Computational prediction of protein–protein binding affinities
Open accessAbstract
Protein–protein interactions form central elements of almost all cellular processes. Knowledge of the structure of protein–protein complexes but also of the binding affinity is of major importance to understand the biological function of protein–protein interactions. Even weak transient protein–protein interactions can be of functional relevance for the cell during signal transduction or regulation of metabolism. The structure of a growing number of protein–protein complexes has been solved in recent years. Combined with docking approaches or template‐based methods, it is possible to generate structural models of many putative protein–protein complexes or to design new protein–protein interactions. In order to evaluate the functional relevance of putative or predicted protein–protein complexes, realistic binding affinity prediction is of increasing importance. Several computational tools ranging from simple force‐field or knowledge‐based scoring of single protein–protein complexes to ensemble‐based approaches and rigorous binding free energy simulations are available to predict relative and absolute binding affinities of complexes. With a focus on molecular mechanics force‐field approaches the present review aims at presenting an overview on available methods and discussing advantages, approximations, and limitations of the various methods. This article is categorized under: Molecular and Statistical Mechanics > Molecular Interactions Molecular and Statistical Mechanics > Free Energy Methods Software > Molecular Modeling
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Alchemical decoupling of a complete protein molecule will usually result in poor convergence due to large free energy changes.
“While in theory an alchemical pathway could be constructed via decoupling a binding partner in a protein–protein complex from its environment in the bound and unbound states, the alchemical decoupling of a complete protein molecule will usually result in poor convergence61 due to large free energy changes.62”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [61]
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“125 It is also possible to extract kinetics and binding free energies from these simulations.”
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Efficient flexible backbone protein-protein docking for challenging targets
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“…based on bioinformatics analysis are available, this information can be included either directly during the docking search, for example, in the HADDOCK, 45 ATTRACT, 43 or RosettaDock 46,47 approaches, or can be used to screen and rerank the docking solutions obtained from rapid FFT-based methods.”
MethodFrontiers in free‐energy calculations of biological systems
Chipot, C. J. (2013) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1157 · Cited by 170 · cited 2× · see citing works
“111,129,130 Significant improvements can often be achieved by coupling Umbrella sampling with H-REMD allowing exchanges of sampled states between separate U.S. windows.”
BackgroundSCOPPI: a structural classification of protein–protein interfaces
Winter, C., Henschel, A., Kim, W., & Schroeder, M. (2005) · Nucleic Acids Res. · doi:10.1093/nar/gkj099 · Cited by 167 · cited 1× · see citing works
“Databases of protein domain – domain interactions such as 3DiD, 22 SCOPPI, 23 or PIBASE 24 are available that allow identification of interfaces and are helpful to predict the range of binding affinities for domain-mediated protein complexes.”
BackgroundDARS (Decoys As the Reference State) potentials for protein-protein docking.
Chuang, G., Kozakov, D., Brenke, R., Comeau, S. R., & Vajda, S. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.135814 · Cited by 162 · see citing works
Accounting for conformational changes during protein-protein docking.
Zacharias, M. (2010) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2010.02.001 · Cited by 159 · cited 3× · see citing works
“Alternatively, molecular dynamics (MD), Brownian dynamics, Monte Carlo, or multistart energy minimization can be used to generate locally optimized protein–protein docking complexes.(16) To enhance the speed, often coarse-grained (CG) instead of atomistic protein models are used.”
BackgroundMethodInfluentialSelf-citationSwarmDock and the Use of Normal Modes in Protein-Protein Docking
Moal, I., & Bates, P. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11103623 · Cited by 154 · cited 1× · see citing works
“44 If experimental data or data based on bioinformatics analysis are available, this information can be included either directly during the docking search, for example, in the HADDOCK, 45 ATTRACT, 43 or RosettaDock 46,47 approaches, or can be used to screen and rerank the docking solutions obtained…”
MethodGapsys, V., Michielssens, S., Seeliger, D., & de Groot, B. D. (2016) · Angewandte Chemie · doi:10.1002/anie.201510054 · Cited by 151 · cited 1× · see citing works
“109 – 114 Although typically performed in explicit solvent, it is also possible to perform alchemical transformations in implicit solvent 115 with computational costs similar to MM/PBSA but avoiding the endpoint approximations inherent to endpoint ensemble approaches.”
BackgroundZwier, M. C., Adelman, J. L., Kaus, J. W., Pratt, A. J., Wong, K. F., Rego, N. B., Suárez, E., Lettieri, S., Wang, D. W., Grabe, M., et al. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct5010615 · Cited by 148 · cited 1× · see citing works
“The WE technique allows eventually the extraction of both kinetic and thermodynamic data along the binding RC. 123 Although so far only applied to study small-ligand binding to proteins, 123,124 in principle, it could also be used to study protein – protein binding.”
BackgroundAldeghi, M., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00347 · Cited by 148 · cited 1× · see citing works
“Aldeghi et al. 99 also found a higher sensitivity of the IE term to the simulated ensemble than the other MM-PBSA terms for three sets of bromodomain-inhibitor pairs.”
BackgroundAdhesive water networks facilitate binding of protein interfaces.
Ahmad, M., Gu, W., Geyer, T., & Helms, V. (2011) · Nature Communications · doi:10.1038/ncomms1258 · Cited by 145 · cited 1× · see citing works
“117 However, such association simulations do not allow direct extraction of the associated binding free energy.”
BackgroundDevelopment of unified statistical potentials describing protein-protein interactions.
Lu, H., Lu, L., & Skolnick, J. (2003) · Biophysical Journal · doi:10.1016/s0006-3495(03)74997-2 · Cited by 144 · see citing works
Conformational selection in protein binding and function
Weikl, T., & Paul, F. (2014) · Protein Science · doi:10.1002/pro.2539 · Cited by 141 · cited 1× · see citing works
“11,13 Within this ensemble, there are structures close to the bound and unbound forms and the process of binding to partner molecules shifts the ensemble toward the bound form.”
