐Pruneyre, N., Lecroisey, A., & Czjzek, M. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20001101)41:2 · Cited by 43 · see citing works
Evaluation of short-range interactions as secondary structure energies for protein fold and sequence recognition
Miyazawa, S., & Jernigan, R. L. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990815)36:3 · Cited by 42 · see citing works
Computer simulation of antibody binding specificity
Pellegrini, M. A., & Doniach, S. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340150410 · Cited by 32 · see citing works
Detecting Native Protein Folds among Large Decoy Sets with the OPLS All‐Atom Potential and the Surface Generalized Born Solvent Model
Wallqvist, A., Gallicchio, E., Felts, A. K., & Levy, R. M. (2002) · Advances in chemical physics · doi:10.1002/0471224421.ch8 · Cited by 4 · see citing works
Crystal structure of tropomyosin at 7 �ngstroms resolution
Whitby, F. G., & Phillips, G. N., Jr. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(20000101)38:1

Flagged first, then by citations

Sorted by citations

Comparative Protein Structure Modeling Using Modeller
Eswar, N., Webb, B., Martı́-Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M., Pieper, U., & Šali, A. (2006) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0506s15 · Cited by 4,564 · see citing works
“They include VERIFY3D (Luthy et al., 1992), PROSAII (Sippl, 1993), HARMONY (Topham et al., 1994), ANOLEA (Melo and Feytmans, 1998), and DFIRE (Zhou and Zhou, 2002).”
Comparative Protein Structure Modeling Using MODELLER
Webb, B., & Šali, A. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.3 · Cited by 4,349 · see citing works
“template was used for the modeling. correct equivalences, that is, They include VERIFY3D (Luthy et al., 1992), Prosa2003 (Sippl, 1993; Wiederstein and Sippl, 2007), HARMONY (Topham et al., 1994), ANOLEA (Melo and Feytmans, 1998), DFIRE (Zhou and Zhou, 2002), DOPE (Shen and Sali, 2006), SOAP (Dong et al., 2013), QMEAN…”
Influential
Comparative Protein Structure Modeling Using MODELLER
Eswar, N., Webb, B., Martı́-Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M., Pieper, U., & Šali, A. (2007) · Current Protocols in Protein Science · doi:10.1002/0471140864.ps0209s50 · Cited by 2,862 · see citing works
“They include VERIFY3D (Luthy et al., 1992), PROSAII (Sippl, 1993), HARMONY (Topham et al., 1994), ANOLEA (Melo and Feytmans, 1998), and DFIRE (Zhou and Zhou, 2002).”
Method
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
Toward the estimation of the absolute quality of individual protein structure models
Benkert, P., Biasini, M., & Schwede, T. F. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq662 · Cited by 2,259 · see citing works
“The performance of QMEAN with respect to other stateof-the-art methods such as ProSA (Sippl, 1993) and DFIRE (Zhou and Zhou, 2002) has also been recently assessed in an independent study (Rykunov and Fiser, 2010).”
Background
I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure
Capriotti, E., Fariselli, P., & Casadio, R. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki375 · Cited by 2,058 · see citing works
The SWISS-MODEL Repository and associated resources
Kiefer, F., Arnold, K., Künzli, M., Bordoli, L., & Schwede, T. F. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn750 · Cited by 2,003 · see citing works
“Therefore, entries from the Repository can be directly submitted to the Workspace for quality assessment using different global and local quality scores such as DFire ( 45 ), ProQRes (46) or QMEAN (47).”
Method
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works
Improving physical realism, stereochemistry, and side‐chain accuracy in homology modeling: Four approaches that performed well in CASP8
Krieger, E., Joo, K., Lee, J., Lee, J., Raman, S., Thompson, J., Tyka, M., Baker, D. A., & Karplus, K. J. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22570 · Cited by 1,396 · see citing works
Protein structure homology modeling using SWISS-MODEL workspace
Bordoli, L., Kiefer, F., Arnold, K., Benkert, P., Battey, J. N. D., & Schwede, T. F. (2008) · Nature Protocols · doi:10.1038/nprot.2008.197 · Cited by 1,218 · see citing works
Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation
Zhou, H., & Pang, X. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00305 · Cited by 1,184 · see citing works
Self-citation
Prediction of protein stability changes for single‐site mutations using support vector machines
Cheng, J. J., Randall, A. Z., & Baldi, P. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20810 · Cited by 1,165 · see citing works
“Statistical potential approaches [16, 18, 19, 20, 21, 22, 23, 24, 25] derive potential functions using statistical analysis of the environmental propensities, substitution frequencies, and correlations of contacting residues in solved tertiary structures.”
