Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method
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A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).
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Similar patterns regarding mutation sites and energy changes were reported in the literature.
“Similar patterns regarding mutation sites and energy changes were reported in the literature49.”
From A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation (2020) · cites this paper as [49]
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Wang, S., Tang, H., Shan, P., Wu, Z., & Zuo, L. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107952 · Cited by 41 · see citing works
Yaacob, N., Kamarudin, N. H. A., Leow, T. C., Salleh, A. B., Rahman, R. N. Z. R. A., & Ali, M. S. M. (2017) · Molecules · doi:10.3390/molecules22081312 · Cited by 43 · see citing works
“The effects of single amino acid substitutions on the electrostatic component of the binding free energy would probably allow the substituted residue to experience flexible charge approach which could benefit these enzymes in different pH and salt concentrations [8].”
BackgroundStrokach, A. V., Lu, T., & Kim, P. M. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166810 · Cited by 43 · see citing works
Li, S., Wu, S., Wang, L., Li, F., Jiang, H., & Bai, F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102344 · Cited by 42 · see citing works
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
Self-citationdbMPIKT: 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
Modeling Electrostatic Force in Protein-Protein Recognition
Shashikala, H. B. M., Chakravorty, A., & Alexov, E. G. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00094 · Cited by 37 · see citing works
“…given by the difference of the total electrostatic energy of the complex (Ecomplex) and of the free molecules (E1 and E2) as: 1Eele = Ecomplex − E1 − E2 (5) Here Ecomplex, E1and E2 are the total electrostatic energies of the complex and the individual monomers, respectively (Petukh et al., 2015b).”
BackgroundSelf-citationArtificial intelligence based methods for hot spot prediction
Ovek, D., Abali, Z., Zeylan, M. E., Keskin, Ö., Gürsoy, A., & Tunçbağ, N. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.003 · Cited by 36 · see citing works
Peng, Y., Norris, J. W., Schwartz, C. E., & Alexov, E. G. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17010077 · Cited by 35 · see citing works
“Another program developed in our lab, SAAMBE [28], was also used to calculate dimer affinity change.”
MethodSelf-citationComputational 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
“Another similar method, SAAMBE, is based on modified MM/PBSA-based components along with a set of statistical terms derived from physico-chemical properties of protein complexes [50,51].”
MethodSelf-citationStudy of SHMT2 Inhibitors and Their Binding Mechanism by Computational Alanine Scanning
He, L., Bao, J., Yang, Y., Dong, S., Zhang, L., Qi, Y., & Zhang, J. Z. H. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00370 · Cited by 33 · see citing works
Pore-modulating toxins exploit inherent slow inactivation to block K + channels
Karbat, I., Altman-Gueta, H., Fine, S., Szanto, T. G., Hamer-Rogotner, S., Dym, O., Frolow, F., Gordon, D., Panyi, G., Gurevitz, M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908903116 · Cited by 33 · see citing works
“The Single Amino Acid Mutation related change of Binding Energy (SAAMBE) method (30) was employed to assess the correlation of the docked model with the experimental results (Fig.”
MethodComputational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction
Huang, D., Wen, W. X., Liu, X., Li, Y., & Zhang, J. Z. H. (2019) · RSC Advances · doi:10.1039/c9ra01369e · Cited by 32 · see citing works
Al-Thiabat, M. G., Saqallah, F. G., Gazzali, A. M., Mohtar, N., Yap, B. K., Choong, Y. S., & Wahab, H. A. (2021) · Molecules · doi:10.3390/molecules26041079 · Cited by 32 · see citing works
“According to the MM-PBSA method [77], the Gibbs free binding energy ∆Gbind for every system can be conceptually defined as the following Equation (1):”
MethodIn silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors
Çetin, A. (2021) · Chemical Physics Letters · doi:10.1016/j.cplett.2021.138563 · Cited by 32 · see citing works
“The Poisson-Boltzmann calculations were performed using the with DelphiForce program [28].”
Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions
Strokach, A. V., Corbi‐Verge, C., Teyra, J., & Kim, P. M. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8736-8_1 · Cited by 31 · see citing works
Williams, A. H., & Zhan, C. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c00869 · Cited by 31 · see citing works
Conjugated β-Cyclodextrin Enhances the Affinity of Folic Acid towards FRα: Molecular Dynamics Study
Al-Thiabat, M. G., Gazzali, A. M., Mohtar, N., Murugaiyah, V., Kamarulzaman, E. E., Yap, B. K., Rahman, N. A., Othman, R., & Wahab, H. A. (2021) · Molecules · doi:10.3390/molecules26175304 · Cited by 30 · see citing works
“In this study, the Molecular Mechanics-Poisson Boltzmann surface area (MM-PBSA) program implemented in AMBER 18 [39,40] was used to calculate the free binding energies of FA and FA–βCD with FRα, with a neglected entropic contribution.”
MethodPandey, B., Grover, S., Goyal, S., Kumari, A., Singh, A. P., Jamal, S., Kaur, J., & Grover, A. (2018) · Scientific Reports · doi:10.1038/s41598-017-19075-2 · Cited by 30 · 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
“Mutational analyses of PPI interfaces, such as alanine scanning, verified that binding affinity is not evenly distributed across the surfaces, but rather that most of the Gibbs binding energy is conferred by a small patch of amino-acid residues, often near the center of the interface, referred to as a “hot spot” [29-3…”
BackgroundÇetin, A. (2022) · Current Computer - Aided Drug Design · doi:10.2174/1573409918666220816113516 · Cited by 28 · see citing works
Calculation of hot spots for protein–protein interaction in p53/PMI‐MDM2/MDMX complexes
Huang, D., Qi, Y., Song, J., & Zhang, J. Z. H. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25592 · 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
“The method predicted the binding free energy change upon single-point mutations achieving a Pearson correlation coefficient of 0.62 [37].”
MethodInfluentialWilliams, A. H., & Zhan, C. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c10718 · Cited by 24 · see citing works
You, W., Huang, Y. M. M., Kizhake, S., Natarajan, A., & Chang, C. A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005057 · Cited by 22 · 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
Self-citationMeseguer, 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
“16 Some molecular mechanical models provide a detailed approach requiring less computational demands, 17,18 for example, using the Poisson – Boltzmann/Surface Accessibility to Solvent (MM/PBSA).”
MethodOchoa, R., Soler, M. A., Laio, A., & Cossio, P. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp03826k · Cited by 20 · see citing works
Chakravorty, A., Li, L., & Alexov, E. G. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24475 · Cited by 19 · see citing works
Self-citationGame theory as a conceptual framework for managing insect pests
Brown, J. S., & Staňková, K. (2017) · Current Opinion in Insect Science · doi:10.1016/j.cois.2017.05.007 · Cited by 19 · see citing works
Robust principal component analysis‐based prediction of protein‐protein interaction hot spots
Sitani, D., Giorgetti, A., Alfonso‐Prieto, M., & Carloni, P. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26047 · Cited by 18 · see citing works
“The authors of the benchmark HB-34 dataset(24) extracted the alanine-mutation data from four databases: Alanine scanning energetics (ASEdb),(42) SKEMPI database,(43) Ab+ data,(44) and Alexov_sDB.(45) Then, they excluded the complexes present in the BID dataset(46) and removed the redundant proteins,”
MethodRawa, M. S. A., Al-Thiabat, M. G., Nogawa, T., Futamura, Y., Okano, A., & Wahab, H. A. (2022) · Pharmaceuticals · doi:10.3390/ph15070902 · Cited by 18 · see citing works
“The free binding energies with a neglected entropic contribution of the simulated systems were determined based on the Molecular Mechanics-Poisson Boltzmann surface area (MMPBSA) program [35] implemented in AMBER 18.”
MethodRepertoire-scale measures of antigen binding
Arora, R., & Arnaout, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2203505119 · Cited by 17 · see citing works
“Despite great interest and progress in this area (90, 91), predicting ΔΔG, let alone measuring it at repertoire-scale throughput to populate similarity matrices (Z) with billions of entries, remains challenging.”
BackgroundGenerative adversarial networks in protein and ligand structure generation: a case study
Faizi, S. A. A., Singh, N. K., Kamal, A., & Raza, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22299-3.00014-1 · Cited by 16 · see citing works
Nabe: an energetic database of amino acid mutations in protein–nucleic acid binding interfaces
Liu, J., Liu, S., Liu, C., Zhang, Y., Pan, Y., Wang, Z., Wang, J., Wen, T., & Deng, L. (2021) · Database · doi:10.1093/database/baab050 · Cited by 16 · see citing works
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
“Other methodologies predicted ΔΔ G bind utilizing statistical potential energies and coarse-grained protein models (Dehouck et al. 2013; Petukh et al. 2015).”
Demir, Z., Çetin, A., Oğuz, E., Kazancioğlu, M. Z., Kazancıoğlu, E. A., & Türkan, F. (2025) · ChemistrySelect · doi:10.1002/slct.202502666 · Cited by 15 · see citing works
Using machine‐learning‐driven approaches to boost hot‐spot's knowledge
Rosário‐Ferreira, N., Bonvin, A. M. J. J., & Moreira, I. S. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1602 · Cited by 14 · see citing works
“More recently, other groups have been implementing a set of different ε to analyze the mutation effect at various types of interfaces.(192,193) These methods allow to access protein heterogeneous ensembles of fluctuating conformations and consider dynamics, flexibility, formation of transient interactions, and pockets.”
BackgroundWang, X., Song, K., Li, Y., Tang, L., & Deng, X. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20061410 · Cited by 14 · see citing works
“The detailed calculation was based on previous reports in the literature [40,41].”
MethodStructural dynamics behind variants in pyrazinamidase and pyrazinamide resistance
Khan, M. T., & Malik, S. I. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1650113 · Cited by 14 · see citing works
KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability
Wilson, C. J., Chang, M., Karttunen, M., & Choy, W. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22105408 · Cited by 13 · see citing works
Gromiha, M. M., & Yugandhar, K. (2017) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2017.01.001 · Cited by 12 · see citing works
“…such as interface structure profiles, solvation energy, scoring functions, optimization 14 techniques and graph-based signatures have been utilized for predicting the binding affinity change upon 15 mutation (Brender and Zhang, 2015; Petukh et al. 2015; Pires and Ascher, 2016; Li et al. 2016).”
MethodA Molecular Lid Mechanism of K+ Channel Blocker Action Revealed by a Cone Peptide
Saikia, C., Dym, O., Altman-Gueta, H., Gordon, D., Reuveny, E., & Karbat, I. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.166957 · Cited by 12 · see citing works
Zhou, Y., Yao, Y., Yang, Z., Tang, Y., & Wei, G. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp00340j · Cited by 11 · see citing works
Robustness and Efficiency of Poisson–Boltzmann Modeling on Graphics Processing Units
Qi, R., & Luo, R. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00761 · Cited by 11 · see citing works
“These challenges are more pronounced when incorporating the PBE in typical biomolecular applications such as post processing binding free energy calculations and modeling amino acid mutation effects 114, 115 that involve tens of thousands of snapshots.”
BackgroundInsights from engineering the Affibody-Fc interaction with a computational-experimental method
Nosrati, M., Solbak, S. M. Ø., Nordesjö, O., Nissbeck, M., Dourado, D. F. A. R., Andersson, K. G., Housaindokht, M. R., Löfblom, J., Virtanen, A., Danielson, U. H., et al. (2017) · Protein Engineering Design and Selection · doi:10.1093/protein/gzx023 · Cited by 10 · see citing works
“Modeling this adequately is crucial, but using explicit water is often less effective (and almost always more computationally expensive) than using implicit representations (Li et al., 2015), as is done in the FoldX (Guerois et al., 2002) and other (Petukh et al., 2015) potentials.”
BackgroundComputational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands
Shao, G., Bao, J., Pan, X., He, X. D., Qi, Y., & Zhang, J. Z. H. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.646524 · Cited by 10 · see citing works
“our previous protocol, the dielectric constant for nonpolar, polar, and charged residues are 1, 3, 5, respectively (Hou et al., 2011; Petukh et al., 2015).”
BackgroundPeng, Y., & Alexov, E. G. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25162 · Cited by 9 · see citing works
Self-citationIvanov, S. M., Cawley, A., Huber, R. G., Bond, P. J., & Warwicker, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185928 · Cited by 9 · see citing works
The computational approach to variant interpretation
Özkan, S., Padilla, N., Moles‐Fernández, A., Dı́ez, O., Gutiérrez‐Enríquez, S., & de la Cruz, X. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820519-8.00007-7 · Cited by 7 · see citing works
Zheng, L., Yang, Y., Bao, J., He, L., Qi, Y., & Zhang, J. Z. H. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14027 · Cited by 8 · see citing works
“In our AS-IE calculations, different dielectric constant of 1, 3, and 5 for nonpolar, polar, and charged residues (Hou et al., 2011; Petukh et al., 2015).”
BackgroundWilliams, A. H., & Zhan, C. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c02123 · Cited by 7 · see citing works
An unconventional family 1 uracil DNA glycosylase in Nitratifractor salsuginis
Li, J., Chen, R., Yang, Y., Zhang, Z., Fang, G., Xie, W., & Cao, W. (2017) · FEBS Journal · doi:10.1111/febs.14285 · Cited by 7 · see citing works
“To further explain the lack of Ugi inhibition, molecular dynamic simulations was performed and the free binding energy between Nsa UNG/human UNG and Ugi was calculated using the MM/PBSA method [47].”
MethodDecoding the effects of mutation on protein interactions using machine learning
Wang, X., Li, A., Zhao, Y., & Peng, Y. (2025) · Biophysics Reviews · doi:10.1063/5.0249920 · Cited by 6 · see citing works
Shao, G., Bao, J., Pan, X., He, X. D., Qi, Y., & Zhang, J. Z. H. (2021) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13848 · Cited by 6 · see citing works
“Following our previous protocol, the dielectric constants were set to 1, 3, and 5 for nonpolar, polar, and charged residues, respectively (Hou et al., 2011; Petukh et al., 2015).”
BackgroundGonzález, J. E. H., & de Araújo, A. S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00972 · Cited by 6 · see citing works
Madani, M., & Tarakanova, A. (2024) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2023.105531 · Cited by 5 · see citing works
Novel Genetic Markers for Early Detection of Elevated Breast Cancer Risk in Women
Wu, B., Peng, Y., Eggert, J. A., & Alexov, E. G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20194828 · Cited by 5 · see citing works
Self-citationModeling and Design of Peptidomimetics to Modulate Protein–Protein Interactions
Watkins, A. M., Bonneau, R., & Arora, P. S. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6798-8_17 · Cited by 4 · see citing works
Mining the Protein Data Bank to improve prediction of changes in protein-protein binding
Flores, S. C., Alexiou, A., & Glaros, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257614 · Cited by 4 · see citing works
“by Molecular Dynamics or MD) [9, 10], especially when the mutation induces small conformational changes [11].”
BackgroundMasri, C. A., & Yu, J. H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01143 · Cited by 3 · see citing works
Jacquemin, M., Ribeiro, F., Aliane, K., Broggio, D., Franck, D., & Desbrée, A. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abe555 · Cited by 3 · 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
“Unfortunately, this method is unable to predict conformational changes from binding, but it has the ability of mimicking the electrostatic effects using amino acid specific dielectric constants [22].”
MethodKoirala, M., & Alexov, E. G. (2019) · Journal of Theoretical and Computational Chemistry · doi:10.1142/s0219633619300015 · Cited by 2 · see citing works
Self-citationDiscovery of novel inhibitors of SARS-CoV-2 main protease
Zheng, L., Chen, Y., Bao, J., He, L., Dong, S., Qi, Y., & Zhang, J. Z. H. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1972041 · Cited by 2 · see citing works
“In our ASIE calculation, different dielectric constant of 1, 3, and 5 are used for nonpolar, polar, and charged residues (Hou et al., 2011; Petukh et al., 2015).”
BackgroundWilliams, A. H., & Zhan, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.14.512203 · Cited by 1 · see citing works
“Our methodology utilizing energy minimization/MM-PBSA to determine the changes in binding affinity has been used with multiple types of therapeutics including small-molecule based inhibitors,[16-22] engineered-protein therapeutics,[23-25] and with multiple variants of concern (VOC) of the SARS-CoV-2 virus.”
MethodPetukh, M. G., Ortega, D. R., Baudry, J. Y., & Zhulin, I. B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/745117
“The entropy of the system was estimated with a previously described empirical formula (41, 42), based on the maximal number of side chain rotamers (R) (43):”
MethodSelf-citationImplementation of adaptive integration method for free energy calculations in molecular systems
Mirabzadeh, C. A., & Ytreberg, F. M. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.264
Zakon, H. H. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736488644.793578009
- Authors
- Petukh, M., Li, M., & Alexov, E.
- Affiliations
- Clemson University (US)
- National Institutes of Health (US)
- Corresponding author
- Alexov, Emil
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-07-06
- Volume
- 11
- Issue
- 7
- Pages
- e1004276
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4492929
- OpenAlex
- W769759530
- Cited by
- 107
- References
- 51
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 10 · 2021: 17 · 2020: 9 · 2019: 16 · 2018: 14 · 2017: 14 · 2016: 9 · 2015: 1
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS Computational Biology (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM093937)
- U.S. National Library of Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
- Keywords
- point mutations, protein-protein complex, MM-PBSA, electrostatics, conformational change, physicochemical properties, protein binding prediction
- MeSH terms
- Amino Acid Sequence, Binding Sites, Computer Simulation, Energy Transfer, Models, Chemical, Molecular Sequence Data, Protein Binding, Proteins, Models, Statistical, Point Mutation, Amino Acid Substitution, Sequence Analysis, Protein, Protein Interaction Mapping
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