Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

VMD: visual molecular dynamics

Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · see citing works

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works

Canonical dynamics: Equilibrium phase-space distributions

Hoover, W. G. (1985) · Physical Review A · doi:10.1103/physreva.31.1695 · Cited by 23,453 · see citing works

Scalable molecular dynamics with NAMD

Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914 · see citing works

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

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

Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models

Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., et al. (2000) · Accounts of Chemical Research · doi:10.1021/ar000033j · Cited by 4,978 · see citing works

Analytical molecular surface calculation

Connolly, M. L. (1983) · Journal of Applied Crystallography · doi:10.1107/s0021889883010985 · Cited by 2,536 · 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

Fast, efficient generation of high-quality atomic charges. AM1-BCC model: I. Method

Jakalian, A., Bush, B. L., Jack, D. B., & Bayly, C. I. (2000) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(20000130)21:2 · Cited by 1,695 · see citing works

Electrostatic Interactions in Macromolecules: Theory and Applications

Sharp, K. A., & Honig, B. H. (1990) · Annual Review of Biophysics and Biophysical Chemistry · doi:10.1146/annurev.bb.19.060190.001505 · Cited by 1,336 · see citing works

Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes.

Gohlke, H., Kiel, C., & Case, D. (2003) · Journal of Molecular Biology · doi:10.1016/s0022-2836(03)00610-7 · Cited by 1,087 · see citing works

Principles of protein–protein recognition

Chothia, C., & Janin, J. (1975) · Nature · doi:10.1038/256705a0 · Cited by 1,014 · see citing works

Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA

Rastelli, G., Río, A. D., Degliesposti, G., & Sgobba, M. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21372 · Cited by 838 · see citing works

Computational Alanine Scanning To Probe Protein−Protein Interactions: A Novel Approach To Evaluate Binding Free Energies

Massova, I., & Kollman, P. A. (1999) · Journal of the American Chemical Society · doi:10.1021/ja990935j · Cited by 778 · see citing works

How to mesh up Ewald sums. I. A theoretical and numerical comparison of various particle mesh routines

Deserno, M., & Holm, C. (1998) · The Journal of Chemical Physics · doi:10.1063/1.477414 · Cited by 672 · see citing works

Role of conformational sampling in computing mutation‐induced changes in protein structure and stability

Kellogg, E. H., Leaver‐Fay, A., & Baker, D. A. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22921 · Cited by 655 · see citing works

Scalable Molecular Dynamics with NAMD

Kale, L. V., & Bhatele, A. (2016) · CRC Press eBooks · doi:10.1201/b16251-15 · Cited by 645 · see citing works

Structural Characterisation and Functional Significance of Transient Protein–Protein Interactions

Nooren, I., & Thornton, J. M. (2003) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)01281-0 · Cited by 587 · see citing works

Calculating total electrostatic energies with the nonlinear Poisson-Boltzmann equation

Sharp, K. A., & Honig, B. H. (1990) · The Journal of Physical Chemistry · doi:10.1021/j100382a068 · Cited by 564 · see citing works

Continuum solvation model: Computation of electrostatic forces from numerical solutions to the Poisson-Boltzmann equation

Im, W., Beglov, D. B., & Roux, B. (1998) · Computer Physics Communications · doi:10.1016/s0010-4655(98)00016-2 · Cited by 541 · see citing works

How to Mesh up Ewald Sums. I. A Theoretical and Numerical Comparison of Various Particle Mesh Routines

Deserno, M., & Holm, C. (1998) · KiltHub Repository · Cited by 529 · see citing works

Validation and Use of the MM-PBSA Approach for Drug Discovery

Kuhn, B., Gerber, P. R., Schulz‐Gasch, T., & Ståhl, M. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049081q · Cited by 465 · see citing works

The dielectric constant of a folded protein

Gilson, M. K., & Honig, B. H. (1986) · Biopolymers · doi:10.1002/bip.360251106 · Cited by 462 · see citing works

Atomic Radii for Continuum Electrostatics Calculations Based on Molecular Dynamics Free Energy Simulations

Nina, M., Beglov, D., & Roux, B. (1997) · The Journal of Physical Chemistry B · doi:10.1021/jp970736r · Cited by 399 · see citing works

Prediction of water and metal binding sites and their affinities by using the Fold-X force field

Schymkowitz, J. W. H., Rousseau, F., Martins, I. C., Ferkinghoff‐Borg, J., Stricher, F., & Serrano, L. G. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0501980102 · Cited by 340 · see citing works

BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations

Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt450 · Cited by 338 · see citing works

Computational alanine scanning of the 1:1 human growth hormone–receptor complex

Huo, S., Massova, I., & Kollman, P. A. (2001) · Journal of Computational Chemistry · doi:10.1002/jcc.1153 · Cited by 305 · see citing works

Computational Design of Peptides That Target Transmembrane Helices

Yin, H. H., Slusky, J. S. G., Berger, B. W., Walters, R. F. S., Vilaire, G., Игоревич, Л. Р., Lear, J. D., Caputo, G. A., BENNETT, J. S., & DeGrado, W. F. (2007) · Science · doi:10.1126/science.1136782 · Cited by 289 · see citing works

SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models

Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works

Calculations of electrostatic energies in proteins

Russell, S. T., & Warshel, A. (1985) · Journal of Molecular Biology · doi:10.1016/0022-2836(85)90411-5 · Cited by 270 · see citing works

Ligand Affinities Predicted with the MM/PBSA Method: Dependence on the Simulation Method and the Force Field

Weis, A., Katebzadeh, K., Söderhjelm, P., Nilsson, I., & Ryde, U. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608210 · Cited by 267 · 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

Relative Solvent Accessible Surface Area Predicts Protein Conformational Changes upon Binding

Marsh, J. A., & Teichmann, S. A. (2011) · Structure · doi:10.1016/j.str.2011.03.010 · Cited by 258 · see citing works

A simple definition of structural regions in proteins and its use in analyzing interface evolution

Levy, E. D. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.09.028 · Cited by 254 · see citing works

PBEQ-Solver for online visualization of electrostatic potential of biomolecules

Jo, S., Vargyas, M., Vasko-Szedlar, J., Roux, B., & Im, W. (2008) · Nucleic Acids Res. · doi:10.1093/nar/gkn314 · Cited by 251 · see citing works

The Effects of Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) on Protein–Protein Interactions

Yates, C. M., & Sternberg, M. J. E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.012 · Cited by 239 · see citing works

The modular architecture of protein–protein binding interfaces

Reichmann, D., Rahat, O., Albeck, S., Meged, R., Dym, O., & Schreiber, G. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0407280102 · Cited by 239 · see citing works

Predicting free energy changes using structural ensembles

Benedix, A., Becker, C. M., de Groot, B. L., Caflisch, A., & Böckmann, R. A. (2009) · Nature Methods · doi:10.1038/nmeth0109-3 · Cited by 234 · see citing works

Automated design of the surface positions of protein helices

Dahiyat, B. I., Gordon, D. B., & Mayo, S. L. (1997) · Protein Science · doi:10.1002/pro.5560060622 · Cited by 231 · see citing works

Mechanisms of protein oligomerization, the critical role of insertions and deletions in maintaining different oligomeric states

Hashimoto, K., & Panchenko, A. R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1012999107 · Cited by 219 · see citing works

Calculation of Absolute Protein-Ligand Binding Affinity Using Path and Endpoint Approaches

Lee, M. S., & Olson, M. A. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.071589 · Cited by 193 · see citing works

Influence of the membrane potential on the free energy of an intrinsic protein

Roux, B. T. (1997) · Biophysical Journal · doi:10.1016/s0006-3495(97)78327-9 · Cited by 190 · see citing works

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Okimoto, N., Futatsugi, N., Fuji, H., Suenaga, A., Morimoto, G., Yanai, R., Ohno, Y., Narumi, T., & Taiji, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000528 · Cited by 190 · see citing works

Parallel Generalized Born Implicit Solvent Calculations with NAMD

Tanner, D. E., Chan, K., Phillips, J. C., & Schulten, K. J. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct200563j · Cited by 154 · see citing works

Computational Design of a New Hydrogen Bond Network and at Least a 300-fold Specificity Switch at a Protein−Protein Interface

Joachimiak, Ł. A., Kortemme, T., Stoddard, B. L., & Baker, D. A. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.05.022 · Cited by 142 · see citing works

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

Modeling Effects of Human Single Nucleotide Polymorphisms on Protein-Protein Interactions

Teng, S., Madej, T., Panchenko, A. R., & Alexov, E. G. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.12.3904 · Cited by 125 · see citing works

The physics and bioinformatics of binding and folding—an energy landscape perspective

Papoian, G. A., & Wolynes, P. G. (2003) · Biopolymers · doi:10.1002/bip.10286 · Cited by 124 · see citing works