BackgroundAn Integrated Suite of Fast Docking Algorithms
Mashiach, E., Schneidman-Duhovny, D., Peri, A., Shavit, Y., Nussinov, R., & Wolfson, H. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22790 · Cited by 141 · cited 1× · see citing works
“Among the most common are geometric hashing methods to rapidly match geometric surface descriptors of proteins 38,39 and fast Fourier transform (FFT) correlation techniques 16,37,40,41 to efficiently locate overlaps between complementary protein surfaces.”
cellPACK: A Virtual Mesoscope to Model and Visualize Structural Systems Biology
Johnson, G. T., Autin, L., Al-Alusi, M., Goodsell, D., Sanner, M. F., & Olson, A. (2014) · Nature Methods · doi:10.1038/nmeth.3204 · Cited by 140 · cited 1× · see citing works
“Numerous interactions between proteins in a cell are in principle possible but only a fraction of putative complexes and assemblies is indeed formed and of functional relevance.(3) The associated binding free energy of protein–protein interactions determines the stability of association and the conditions for complex…”
BackgroundThe interface of protein-protein complexes: Analysis of contacts and prediction of interactions
Bahadur, R., & Zacharias, M. (2008) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-007-7451-x · Cited by 140 · see citing works
Self-citationKastritis, P., Rodrigues, J., Folkers, G., Boelens, R., & Bonvin, A. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.04.017 · Cited by 137 · cited 1× · see citing works
“55 In an extension of the BSA approach for binding prediction, Vangone and Bonvin 56 suggested a contact-based scoring with optimal weights on various types of contacts at an interface (e.g., polar – polar, polar – nonpolar, etc.) that showed improved correlation with experimental binding affinity…”
MethodTowards the prediction of protein interaction partners using physical docking
Wass, M., Fuentes, G., Pons, C., Pazos, F., & Valencia, A. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.3 · Cited by 129 · cited 1× · see citing works
“33 In the second and third sections, we will focus on rapid approaches to rank predicted complexes and to estimate binding affinities based on single complex structures or ensembles of structures with a focus on force field-based methods.”
BackgroundProtein–protein interactions: scoring schemes and binding affinity
Gromiha, M. M., Yugandhar, K., & Jemimah, S. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.10.016 · Cited by 127 · cited 3× · see citing works
“30 – 32 These sets can serve as references for evaluating computational efforts; however, one should always be aware of experimental conditions and the method for binding affinity determination compared to the computational setup.”
BackgroundMethodInteraction entropy for protein-protein binding
Sun, Z., Yan, Y. N., Yang, M., & Zhang, J. Z. H. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4978893 · Cited by 117 · cited 1× · see citing works
“IE has been successfully applied in studies in combination with MM-PBSA and MM-GBSA calculations of protein – protein 96,97 or protein – ligand 95,97 binding affinities.”
MethodPotential of mean force for protein–protein interaction studies
Jiang, L., Gao, Y.-D., Mao, F., Liu, Z., & Lai, L. (2002) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10031 · Cited by 115 · see citing works
Automated Prediction of Protein Association Rate Constants
Qin, S., Pang, X., & Zhou, H.-X. (2011) · Structure · doi:10.1016/j.str.2011.10.015 · Cited by 113 · cited 1× · see citing works
“144,145 Finally, with increasing computational resources and development of improved advanced sampling methods, the option to directly follow protein – protein association and dissociation offers the opportunity for an in depth understanding of transient nonspecific and specific binding.”
BackgroundATTRACT: Protein–protein docking in CAPRI using a reduced protein model
Zacharias, M. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20566 · Cited by 113 · cited 1× · see citing works
“Some approaches included conformational changes during docking already during the initial search including soft collective normal mode (NM) directions as additional variables during docking by energy minimization 42,43 or in using swarm optimization.”
MethodSelf-citationPetukh, M., Li, M., & Alexov, E. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004276 · Cited by 107 · cited 2× · see citing works
“Using an extension of the MM/PBSA method with residue specific dielectric constants, Petukh et al. 105 achieved a high correlation (correlation coefficient of − 0.62) with experiment for a set of 1,300 mutations in 43 protein – protein complexes (several other applications are reviewed in Reference 79).”
MethodInfluentialGeng, 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
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 · cited 1× · see citing works
“116”
Riniker, S., Christ, C. D., Hansen, H. S., Hünenberger, P., Oostenbrink, C., Steiner, D., & van Gunsteren, W. V. (2011) · Journal of Physical Chemistry B · doi:10.1021/jp204303a · Cited by 87 · see citing works
Dockground: A comprehensive data resource for modeling of protein complexes
Kundrotas, P. J., Anishchenko, I. V., Dauzhenka, T. A., Kotthoff, I., Mnevets, D., Copeland, M. M., & Vakser, I. A. (2017) · Protein Science · doi:10.1002/pro.3295 · Cited by 82 · cited 1× · see citing works
“50 In fact, it has been found that the majority of stable natural protein – protein interactions can in principle be modeled by a template-based approach.”
BackgroundRigorous Free Energy Calculations in Structure‐Based Drug Design
Michel, J., Foloppe, N., & Essex, J. (2010) · Molecular Informatics · doi:10.1002/minf.201000051 · Cited by 73 · see citing works
Quantitative Analysis of Protein–Protein Interactions
Wilkinson, K. D. (2004) · Humana Press eBooks · doi:10.1385/1-59259-762-9:015 · Cited by 70 · cited 2× · see citing works
“26 Methods based on separation of complex and unbound partners require long lifetimes of the complex (small dissociation rate constants) beyond the time scale of the experiment.”
BackgroundChong, S., & Ham, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-09466-w · Cited by 69 · see citing works
Improved Computation of Protein–Protein Relative Binding Energies with the Nwat-MMGBSA Method
Maffucci, I., & Contini, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00196 · Cited by 68 · cited 1× · see citing works
“90 The inclusion of an explicit water model has been proven to give good results in various protein – protein binding affinity predictions for single complexes (e.g., Ulucan et al. 93 ).”
ResultOptical methods in the study of protein-protein interactions.
Masi, A., Cicchi, R., Carloni, A., Pavone, F., & Arcangeli, A. (2010) · Advances in Experimental Medicine and Biology · doi:10.1007/978-1-4419-6066-5_4 · Cited by 66 · cited 1× · see citing works
“26,28 In the surface plasmon resonance technique, one partner is immobilized on a sensor surface.”
BackgroundChallenges in structural approaches to cell modeling
Im, W., Liang, J., Olson, A. J., Zhou, H., Vajda, S., & Vakser, I. A. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.05.024 · Cited by 66 · cited 2× · see citing works
“5,6 Within different protein families, these domains can differ in sequence resulting in different binding specificities and in different combinations of possible interactions.”