BackgroundMethodInfluential
Comparative Protein Structure Modeling Using MODELLER
Webb, B., & Šali, A. (2014) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0506s47 · Cited by 1,141 · see citing works
Improving the Physical Realism and Structural Accuracy of Protein Models by a Two-Step Atomic-Level Energy Minimization
Xu, D. H., & Zhang, Y. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.10.024 · Cited by 1,027 · see citing works
“Edfire is the pairwise statistical potential from DFIRE (21), and ELJ is the pairwise physicsbased Lennard-Jones 12-6 potential (22).”
Background
QMEAN: A comprehensive scoring function for model quality assessment
Benkert, P., Tosatto, S. C. E., & Schomburg, D. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21715 · Cited by 1,011 · see citing works
“…from data of known protein structures and are usually formalized as either distance-dependent or -independent pairwise potentials of mean force.12 –20 Alternatively, statistical potentials have been derived for other structural features such as torsion angles21–25 and solvent accessibility.25 –27…”
Background
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 884 · see citing works
QMEAN server for protein model quality estimation
Benkert, P., Künzli, M., & Schwede, T. F. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp322 · Cited by 874 · see citing works
PIPER: An FFT‐based protein docking program with pairwise potentials
Kozakov, D., Brenke, R., Comeau, S. R., & Vajda, S. (2006) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21117 · Cited by 865 · see citing works
Ab initio protein structure assembly using continuous structure fragments and optimized knowledge‐based force field
Xu, D. H., & Zhang, Y. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24065 · Cited by 853 · see citing works
Focused Evolution of HIV-1 Neutralizing Antibodies Revealed by Structures and Deep Sequencing
Wu, X., Zhou, T., Zhu, J., Zhang, B., Georgiev, I. S., Wang, C., Chen, X., Longo, N. S., Louder, M. K., McKee, K., et al. (2011) · Science · doi:10.1126/science.1207532 · Cited by 785 · see citing works
Comparative protein structure modeling using MODELLER
Webb, B., & Sali, A. (2016) · Current Protocols in Protein Science · doi:10.1002/cpps.20 · Cited by 730 · see citing works
“…(Luthy et al., 1992), Prosa, 2003 (Sippl, 1993; Wiederstein and Sippl, 2007), HARMONY (Topham et al., 1994), ANOLEA (Melo and Feytmans, 1998), DFIRE (Zhou and Zhou, 2002), DOPE (Shen and Sali, 2006), SOAP (Dong et al., 2013), QMEAN local (Benkert et al., 2011), and TSVMod (Eramian et al., 2008).”
Background
CUPSAT: prediction of protein stability upon point mutations
Parthiban, V., Gromiha, M. M., & Schomburg, D. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl190 · Cited by 697 · see citing works
Protein structure determination from NMR chemical shifts
Cavalli, A., Salvatella, X., Dobson, C. M., & Vendruscolo, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610313104 · Cited by 557 · see citing works
HADDOCK versus HADDOCK: New features and performance of HADDOCK2.0 on the CAPRI targets
de Vries, S. D., van Dijk, A. V., Krzeminski, M., van Dijk, M. V., Thureau, A., Hsu, V. L., Wassenaar, T., & Bonvin, A. M. J. J. (2007) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21723 · Cited by 583 · see citing works
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality
Dehouck, Y., Kwasigroch, J. M., Gilis, D., & Rooman, M. J. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-151 · Cited by 534 · see citing works
“We may distinguish the approaches based on statistical potentials extracted from datasets of protein structures [15-19], from those based on empirical potentials built from optimised combinations of various physical energy terms [20-23].”
Method
Analysis of a Clonal Lineage of HIV-1 Envelope V2/V3 Conformational Epitope-Specific Broadly Neutralizing Antibodies and Their Inferred Unmutated Common Ancestors
Bonsignori, M., Hwang, K.-K., Chen, X., Tsao, C.-Y., Morris, L., Gray, E. S., Marshall, D. J., Crump, J. A., Kapiga, S., Sam, N. E., et al. (2011) · Journal of Virology · doi:10.1128/jvi.05045-11 · Cited by 424 · see citing works
“evaluated by using a normalized DFIRE score (63).”
Method
The Stability Effects of Protein Mutations Appear to be Universally Distributed
Tokuriki, N., Stricher, F., Schymkowitz, J. W. H., Serrano, L. G., & Tawfik, D. S. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.03.069 · Cited by 415 · 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
“Dmutant exploits atom-based distance potentials with a finite ideal gas reference state (Zhou and Zhou, 2002).”
