Predicting free energy changes using structural ensembles

Benedix, Alexander; Becker, Caroline M; de Groot, Bert L; Caflisch, Amedeo; Böckmann, Rainer A

doi:10.1038/nmeth0109-3

Cited by 234Protein Structure and Dynamicsthermodynamics and calorimetric analysesRNA and protein synthesis mechanisms

Predicting free energy changes using structural ensembles

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Benedix, Alexander, Becker, Caroline M, de Groot, Bert L, Caflisch, Amedeo, Böckmann, Rainer A·2009·Nature Methods

DOI 10.1038/nmeth0109-3

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Empirical energy-based methods usually take linear forms, often with only energy-based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.
“They usually take a form of linear functions, often with only energy‐based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [8]
CC/PBSA's performance on the NM test set might be over-estimated because it used the NM data for training.
“Note that since CC/PBSA did use the NM data for training,8 its performance might be over‐estimated.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [8]
CC/PBSA used the NM data for training.
“Note that since CC/PBSA did use the NM data for training,8 its performance might be over‐estimated.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [8]
The NM test subset used to evaluate iSEE consisted of 19 single point mutations in one heterodimer (PDB ID: 1IAR) that were not represented in the training data.
“We only selected data from the NM data set for mutations that were not represented in the training data, which left 19 single point mutations for one complex (heterodimer, PDB ID: 1IAR. Supporting Information Table S6).”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [8]

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Fluorescence and conformational stability studies of Staphylococcus nuclease and its mutants, including the less stable nuclease-concanavalin A hybrids
Eftink, M. R., Ghiron, C. A., Kautz, R. A., & Fox, R. O. (1991) · Biochemistry · doi:10.1021/bi00219a005 · Cited by 40 · see citing works
Superadditive and Subadditive Effects of “Hot Spot” Mutations within the Interfaces of Placental Ribonuclease Inhibitor with Angiogenin and Ribonuclease A
Chen, C., & Shapiro, R. (1999) · Biochemistry · doi:10.1021/bi990762a · Cited by 38 · see citing works
Thermal unfolding of staphylococcal nuclease and several mutant forms thereof studied by differential scanning calorimetry
Tanaka, A., Sturtevant, J. M., & Flanagan, J. M. (1993) · Protein Science · doi:10.1002/pro.5560020408 · Cited by 38 · see citing works
Effect of proline mutations on the stability and kinetics of folding of staphylococcal nuclease
Nakano, T., Antonino, L. C., Fox, R. O., & Fink, A. L. (1993) · Biochemistry · doi:10.1021/bi00061a010 · Cited by 35 · see citing works
Correlation between mutational destabilization of phage T4 lysozyme and increased unfolding rates
Klemm, J. D., Wozniak, J. A., Alber, T., & Goldenberg, D. P. (1991) · Biochemistry · doi:10.1021/bi00216a038 · Cited by 33 · see citing works
Development of an in vivo method to identify mutants of phage T4 lysozyme of enhanced thermostability
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Molecular Determinants of Major Histocompatibility Complex Class I Complex Stability
Narzi, D., Winkler, K., Saidowsky, J., Misselwitz, R., Ziegler, A., Böckmann, R. A., & Alexiev, U. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m710234200 · Cited by 22 · see citing works
Self-citation
Thermodynamic Consequences of Grafting Enhanced Affinity toward the Mutated Antigen onto an Antibody
Nishimiya, Y., Tsumoto, K., Shiroishi, M., Yutani, K., & Kumagai, I. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.275.17.12813 · Cited by 21 · see citing works
Perturbation of Trp 138 in T4 lysozyme by mutations at Gln 105 used to correlate changes in structure, stability, solvation, and spectroscopic properties
Pjura, P. E., McIntosh, L. P., Wozniak, J. A., & Matthews, B. W. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340150407 · Cited by 17 · see citing works
Preliminary X-ray crystallographic analysis of the complex between the DNAase domain of colicin E9 and its cognate immunity protein
Kühlmann, U. C., Kleanthous, C., James, R., Moore, G. R., & Hemmings, A. M. (1999) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0108444998002590 · Cited by 6 · see citing works