BackgroundSchindler, C., de Vries, S. J., & Zacharias, M. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24728 · Cited by 65 · cited 1× · see citing works
“Besides of using knowledge-based statistical potentials for ranking of docked complexes, it can also be based on a molecular mechanics (MM) type force field description of binding partners.(45,46,73) Similar to the statistical potentials discussed above, these scoring potentials are mostly used to provide a relative r…”
BackgroundSelf-citationDAPI binding to the DNA minor groove: a continuum solvent analysis
Castro, L. F. P. D., & Zacharias, M. (2002) · Journal of Molecular Recognition · doi:10.1002/jmr.581 · Cited by 60
Self-citationLiu, X., Peng, L., & Zhang, J. Z. H. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00248 · Cited by 60 · see citing works
Evaluation of Predicted Protein–Protein Complexes by Binding Free Energy Simulations
Siebenmorgen, T., & Zacharias, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b01022 · Cited by 59 · cited 2× · see citing works
“143 The calculated binding free energies for the near-native complex geometries were in reasonable agreement with experiment.”
BackgroundResultSelf-citationHigh-resolution cryo-EM: the nuts and bolts.
Elmlund, D., Le, S. N., & Elmlund, H. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.03.003 · Cited by 58 · cited 1× · see citing works
“17,18 The CryoEM method is limited to complexes above a certain size limit (~100 kD) to achieve sufficient contrast of the image relative to a noisy background but no crystals of the complex are necessary.”
MethodAnalysis of biomolecules using surface plasmons.
Willander, M., & Al-Hilli, S. (2009) · Methods in molecular biology · doi:10.1007/978-1-59745-483-4_14 · Cited by 57 · cited 1× · see citing works
“29 With the method, it is also possible to analyze the time dependence of binding and to determine kinetic constants of protein – protein association and dissociation.”
MethodKokubo, H., Tanaka, T., & Okamoto, Y. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21860 · Cited by 54 · see citing works
What is a desirable statistical energy functions for proteins and how can it be obtained?
Zhou, Y., Zhou, H.-Y., Zhang, C., & Liu, S. (2007) · Cell Biochemistry and Biophysics · doi:10.1385/cbb:46:2:165 · Cited by 54 · see citing works
Ben-Shalom, I., Pfeiffer-Marek, S., Baringhaus, K., & Gohlke, H. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00373 · Cited by 52 · cited 1× · see citing works
“Methods have also been developed to just obtain the change in translational and orientational (external) entropy of one partner with respect to the other.(83,84) MM-GBSA and MM-PBSA have been used both for the calculation of absolute protein–protein binding affinities and to evaluate docked protein–protein complex str…”
MethodSimões, I. C. M., Costa, I. P. D., Coimbra, J. T. S., Ramos, M. J., & Fernandes, P. A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00378 · Cited by 52 · cited 1× · see citing works
“The ensemblesbased MM/PBSA or MM/GBSA approaches can also be used to identify hot spots and to calculate the change in the binding free energy upon mutation of interface residues.(104) The hot spots of 15 protein–protein complexes were calculated recently with MM/PBSA using residue type specific dielectric constants (…”
MethodCoarse-grained versus atomistic simulations: realistic interaction free energies for real proteins
May, A., Pool, R., van Dijk, E., Bijlard, J., Abeln, S., Heringa, J., & Feenstra, K. (2014) · Bioinform. · doi:10.1093/bioinformatics/btt675 · Cited by 51 · cited 2× · see citing works
“142 However, one should keep in mind that a CG model misses many details of intermolecular interactions such as hydrogen bonds and several conformational degrees of freedom are merged into effective interaction potentials.”
BackgroundMethodComputational modeling of protein assemblies
Soni, N., & Madhusudhan, M. S. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.04.006 · Cited by 50 · cited 2× · see citing works
“15,16,25,35 In addition, the de novo design of protein – protein interactions requires docking or modeling of new protein – protein interfaces.”
BackgroundProtein–protein docking using 3D‐Dock in rounds 3, 4, and 5 of CAPRI
Carter, P., Lesk, V., Islam, S., & Sternberg, M. J. E. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20571 · Cited by 45 · cited 1×
“Among the most common are geometric hashing methods to rapidly match geometric surface descriptors of proteins 38,39 and fast Fourier transform (FFT) correlation techniques 16,37,40,41 to efficiently locate overlaps between complementary protein surfaces.”
Protein-protein interactions : methods and applications
Fu, H. (2004) · doi:10.1385/1592597629 · Cited by 45
Stein, A., Rueda, M., Panjkovich, A., Orozco, M., & Aloy, P. (2011) · Structure · doi:10.1016/j.str.2011.03.009 · Cited by 43 · cited 1× · see citing works
“14 However, every conformation is, in principle, accessible even in the unbound state albeit with a potentially very low statistical weight.”
BackgroundMiyazawa, S., & Jernigan, R. L. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990815)36:3 · Cited by 42 · see citing works
Das Schüssel‐Schloß‐Prinzip und die Induced‐fit‐Theorie
Koshland, D. (1994) · Angewandte Chemie · doi:10.1002/ange.19941062306 · Cited by 41 · cited 2× · see citing works
“10,11 During binding, proteins appear to induce conformational changes in the partners that are a prerequisite for specific complex formation.”
BackgroundZacharias, M. (2003) · doi:10.1021/jp027598c · Cited by 40
Self-citationGarcía-Sosa, A. T., & Mancera, R. L. (2010) · Molecular Informatics · doi:10.1002/minf.201000007 · Cited by 39 · see citing works
Simple Entropy Terms for End-Point Binding Free Energy Calculations
Menzer, W. M., Li, C., Sun, W.-D., Xie, B., & Minh, D. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00418 · Cited by 36 · cited 1× · see citing works
“Furthermore, an external entropy correction alone has been shown to not necessarily improve the correlation to experimental binding affinities for protein–ligand systems.(100)”
BackgroundPerthold, J. W., & Oostenbrink, C. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00706 · Cited by 34 · cited 2× · see citing works
“138,140,141 These examples indicate that the methodology, in principle, allow accurate calculation of protein – protein binding free energies if the bound complex structure is known.”
BackgroundComputational design of protein-protein interactions.
Kortemme, T., & Baker, D. (2004) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2003.12.008 · Cited by 34 · cited 2× · see citing works
“7 Second, for the majority of natural stable protein – protein interactions one often finds homologous complex geometries in the data base of experimentally determined complexes.”