BackgroundInfluential
Components of Coated Vesicles and Nuclear Pore Complexes Share a Common Molecular Architecture
Devos, D. P., Dokudovskaya, S., Alber, F., Williams, R., Chait, B. T., Šali, A., & Rout, M. P. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020380 · Cited by 390 · see citing works
“The aim was to improve model assessment by statistical potentials of ProsaII (Sippl 1993) and DFIRE (Zhou and Zhou 2002), and by a composite model evaluation criterion (Melo et al.”
Method
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation
Capriotti, E., Calabrese, R., Fariselli, P., Martelli, P. L., Altman, R. B., & Casadio, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s3-s6 · Cited by 371 · see citing works
“Thus, during the last few years, several methods have been developed to predict the impact of nsSNPs on protein stability [10-16], protein function [17,18] and to detect their pathologic effect [19-29].”
Background
Design of protein-interaction specificity gives selective bZIP-binding peptides
Grigoryan, G., Reinke, A. W., & Keating, A. E. (2009) · Nature · doi:10.1038/nature07885 · Cited by 343 · see citing works
“Large-scale interaction experiments are becoming more common, and general-purpose models to describe protein structures and energies are under development33, 35–37.”
Molecular Mechanisms of Disease-Causing Missense Mutations
Stefl, S. K., Nishi, H., Petukh, M. G., Panchenko, A. R., & Alexov, E. G. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.014 · Cited by 334 · see citing works
“It is outside of the scope of this review to rank the performance of the existing tools (including Dmutant [73], MultiMutate [74], SCide [75], Scpred [76], SRide [77], nsSNPAnalyzer [78], Panther [79], PhD-SNP [6], SNAP [80] and SNPs&GO [81], among other well-known tools) because different benchmarking papers (see rev…”
Background
A three-state prediction of single point mutations on protein stability changes
Capriotti, E., Fariselli, P., Rossi, I., & Casadio, R. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-s2-s6 · Cited by 328 · see citing works
“Some methods developed different energy functions, suited to compute the stability free energy [1-11], while other machine learning approaches [12-15].”
Method
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates
Yang, Y., Faraggi, E., Zhao, H., & Zhou, Y. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr350 · Cited by 325 · see citing works
“After that, these different models are linked and steric clashes are removed by using the DFIRE potential functions (Yang and Zhou, 2008; Zhou and Zhou, 2002).”
Method
Performance of protein stability predictors
Khan, S., & Vihinen, M. (2010) · Human Mutation · doi:10.1002/humu.21242 · Cited by 324 · see citing works
STRUM: structure-based prediction of protein stability changes upon single-point mutation
Quan, L., Lv, Q., & Zhang, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw361 · Cited by 324 · see citing works
“The first group of energy potentials is the knowledge-based atomic contact potentials by RW/RWplus (Zhang and Zhang, 2010) and Dfire/ dDfire (Yang and Zhou, 2008; Zhou and Zhou, 2002).”
Background
MAESTRO - multi agent stability prediction upon point mutations
Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S., & Lackner, P. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0548-6 · Cited by 310 · see citing works
“Structure based tools AUTO-MUTE [7], CUPSAT [8], Dmu-tant [9], FoldX [10], Eris [11], PoPMuSiC [12], SDM [13] or mCSM [14] usually perform better than the sequence based counterparts.”
Method
A Knowledge-Based Energy Function for Protein−Ligand, Protein−Protein, and Protein−DNA Complexes
Zhang, C., Liu, S., Zhu, Q., & Zhou, Y. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049314d · Cited by 287 · 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
“For example, in dDFIRE, Yang andZhou (31,34) introduced intoDFIRE the orientation dependence of polar atom interactions (treated as dipoles), which includes hydrogen-bonding interactions, and some improvement over DFIRE (14) in refolding the protein terminal regions with secondary structures was observed.”
BackgroundMethodInfluentialSelf-citation
Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design
Grinter, S. Z., & Zou, X. (2014) · Molecules · doi:10.3390/molecules190710150 · Cited by 264 · see citing works
“A simple ideal gas approximation may be used, and many alternatives have been proposed [81,87–89].”
