The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts
Open accessAbstract
Understanding how gene-level mutations affect the binding affinity of protein-protein interactions is a key issue of protein engineering. Due to the complexity of the problem, using physical force field to predict the mutation-induced binding free-energy change remains challenging. In this work, we present a renewed approach to calculate the impact of gene mutations on the binding affinity through the structure-based profiling of protein-protein interfaces, where the binding free-energy change (ΔΔG) is counted as the logarithm of relative probability of mutant amino acids over wild-type ones in the interface alignment matrix; three pseudo-counts are introduced to alleviate the limit of the current interface library. Compared with a previous profile score that was based on the log-odds likelihood calculation, the correlation between predicted and experimental ΔΔG of single-site mutations is increased in this approach from 0.33 to 0.68. The structure-based profile score is found complementary to the physical potentials, where a linear combination of the profile score with the FoldX potential could increase the ΔΔG correlation from 0.46 to 0.74. It is also shown that the profile score is robust for counting the coupling effect of multiple individual mutations. For the mutations involving more than two mutation sites where the correlation between FoldX and experimental data vanishes, the profile-based calculation retains a strong correlation with the experimental measurements.
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In the citing paper
“A subset consisting of 1,131 single-point mutations (S1131) (39) was used to benchmark the model and other baselines.”
From Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization (2022) · cites this paper as [39]
In the citing paper
“These datasets have been widely used to test the predictive power of the PPI prediction methods [12, 13, 27–29, 29–35].”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [30]
In the citing paper
“Xiong et al. recently selected a subset of 1,131 non-redundant interface single-point mutations (denoted set S1131) from SKEMPI set S231740.”
From A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation (2020) · cites this paper as [40]
In the citing paper
“However, since the conservation measure they used is structure‐based, relying on the availability of structural homologs,13, 19”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [19]
In the citing paper
“the application and prediction of these ΔΔG predictors are largely limited by the availability and the number of such homologs.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [19]
In the citing paper
“Interface conversation has been used in several of the best performing ΔΔG predictors.13, 19”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [19]
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Citations
Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302 · cited 1×
“Since the number of collected sequences is often much lower than that required for stable Boltzmann statistics, here it is necessary to make corrections to the amino acid probability in the interface multiple structural alignment (iMSA) according to amino acid substitutionmatrix [12].”
Amino acid substitution matrices from protein blocks.
Henikoff, S., & Henikoff, J. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.22.10915 · Cited by 6,218
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774 · cited 1×
“To calculate the BindProfX score, we first constructed the complex structures by overlaying the unbound monomer structure to the template complex structure provided by ZEMu, using the TM-align structural alignment program [22].”
MethodSelf-citationThe FoldX web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki387 · Cited by 2,759 · cited 5×
“118), which was based on FoldX potential [14] using the complex structure refined from MacroMoleculeBuilder (MMB) simulations after the structural-alignment overlay [21].”
BackgroundMethodResultInfluentialProfile analysis: detection of distantly related proteins.
Gribskov, M., McLachlan, A. D., & Eisenberg, D. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.13.4355 · Cited by 1,325 · cited 3×
“One of themostly usedmethods is the log-odd likelihood score [9] that is equivalent to the averageof the substitution scales from theDayhoff [10] or BLOSUM [11] matrix between target amino acid and all amino acids at the corresponding position in the multiple interface alignments; this approach was taken in the former…”
BackgroundMethodmCSM: Predicting the effects of mutations in proteins using graph-based signatures
Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt691 · Cited by 1,066 · cited 1×
“As expected, the difference between the training and test becomes smaller in the mutation-level cross-validation compared to the protein-level ones, probably due to the effect of homologous correlation between test and training samples, which is consistent with the observation made on the cross-validation of monomer-c…”
ResultBeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations
Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt450 · Cited by 338 · cited 2×
“In addition to the physics-based approach of FoldX, we reported in Table 1 the results obtained from two statistical potentials: BeAtMuSiC [19] and Dcomplex [20].”
MethodDouble-mutant cycles: a powerful tool for analyzing protein structure and function.
Horovitz, A. (1996) · Folding & design · doi:10.1016/s1359-0278(96)00056-9 · Cited by 322 · cited 2×
“org/1 coupling effect from individual residues could further complicate the problem [3].”
BackgroundSTRUM: structure-based prediction of protein stability changes upon single-point mutation
Quan, L., Lv, Q., & Zhang, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw361 · Cited by 317 · cited 1×
“As expected, the difference between the training and test becomes smaller in the mutation-level cross-validation compared to the protein-level ones, probably due to the effect of homologous correlation between test and training samples, which is consistent with the observation made on the cross-validation of monomer-c…”
ResultSelf-citation
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Jankauskaitė, J., Jiménez-García, B., Dapkūnas, J., Fernández-Recio, J., & Moal, I. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty635 · Cited by 389
Wang, M., Cang, Z., & Wei, G.-W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0149-6 · Cited by 202
MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions
Zhang, N., Chen, Y., Lu, H., Zhao, F., Alvarez, R. V., Goncearenco, A., Panchenko, A. R., & Li, M. (2020) · iScience · doi:10.1016/j.isci.2020.100939 · Cited by 199
Shan, S., Luo, S., Yang, Z., Hong, J., Su, Y., Ding, F., Fu, L., Li, C., Chen, P., Ma, J., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2122954119 · Cited by 157
Deep geometric representations for modeling effects of mutations on protein-protein binding affinity
Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009284 · Cited by 140
“A number of previous methods have been tested on this dataset, such as BindProfX (Xiong et al., 2017), Profilescore (Lensink and Wodak, 2013), BeAtMuSiC (Lensink and Wodak, 2013), SAMMBE (Petukh et al.”
Method
Metadata
- Authors
- Xiong, P., Zhang, C., Zheng, W., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Zhang, Yang
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 429
- Issue
- 3
- Pages
- 426-434
- ISSN
- 0022-2836 (ISSN-L)
- PMC
- PMC5963940
- OpenAlex
- W2554576124
- Cited by
- 140
- References
- 26
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 20 · 2023: 20 · 2022: 18 · 2021: 12 · 2020: 15 · 2019: 15 · 2018: 8 · 2017: 6
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Molecular Biology (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (GM083107, GM116960)
- National Science Foundation (DBI1564756)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein-protein interaction, pseudo counts, point mutations, protein engineering
- MeSH terms
- Algorithms, Amino Acid Sequence, Models, Molecular, Mutation, Protein Conformation, Proteins, Protein Engineering, Computational Biology, Protein Interaction Maps
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