BackgroundFornes, O., García-García, J., Bonet, J., & Oliva, B. (2014) · Advances in Protein Chemistry and Structural Biology · doi:10.1016/b978-0-12-800168-4.00004-4 · Cited by 31 · see citing works
Exploring biomolecular dynamics and interactions using advanced sampling methods
Luitz, M. P., Bomblies, R., Ostermeir, K., & Zacharias, M. (2015) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/27/32/323101 · Cited by 31 · cited 2× · see citing works
“111,129,130 Significant improvements can often be achieved by coupling Umbrella sampling with H-REMD allowing exchanges of sampled states between separate U.S. windows.”
BackgroundSelf-citationEnergetics of Hydrophilic Protein-Protein Association and the Role of Water.
Ulucan, O., Jaitly, T., & Helms, V. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct5001796 · Cited by 28 · cited 1×
“90 The inclusion of an explicit water model has been proven to give good results in various protein – protein binding affinity predictions for single complexes (e.g., Ulucan et al. 93 ).”
ResultZeller, F., & Zacharias, M. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400689h · Cited by 23 · cited 1× · see citing works
“133 Alternatively, it is possible to use Umbrella integration, 134 metadynamics, 135 – 137 or to apply an adaptive force (ABF) 135,136 along the RC to offset the effective interaction between partners.”
MethodSelf-citationCuruksu, J., Šponer, J., & Zacharias, M. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.07.031 · Cited by 23 · cited 1×
“129,131,132 Another possibility is to calculate first an approximate PMF and add this to bias the simulations along the RC to smooth the effective interaction for calculating a precise PMF in a second step or iteratively in several steps.”
BackgroundSelf-citationBinding free energy analysis of protein-protein docking model structures by evERdock
Takemura, K., Matubayasi, N., & Kitao, A. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019864 · Cited by 22 · cited 1× · see citing works
“On a smaller test set of four proteins, crystal water molecules were added in the evERdock approach that improved water mediated contacts so that the identification of near-native binding decoys could be improved.(94) SIEBENMORGEN AND ZACHARIAS 7 of 18”
BackgroundKohut, G., Liwo, A., Bősze, S. E., Beke‐Somfai, T., & Samsonov, S. A. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b03658 · Cited by 19 · cited 2× · see citing works
“95,97,98 In a recent study of Sun et al., 97 entropy effects on the performance of endpoint methods of over 1,500 protein – ligand systems were assessed.”
BackgroundUpdate of the ATTRACT force field for the prediction of protein-protein binding affinity
Chéron, J.-B., Zacharias, M., Antonczak, S., & Fiorucci, S. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24836 · Cited by 17 · see citing works
Self-citationNew Computational Approach for External Entropy in Protein-Protein Binding.
Chong, S., & Ham, S. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00174 · Cited by 14
Novel Nonlinear Knowledge-Based Mean Force Potentials Based on Machine Learning
Dong, Q., & Zhou, S. (2011) · IEEE/ACM Transactions on Computational Biology & Bioinformatics · doi:10.1109/tcbb.2010.86 · Cited by 14 · see citing works
Interplay between binding affinity and kinetics in protein–protein interactions
Cao, H., Huang, Y.-Q., & Liu, Z.-R. (2016) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25041 · Cited by 13 · see citing works
Ostermeir, K., & Zacharias, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172072 · Cited by 12 · cited 1× · see citing works
“An approach using a restraining potential to avoid trapping of protein–protein complexes in nonspecific transient states but at the same time restraining the partners not to separate has been introduced to refine docked complexes.(126) Different levels of the biasing potential are applied in an H-REMD simulation with…”
MethodSelf-citationLuitz, M., & Zacharias, M. (2013) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24203 · Cited by 12 · cited 1×
“109 – 114 Although typically performed in explicit solvent, it is also possible to perform alchemical transformations in implicit solvent 115 with computational costs similar to MM/PBSA but avoiding the endpoint approximations inherent to endpoint ensemble approaches.”
BackgroundSelf-citationSasse, A., de Vries, S. J., Schindler, C., de Beauchêne, I. C., & Zacharias, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170625 · Cited by 12 · cited 1× · see citing works
“69 – 72 The potentials also vary in the number of atom or pseudo atom types or the reference state from which the expected contact or distance probability for atom-pairs are derived.”
BackgroundSelf-citationRapid alchemical free energy calculation employing a generalized born implicit solvent model.
Ostermeir, K., & Zacharias, M. (2015) · Journal of Physical Chemistry B · doi:10.1021/jp506367y · Cited by 8 · cited 1× · see citing works
“109 – 114 Although typically performed in explicit solvent, it is also possible to perform alchemical transformations in implicit solvent 115 with computational costs similar to MM/PBSA but avoiding the endpoint approximations inherent to endpoint ensemble approaches.”
BackgroundSelf-citation
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The SARS-CoV-2 Spike Variant D614G Favors an Open Conformational StatePreprint
Mansbach, R., Chakraborty, S., Nguyen, K., Montefiori, D., Korber, B., & Gnanakaran, G. (2020) · bioRxiv · doi:10.1101/2020.07.26.219741 · Cited by 207 · see citing works
The SARS-CoV-2 Spike variant D614G favors an open conformational state
Mansbach, R. A., Chakraborty, S., Nguyen, K., Montefiori, D. C., Korber, B., & Gnanakaran, S. (2021) · Science Advances · doi:10.1126/sciadv.abf3671 · Cited by 206 · see citing works
“While this method has been known to overestimate energies, it performs reasonably well for relative comparison of binding strength (41, 42).”
Protein–protein interaction prediction with deep learning: A comprehensive review
Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W. J., & Spinello, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.070 · Cited by 195 · see citing works
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 200 · see citing works
Forouzesh, N., & Mishra, N. (2021) · Molecules · doi:10.3390/molecules26082383 · Cited by 143 · see citing works
RosettaDDGPrediction for high-throughput mutational scans: from stability to binding
Sora, V., Laspiur, A. O., Degn, K., Arnaudi, M., Utichi, M., Beltrame, L., De Menezes, D., Orlandi, M., Rigina, O., Sackett, P. W., et al. (2022) · doi:10.1101/2022.09.02.506350 · Cited by 90 · see citing works
“However, more rigorous strategies are available to predict both the effect of mutations on the folding free energy and the binding free energy (Benedix et al., 2009; Kumari et al., 2014b; Seeliger & de Groot, 2010; Siebenmorgen & Zacharias, 2020).”
BackgroundBFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations
Fu, H., Chen, H., Cai, W., Shao, X., & Chipot, C. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00269 · Cited by 83 · see citing works
MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery
Siebenmorgen, T., Menezes, F., Benassou, S., Merdivan, E., Didi, K., Mourão, A. S. D., Kitel, R., Liò, P., Kesselheim, S., Piraud, M., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00627-2 · Cited by 75 · see citing works
“…the context of AI for DD it is of utmost importance to train the models on a dataset structure-based DD include semi-empirical quantum mechanical (QM) methods 5 , molecular dynamics (MD) simulations 6,7 , docking 8 and coarse-grained simulations 9 , which can also be combined to be more efficient.”
Self-citationMachine learning solutions for predicting protein–protein interactions
Casadio, R., Martelli, P. L., & Savojardo, C. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1618 · Cited by 80 · see citing works
“Supervised machine-learning is applied for developing protein-targeted scoring functions for the prediction of binding affinity 41,42 (for an extensive description of recent docking methods, see Reference 40 and references therein).”
MethodBinding affinity estimation from restrained umbrella sampling simulations
Kumar, V. G., Polasa, A., Agrawal, S., Kumar, T. K. S., & Moradi, M. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00389-9 · Cited by 72 · see citing works
“Combining equations (5) and (7), we have:”
Wang, J., Ishchenko, A., Zhang, W., Razavi, A., & Langley, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-05875-8 · Cited by 55 · see citing works
“(1) In the potential of mean force methods, a complex is dissociated by forcing one of the binding partners to move relative to the other along a userdefined path.”
BackgroundMethodInfluentialHow the ovalbumin modulates the conformation of zein through protein-protein interactions
Xie, H., Li, H., Liu, L., Cai, X., Wang, T., Jiao, Q.-B., Lv, N., Huang, M., Wu, R., Cao, Z., et al. (2024) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2024.110696 · Cited by 55 · see citing works
A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations
Martin, J., & Frezza, E. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.970109 · Cited by 54 · see citing works
“On the one hand, for example, alchemical calculations are quite accurate, but they are still very challenging for protein-protein complexes (Gapsys et al., 2016; Mey et al., 2020; Siebenmorgen and Zacharias, 2020).”
BackgroundNative or Non-Native Protein–Protein Docking Models? Molecular Dynamics to the Rescue
Jandová, Z., Vargiu, A. V., & Bonvin, A. M. J. J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00336 · Cited by 52 · see citing works
On the accuracy of molecular simulation-based predictions of koff values: a Metadynamics study
Capelli, R., Lyu, W., Bolnykh, V., Meloni, S., Olsen, J. M. H., Rothlisberger, U., Parrinello, M., & Carloni, P. (2020) · bioRxiv · doi:10.1101/2020.03.30.015396 · Cited by 47
Duboué-Dijon, E., & Hénin, J. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0046853 · Cited by 46 · see citing works
How the strength of proteins interactions affects the phase behavior of protein complexes
Jiao, Q.-B., Ye, H.-X., Lv, N., Huang, M., Wu, R., Yang, T., Cao, Z., Lei, Q.-F., Fang, W., & Xie, H.-J. (2023) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2023.109654 · Cited by 45
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
“…thermodynamics and kinetics play foundational roles in regulating biological processes [1–4], such as the processes of protein–protein interactions [5], protein-metabolite recognitions [6] and protein-nucleic acids interactions [7, 8], where binding free energy between the systems controls the…”
BackgroundJafary, F., Jafari, S., & Ganjalikhany, M. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-86380-2 · Cited by 42 · see citing works
2D Zernike polynomial expansion: Finding the protein-protein binding regions
Milanetti, E., Miotto, M., Rienzo, L. D., Monti, M., Gosti, G., & Ruocco, G. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.051 · Cited by 42 · see citing works
“However, it is known that proteins are dynamic objects and their structure undergoes conformational changes, both under the natural effect of thermal noise and as a result of binding with a molecular partner [34].”
BackgroundCurrent Methods for Detecting Cell Membrane Transient Interactions
Bagheri, Y., Ali, A., & You, M.-X. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.603259 · Cited by 38 · see citing works
“There are several great reviews on using computational methods to studymembrane dynamic interactions (Corradi et al., 2019; Muller et al., 2019; Siebenmorgen and Zacharias, 2020).”
BackgroundPerspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations
Souza, P. C. T., Limongelli, V., Wu, S., Marrink, S., & Monticelli, L. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.657222 · Cited by 37 · see citing works
Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy
Fu, H., Zhou, Y.-B., Xiang, J., Shao, X., & Cai, W. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00796 · Cited by 34 · see citing works
Towards design of drugs and delivery systems with the Martini coarse-grained model
Kjølbye, L. R., Pereira, G. P., Bartocci, A., Pannuzzo, M., Albani, S., Marchetto, A., Jiménez‐García, B., Martin, J., Rossetti, G., Cecchini, M., et al. (2022) · QRB Discovery · doi:10.1017/qrd.2022.16 · Cited by 39 · see citing works
“Some of these methods rely on atomistic molecular dynamics (MD) simulations to produce configurational ensembles (Siebenmorgen and Zacharias, 2020).”
MethodImplementing and Assessing an Alchemical Method for Calculating Protein–Protein Binding Free Energy
Patel, D., Patel, J. S., & Ytreberg, F. M. (2020) · bioRxiv · doi:10.1101/2020.10.02.324442 · Cited by 31
Panday, S. K., & Alexov, E. G. (2022) · ACS Omega · doi:10.1021/acsomega.1c07037 · Cited by 32 · see citing works
Quantitative predictions from molecular simulations using explicit or implicit interactions
Spoel, D., Zhang, J., & Zhang, H. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1560 · Cited by 29
“221”
Wang, J., Wang, X., Chu, Y., Li, C., Li, X., Meng, X., Fang, Y., No, K. T., Mao, J., & Zeng, X. (2024) · doi:10.1101/2024.02.24.581708 · Cited by 26 · see citing works
Wang, G., Zhang, H., Shao, M., Feng, Y., Cao, C., & Hu, X.-W. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00938-6 · Cited by 24
Computer aided drug design in the development of proteolysis targeting chimeras
Tunjic, T. M., Weber, N., & Brunsteiner, M. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.042 · Cited by 27 · see citing works
“A few papers that discuss, both, refinement of poses and scoring of PPIs have already been discussed in the previous paragraph [50–53 , 61 ] In an early study, Cuendet et al. studied the binding free energy of two protein-protein complexes that differ only by a single mutation [62].”