Cancer Missense Mutations Alter Binding Properties of Proteins and Their Interaction Networks

Nishi, H., Tyagi, M. G., Teng, S., Shoemaker, B. A., Hashimoto, K., Alexov, E. G., Wuchty, S., & Panchenko, A. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066273 · Cited by 117 · see citing works

Exploring the origins of binding specificity through the computational redesign of calmodulin

Shifman, J. M., & Mayo, S. L. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2234277100 · Cited by 113 · see citing works

Protein–protein binding affinity prediction on a diverse set of structures

Moal, I. H., Agius, R., & Bates, P. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr513 · Cited by 112 · see citing works

Solvent dramatically affects protein structure refinement

Chopra, G., Summa, C. M., & Levitt, M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810818105 · Cited by 97 · see citing works

Computational analysis of missense mutations causing Snyder-Robinson syndrome

Zhang, Z., Teng, S., Wang, L., Schwartz, C. E., & Alexov, E. G. (2010) · Human Mutation · doi:10.1002/humu.21310 · Cited by 90 · see citing works

Predicting folding free energy changes upon single point mutations

Zhang, Z., Wang, L., Gao, Y., Zhang, J., Zhenirovskyy, M., & Alexov, E. G. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts005 · Cited by 90 · see citing works

pH‐selective mutagenesis of protein–protein interfaces: In silico design of therapeutic antibodies with prolonged half‐life

Spassov, V. Z., & Yan, L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24230 · Cited by 85 · see citing works

Comparing experimental and computational alanine scanning techniques for probing a prototypical protein–protein interaction

Bradshaw, R. T., Patel, B. H., Tate, E. W., Leatherbarrow, R. J., & Gould, I. R. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq047 · Cited by 83 · see citing works

Unraveling the Importance of Protein−Protein Interaction: Application of a Computational Alanine-Scanning Mutagenesis to the Study of the IgG1 Streptococcal Protein G (C2 Fragment) Complex

Moreira, I. S., Fernandes, P. A., & Ramos, M. J. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp054760d · Cited by 70 · see citing works

Testing computational prediction of missense mutation phenotypes: Functional characterization of 204 mutations of human cystathionine beta synthase

Wei, Q., Wang, L., Wang, Q., Kruger, W. D., & Dunbrack, R. L. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22722 · Cited by 61 · see citing works

Impact of Mutations on the Allosteric Conformational Equilibrium

Weinkam, P., Chen, Y. C., Pons, J., & Šali, A. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.11.041 · Cited by 52 · see citing works

Designing specific protein–protein interactions using computation, experimental library screening, or integrated methods

Chen, T. S., & Keating, A. E. (2012) · Protein Science · doi:10.1002/pro.2096 · Cited by 45 · see citing works

Molecular origins of binding affinity: seeking the Archimedean point

Kastritis, P. L., & Bonvin, A. M. J. J. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.07.001 · Cited by 43 · see citing works

Probing the Structural and Energetic Basis of Kinesin–Microtubule Binding Using Computational Alanine-Scanning Mutagenesis

Li, M., & Zheng, W. (2011) · Biochemistry · doi:10.1021/bi2008257 · Cited by 41 · see citing works

Oncogenic potential is related to activating effect of cancer single and double somatic mutations in receptor tyrosine kinases

Hashimoto, K., Rogozin, I. B., & Panchenko, A. R. (2012) · Human Mutation · doi:10.1002/humu.22145 · Cited by 28 · see citing works

Triathlon for energy functions: Who is the winner for design of protein–protein interactions?

Sharabi, O., Dekel, A., & Shifman, J. M. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22977 · Cited by 21 · see citing works

Mutations in DNA-Binding Loop of NFAT5 Transcription Factor Produce Unique Outcomes on Protein–DNA Binding and Dynamics

Li, M., Shoemaker, B. A., Thangudu, R. R., Ferraris, J. D., Burg, M. B., & Panchenko, A. R. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp403310a · Cited by 16 · see citing works

Probing the Structural and Energetic Basis of Kinesin-Microtubule Binding using Computational Alanine-Scanning Mutagenesis

Zheng, W., & Li, M. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2011.11.2003 · Cited by 9 · 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

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z. H., & Hou, T. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00055 · Cited by 2,266 · see citing works

Recent Developments and Applications of the MMPBSA Method

Wang, C.-H., Greene, D., Xiao, L., Qi, R.-X., & Luo, R. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2017.00087 · Cited by 608 · see citing works

Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein–Protein Binding Affinity upon Mutation

Barlow, K. A., Conchúir, S. Ó., Thompson, S., Suresh, P., Lucas, J. E., Heinonen, M., & Kortemme, T. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b11367 · Cited by 298 · see citing works

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

MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions

Zhang, N., Chen, Y., Lu, H., Zhao, F., Alvarez, R. V., Goncearenco, A., Panchenko, A. R., & Li, M. (2020) · iScience · doi:10.1016/j.isci.2020.100939 · Cited by 199 · see citing works

On Human Disease-Causing Amino Acid Variants: Statistical Study of Sequence and Structural Patterns

Petukh, M. G., Kucukkal, T. G., & Alexov, E. G. (2015) · Human Mutation · doi:10.1002/humu.22770 · Cited by 166 · see citing works

Interactome INSIDER: a structural interactome browser for genomic studies

Meyer, M. J., Beltrán, J. F., Liang, S., Fragoza, R., Rumack, A., Liang, J. H., Wei, X., & Yu, H. (2018) · Nature Methods · doi:10.1038/nmeth.4540 · Cited by 162 · see citing works

Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health

Jubb, H. C., Pandurangan, A. P., Turner, M. A., Ochoa‐Montaño, B., Blundell, T. L., & Ascher, D. B. (2016) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2016.10.002 · Cited by 153 · see citing works

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

Brender, J. R., & Zhang, Y. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004494 · Cited by 140 · see citing works

MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions

Li, M., Simonetti, F. L., Goncearenco, A., & Panchenko, A. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw374 · Cited by 118 · see citing works

SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions

Pahari, S., Li, G., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. G. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072563 · Cited by 109 · see citing works

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

Petukh, M., Li, M., & Alexov, E. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004276 · Cited by 107 · see citing works

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng, C., Xue, L. C., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1410 · Cited by 100 · see citing works

Relative binding affinity prediction of charge-changing sequence mutations with FEP in protein-protein interfaces

Clark, A. J., Negron, C., Hauser, K., Sun, M., Wang, L., Abel, R., & Friesner, R. A. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.003 · Cited by 91 · see citing works

‘Latent drivers’ expand the cancer mutational landscape

Nussinov, R., & Tsai, C. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.01.004 · Cited by 86 · see citing works

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations

Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. M. J. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25630 · Cited by 82 · see citing works

Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale

Cafarelli, T. M., Desbuleux, A., Wang, Y., Choi, S. G., Ridder, D. D., & Vidal, M. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.05.003 · Cited by 72 · see citing works

SAAMBE: webserver to predict the charge of binding free energy caused by amino acids mutations

Petukh, M., Dai, L., & Alexov, E. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17040547 · Cited by 65 · see citing works

SSIPe: accurately estimating protein–protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

Huang, X., Zheng, W. H., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz926 · Cited by 59 · see citing works

A multiscale approach to predicting affinity changes in protein-protein interfaces: Multiscale Method for Predicting ΔΔG

Dourado, D. F. A. R., & Flores, S. C. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24634 · Cited by 56 · see citing works

PremPDI estimates and interprets the effects of missense mutations on protein-DNA interactions

Zhang, N., Chen, Y., Zhao, F., Yang, Q., Simonetti, F. L., & Li, M. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006615 · Cited by 56 · see citing works

Fast Calculation of Protein–Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model

Patel, J. S., & Ytreberg, F. M. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00660 · Cited by 48 · see citing works

Free energy perturbation calculations of the thermodynamics of protein side-chain mutations

Steinbrecher, T., Abel, R., Clark, A., & Friesner, R. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.03.002 · Cited by 46 · see citing works

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Yu, Y., Wang, Z., Wang, L.-L., Tian, S., Hou, T.-J., & Sun, H.-Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00639-y · Cited by 42 · see citing works

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

Sun, T., Chen, Y., Wen, Y., Zhu, Z., & Li, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02826-3 · Cited by 43 · see citing works

SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein–protein binding affinity

Li, G., Pahari, S., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa761 · Cited by 41 · see citing works

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

Goncearenco, A., Li, M., Simonetti, F. L., Shoemaker, B. A., & Panchenko, A. R. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7201-2_15 · Cited by 41 · see citing works

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

Peng, Y., Alexov, E. G., & Basu, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20030548 · Cited by 41 · see citing works

dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions

Liu, Q., Chen, P., Wang, B., Zhang, J., & Li, J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2493-7 · Cited by 38 · see citing works