Method
PremPS: Predicting the impact of missense mutations on protein stability
Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., & Li, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008543 · Cited by 257 · see citing works
Bioinformatics challenges for personalized medicine
Fernald, G. H., Capriotti, E., Daneshjou, R., Karczewski, K. J., & Altman, R. B. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr295 · Cited by 260 · see citing works
“…structural information describes the structural the protein stability change upon mutation (Capriotti et al. , , 2005; Schymkowitz et al. , 2005; Zhou and Zhou, 2002). of the most important features for the prediction of the impact acids are often conserved in protein families and so changes…”
Improved protein structure prediction by deep learning irrespective of co-evolution information
(2021) · doi:10.1038/s42256-021-00348-5 · Cited by 256 · see citing works
Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods
Thusberg, J., & Vihinen, M. (2009) · Human Mutation · doi:10.1002/humu.20938 · Cited by 251 · see citing works
“, 2005], DMUTANT [Zhou and Zhou, 2002], and PoPMuSic [Gilis and Rooman, 2000] calculate mutational free energy changes of the protein based on its 3D structure.”
Background
DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks
Cao, H., Wang, J., He, L., Qi, Y., & Zhang, J. Z. H. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00697 · Cited by 239 · see citing works
Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark.
Kastritis, P., & Bonvin, A. (2010) · Journal of Proteome Research · doi:10.1021/pr9009854 · Cited by 267 · see citing works
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
Self-citation
Advances in protein structure prediction and de novo protein design: A review
Floudas, C. A., Fung, H. K., McAllister, S. R., Mönnigmann, M., & Rajgaria, R. (2005) · Chemical Engineering Science · doi:10.1016/j.ces.2005.04.009 · Cited by 245 · see citing works
“Zhang et al. (2004) presented a simplified version of this all-atom based force field (Zhou and Zhou, 2002) by using the center of mass of side chains.”
Influential
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
“RAPDF [9], KBP [10], DFIRE [11], DOPE [12], OPUS-PSP [13,14], free-rotating chain-based potential [15], or the more composite TASSER/ITASSER [16,17,18] and ROSETTA [19] potentials], which is derived from the statistical regularities [20] of the solved protein structures in the PDB library [21].”
BackgroundMethodResultInfluential
A heavy metal P-type ATPase OsHMA4 prevents copper accumulation in rice grain
Huang, X., Deng, F., Yamaji, N., Pinson, S. R. M., Fujii‐Kashino, M., Danku, J., Douglas, A., Guerinot, M. L., Salt, D. E., & Feng, J. X. (2016) · Nature Communications · doi:10.1038/ncomms12138 · Cited by 237 · see citing works
iStable: off-the-shelf predictor integration for predicting protein stability changes
Chen, C.-W., Lin, J., & Chu, Y.-W. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s2-s5 · Cited by 236 · see citing works
“Some of these researched the physical potential [5-7], some were based on statistical potentials [6,8-13] and some on empirical approaches that combined physical and statistical potentials to confer how the protein stability would change upon mutations [14-18]; still others were based on machine learning theories, by…”
Act1, a U-box E3 Ubiquitin Ligase for IL-17 Signaling
Liu, C., Qian, W., Qian, Y., Giltiay, N. V., Lu, Y., Swaidani, S., Misra, S., Deng, L., Chen, Z. J., & Li, X. (2009) · Science Signaling · doi:10.1126/scisignal.2000382 · Cited by 224 · see citing works
“The overall reasonableness of the model geometry and conformation were verified by a number of parameters, including pseudo-energies calculated by QMEAN (−1204 kJ/mol) (25) and DFIRE (−3250 kJ/mol) (26).”
Method
Host Species Restriction of Middle East Respiratory Syndrome Coronavirus through Its Receptor, Dipeptidyl Peptidase 4
van Doremalen, N., Miazgowicz, K. L., Milne-Price, S., Bushmaker, T., Robertson, S. J., Scott, D. P., Kinne, J., McLellan, J. S., Zhu, J., & Munster, V. J. (2014) · Journal of Virology · doi:10.1128/jvi.00676-14 · Cited by 223 · see citing works
Crystal Structure of PG16 and Chimeric Dissection with Somatically Related PG9: Structure-Function Analysis of Two Quaternary-Specific Antibodies That Effectively Neutralize HIV-1
Pancera, M., McLellan, J. S., Wu, X., Zhu, J., Changela, A., Schmidt, S. D., Yang, Y., Zhou, T., Phogat, S. K., Mascola, J. R., et al. (2010) · Journal of Virology · doi:10.1128/jvi.00966-10 · Cited by 222 · see citing works
The state-of-the-art strategies of protein engineering for enzyme stabilization
Liu, Q., Xun, G., & Feng, Y. F. (2018) · Biotechnology Advances · doi:10.1016/j.biotechadv.2018.10.011 · Cited by 217 · see citing works
A coarse‐grained protein force field for folding and structure prediction
Maupetit, J., Tufféry, P., & Derreumaux, P. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21505 · Cited by 216 · see citing works
Infection with MERS-CoV Causes Lethal Pneumonia in the Common Marmoset
Falzarano, D., de Wit, E., Feldmann, F., Rasmussen, A. L., Okumura, A., Peng, X., Thomas, M. J., van Doremalen, N., Haddock, E., Nagy, L., et al. (2014) · PLoS Pathogens · doi:10.1371/journal.ppat.1004250 · Cited by 209 · see citing works
“For the marmoset and ferret DPP4, the RBD/ DPP4 complex model was generated by merging the RBD domain (PDB ID: 4KR0, Chain B) with the DPP4 model, which was then subjected to the binding energy calculation using an all-atom distance-dependent pairwise statistical potential, DFIRE [38].”