BackgroundAssessment of software methods for estimating protein-protein relative binding affinities
Gonzalez, T. R., Martin, K., Barnes, J. E., Patel, J. S., & Ytreberg, F. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240573 · Cited by 21 · see citing works
Siebenmorgen, T., & Zacharias, M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00853 · Cited by 33 · see citing works
Self-citationMISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery
(2023) · doi:10.1101/2023.05.24.542082 · Cited by 18 · see citing works
Self-citationDeep learning methods for protein structure prediction
Qin, Y., Chen, Z., Ye, P., Xiao, Y., Zhong, T., & Yu, X. (2024) · MedComm – Future Medicine · doi:10.1002/mef2.96 · Cited by 29 · see citing works
Standard Binding Free-Energy Calculations: How Far Are We from Automation?
Fu, H., Chipot, C. J., Shao, X., & Cai, W. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04370 · Cited by 18 · see citing works
Das, N., Chakraborty, P., Bayry, J., & Mukherjee, S. (2023) · Antibodies · doi:10.3390/antib12010017 · Cited by 17 · see citing works
“Binding affinity (∆G) and the dissociation constant (Kd) were to determined accordingly [44,45].”
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
“Because Kd1/Kd2 cannot yet be predicted precisely for a given reference–variant pair (55, 56), the model’s estimates were expected to be imprecise for any given pair of antibodies or TCRs; however, the law of large numbers—on the order of 10(10) pairwise comparisons per repertoire—provides for accuracy of the desired…”
BackgroundParate, S., Rampogu, S., Lee, G., Hong, J.-C., & Lee, K. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.655035 · Cited by 17 · see citing works
“Binding Free Energy Calculations using MM/PBSA and MM/GBSA Method The free intermolecular binding energy of C-Raf with RKIP and its variants was estimated using Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/ Generalized Born Surface Area (MM/GBSA) methodology (Chen et al., 2016;…”
MethodChakrabarti, M., Joshi, N., Kumari, G., Singh, P., Shoaib, R., Munjal, A., Kumar, V., Behl, A., Abid, M., Garg, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-83513-5 · Cited by 16
Yang, Y. X., Wang, P., & Zhu, B. T. (2021) · Biophysical Chemistry · doi:10.1016/j.bpc.2022.106762 · Cited by 22 · see citing works
Raghavan, S. S., Iqbal, S., Ayyadurai, N., & Gunasekaran, K. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1871955 · Cited by 15 · see citing works
“It has superior performance predicting binding free energy with respect to computation time in comparison with other free energy perturbation methods, hence MMGBSA-based free energy approach was utilized (Siebenmorgen & Zacharias, 2020; Su et al., 2015; Su et al., 2015; Wang et al., 2019).”
MethodZheng, 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
Sajeevan, K. A., Acharya, B., Ferdous, S., Park, D. M., Cotruvo, J., & Chowdhury, R. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.005 · Cited by 14
Wang, Y.-T., Liu, M., Li, J.-Y., Wei, H., & Zhang, K. (2021) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2021.10.201 · Cited by 14
Peptide Affinity Chromatography Applied to Therapeutic Antibodies Purification
Barredo-Vacchelli, G. R., Giudicessi, S., Martínez-Ceron, M. C., Cascone, O., & Camperi, S. (2021) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-021-10299-5 · Cited by 14
Lauro, A. D., Rienzo, L. D., Miotto, M., Olimpieri, P. P., Milanetti, E., & Ruocco, G. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.874296 · Cited by 15 · see citing works
“The aim of these methods are various, varying from binding site identification [5, 6] to binding affinity prediction [7, 8] or protein-protein docking guide [9–12].”
MethodAccelerating the repurposing of FDA-approved drugs against coronavirus disease-19 (COVID-19)
Vita, S. D., Chini, M. G., Lauro, G., & Bifulco, G. (2020) · RSC Advances · doi:10.1039/d0ra09010g · Cited by 14
Crean, R. M., MacLachlan, B., Madura, F., Whalley, T., Rizkallah, P., Holland, C. J., McMurran, C., Harper, S., Godkin, A., Sewell, A., et al. (2020) · Molecular Therapy: Oncolytics · doi:10.1016/j.omto.2020.07.008 · Cited by 14
“This is consistent with previous findings that there is poor correlation between number of contacts and affinity.(31) For the A6_c134 TCR, although our MD simulations suggested a smaller increase in the average number of contacts compared to the analysis of the crystal structure, increases were observed in the region…”
ResultChen, S.-F., Cheng, W.-W., Liu, W.-R., Li, E.-N., Xiao, F., He, J.-L., Liu, L.-L., Han, S., & Xu, B.-C. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.147191 · Cited by 13
Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning
Wee, J., & Wei, G. (2025) · Virus Evolution · doi:10.1093/ve/veaf026 · Cited by 12 · see citing works
“…interactions or protein–protein inter-© actions (PPIs) Geng et al. (2019); Pandurangan and Blundell (2020); Geng et al. (2019); Li et al. (2021), Siebenmorgen and Zacharias (2020); Dehouck et al. (2013); Romero-Molina et al. (2022); Xue et al. (2016); Yang et al. (2023); Wang et al. (2020).”
The importance of protein domain mutations in cancer therapy
Chitluri, K. K., & Emerson, I. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27655 · Cited by 13 · see citing works
“Protein domains frequently mediate protein-protein interactions; mutations in these domains can change a protein ’ s capacity to interact with other proteins [93].”
BackgroundMultilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction
Ciura, P., Smardz, P., Spodzieja, M., Sieradzan, A., & Krupa, P. (2024) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c02255 · Cited by 12 · see citing works
Zhang, Y., Qiu, Y.-J., & Zhang, H. (2022) · ACS Omega · doi:10.1021/acsomega.2c00032 · Cited by 12
Perthold, J. W., & Oostenbrink, C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00687 · Cited by 13 · see citing works
From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics
Falbo, E., Delre, P., & Lavecchia, A. (2025) · ACS Omega · doi:10.1021/acsomega.4c11128 · Cited by 11 · see citing works
Conti, S., Lau, E. Y., & Ovchinnikov, V. (2022) · Antibodies · doi:10.3390/antib11030051 · Cited by 11 · see citing works
“[1] for a review of the underlying theory).”