Structure-Based Design of Peptidic Inhibitors of the Interaction between CC Chemokine Ligand 5 (CCL5) and Human Neutrophil Peptides 1 (HNP1)

Wichapong, K., Alard, J.-É., Ortega‐Gómez, A., Weber, C., Hackeng, T. M., Soehnlein, O., & Nicolaes, G. A. F. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01952 · Cited by 38 · see citing works

Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics

Kucukkal, T. G., Yang, Y., Chapman, S. C., Cao, W., & Alexov, E. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15069670 · Cited by 36 · see citing works

Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation

Li, M., Kales, S. C., Ma, K., Shoemaker, B. A., Crespo-Barreto, J., Cangelosi, A. L., Lipkowitz, S., & Panchenko, A. R. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-3812 · Cited by 37 · see citing works

Atomistic simulations and network-based modeling of the Hsp90-Cdc37 chaperone binding with Cdk4 client protein: A mechanism of chaperoning kinase clients by exploiting weak spots of intrinsically dynamic kinase domains

Czemeres, J., Buse, K., & Verkhivker, G. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0190267 · Cited by 32 · see citing works

Computational Approaches to Prioritize Cancer Driver Missense Mutations

Zhao, F., Zheng, L., Goncearenco, A., Panchenko, A. R., & Li, M. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19072113 · Cited by 33 · see citing works

Important amino acid residues involved in folding and binding of protein–protein complexes

Kulandaisamy, A., Lathi, V., ViswaPoorani, K., Yugandhar, K., & Gromiha, M. M. (2016) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2016.10.045 · Cited by 32 · see citing works

Oligomeric interactions maintain active‐site structure in a noncooperative enzyme family

Li, Y., Zhang, R., Wang, C., Forouhar, F., Clarke, O. B., Vorobiev, S. M., Singh, S., Montelione, G. T., Szyperski, T., Xu, Y., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021108368 · Cited by 31 · see citing works

On the energy components governing molecular recognition in the framework of continuum approaches

Li, L., Wang, L., & Alexov, E. (2015) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2015.00005 · Cited by 31 · see citing works

Implementing and Assessing an Alchemical Method for Calculating Protein–Protein Binding Free Energy

Patel, D. J., Patel, J. S., & Ytreberg, F. M. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01045 · Cited by 32 · see citing works

Screening-based approaches to identify small molecules that inhibit protein–protein interactions

Choi, S., & Choi, K.-Y. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1280456 · Cited by 29 · see citing works

Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine

Kumar, A., Butler, B. M., Kumar, S., & Ozkan, S. B. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.11.002 · Cited by 29 · see citing works

Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes

Geng, C., Vangone, A., & Bonvin, A. M. J. J. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw020 · Cited by 28 · see citing works

Personalized Biochemistry and Biophysics

Kroncke, B. M., Vanoye, C. G., Meiler, J., George, A. L., & Sanders, C. R. (2015) · Biochemistry · doi:10.1021/acs.biochem.5b00189 · Cited by 27 · see citing works

PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks

Lu, H., Braga, J. H., & Fraternali, F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw153 · Cited by 26 · see citing works

Mutations in the KDM5C ARID Domain and Their Plausible Association with Syndromic Claes-Jensen-Type Disease

Peng, Y., Suryadi, J., Yang, Y., Kucukkal, T. G., Cao, W., & Alexov, E. G. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161126022 · Cited by 26 · see citing works

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Ammar, A., Cavill, R., Evelo, C. T., & Willighagen, E. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00701-3 · Cited by 25 · see citing works

A mutation-induced drug resistance database (MdrDB)

Yang, Z., Ye, Z., Qiu, J., Feng, R., Li, D., Hsieh, C., Allcock, J., & Zhang, S. (2023) · Communications Chemistry · doi:10.1038/s42004-023-00920-7 · Cited by 24 · see citing works

Evolution and Diversification of FRUITFULL Genes in Solanaceae

Maheepala, D. C., Emerling, C. A., Rajewski, A. C., Macon, J., Strahl, M., Pabón‐Mora, N., & Litt, A. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00043 · Cited by 22 · see citing works

Computational protein design for given backbone: recent progresses in general method-related aspects

Liu, H., & Chen, Q. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.06.013 · Cited by 23 · see citing works