Method
Predicting protein stability changes from sequences using support vector machines
Capriotti, E., Fariselli, P., Calabrese, R., & Casadio, R. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1109 · Cited by 195 · see citing works
A physical reference state unifies the structure-derived potential of mean force for protein folding and binding
Liu, S., Zhang, C., Zhou, H., & Zhou, Y. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20019 · Cited by 193 · see citing works
“) The DFIRE-based potential derived from a structure database of single-chain proteins.(21) The two numbers in each cell represent the rank of native structure and the Z score, respectively.”
BackgroundMethodResultInfluentialSelf-citation
VoroMQA: Assessment of protein structure quality using interatomic contact areas
Olechnovič, K., & Venclovas, Č. (2017) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25278 · Cited by 181 · see citing works
“The last required information is the mean and standard deviation values used in equation (9).”
Single‐body residue‐level knowledge‐based energy score combined with sequence‐profile and secondary structure information for fold recognition
Zhou, H., & Zhou, Y. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20007 · Cited by 193 · see citing works
Self-citation
ENCoM server: exploring protein conformational space and the effect of mutations on protein function and stability
Frappier, V., Chartier, M., & Najmanovich, R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv343 · Cited by 181 · see citing works
“RMSE and variance for different linear combinations of methods Methods Parameters Median bootstrapped RMSE Variance A B All Stabilizing Destabilizing A B Shared ENCoM FoldX − 1.12 0.38 1.24 ± 0.14 1.45 ± 0.39 1.40 ± 0.17 0.12 0.24 0.05 ENCoM Rosetta − 1.14 0.46 1.32 ± 0.16 1.70 ± 0.41 1.43 ± 0.21 0.14 0.15 0.04 FoldX…”
BackgroundInfluential
<scp>AB</scp> ‐Bind: Antibody binding mutational database for computational affinity predictions
Sirin, S., Apgar, J. R., Bennett, E. M., & Keating, A. E. (2016) · Protein Science · doi:10.1002/pro.2829 · Cited by 179 · see citing works
“In the top 5% of the entire 1101-variant database, DFIRE, FoldX, and Discovery Studio identified different stabilized variants; only 11 out of 89 total variants with DD G < 2 1.0 kcal/mol were found by all three methods.”
MethodInfluential
A neural-network-based method for predicting protein stability changes upon single point mutations
Capriotti, E., Fariselli, P., & Casadio, R. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth928 · Cited by 180 · see citing works
“Differently from our approach, all the methods available use the molecular protein structure at different level of representation for predicting the G values after the protein mutation (Gilis and Rooman, 1997; Funahashi et al., 2001; Guerois et al., 2002; Kwasigroch et al., 2002; Zhou and Zhou, 2002).”
BackgroundMethodInfluential
MetaMQAP: A meta-server for the quality assessment of protein models
Pawlowski, M., Gajda, M. J., Matlak, R., & Bujnicki, J. M. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-403 · Cited by 176 · see citing works
“A set of models for given target sequence is clustered together and the clusters are evaluated by MODELLER scoring function [43] and/or DFIRE energy [44].”
Method
Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)
Studer, G., Biasini, M., & Schwede, T. F. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu457 · Cited by 174 · see citing works
“However, most scoring functions using knowledge-based methods (Benkert et al., 2011; Luthy et al., 1992; Ray et al., 2012; Sippl, 1993; Zhou and Zhou, 2002) have been trained on soluble proteins.”
MethodInfluential
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
DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations
Montanucci, L., Capriotti, E., Frank, Y., Ben-Tal, N., & Fariselli, P. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2923-1 · Cited by 167 · see citing works
“Known structure-based algorithms include Dmutant [3], FoldX [4], I-Mutant2.”
Background
Selected‐fit versus induced‐fit protein binding: Kinetic differences and mutational analysis
Weikl, T. R., & von Deuster, C. I. E. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22223 · Cited by 169 · see citing works
“Empirical methods to calculate mutation-induced stability changes of proteins have been investigated intensively in the past years [20, 21, 22, 23, 24, 25, 26, 27, 28, 29].”