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Ultrasensitive proteome analysis using paramagnetic bead technology
Hughes, C. S., Föehr, S., Garfield, D. A., Furlong, E. E. M., Steinmetz, L. M., & Krijgsveld, J. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145625 · Cited by 1,194 · see citing works
Binding Mechanisms in Supramolecular Complexes
Schneider, H. (2009) · Angewandte Chemie International Edition · doi:10.1002/anie.200802947 · Cited by 1,149 · 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
Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules
Park, H., Bradley, P. H., Greisen, P. J., Liu, Y., Mulligan, V. K., Kim, D. E., Baker, D. A., & DiMaio, F. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00819 · Cited by 564 · see citing works
On the binding affinity of macromolecular interactions: daring to ask why proteins interact
Kastritis, P. L., & Bonvin, A. M. J. J. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0835 · Cited by 519 · see citing works
Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details
Potapov, V., Cohen, M., & Schreiber, G. (2009) · Protein Engineering Design and Selection · doi:10.1093/protein/gzp030 · Cited by 349 · see citing works
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
Performance of protein stability predictors
Khan, S., & Vihinen, M. (2010) · Human Mutation · doi:10.1002/humu.21242 · Cited by 324 · 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
FoldX as Protein Engineering Tool: Better Than Random Based Approaches?
Buß, O., Rudat, J., & Ochsenreither, K. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.002 · Cited by 288 · 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
pmx: Automated protein structure and topology generation for alchemical perturbations
Gapsys, V., Michielssens, S., Seeliger, D., & de Groot, B. L. (2014) · Journal of Computational Chemistry · doi:10.1002/jcc.23804 · Cited by 269 · see citing works
Strigolactone Promotes Degradation of DWARF14, an α/β Hydrolase Essential for Strigolactone Signaling in Arabidopsis
Chevalier, F., Nieminen, K. M., Sánchez-Ferrero, J. C., Rodríguez, M. L., Chagoyen, M., Hardtke, C. S., & Cubas, P. (2014) · The Plant Cell · doi:10.1105/tpc.114.122903 · Cited by 239 · see citing works
Biophysics of protein evolution and evolutionary protein biophysics
Sikosek, T., & Chan, H. S. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0419 · Cited by 220 · see citing works
Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins
Kucukkal, T. G., Petukh, M. G., Li, L., & Alexov, E. G. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.01.003 · Cited by 220 · see citing works
Protein Thermostability Calculations Using Alchemical Free Energy Simulations
Seeliger, D., & de Groot, B. L. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.01.051 · Cited by 217 · see citing works
Mitochondria-targeted fluorescent thermometer monitors intracellular temperature gradient
Arai, S., Suzuki, M., Park, S. J., Yoo, J. S., Wang, L., Kang, N.-Y., Ha, H., & Chang, Y. (2015) · Chemical Communications · doi:10.1039/c5cc01088h · Cited by 211 · see citing works
ERBB2 drives YAP activation and EMT-like processes during cardiac regeneration
Aharonov, A., Shakked, A., Umansky, K. B., Savidor, A., Genzelinakh, A., Kain, D., Lendengolts, D., Revach, O., Morikawa, Y., Dong, J., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-00588-4 · Cited by 214 · see citing works
A Molecular Fluorescent Probe for Targeted Visualization of Temperature at the Endoplasmic Reticulum
Arai, S., Lee, S.-C., Zhai, D., Suzuki, M., & Chang, Y. (2014) · Scientific Reports · doi:10.1038/srep06701 · Cited by 193 · see citing works
Bindungsmechanismen in supramolekularen Komplexen
Schneider, H. (2009) · Angewandte Chemie · doi:10.1002/ange.200802947 · Cited by 183 · see citing works
DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein–protein interactions
ger, D. M. K. ⁄., & Gohlke, H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq471 · Cited by 169 · 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
Midkine rewires the melanoma microenvironment toward a tolerogenic and immune-resistant state
Cerezo‐Wallis, D., Contreras-Alcalde, M., Troulé, K., Catena, X., Mucientes, C., Calvo, T. G., Cañón, E., Tejedo, C., Pennacchi, P. C., Hogan, S. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1073-3 · Cited by 161 · see citing works
Computational Design of Stable and Soluble Biocatalysts
Musil, M., Konegger, H., Hon, J., Bednář, D., & Damborský, J. (2018) · ACS Catalysis · doi:10.1021/acscatal.8b03613 · Cited by 155 · see citing works