Computational and Experimental Characterization of Patient Derived Mutations Reveal an Unusual Mode of Regulatory Spine Assembly and Drug Sensitivity in EGFR Kinase

Ruan, Z., Katiyar, S., & Kannan, N. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00572 · Cited by 23 · see citing works

PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions

Zhang, N., Lu, H., Chen, Y., Zhu, Z., Yang, Q., Wang, S., & Li, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21155560 · Cited by 22 · see citing works

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Meseguer, A., Dominguez, L., Bota, P. M., Aguirre‐Plans, J., Bonet, J., Fernández‐Fuentes, N., & Oliva, B. (2020) · Protein Science · doi:10.1002/pro.3930 · Cited by 22 · see citing works

Localized structural frustration for evaluating the impact of sequence variants

Kumar, S., Clarke, D., & Gerstein, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkw927 · Cited by 21 · see citing works

Examining the Assumptions Underlying Continuum-Solvent Models

Harris, R. C., & Pettitt, B. M. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00684 · Cited by 19 · see citing works

Electrostatic component of binding energy: Interpreting predictions from poisson–boltzmann equation and modeling protocols

Chakravorty, A., Li, L., & Alexov, E. G. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24475 · Cited by 19 · see citing works

LYRUS: a machine learning model for predicting the pathogenicity of missense variants

Lai, J., Yang, J., Uzun, E. D. G., Rubenstein, B. M., & Sarkar, I. N. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab045 · Cited by 19 · see citing works

Decoding the functional impact of the cancer genome through protein–protein interactions

Fu, H., Mo, X., & Ivanov, A. A. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-024-00784-6 · Cited by 21 · see citing works

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

Li, M., Goncearenco, A., & Panchenko, A. R. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6747-6_17 · Cited by 19 · see citing works

Rosetta custom score functions accurately predict ΔΔ G of mutations at protein–protein interfaces using machine learning

Shringari, S. R., Giannakoulias, S. G., Ferrie, J. J., & Petersson, E. J. (2020) · Chemical Communications · doi:10.1039/d0cc01959c · Cited by 22 · see citing works

Problems of Robustness in Poisson–Boltzmann Binding Free Energies

Harris, R. C., Mackoy, T., & Fenley, M. O. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct5005017 · Cited by 17 · see citing works

Chronic Beryllium Disease: Revealing the Role of Beryllium Ion and Small Peptides Binding to HLA-DP2

Petukh, M. G., Wu, B., Stefl, S. K., Smith, N., Hyde-Volpe, D., Wang, L., & Alexov, E. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111604 · Cited by 16 · see citing works

ProBASS—a language model with sequence and structural features for predicting the effect of mutations on binding affinity

Gurusinghe, S. N. S., Wu, Y., DeGrado, W. F., & Shifman, J. M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf270 · Cited by 15 · see citing works

Interpreting the Mechanism of APOE (p.Leu167del) Mutation in the Incidence of Familial Hypercholesterolemia; An In-silico Approach

Rashidi, O. M., H.Nazar, F. A., Alama, M. N., & Awan, Z. A. (2017) · The Open Cardiovascular Medicine Journal · doi:10.2174/1874192401711010084 · Cited by 15 · see citing works

Models for the solubility calculation of a CO2/polymer system: A review

Li, M., Zhang, J., Zou, Y., Wang, F., Chen, B., Guan, L., & Yan, W. (2020) · Materials Today Communications · doi:10.1016/j.mtcomm.2020.101277 · Cited by 15 · see citing works

Effects of Acetylation and Phosphorylation on Subunit Interactions in Three Large Eukaryotic Complexes

Šoštarić, N., O’Reilly, F. J., Giansanti, P., Heck, A. J. R., Gavin, A., & van Noort, V. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000892 · Cited by 15 · see citing works

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.018 · Cited by 14 · see citing works

Protein-Protein Interactions: Structures and Druggability

Ascher, D. B., Jubb, H. C., Pires, D. E. V., Ochi, T., Higueruelo, A. P., & Blundell, T. L. (2015) · NATO science for peace and security series. A, Chemistry and biology · doi:10.1007/978-94-017-9719-1_12 · Cited by 13 · see citing works

Extraction of molecular features for the drug discovery targeting protein‐protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2

Junaid, M., Li, C., Shah, M., Khan, A., Guo, H., & Wei, D. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25748 · Cited by 12 · see citing works

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation

Rana, M. M., & Nguyen, D. D. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c02679 · Cited by 11 · see citing works