Method
Computational Protein Design with Deep Learning Neural Networks
Wang, J., Cao, H., Zhang, J. Z. H., & Qi, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24760-x · Cited by 161 · see citing works
“58 SPIN was trained on 1532 non-redundant proteins and reaches a sequence identity of 30.3% on a test set containing 500 proteins.”
MethodInfluential
QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information
Benkert, P., Schwede, T. F., & Tosatto, S. C. E. (2009) · BMC Structural Biology · doi:10.1186/1472-6807-9-35 · Cited by 167 · see citing works
“…taking into account evolutionary information [16-18], stereochemical plausibility of the models [19,20] and the environment compatibility of their residues [21] as well as energy-based methods which include physics-based energy functions [22,23] and knowledge-based statistical potentials [24-29].”
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
Evolution of the B3 DNA Binding Superfamily: New Insights into REM Family Gene Diversification
Romanel, E. A. D. C., Schrago, C. G., Couñago, R. M., Russo, C., & Alves‐Ferreira, M. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005791 · Cited by 166 · see citing works
“QMEAN [80] and DFire [84] were used to estimate global model quality.”
Method
A composite score for predicting errors in protein structure models
Eramian, D., Shen, M., Devos, D. P., Melo, F., Šali, A., & Martı́-Renom, M. A. (2006) · Protein Science · doi:10.1110/ps.062095806 · Cited by 166 · see citing works
“Examples of statistical potentials include ProsaII (Sippl 1993a, b), ANOLEA (Melo and Feytmans 1997, 1998), and DFIRE (Zhou and Zhou 2002; Zhang et al. 2004).”
Background
Analyzing Effects of Naturally Occurring Missense Mutations
Zhang, Z., Miteva, M. A., Wang, L., & Alexov, E. G. (2012) · Computational and Mathematical Methods in Medicine · doi:10.1155/2012/805827 · Cited by 165 · see citing works
“(ii) Severe form of phenylketonuria: G46S; human phenylalanine hydroxylase (hPAH); [176] DFIRE [55, 177, 178] PoPMuSiC-2.”
Background
The Hydrophobic Temperature Dependence of Amino Acids Directly Calculated from Protein Structures
van Dijk, E., Hoogeveen, A., & Abeln, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004277 · Cited by 162 · see citing works
“[22–27].”
Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis
Masso, M., & Vaisman, I. I. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn353 · Cited by 160 · see citing works
“These techniques are derived either from knowledge-based statistical potentials (Gilis and Rooman, 1996, 1997; Hoppe and Schomburg, 2005; Kwasigroch et al., 2002; Meyerguz et al., 2007; Ota et al., 1995; Parthiban et al., 2007; Topham et al., 1997; Wang et al., 1998; Zhou and Zhou, 2002), or from physical descriptions…”
MethodResult
Modeling Backbone Flexibility Improves Protein Stability Estimation
Yin, S., Ding, F., & Dokholyan, N. V. (2007) · Structure · doi:10.1016/j.str.2007.09.024 · Cited by 160 · see citing works
“To our knowledge, protein dynamics and flexibility have not been explicitly modeled inpreviousDDGpredictionmethods.Ignoringproteinflexibility limits the application of current prediction methods to a small range of mutations (Guerois et al., 2002; Zhou and Zhou, 2002 ).”
BackgroundMethodResultInfluential
Regulation of Calcium/Calmodulin-dependent Kinase IV by O-GlcNAc Modification
Dias, W. B., Cheung, W. D., Wang, Z., & Hart, G. W. (2009) · Journal of Biological Chemistry · doi:10.1074/jbc.m109.007310 · Cited by 155 · see citing works
Improving the prediction of disease-related variants using protein three-dimensional structure
Capriotti, E., & Altman, R. B. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s4-s3 · Cited by 150 · see citing works
“These algorithms are able to predict the effect of the mutation on protein stability [13,14,19,23-26], protein functional activity [9,21], and insurgence of human pathologies [7,8,10-12,1518,20-22].”
BackgroundMethod
Protein thermostability engineering
Modarres, H. P., Mofrad, M. R. K., & Sanati‐Nezhad, A. (2016) · RSC Advances · doi:10.1039/c6ra16992a · Cited by 150 · see citing works
Automated Protein Structure Modeling with SWISS-MODEL Workspace and the Protein Model Portal
Bordoli, L., & Schwede, T. F. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-588-6_5 · Cited by 149 · see citing works
“The development of more sophisticated scoring methods, taking into account various aspects of structural and sequence information in order to be able to judge the quality of obtained models (41–45), is currently matter of intensive research.”