BackgroundForouzesh, N., & Onufriev, A. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.25.267625 · Cited by 15 · see citing works
Assembly mechanism and interfacial behavior of potato protein-zein nanoparticles
Fei, X.-Y., Luo, J., Xu, C., Gong, D.-M., & Zhang, G.-W. (2026) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2025.112167 · Cited by 10
Hasan, M., Sharma, S., Mallen, J. J., & Saha, A. (2025) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c04085 · Cited by 9 · see citing works
Chen, B., Bülbül, E. F., Bang, S., Minas, H. A., & Bozhüyük, K. A. J. (2025) · Natural Product Reports · doi:10.1039/d5np00041f · Cited by 12 · see citing works
Lombardi, G., & Carbone, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.24.609515 · Cited by 10 · see citing works
“They include molecular dynamics simulations reaching very accurate estimations, but requiring extensive computational resources which limit their large-scale application (Siebenmorgen and Zacharias, 2020).”
BackgroundFu, H., Chipot, C. J., Shao, X., & Cai, W. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00487 · Cited by 9 · see citing works
Bhadane, R. N., & Salo‐Ahen, O. M. H. (2022) · Biomedicines · doi:10.3390/biomedicines10112779 · Cited by 10 · see citing works
Mohanty, A., & Kumar, M. S. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.2008497 · Cited by 9 · see citing works
Predicting stable binding modes from simulated dimers of the D76N mutant of β2-microglobulin
Oliveira, N., Rodrigues, F. E. P., Vitorino, J. N. M., Loureiro, R., Faísca, P., & Machuqueiro, M. (2021) · bioRxiv · doi:10.1016/j.csbj.2021.09.003 · Cited by 9
A paired sequence language model for protein-protein interaction modeling
Liu, J., Chen, H., & Zhang, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-70457-5 · Cited by 8 · see citing works
“Beyond predicting inter-protein contacts, accurately identifying interface residues is essential for understanding complex formation, signaling, and functional regulation of protein assemblies 46 .”
Viral entry mechanisms: the role of molecular simulation in unlocking a key step in viral infections
Valério, M., Buga, C. C., Melo, M. N., Soares, C. M., & Lousa, D. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13908 · Cited by 8 · see citing works
“This can be done by analyzing the details of fusion protein-receptor interactions using molecular docking or molecular dynamics simulations coupled with free energy calculations [50 – 52].”
González, 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
Yi, C., Taylor, M. L., Ziebarth, J., & Wang, Y.-M. (2023) · bioRxiv · doi:10.1021/acsomega.3c06996 · Cited by 7 · see citing works
“During the past two decades, three general classes of computational approaches have been used to predict BA [11,12]: (1) physics-based ab-initio methods such as free energy perturbation and thermodynamics integration [13,14]; (2) knowledge-based statistical potentials such as DFIRE [15,16] or other empirical scoring f…”
Zaccaria, M., Genovese, L., Dawson, W., Cristiglio, V., Nakajima, T., Johnson, W., Farzan, M., & Momeni, B. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac180 · Cited by 7 · see citing works
“S6) (43, 44).”
Sun, Q., Vijayan, R. S. K., Levy, R. M., & Deng, N. (2020) · Protein Science · doi:10.1002/pro.4004 · Cited by 7 · see citing works
A Complementarity‐Based Approach to De Novo Binder Design
Maksymenko, K., Hatskovska, V., Coles, M. P., Aghaallaei, N., Pashkovskaia, N., Borbarán‐Bravo, N., Pilz, M., Bucher, P., Volz, M., Pereira, J., et al. (2025) · Advanced Science · doi:10.1002/advs.202502015 · Cited by 5 · see citing works
Geometric deep learning assists protein engineering. Opportunities and Challenges
Vinuesa, J. A. G. , Rojas, J. A., Soto-García, N., Martínez, N., Álvarez-Saravia, D., Uribe-Paredes, R., Davari, M. D., Conca, C., Asenjo, J. A., & Medina-Ortiz, D. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108790 · Cited by 5 · see citing works
“…in machine learning (ML) have begun to reshape the landscape of protein engineering, offering data-driven strategies to predict relevant properties, including protein stability (Musil et al., 2024), binding affinities (Siebenmorgen and Zacharias, 2020), and catalytic properties (Chen et al., 2022).”
drMD: Molecular Dynamics for Experimentalists
(2024) · doi:10.1101/2024.10.29.620839 · Cited by 6 · see citing works
“MD has been used to investigate a wide range of biologically important properties, such as the role of single-point mutations on enzyme structure [3,4] or activity [5,6] and the quantitative determination of binding energies for both protein-ligand [7,8] and protein - protein complexes [9].”
Yang, J.-I., Jung, H.-C., Oh, H.-M., Choi, B. G., Lee, H., & Kang, S. (2023) · Applied and Environmental Microbiology · doi:10.1128/aem.01474-23 · Cited by 6 · see citing works
“…nuclear magnetic resonance (NMR), or cryo-electron microscopy (cryo-EM), the interaction partner of frhAGB -encoded hydrogenase could have been easily predicted using computational methods such as protein–protein docking, based entirely on three-dimensional protein structures (36, 37).”
MethodPrediction of Protein-Protein Binding Affinities from Unbound Protein Structures.
Meseguer, A., Bota, P. M., Fernández-Fuentes, N., & Oliva, B. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1767-0_16 · Cited by 6 · see citing works
Blake, S., Hemming, I., Heng, J., & Agostino, M. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.0c00758 · Cited by 6 · see citing works
Structural maturation of the matrix lattice is not required for HIV-1 particle infectivity
Chen, L., Hikichi, Y., Rey, J. S., Akıl, C., Zhu, Y., Veler, H., Shen, Y., Perilla, J. R., Freed, E. O., & Zhang, P. (2025) · Science Advances · doi:10.1126/sciadv.adv4356 · Cited by 6 · see citing works
“MM-PBSA/GBSA has been used recently to estimate protein-protein binding affinities in molecular complexes ( 47 , 106 , 107 ).”