Long-range electrostatic interactions significantly modulate the affinity of dynein for microtubules

Pabbathi, A., Coleman, L. B., Godar, S. C., Paul, A., Garlapati, A., Spencer, M., Eller, J., & Alper, J. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.03.029 · Cited by 11 · see citing works

Structural Principles Governing Disease-Causing Germline Mutations

Dobson, L., Mészáros, B., & Tusnády, G. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.10.005 · Cited by 11 · see citing works

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

Conti, S., Lau, E. Y., & Ovchinnikov, V. (2022) · Antibodies · doi:10.3390/antib11030051 · Cited by 11 · see citing works

BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms

Mueller, S. C., Backes, C., Kalinina, O. V., Meder, B., Stöckel, D., Lenhof, H., Meese, E. U., & Keller, A. (2015) · Genome Medicine · doi:10.1186/s13073-015-0190-y · Cited by 10 · see citing works

Integrated genomic approaches delineate a novel role ofROP1 ENHANCER1in controlling seed protein content of chickpea

Chakraborty, A., Junaid, A., Parida, S. K., & Bhatia, S. (2022) · Journal of Experimental Botany · doi:10.1093/jxb/erac452 · Cited by 10 · see citing works

Identification of NRAS Diagnostic Biomarkers and Drug Targets for Endometrial Cancer—An Integrated in Silico Approach

Alessandro, L., Low, K.-J. E., Abushelaibi, A. A., Lim, S. E., Cheng, W.-H., Chang, S.-K., Lai, K., Sum, Y. W., & Maran, S. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232214285 · Cited by 8 · see citing works

Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders

Ochoa, R., Laskowski, R. A., Thornton, J. M., & Cossio, P. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.636562 · Cited by 7 · see citing works

Applications of Circular Dichroism Spectroscopy in Studying Protein Folding, Stability, and Interaction

Gupta, P., Islam, A., Ahmad, F., & Hassan, M. I. (2023) · Springer eBooks · doi:10.1007/978-981-99-2079-2_1 · Cited by 7 · see citing works

MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes

Ridha, F., & Gromiha, M. M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae598 · Cited by 7 · see citing works

Prognostic Modeling of Deleterious IDUA Mutations L238Q and P385R in Hurler Syndrome Through Molecular Dynamics Simulations

Kumar, M. P. N., Selvamani, E. D., Panemangalore, A. P., Kumar, S., Vasudevan, V., & Ramasamy, M. (2025) · Pharmaceuticals · doi:10.3390/ph18060922 · Cited by 6 · see citing works

New Sources of Resistance to Terbinafine Revealed and Squalene Epoxidase Modelled in the Dermatophyte Fungus Trichophyton interdigitale From Australia

Mechidi, P., Holien, J. K., Grando, D., Huynh, T. T., & Lawrie, A. C. (2024) · Mycoses · doi:10.1111/myc.13795 · Cited by 6 · see citing works

Developing similarity matrices for antibody-protein binding interactions

Islam, S., & Pantazes, R. J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293606 · Cited by 6 · see citing works

VariBench, new variation benchmark categories and data sets

Shirvanizadeh, N., & Vihinen, M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1248732 · Cited by 4 · see citing works

Identification and mechanism determination of the efflux pump subunit amrB gene mutations linked to gentamicin susceptibility in clinical Burkholderia pseudomallei from Malaysian Borneo

Hussin, A., Nathan, S., Shahidan, M. A., Rahim, M. Y. N., Zainun, M. Y., Khairuddin, N. A. N., & Ibrahim, N. (2024) · Molecular Genetics and Genomics · doi:10.1007/s00438-024-02105-w · Cited by 4 · see citing works

Weak purifying selection in allelic diversity of the ADSL gene in indigenous and local chicken breeds and red junglefowl in Thailand

Budi, T., Kumnan, N., Singchat, W., Chalermwong, P., Thong, T., Wongloet, W., Toky, R. F. M., Pathomvanich, P., Panthum, T., Wattanadilokchatkun, P., et al. (2024) · Gene · doi:10.1016/j.gene.2024.148587 · Cited by 4 · see citing works

Protein domain-specific genotype–phenotype correlation study of neurofibromatosis type 1

Ou, M., Ho, S. K., Luk, H., Cheng, S. S., Lee, M. M.-K., Lam, T., Lo, I. F. M., & Luo, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-32422-y · Cited by 3 · see citing works

The capricious electrostatic force: Revealing the signaling pathway in integrin α2-I domain