Method
An accurate, residue‐level, pair potential of mean force for folding and binding based on the distance‐scaled, ideal‐gas reference state
Zhang, C., Liu, S., Zhou, H., & Zhou, Y. (2004) · Protein Science · doi:10.1110/ps.03348304 · Cited by 148 · see citing works
“For all three statistical potentials of mean force (KBP, Lu and Skolnick 2001; RAPDF, Samudrala and Moult 1998; DFIRE, Zhou and Zhou 2002), the potential based on the side-chain center of mass (or center of geometry) is the most accurate one.”
BackgroundInfluentialSelf-citation
INPS: predicting the impact of non-synonymous variations on protein stability from sequence
Fariselli, P., Martelli, P. L., Savojardo, C., & Casadio, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv291 · Cited by 147 · see citing works
“Some of them require the knowledge of the protein structure: AUTO-MUTE (Masso and Vaisman, 2008), CUPSAT (Parthiban et al., 2006), Dmutant (Zhou and Zhou, 2002), FoldX (Guerois et al., 2002), Eris (Yin et al., 2007), PoPMuSiC (Dehouck et al., 2009), SDM (Topham et al., 1997; Worth et al., 2011), mCSM (Pires et al., 20…”
BackgroundInfluential
How well can the accuracy of comparative protein structure models be predicted?
Eramian, D., Eswar, N., Shen, M., & Šali, A. (2008) · Protein Science · doi:10.1110/ps.036061.108 · Cited by 147 · see citing works
“2002; McGuffin and Jones 2003; Melo and Sali 2007), (2) discriminating between the native and near-native states (Sippl 1993; Melo and Feytmans 1997, 1998; Park et al. 1997; Fiser et al. 2000; Lazaridis and Karplus 2000; Zhou and Zhou 2002; Seok et al. 2003; Tsai et al. 2003; Shen and Sali 2006), (3) selecting the mos…”
Background
Loop modeling: Sampling, filtering, and scoring
Soto, C. S., Fasnacht, M., Zhu, J., Forrest, L. R., & Honig, B. H. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21612 · Cited by 144 · see citing works
Near-native structure refinement using in vacuo energy minimization
Summa, C. M., & Levitt, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611593104 · Cited by 142 · 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
“Although these potentials describe their reference state as an ideal gas, in reality, this state is closer to an ideal solution of noninteracting uniform-size particles [26 ,27].”
Background
Protein threading using context-specific alignment potential
Ma, J., Wang, S., Zhao, F., & Xu, J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt210 · Cited by 140 · see citing works
“Our large-scale experimental test also indicates EPAD is much better than those context-independent distance-based pairwise potentials, such as DOPE (Shen and Sali, 2009), DFIRE (Zhou and Zhou, 2009) and RW (Zhang and Zhang, 2010) in ranking protein alignments (or templatebased models) generated by both threading and…”
BackgroundMethod
Single nucleotide variations: Biological impact and theoretical interpretation
Katsonis, P., Koire, A., Wilson, S. J., Hsu, T.-K., Lua, R., Wilkins, A., & Lichtarge, O. (2014) · Protein Science · doi:10.1002/pro.2552 · Cited by 126 · see citing works
“Some of the first methods to predict the impact of nsSNVs were based solely on structure.(54,55) They”
Method
LightDock: a new multi-scale approach to protein–protein docking
Jiménez‐García, B., Roel‐Touris, J., Romero‐Durana, M., Vidal, M., Jiménez-González, D., & Fernández‐Recio, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx555 · Cited by 139 · see citing works
“We tested the performance of LightDock (using default parameters; see Methods) with DFIRE (Zhou and Zhou, 2002) scoring function (LightDock-DFIRE), as well as that of LightDock with a faster implementation of the pyDock (Cheng et al., 2007) scoring function (Supplementary Methods 1.6) called…”
MethodInfluential
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
“…total of 30 bins, 158 residue-specific atom types with identical atom types merged (e.g., NH1 and NH2 in Arg), while the original DFIRE used a variable bin width (2 Å for the first 2 Å, 0.5 Å from 2 Å and 8 Å, and 1 Å from 8 to 15 Å) and 167 residue-specific atom types (Zhou and Zhou 2002).”
BackgroundMethodInfluential
Effective protein model structure refinement by loop modeling and overall relaxation
Lee, G. R., Heo, L., & Seok, C. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24858 · Cited by 136 · see citing works
“10,11 Another popular refinement approach is using knowledge-based potentials such as DFIRE,20–22 dDFIRE,11,23 and RAPDF22,24–26 for sampling and/or scoring.”