Computational analyses of the interactome between TNF and TNFR superfamilies
Dhusia, K., Su, Z.-Q., & Wu, Y. (2023) · Comput. Biol. Chem. · doi:10.1016/j.compbiolchem.2023.107823 · Cited by 5 · 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
“Transient interactions are also often mediated by disordered protein segments, small linear motifs and can require posttranslational modifications (PTMs),(37,39) complicating even further the understanding of their binding mechanisms.(10) Additionally, PTMs at functional sites can create new binding sites within trans…”
BackgroundStark, L. E., Guan, W., Colvin, M., & LiWang, P. (2021) · Biomedical Journal · doi:10.1016/j.bj.2021.07.004 · Cited by 5 · see citing works
Spankie, T. J., Haywood, A. L., Dottorini, T., Barrow, P., & Hirst, J. (2020) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2020.107723 · Cited by 5
Notari, E., Wood, C. W., & Michel, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01695 · Cited by 4 · see citing works
Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
Chung, J., Hahn, H., Flores-Espinoza, E., & Thomsen, A. R. B. (2025) · Biomolecules · doi:10.3390/biom15030423 · Cited by 9 · see citing works
“Based on this alignment, a 3D model of the target protein can be constructed (Figure 3A) [56,57].”
The physical logic of protein machinesPreprint
Mcbride, J., & Tlusty, T. (2023) · bioRxiv · doi:10.1101/2023.11.16.567487 · Cited by 4 · see citing works
Pasha, U., Nisar, H., Nisar, H., Abid, R., Ashraf, N., & Sadaf, S. (2023) · Journal of Interferon and Cytokine Research · doi:10.1089/jir.2022.0216 · Cited by 4
“Wealso calculated the interaction energies of protein– protein interaction (Siebenmorgen and Zacharias, 2020).”
Su, Z., & Wu, Y. (2022) · Journal of Cell Communication and Signaling · doi:10.1007/s12079-022-00701-2 · Cited by 3 · see citing works
“However, most of these applications focused on the binding of biological macromolecules to drug, ion, or other small organic molecules (Siebenmorgen and Zacharias 2020).”
BackgroundAnalysis of amyloidogenic transthyretin mutations using continuum solvent free energy calculations
Hartmann, J., & Zacharias, M. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26399 · Cited by 4
“model (MMGBSA: Molecular Mechanics Generalized Born Surface Area method).(35,36) To avoid large conformational changes during the simulation's weak positional restraints on the structures were included, assuming that the mutations do not alter the structure sig-”
MethodSelf-citationForouzesh, N. (2020) · doi:10.1145/3388440.3414712 · Cited by 4
“Despite decades of research, efficient and accurate computational prediction of binding free energies is still a challenge [30, 39, 42, 50, 54].”
BackgroundChoi, Y., Jeong, S., Choi, J., NDong, C., Bailey-Kellogg, C., Griswold, K., & Kim, H.-S. (2019) · bioRxiv · doi:10.1371/journal.pcbi.1008150 · Cited by 4
From Intermolecular Poses to Thermodynamics Using Subdivided Spheres
Vinterbladh, I., Bye, J., Curtis, R., Hatch, H., Grudinin, S., & Lund, M. (2026) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c01665 · Cited by 3 · see citing works
Ayyildiz, M., Noske, J., Gisdon, F. J., Kynast, J. P., & Höcker, B. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70160 · Cited by 4 · see citing works
“Machine-learning based scoring functions have been developed for the approximation of binding specificity [7, 8], however, they often do not include routines for preparing mutated structural models or ensembles of models.”
Grassmann, G., Miotto, M., Alessandrini, F., Bo’, L., Ruocco, G., Milanetti, E., & Giansanti, A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.10.033 · Cited by 3 · see citing works
“Despite decades of theoretical and computational advancements in predicting protein-protein binding affinity, as reviewed by Siebenmorgen et al. [11], the problem remains highly complex and unsolved.”
Structure and dimerization properties of the plant-specific copper chaperone CCH
Dluhosch, D., Kersten, L. S., Schott-Verdugo, S. N., Hoppen, C., Schwarten, M., Willbold, D., Gohlke, H., & Groth, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-69532-y · Cited by 3 · see citing works
“For this MM-GBSA 54 calculations employing the “single trajectory approach” in MMPBSA.py of AMBER22 80 , considering gas-phase energies and solvation free energies but not configurational entropies 55 , were performed.”
Mokhtari-Abpangoui, M., Lohrasbi-Nejad, A., Zolala, J., Torkzadeh-Mahani, M., & Ghanbari, S. (2021) · Molecular Biotechnology · doi:10.1007/s12033-021-00342-7 · Cited by 3
“The use of technical computers increases our knowledge about mechanisms of interaction, 3D-structural information of protein–protein complex, and prediction-binding affinity [30].”
BackgroundAbbasi, W. A., Abbas, S. A., & Andleeb, S. (2021) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720021500153 · Cited by 8 · see citing works
“Mutations in protein sequences modulate or often disrupt protein interactions by changing associated binding affinity which may forbid normal protein function and cause disease (Cargill et al., 1999; Li et al., 2016; Siebenmorgen and Zacharias, 2019).”
BackgroundAminorroayaei, A., Alghamdi, S. S., Alshehri, S. A., Majrashi, T. A., Farhadian, S., Shareghi, B., Atiya, A., gholizadeh, M., Alsayari, A., Muhsinah, A. B., et al. (2026) · International Dairy Journal · doi:10.1016/j.idairyj.2025.106471 · Cited by 3
Yao, Y., Hong, C., Teal, C. J., Bahsoun, N. E., Lu, S. P., Fernandes, I., Keller, G. M., & Shoichet, M. (2025) · Acta Biomaterialia · doi:10.1016/j.actbio.2025.12.034 · Cited by 2 · see citing works
Zhu, J., Yan, J.-N., Zheng, M.-Z., Xu, X.-Y., Zhang, H., Wu, Y.-Z., Zhao, C., & Liu, J.-S. (2025) · Food Chemistry · doi:10.1016/j.foodchem.2025.146836 · Cited by 2 · see citing works
- Authors
- Siebenmorgen, T., & Zacharias, M.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Zacharias, Martin
- Venue
- WIREs Computational Molecular Science
- Publisher
- Wiley
- Published
- 2019-10-29
- Volume
- 10
- Issue
- 3
- ISSN
- 1759-0876 (ISSN-L)
- 1759-0884
- OpenAlex
- W2982586377
- Cited by
- 184
- References
- 148
- Citations by year
- 2026: 9 · 2025: 42 · 2024: 25 · 2023: 27 · 2022: 32 · 2021: 35 · 2020: 12 · 2019: 2
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Wiley Interdisciplinary Reviews Computational Molecular Science (publishedVersion, cc-by)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- Funders
- Deutsche Forschungsgemeinschaft (SFB863/A10)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-protein interaction, protein-protein complex, knowledge-based scoring, binding free energy, protein-protein docking, template-based modeling, protein-protein complex structure
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