Protein stability: computation, sequence statistics, and new experimental methods
Magliery, T. J. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.09.002 · 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
mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures
Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw458 · Cited by 134 · see citing works
EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models
Folkman, L., Stantić, B., Sattar, A., & Zhou, Y. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.01.012 · Cited by 126 · see citing works
AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Schauperl, M., & Denny, R. A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00026 · Cited by 122 · see citing works
Calculation of Binding Free Energies
Gapsys, V., Michielssens, S., Peters, J. H., de Groot, B. L., & Leonov, H. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1465-4_9 · Cited by 125 · see citing works
NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation
Giollo, M., Martín, A. J. M., Walsh, I., Ferrari, C., & Tosatto, S. C. E. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s4-s7 · Cited by 121 · see citing works
The scoring of poses in protein-protein docking: current capabilities and future directions
Moal, I. H., Torchala, M., Bates, P. A., & Fernández‐Recio, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-286 · Cited by 116 · see citing works
Prediction of Protein Mutational Free Energy: Benchmark and Sampling Improvements Increase Classification Accuracy
Frenz, B., Lewis, S. M., King, I., Park, H., DiMaio, F., & Song, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.558247 · Cited by 114 · 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
Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity
Li, M., Petukh, M., Alexov, E., & Panchenko, A. R. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct401022c · Cited by 111 · see citing works
Distribution and Functions of TonB-Dependent Transporters in Marine Bacteria and Environments: Implications for Dissolved Organic Matter Utilization
Tang, K., Jiao, N., Liu, K., Zhang, Y., & Li, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041204 · Cited by 111 · 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
FireProt 2.0: web-based platform for the fully automated design of thermostable proteins
Musil, M., Jezik, A., Horackova, J., Borko, S., Kabourek, P., Damborský, J., & Bednář, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad425 · Cited by 112 · see citing works
Assessing predictors of changes in protein stability upon mutation using self-consistency
Thiltgen, G., & Goldstein, R. A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046084 · Cited by 110 · see citing works
Peptiderive server: derive peptide inhibitors from protein–protein interactions
Sedan, Y., Marcu, O., Lyskov, S., & Schueler‐Furman, O. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw385 · Cited by 109 · see citing works
Computational design of enzymes for biotechnological applications
Planas-Iglesias, J., Marques, S. M., Pinto, G., Musil, M., Štourač, J., Damborský, J., & Bednář, D. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107696 · Cited by 109 · see citing works
RosettaBackrub--a web server for flexible backbone protein structure modeling and design
Lauck, F., Smith, C. A., Friedland, G. F., Humphris, E. L., & Kortemme, T. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq369 · Cited by 108 · see citing works
Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation
Tang, H., & Thomas, P. D. (2016) · Genetics · doi:10.1534/genetics.116.190033 · Cited by 108 · see citing works
Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations
Steinbrecher, T. B., Zhu, C., Wang, L., Abel, R., Negron, C., Pearlman, D. A., Feyfant, E., Duan, J., & Sherman, W. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.12.007 · Cited by 108 · 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
“We compiled a dataset, containing experimentally measured values of changes in binding free energy of protein-protein complexes due to single amino acid substitutions, by combining three sets of data mentioned in the following references: [25], [23] (Ala scanning database), and Skempi database [17].”
BackgroundMethod
Backbone flexibility in computational protein design
Mandell, D. J., & Kortemme, T. (2009) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2009.07.006 · Cited by 106 · see citing works
Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning
Pandurangan, A. P., & Blundell, T. L. (2019) · Protein Science · doi:10.1002/pro.3774 · Cited by 99 · see citing works
Modulating protein stability – directed evolution strategies for improved protein function
Socha, R. D., & Tokuriki, N. (2013) · FEBS Journal · doi:10.1111/febs.12354 · Cited by 101 · 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 101 · see citing works
Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines
Lise, S., Buchan, D. W. A., Pontil, M., & Jones, D. T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016774 · Cited by 94 · see citing works
Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation
Usmanova, D. R., Bogatyreva, N. S., Ariño Bernad, J., Eremina, A. A., Gorshkova, A. A., Kanevskiy, G. M., Lonishin, L. R., Meister, A. V., Yakupova, A. G., Kondrashov, F. A., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty340 · Cited by 93 · see citing works
Predicting and Experimentally Validating Hot-Spot Residues at Protein–Protein Interfaces
Ibarra, A. Á., Bartlett, G. J., Hegedüs, Z., Dutt, S., Hóbor, F., Horner, K. A., Hetherington, K., Spence, K., Nelson, A. S., Edwards, T. A., et al. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00560 · Cited by 88 · see citing works
RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding
(2022) · doi:10.1002/pro.4527 · 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
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design
Conchúir, S. Ó., Barlow, K. A., Pache, R. A., Ollikainen, N., Kundert, K., O’Meara, M. J., Smith, C. A., & Kortemme, T. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130433 · Cited by 83 · 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
Computational and Theoretical Methods for Protein Folding
Compiani, M., & Capriotti, E. (2013) · Biochemistry · doi:10.1021/bi4001529 · Cited by 81 · see citing works
SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach
Getov, I., Petukh, M. G., & Alexov, E. G. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17040512 · Cited by 80 · see citing works
Predicting changes in protein thermostability brought about by single- or multi-site mutations
Tian, J., Wu, N., Chu, X., & Fan, Y. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-370 · Cited by 80 · see citing works
Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning
Zhao, N., Han, J. G., Shyu, C., & Korkin, D. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003592 · Cited by 79 · see citing works
Mutation effect estimation on protein–protein interactions using deep contextualized representation learning
Zhou, G., Chen, M., Ju, C. J. ‐., Wang, Z., Jiang, J., & Wang, W. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa015 · Cited by 70 · see citing works
Mutations of the aminoacyl-tRNA-synthetases SARS and WARS2 are implicated in the etiology of autosomal recessive intellectual disability
Musante, L., Püttmann, L., Kahrizi, K., Garshasbi, M., Hu, H., Stehr, H., Lipkowitz, B., Otto, S., Jensen, L. R., Tzschach, A., et al. (2017) · Human Mutation · doi:10.1002/humu.23205 · Cited by 70 · see citing works
Predicting the stability of mutant proteins by computational approaches: an overview
Marabotti, A., Scafuri, B., & Facchiano, A. M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa074 · Cited by 68 · see citing works
Functional regions of the N‐terminal domain of the antiterminator RfaH
Belogurov, G. A., Sevostyanova, A., Svetlov, V. V., & Artsimovitch, I. (2010) · Molecular Microbiology · doi:10.1111/j.1365-2958.2010.07056.x · Cited by 66 · see citing works
Effect of Oxidative Damage on the Stability and Dimerization of Superoxide Dismutase 1
Petrov, D., Daura, X., & Žagrović, B. (2016) · Biophysical Journal · doi:10.1016/j.bpj.2016.02.037 · Cited by 62 · see citing works
Alternative Fusion Protein Strategies to Express Recalcitrant QconCAT Proteins for Quantitative Proteomics of Human Drug Metabolizing Enzymes and Transporters
Russell, M. R., Achour, B., McKenzie, E. A., Lopez, R., Harwood, M. D., Rostami‐Hodjegan, A., & Barber, J. (2013) · Journal of Proteome Research · doi:10.1021/pr400279u · Cited by 59 · see citing works
Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site
Wainreb, G., Wolf, L., Ashkenazy, H., Dehouck, Y., & Ben‐Tal, N. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr576 · Cited by 59 · 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
Modulating Protein-Protein Interactions: From Structural Determinants of Binding to Druggability Prediction to Application