Method
Quantifying the effect of burial of amino acid residues on protein stability
Zhou, H., & Zhou, Y. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10584 · Cited by 135 · see citing works
“61 is best suited for finite proteins.(6) The potential was generated by using a nonhomologous ( 30% homology) structural database of 1011 soluble proteins with resolution 2 Å.”
BackgroundMethodInfluentialSelf-citation
Deep geometric representations for modeling effects of mutations on protein-protein binding affinity
Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140 · see citing works
“, 2015), DFIRE (Zhou and Zhou, 2002), dDFIRE(Yang and Zhou, 2008), and Rosetta (Humphris and Kortemme, 2008).”
A New Generation of Statistical Potentials for Proteins
Dehouck, Y., Gilis, D., & Rooman, M. J. (2006) · Biophysical Journal · doi:10.1529/biophysj.105.079434 · Cited by 134 · see citing works
“Several potentials have been tested on the group of decoy sets D(1)str (30,47); the results are summarized in Table 3.”
Background
A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation
Fang, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz071 · Cited by 130 · see citing works
“Force field-based algorithms were also introduced decades ago and are still being actively researched [13–15].”
Method
Transferable Coarse Grain Nonbonded Interaction Model for Amino Acids
Devane, R. H., Shinoda, W., Moore, P. B., & Klein, M. L. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct800441u · Cited by 129 · see citing works
Optimized atomic statistical potentials: assessment of protein interfaces and loops
Dong, G. Q., Fan, H., Schneidman‐Duhovny, D., Webb, B., & Šali, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt560 · Cited by 129 · see citing works
“First, if we assume that atoms are placed randomly within the protein shell, a recovery function will be similar to the DFIRE and DOPE reference states based on the ideal gas assumption (Shen and Sali, 2006; Zhou and Zhou, 2002).”
BackgroundMethodInfluential
Exploring Global Motions and Correlations in the Ribosome
Trylska, J., Tozzini, V., & McCammon, J. A. (2005) · Biophysical Journal · doi:10.1529/biophysj.104.058495 · Cited by 128 · see citing works
“Boltzmann inversion, whereas molecules are finite systems composed of interacting particles (31,32).”
Background

An accurate, residue‐level, pair potential of mean force for folding and binding based on the distance‐scaled, ideal‐gas reference state
Zhang, C., Liu, S., Zhou, H., & Zhou, Y. (2004) · Protein Science · doi:10.1110/ps.03348304 · Cited by 148 · see citing works
Self-citation
Statistical mechanics‐based method to extract atomic distance‐dependent potentials from protein structures
Huang, S., & Zou, X. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23086 · Cited by 68 · see citing works
Analyses on hydrophobicity and attractiveness of all‐atom distance‐dependent potentials
Shirota, M., Ishida, T., & Kinoshita, K. (2009) · Protein Science · doi:10.1002/pro.201 · Cited by 3 · see citing works
A Physics-Based Iterative Method to Extract Distance-Dependent All-Atom Potentials for Protein Structure Prediction
Huang, S., & Zou, X. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2009.12.2515
A distance-dependent atomic knowledge-based potential for improved protein structure selection, Proteins 44:223–232
Lu, H., & Skolnick, J. (2001)

Authors: Zhou, H., & Zhou, Y.
Affiliations: Howard Hughes Medical Institute (US)
Corresponding author: Zhou, Yaoqi
ORCID: Zhou, Hongyi
Zhou, Yaoqi
Venue: Protein Science
Publisher: Wiley
Published: 2002-11
Volume: 11
Issue: 11
Pages: 2714-2726
ISSN: 0961-8368 (ISSN-L)
1469-896X
DOI: 10.1110/ps.0217002
PMC: PMC2373736
OpenAlex: W2125677968
Cited by: 902
References: 62
Citations by year: 2026: 10 · 2025: 15 · 2024: 15 · 2023: 23 · 2022: 20 · 2021: 34 · 2020: 34 · 2019: 44 · 2018: 40 · 2017: 48 · 2016: 37 · 2015: 45 · 2014: 64 · 2013: 64 · 2012: 49
FWCI: 5.58
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 2355 d)
Locations: Protein Science (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Howard Hughes Medical Institute
State University of New York
University at Buffalo
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: knowledge-based potentials, protein structure prediction, protein stability prediction, protein mutants, protein structure database
MeSH terms: Mathematics, Models, Theoretical, Protein Conformation, Proteins, Statistics as Topic, Thermodynamics, Protein Folding, Computational Biology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks