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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A subset consisting of 1,131 single-point mutations (S1131) was used to benchmark the model and other baselines.
“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]
The six benchmark datasets have been widely used to test the predictive power of PPI prediction methods.
“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]
Xiong et al. selected a subset of 1,131 non-redundant interface single-point mutations (S1131) from SKEMPI S2317.
“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]
The conservation measure used in those ΔΔG predictors is structure-based and relies on the availability of structural homologs.
“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]
The application and prediction of those structure-based conservation ΔΔG predictors are largely limited by the availability and number of structural homologs.
“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]
Interface conservation has been used in several of the best performing ΔΔG predictors.
“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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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 · see citing works
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 · see citing works
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327 · see citing works
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 · see citing works
Self-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× · see citing works
“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 · see citing works
mCSM: 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 · see citing works
BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations
Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt450 · Cited by 338 · see citing works
STRUM: 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 324 · see citing works
Self-citationDouble-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 · see citing works
Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works
Docking, scoring, and affinity prediction in CAPRI
Lensink, M. F., & Wodak, S. J. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24428 · Cited by 249 · see citing works
Yates, C. M., & Sternberg, M. J. E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.012 · Cited by 239 · see citing works
Liu, S., Zhang, C., Zhou, H., & Zhou, Y. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20019 · Cited by 193 · see citing works
Self-citationAssessing predictions of protein–protein interaction: The CAPRI experiment
Janin, J. (2005) · Protein Science · doi:10.1110/ps.041081905 · Cited by 179 · see citing works
Protein-Protein Complex Structure Predictions by Multimeric Threading and Template Recombination
Mukherjee, S., & Zhang, Y. (2011) · Structure · doi:10.1016/j.str.2011.04.006 · Cited by 174 · see citing works
Self-citationTemplate-based structure modeling of protein–protein interactions
Szilágyi, A., & Zhang, Y. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.11.005 · Cited by 168 · see citing works
Self-citationBrender, J. R., & Zhang, Y. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004494 · Cited by 140 · see citing works
Self-citationInsights into disease-associated mutations in the human proteome through protein structural analysis
Gao, M., Zhou, H., & Skolnick, J. (2015) · Structure · doi:10.1016/j.str.2015.03.028 · Cited by 113 · see citing works
iAlign: a method for the structural comparison of protein–protein interfaces
Gao, M., & Skolnick, J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq404 · Cited by 98 · see citing works
Non-Redundant Unique Interface Structures as Templates for Modeling Protein Interactions
Cukuroglu, E., Gürsoy, A., Nussinov, R., & Keskin, Ö. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086738 · Cited by 76 · see citing works
Mapping Monomeric Threading to Protein–Protein Structure Prediction
Guerler, A., Govindarajoo, B., & Zhang, Y. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300579r · Cited by 73 · see citing works
Self-citationAn Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis
Mitra, P., Shultis, D., Brender, J., Czajka, J. J., Marsh, D., Gray, F. L. V., Cierpicki, T., & Zhang, Y. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003298 · Cited by 57 · see citing works
Self-citationHow Many Protein-Protein Interactions Types Exist in Nature?
Garma, L. D., Mukherjee, S., Mitra, P., & Zhang, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038913 · Cited by 33 · see citing works
Self-citationModeling and fitting protein-protein complexes to predict change of binding energy
Dourado, D. F. A. R., & Flores, S. C. (2016) · Scientific Reports · doi:10.1038/srep25406 · Cited by 15 · see citing works
From an SNP to a Disease: A Comprehensive Statistical Analysis
Elber, R. (2015) · Structure · doi:10.1016/j.str.2015.06.005 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Jankauskaitė, J., Jiménez-García, B., Dapkūnas, J., Fernández-Recio, J., & Moal, I. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty635 · Cited by 389 · see citing works
Wang, M., Cang, Z., & Wei, G.-W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0149-6 · Cited by 202 · see citing works
MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions
Zhang, N., Chen, Y., Lu, H., Zhao, F., Alvarez, R. V., Goncearenco, A., Panchenko, A. R., & Li, M. (2020) · iScience · doi:10.1016/j.isci.2020.100939 · Cited by 199 · see citing works
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 · see citing works
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 · see citing works
EvoEF2: accurate and fast energy function for computational protein design
Huang, X., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz740 · Cited by 119 · see citing works
Self-citationHuang, X., Pearce, R., & Zhang, Y. (2020) · Aging · doi:10.18632/aging.103416 · Cited by 113 · see citing works
Self-citationSAAMBE-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
Geng, C., Xue, L. C., Roel‐Touris, J., & Bonvin, A. M. J. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1410 · Cited by 100 · see citing works
Pearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.028 · Cited by 83 · see citing works
Self-citationGeng, 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
Wang, G., Liu, X., Wang, K., Gao, Y., Li, G., Baptista‐Hon, D. T., Yang, X. H., Xue, K., Tai, W. H., Jiang, Z., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02483-5 · Cited by 83 · see citing works
Recent advances in predicting and modeling protein–protein interactions
Durham, J., Zhang, J., Humphreys, I. R., Pei, J., & Cong, Q. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.03.003 · Cited by 80 · see citing works
Chen, C., Boorla, V. S., Banerjee, D., Chowdhury, R., Cavener, V. S., Nissly, R. H., Gontu, A., Boyle, N. R., Vandegrift, K. J., Nair, M. S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106480118 · Cited by 79 · see citing works
Recent advances in user-friendly computational tools to engineer protein function
Sequeiros-Borja, C. E., Surpeta, B., & Brezovský, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa150 · Cited by 79 · see citing works
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
Chen, J., Qiu, Y., Wang, R., & Wei, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106262 · Cited by 70 · see citing works
Study, D. D. D. (., Zweier, M., Begemann, A., McWalter, K., Cho, M. T., Abela, L., Banka, S., Behring, B., Berger, A., Brown, C. W., et al. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-018-0331-z · Cited by 71 · see citing works
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
Zhou, Y., Myung, Y., Rodrigues, C. H. M., & Ascher, D. B. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae412 · Cited by 65 · see citing works
Glusman, G., Rose, P. W., Prlić, A., Dougherty, J. G., Duarte, J. M., Hoffman, A. S., Barton, G. J., Bendixen, E., Bergquist, T. R., Bock, C., et al. (2017) · Genome Medicine · doi:10.1186/s13073-017-0509-y · Cited by 60 · see citing works
Self-citationBegemann, A., Sticht, H., Begtrup, A., Vitobello, A., Faivre, L., Banka, S., Alhaddad, B., Asadollahi, R., Becker, J., Bierhals, T., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-020-01011-x · Cited by 61 · see citing works
Huang, X., Zheng, W. H., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz926 · Cited by 59 · see citing works
Self-citationDe novo variants in PAK1 lead to intellectual disability with macrocephaly and seizures
Horn, S., Au, M., Basel‐Vanagaite, L., Bayrak‐Toydemir, P., Chapin, A., Cohen, L., Elting, M. W., Graham, J. M., Gonzaga-Jauregui, C. G., Konen, O., et al. (2019) · Brain · doi:10.1093/brain/awz264 · Cited by 53 · see citing works
AlphaFold 3: an unprecedent opportunity for fundamental research and drug development
Fang, Z., Ran, H., Zhang, Y., Chen, C., Ping, L., Zhang, X., & Wu, M. (2025) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbaf015 · Cited by 58 · see citing works
Self-citationAntibody design using deep learning: from sequence and structure design to affinity maturation
Joubbi, S., Micheli, A., Milazzo, P., Maccari, G., Ciano, G., Cardamone, D., & Medini, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae307 · Cited by 54 · see citing works
DIP/Dpr interactions and the evolutionary design of specificity in protein families
Sergeeva, A. P., Katsamba, P. S., Cosmanescu, F., Brewer, J. J., Ahlsen, G., Mannepalli, S., Shapiro, L., & Honig, B. (2020) · Nature Communications · doi:10.1038/s41467-020-15981-8 · Cited by 38 · see citing works
Wee, J., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac024 · Cited by 45 · see citing works
Jemimah, S., Sekijima, M., & Gromiha, M. M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz829 · Cited by 44 · see citing works
Sun, T., Chen, Y., Wen, Y., Zhu, Z., & Li, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02826-3 · Cited by 43 · see citing works
Design of Protein Segments and Peptides for Binding to Protein Targets
Gupta, S., Azadvari, N., & Hosseinzadeh, P. (2022) · BioDesign Research · doi:10.34133/2022/9783197 · Cited by 45 · see citing works
Woldring, D., Holec, P. V., Stern, L. A., Du, Y., & Hackel, B. J. (2017) · Biochemistry · doi:10.1021/acs.biochem.6b01142 · Cited by 44 · see citing works
Li, G., Pahari, S., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa761 · Cited by 41 · see citing works
Kreutzberger, M. A. B., Sobe, R. C., Sauder, A. B., Chatterjee, S., Peña, A., Wang, F., Girón, J. A., Kiessling, V., Costa, T. R. D., Conticello, V. P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29069-y · Cited by 39 · see citing works
Implications of disease-related mutations at protein–protein interfaces
Xiong, D., Lee, D., Li, L., Zhao, Q., & Yu, H. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.11.012 · Cited by 40 · see citing works
Precision medicine — networks to the rescue
Yadav, A., Vidal, M., & Luck, K. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.02.005 · Cited by 36 · see citing works
Accurate prediction of antibody function and structure using bio-inspired antibody language model
Jing, H., Gao, Z., Xu, S. Y., Shen, T., Peng, Z., He, S., You, T., Ye, S., Lin, W. J., & Sun, S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae245 · Cited by 34 · see citing works
Computed cancer interactome explains the effects of somatic mutations in cancers
Zhang, J., Pei, J., Durham, J., Bos, T., & Cong, Q. (2022) · Protein Science · doi:10.1002/pro.4479 · Cited by 31 · see citing works
Zhou, Z., Yin, Y., Han, H., Jia, Y., Koh, J. H., Kong, A. W.-K., & Mu, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01850 · Cited by 30 · see citing works
Protein Function Analysis through Machine Learning
Avery, C., Patterson, J., Grear, T. J., Frater, T. D., & Jacobs, D. J. (2022) · Biomolecules · doi:10.3390/biom12091246 · Cited by 29 · see citing works
Quantification of biases in predictions of protein–protein binding affinity changes upon mutations
Tsishyn, M., Pucci, F., & Rooman, M. J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad491 · Cited by 29 · see citing works
Genetic variants and protein–protein interactions: a multidimensional network-centric view
Laddach, A., Ng, J. C.-F., Chung, S. S., & Fraternali, F. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.12.006 · Cited by 23 · see citing works
AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactionsPreprint
Lu, W., Zhang, J., Rao, J., Zhang, Z., & Zheng, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.25.595871 · Cited by 27 · see citing works
PremPRI: Predicting the Effects of Missense Mutations on Protein–RNA Interactions
Zhang, N., Lu, H., Chen, Y., Zhu, Z., Yang, Q., Wang, S., & Li, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21155560 · Cited by 22 · see citing works
Meseguer, A., Dominguez, L., Bota, P. M., Aguirre‐Plans, J., Bonet, J., Fernández‐Fuentes, N., & Oliva, B. (2020) · Protein Science · doi:10.1002/pro.3930 · Cited by 22 · see citing works
Mohseni Behbahani, Y., Laine, E., & Carbone, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad231 · Cited by 23 · see citing works
Yu, G., Zhao, Q., Bi, X., & Wang, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae232 · Cited by 23 · see citing works
Assessment 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
Jiang, Y., Quan, L., Li, K., Li, Y., Zhou, Y., Wu, T., & Lyu, Q. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3233627 · Cited by 20 · see citing works
Loganathan, L., Gopinath, K., Sankaranarayanan, V. M., Kukreti, R., Rajendran, K., Lee, J.-K., & Muthusamy, K. (2019) · Current Drug Targets · doi:10.2174/1389450120666190808101356 · Cited by 21 · see citing works
Liu, X., Feng, H., Wu, J., & Xia, K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00580 · Cited by 18 · see citing works
Pandey, P., Panday, S. K., Rimal, P., Ancona, N., & Alexov, E. G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241512073 · Cited by 19 · see citing works
Yue, Y., Li, S., Wang, L., Liu, H., Tong, H. H. Y., & He, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad310 · Cited by 19 · see citing works
Shringari, S. R., Giannakoulias, S. G., Ferrie, J. J., & Petersson, E. J. (2020) · Chemical Communications · doi:10.1039/d0cc01959c · Cited by 22 · see citing works
Are transient protein-protein interactions more dispensable?
Ghadie, M. A., & Xia, Y. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010013 · Cited by 18 · see citing works
Changing the Apoptosis Pathway through Evolutionary Protein Design
Shultis, D., Mitra, P., Huang, X., Johnson, J. S., Khattak, N. A., Gray, F. L. V., Piper, C., Czajka, J. J., Hansen, L., Wan, B., et al. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.12.016 · Cited by 18 · see citing works
Self-citationBæk, K. T., & Kepp, K. P. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.26810 · Cited by 17 · see citing works
PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein–Protein Interface
Wu, F., Yang, J., Mei, L., Wang, F., Hao, G., & Yang, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00966 · Cited by 17 · see citing works
Estimating dispensable content in the human interactome
Ghadie, M. A., & Xia, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-11180-2 · Cited by 17 · see citing works
Gurusinghe, S. N. S., Wu, Y., DeGrado, W. F., & Shifman, J. M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf270 · Cited by 15 · see citing works
Viricel, C., de Givry, S., Schiex, T., & Barbe, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty092 · Cited by 15 · see citing works
Montioli, R., Janson, G., Paiardini, A., Bertoldi, M., & Voltattorni, C. B. (2018) · IUBMB Life · doi:10.1002/iub.1718 · Cited by 14 · see citing works
Peka, M., & Balatsky, V. (2023) · Future Virology · doi:10.2217/fvl-2022-0152 · Cited by 13 · see citing works
MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning
Li, P., & Liu, Z. (2024) · Advanced Science · doi:10.1002/advs.202402918 · Cited by 11 · see citing works
Wang, B., Su, Z., & Wu, Y. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2021.03.004 · Cited by 11 · see citing works
Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma
Pei, J., Zhang, J., & Cong, Q. (2023) · FEBS Open Bio · doi:10.1002/2211-5463.13732 · Cited by 11 · see citing works
Rana, M. M., & Nguyen, D. D. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c02679 · Cited by 11 · see citing works
Verkhivker, G. M., Agajanian, S., Öztaş, D. Y., & Gupta, G. (2021) · ACS Omega · doi:10.1021/acsomega.1c03558 · Cited by 11 · see citing works
On the linkage of thermodynamics and pathogenicity
Pandey, P., Ghimire, S., Wu, B., & Alexov, E. G. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102572 · Cited by 11 · see citing works
Santos, V. T., Guerra-Almeida, D., Mury, F. B., Ribeiro, L., Berni, M. A., Araujo, H. M., Logullo, C., Feitosa, N. M., Souza‐Menezes, J., Costa, E. P., et al. (2019) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2019.00379 · Cited by 11 · see citing works
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Li, W. (2022) · Preprints.org · doi:10.20944/preprints202208.0213.v1 · Cited by 10 · see citing works
Towards a Truly General Intermolecular Binding Affinity Calculator for Drug Discovery & DesignPreprint
Li, W., & Vottevor, G. (2023) · Preprints.org · doi:10.20944/preprints202208.0213.v2 · Cited by 11 · see citing works
Abdollahi, S., Lin, P., & Chiang, J.-H. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3093441 · Cited by 10 · see citing works
Persistent Tor-algebra for protein–protein interaction analysis
Liu, X., Feng, H., Lü, Z., & Xia, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad046 · Cited by 10 · see citing works
Insights from engineering the Affibody-Fc interaction with a computational-experimental method
Nosrati, M., Solbak, S. M. Ø., Nordesjö, O., Nissbeck, M., Dourado, D. F. A. R., Andersson, K. G., Housaindokht, M. R., Löfblom, J., Virtanen, A., Danielson, U. H., et al. (2017) · Protein Engineering Design and Selection · doi:10.1093/protein/gzx023 · Cited by 10 · see citing works
Raisinghani, N., Alshahrani, M., Gupta, G., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01857 · Cited by 8 · see citing works
Lan, T., Su, S., Ping, P., Hutvágner, G., Liu, T., Pan, Y. F., & Li, J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00803-z · Cited by 9 · see citing works
Zhang, Y., Dong, M., Deng, J., Wu, J., Zhao, Q., Gao, X., & Xiong, D. (2024) · Communications Biology · doi:10.1038/s42003-024-07066-9 · Cited by 9 · see citing works
Self-citationSu, Z., Wang, B., Almo, S. C., & Wu, Y. (2020) · iScience · doi:10.1016/j.isci.2020.101835 · Cited by 9 · 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
Bioinformatic approach to identifying causative missense polymorphisms in animal genomes
Peka, M., & Balatsky, V. (2024) · BMC Genomics · doi:10.1186/s12864-024-11126-z · Cited by 8 · see citing works
Rimal, P., Panday, S. K., Wang, X., Peng, Y., & Alexov, E. G. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae544 · Cited by 8 · see citing works
Chen, J., & Wu, Y. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.02.013 · Cited by 8 · see citing works
The computational approach to variant interpretation
Özkan, S., Padilla, N., Moles‐Fernández, A., Dı́ez, O., Gutiérrez‐Enríquez, S., & de la Cruz, X. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820519-8.00007-7 · Cited by 7 · see citing works
Floriani, F., Voltattorni, C. B., Cellini, B., & Montioli, R. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043369 · Cited by 8 · see citing works
Specific inter-domain interactions stabilize a compact HIV-1 Gag conformation
Lin, C., Mendoza-Espinosa, P., Rouzina, I. F., Guzmán, O., Moreno-Razo, J. A., Francisco, J. S., & Bruinsma, R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221256 · Cited by 7 · see citing works
Biomolecular Topology: Modelling and Analysis
Liu, J., Xia, K., Wu, J., Yau, S. S. ‐., & Wei, G. (2022) · Acta Mathematica Sinica English Series · doi:10.1007/s10114-022-2326-5 · Cited by 9 · see citing works
Engineered Fully Human Single-Chain Monoclonal Antibodies to PIM2 Kinase
Kaewchim, K., Glab-ampai, K., Mahasongkram, K., Chulanetra, M., Seesuay, W., Chaicumpa, W., & Sookrung, N. (2021) · Molecules · doi:10.3390/molecules26216436 · Cited by 7 · see citing works
Structural basis for neutralization of cytotoxic abrin by monoclonal antibody D6F10
Bansia, H., Bagaria, S., Karande, A. A., & Ramakumar, S. (2018) · FEBS Journal · doi:10.1111/febs.14716 · Cited by 7 · see citing works
Decoding the effects of mutation on protein interactions using machine learning
Wang, X., Li, A., Zhao, Y., & Peng, Y. (2025) · Biophysics Reviews · doi:10.1063/5.0249920 · Cited by 6 · see citing works
Nikam, R., Jemimah, S., & Gromiha, M. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae309 · Cited by 6 · see citing works
Yadav, D., Agarwal, S., Pancham, P., Jindal, D., Agarwal, V., Dubey, P. K., Jha, S. K., Mani, S., Rachana, R., Dey, A., et al. (2022) · Immuno · doi:10.3390/immuno2020022 · Cited by 7 · see citing works
Multiscale Simulation Unravel the Kinetic Mechanisms of Inflammasome Assembly
Su, Z.-Q., & Wu, Y. (2019) · Biochimica et biophysica acta. Molecular cell research · doi:10.1016/j.bbamcr.2019.118612 · Cited by 5 · see citing works
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language ModelPreprint
Jing, H., Gao, Z., Xu, S. Y., Shen, T., Peng, Z., He, S., You, T., Ye, S., Lin, W., & Sun, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.30.555473 · Cited by 6 · see citing works
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202405.1739.v1 · Cited by 6 · see citing works
Barkan, E., Siddiqui, I., Cheng, K. J., Golts, A., Shoshan, Y., Weber, J. K., Mota, Y. C., Ozery-Flato, M., & Sautto, G. A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.038 · Cited by 5 · see citing works
A Multiscale Computational Model for Simulating the Kinetics of Protein Complex Assembly
Chen, J. C., & Wu, Y. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7759-8_26 · Cited by 5 · see citing works
Dhusia, K., Madrid, C., Su, Z., & Wu, Y. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00612 · Cited by 4 · see citing works
VariBench, new variation benchmark categories and data sets
Shirvanizadeh, N., & Vihinen, M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1248732 · Cited by 4 · see citing works
Wang, Y., Shi, X., & Zhou, H. (2023) · IEEE Industrial Electronics Society Annual On-Line Conference (ONCON) · doi:10.1109/oncon60463.2023.10430682 · Cited by 4 · see citing works
Khor, B. Y., Lim, T. S., Noordin, R., & Choong, Y. S. (2017) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2017.07.004 · Cited by 4 · see citing works
- 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 · 2016: 1
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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