Jia, Z., Li, L., Peng, Y., Ding, F., & Alexov, E. G. (2018) · Journal of Theoretical and Computational Chemistry · doi:10.1142/s0219633618400011 · Cited by 3 · see citing works

Assessing the functional impact of protein binding site definition

Nandigrami, P., & Fiser, A. (2024) · Protein Science · doi:10.1002/pro.5026 · Cited by 2 · see citing works

Genomic and computational-aided integrative drug repositioning strategy for EGFR and ROS1 mutated NSCLC

Tripathi, V., Khare, A., Shukla, D., Bharadwaj, S., Kirtipal, N., & Ranjan, V. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.112682 · Cited by 3 · see citing works

Low resolution protein mapping and KB-R7943 drug-protein molecular interaction analysis of long-QT syndrome linked KCNH2 mutations

Awan, Z. A., Bima, A. I. H., Rashidi, O. M., Jamil, K., Khan, I. A., Almukadi, H. S., Bilgrami, A. L., Shaik, N. A., & Banaganapalli, B. (2020) · All Life · doi:10.1080/26895293.2020.1737249 · Cited by 3 · see citing works

Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes

Zhang, Y., Liu, Y.-J., Zhang, Y.-L., Wang, Z., Lu, X., Ge, S.-X., & Min, X.-P. (2025) · Briefings Bioinform. · doi:10.1093/bib/bbaf645 · Cited by 1 · see citing works

Autism-Associated PTCHD1 Missense Variants Bind to the SNARE-Associated Protein SNAPIN but Exhibit Impaired Subcellular Trafficking

Pastore, S. F., Xie, C. T. Y., Derwish, R., Muhammad, T., BLÁHOVÁ, T., El-masri, S. C., Frankland, P. W., Hamel, P. A., & Vincent, J. B. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100492 · Cited by 2 · see citing works

MM-PBSA: Challenges and opportunities

Cavalheiro, J. P. D. V. H., Pires, N. M. M., & Dong, T. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8302303 · Cited by 2 · see citing works

Energetic Aspects of Protein–Protein Interactions (PPIs)

Poluri, K. M., Gulati, K., & Sarkar, S. (2021) · Springer eBooks · doi:10.1007/978-981-16-1594-8_3 · Cited by 2 · see citing works

A Paradigm Shift in Experimental Biochemistry: A Priori Estimation of Physical Parameters of Biomolecules

Kulikov, K. G., Koshlan, T., & Golovitsky, A. P. (2023) · Biophysical Reviews and Letters · doi:10.1142/s1793048023500017 · Cited by 2 · see citing works

Bioinformatics‐Based Comparative Analysis of the Human Retina Proteome

Kim, C. K., Djulbegovic, M. B., Broytman, D., Hadzijahic, N., Antonietti, M., Gonzalez, D. J. T., Uversky, V. N., Yannuzzi, N. A., & Karp, C. L. (2025) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.70012 · Cited by 1 · see citing works

Investigating Errors in Alchemical Free Energy Predictions Using Random Forest Models and GaMD

Sastry, S., & Kim, M. T.-J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01135 · Cited by 1 · see citing works

Explicit and Implicit Water Models for Biomolecular Simulations

Oh, M. I., & Weaver, D. F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.14661-x · Cited by 1 · see citing works

Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep LearningPreprint

Zhuang, J., Li, Z., Wang, S., Zheng, R., & Zhang, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.15.688659 · Cited by 1 · see citing works

Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis

Guclu, T. F., Atilgan, A. R., & Atilgan, C. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04916

Protein Conformational Dynamics In Genomic Analysis

Butler, B. M. (2016)

Analysis to determine the effect of mutations on binding to small chemical molecules

Koshlan, T. V., & Kulikov, K. G. (2022) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720022400030

Predicting single mutation effects on binding affinity at protein-protein interfaces based on MMPBSA calculationsPreprint

Noske, J., Lepoivre, T., Janzen, M., & Höcker, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.22.753561

mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions

Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab273 · Cited by 82 · see citing works

Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes

Beard, H. S., Cholleti, A., Pearlman, D. A., Sherman, W., & Loving, K. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082849 · Cited by 275 · see citing works

mmCSM-NA: accurately predicting effects of single and multiple mutations on protein–nucleic acid binding affinity

Ascher, D. B. (2021) · Apollo (University of Cambridge) · doi:10.17863/cam.77406

BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations

Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt450 · Cited by 338 · see citing works