Metz, A., Ciglia, E., & Gohlke, H. (2012) · Current Pharmaceutical Design · doi:10.2174/138161212802651553 · Cited by 57 · see citing works
Towards a “Golden Standard” for computing globin stability: Stability and structure sensitivity of myoglobin mutants
Kepp, K. P. (2015) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2015.06.002 · Cited by 57 · 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
Computational Modeling of Protein Stability: Quantitative Analysis Reveals Solutions to Pervasive Problems
Broom, A., Trainor, K., Jacobi, Z. E., & Meiering, E. M. (2020) · Structure · doi:10.1016/j.str.2020.04.003 · Cited by 54 · see citing works
Computational enzyme redesign: large jumps in function
Cui, Y., Sun, J., & Wu, B. (2022) · Trends in Chemistry · doi:10.1016/j.trechm.2022.03.001 · Cited by 55 · see citing works
Systematic Investigation of the Data Set Dependency of Protein Stability Predictors
Caldararu, O. F., Mehra, R., Blundell, T. L., & Kepp, K. P. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00591 · Cited by 52 · see citing works
Accounting for conformational entropy in predicting binding free energies of protein‐protein interactions
Kamisetty, H., Ramanathan, A., Bailey‐Kellogg, C., & LANGMEAD, C. J. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22894 · Cited by 53 · see citing works
Application of Rigidity Theory to the Thermostabilization of Lipase A from Bacillus subtilis
Rathi, P. C., Fulton, A., Jaeger, K., & Gohlke, H. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004754 · Cited by 51 · see citing works
Misfolding of galactose 1-phosphate uridylyltransferase can result in type I galactosemia
McCorvie, T. J., Gleason, T. J., Fridovich‐Keil, J. L., & Timson, D. J. (2013) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2013.04.004 · Cited by 47 · see citing works
Trigger loop folding determines transcription rate of Escherichia coli’s RNA polymerase
Mejia, Y. X., Nudler, E., & Bustamante, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421067112 · Cited by 47 · 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
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
Characterization of Arabidopsis FPS Isozymes and FPS Gene Expression Analysis Provide Insight into the Biosynthesis of Isoprenoid Precursors in Seeds
Keim, V., Manzano, D., Fernández, F. J., Closa, M., Andrade, P., Caudepón, D., Bortolotti, C., Vega, M. C., Arró, M., & Ferrer, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049109 · Cited by 45 · see citing works
ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations
Strokach, 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
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
Scoring Ensembles of Docked Protein:Ligand Interactions for Virtual Lead Optimization
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Enhanced Thermostability of Glucose Oxidase through Computer-Aided Molecular Design
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nature | methods Predicting free energy changes using structural ensembles
Benedix, E., Becker, C. M., Groot, B. L. D., Caflisch, A., & Böckmann, R. A. (2013)
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Predicting free energy changes using structural ensembles. Nat. Methods
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Authors: Benedix, A., Becker, C. M., de Groot, B. L., Caflisch, A., & Böckmann, R. A.
Affiliations: Saarland University (DE)
University of Zurich (CH)
ORCID: Caflisch, Amedeo
Böckmann, Rainer A
Venue: Nature Methods
Publisher: Springer Science and Business Media LLC
Published: 2009-01
Volume: 6
Issue: 1
Pages: 3-4
ISSN: 1548-7091 (ISSN-L)
1548-7105
DOI: 10.1038/nmeth0109-3
OpenAlex: W1963697504
Cited by: 234
References: 97
Citations by year: 2025: 4 · 2024: 4 · 2023: 8 · 2022: 8 · 2021: 12 · 2020: 20 · 2019: 11 · 2018: 13 · 2017: 10 · 2016: 17 · 2015: 14 · 2014: 26 · 2013: 13 · 2012: 29
FWCI: 6.15
Type: journal article
Language: en
Open access: green
www.zora.uzh.ch
Licence: other-oa
www.springer.com/tdm (tdm)
Locations: Nature Methods (publishedVersion)
PubMed (publishedVersion)
Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
Max Planck Institute for Plasma Physics (submittedVersion)
GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
Zurich Open Repository and Archive (University of Zurich)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords: free energy prediction, structural ensembles, protein thermal stability, protein stability, protein folding
MeSH terms: Mutation, Protein Binding, Proteins, Thermodynamics, Protein Folding

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