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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
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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×
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BackgroundMethodResultInfluentialProfile analysis: detection of distantly related proteins.
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“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.
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“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
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
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.”
MethodEvoEF2: accurate and fast energy function for computational protein design
Huang, X., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz740 · Cited by 122
Self-citationHuang, X., Pearce, R., & Zhang, Y. (2020) · Aging · doi:10.18632/aging.103416 · Cited by 115
“In previous studies, we also proposed incorporating protein-protein interface evolutionary profiles to model PPIs [19, 41, 42].”
MethodSelf-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. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072563 · Cited by 111
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 104
“By combining Boltzmann probability and FoldX ΔΔ G with a simple linear function, BindProfX shows a further improved PCC of 0.738 on 1,131 single mutations of SKEMPI.”
BackgroundMethodInfluentialPearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.028 · Cited by 83
“Finally, the evolution-based binding affinity change for each mutation at the interface is determined by the logarithm of the relative probability of each mutant amino acid compared to the wild type amino acid in the interface profile [13, 16].”
BackgroundMethodResultInfluentialSelf-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
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
“https://doi.org/10.1038/s41591-023-02483-5 with that of other published models 32–34 (Extended Data Table 1).”
InfluentialRecent 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 83
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 81
“Other methods, such as Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) (56) and BindProfX (57), use a combination of bio-physical energies and residue-level structural properties or sequence-based conservation profiles, respectively.”
MethodRecent 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 81
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
“Protein-protein BFE changes can be carried out in a variety of ways as shown in software packages FOLDX [18], SAAMBE [38], mCSM-AB [39], mCSMPPI2 [42], BindProfX [58], etc.”
BackgroundMethodStudy, 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
Zhou, G., Chen, M., Ju, C. J. ‐., Wang, Z., Jiang, J., & Wang, W. W. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa015 · Cited by 70
“Binding affinity change estimation is a widely attempted task in previous works [6, 11, 16, 29, 43, 51].”
BackgroundMethodInfluentialZhou, Y., Myung, Y., Rodrigues, C. H. M., & Ascher, D. B. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae412 · Cited by 65
Glusman, G., Rose, P. W., Prlić, A., Dougherty, J., Duarte, J. M., Hoffman, A. S., Barton, G. J., Bendixen, E., Bergquist, T., Bock, C., et al. (2017) · Genome Medicine · doi:10.1186/s13073-017-0509-y · Cited by 61
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
Huang, X., Zheng, W. H., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz926 · Cited by 59
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
“Protein modelling and analysis of gene expression Energetic calculations of the observed protein changes were carried out using BindProfX (Xiong et al., 2017) and visualized using UCSC Chimera v.1.12 (Pettersen et al., 2004).”
MethodInfluentialAlphaFold 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 67
“The test results of the SKEMPI database show that, compared to FoldX [ 68 ] and BindProfX [ 69 ], AF3 has unique advantages in estimating binding energy and free energy.”
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 56
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
Wee, J., & Xia, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac024 · Cited by 48
Jemimah, S., Sekijima, M., & Gromiha, M. M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz829 · Cited by 44
Sun, T., Chen, Y., Wen, Y., Zhu, Z., & Li, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02826-3 · Cited by 43
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 47
“interfaces [41] to the interface of interest using iAlign, a computational method developed for aligning and comparing protein-protein interfaces [42].”
MethodWoldring, D., Holec, P. V., Stern, L. A., Du, Y., & Hackel, B. J. (2017) · Biochemistry · doi:10.1021/acs.biochem.6b01142 · Cited by 44
Li, G., Pahari, S., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa761 · Cited by 44
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
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
“The most recently published energy-based methods including BindProfX [32] and SSIPe [33] combined such energies with other information (such as protein interface profiles) to predict DD G and have achieved better performance than other energy-based models.”
MethodPrecision 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 37
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 35
“The testing set is composed of subsets from S1131 [29] and M1707 [30] datasets.”
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
“We evaluated the impact of these SMMs on the binding affinity between two interacting proteins using BindProfX.(51) Majority (74.”
MethodZhou, 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
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 30
“FoldX [427], CC-PBSA [428], BeAtMuSiC [429], BindProfX [430], which uses evolutionary information from homologs, and mCSM [431,432].”
MethodQuantification 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 33
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 24
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
“…have been developed to estimate the effects of mutations, ranging from force field-based methods [14, 15], which derive forces from physical interactions such as van der Waals and electrostatic, or from statistical energy, to profile-based methods [16], which query sequence and structure databases.”
MethodPremPRI: 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 24
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 21
“Recent works, such as BindfProfX 54 and SSIPE, 55 have, respectively, used the structure-based profile and sequence-based profile-profile of the two protein partners to predict the changes of affinity caused by single, and also multiple, amino-acid variants of the interface sequence.”
MethodMohseni Behbahani, Y., Laine, E., & Carbone, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad231 · Cited by 23
“BindProfX combines FoldX with evolutionary interface profiles built from structural homologs.”
MethodResultInfluentialYu, G., Zhao, Q., Bi, X., & Wang, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae232 · Cited by 23
“According to Xiong et al. (2017), S1131 is a subset with 1131 nonredundant interface single point mutations.”
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 22
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 21
“several methods can be obtained from the literatures [13], [20].”
MethodInfluentialLoganathan, 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
Liu, X., Feng, H., Wu, J., & Xia, K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00580 · Cited by 19
Pandey, P., Panday, S. K., Rimal, P., Ancona, N., & Alexov, E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241512073 · Cited by 20
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
“Over the years, numerous computational approaches have been developed to predict G [11].”
BackgroundMethodShringari, S. R., Giannakoulias, S. G., Ferrie, J. J., & Petersson, E. J. (2020) · Chemical Communications · doi:10.1039/d0cc01959c · Cited by 22
Are transient protein-protein interactions more dispensable?
Ghadie, M. A., & Xia, Y. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010013 · Cited by 19
“…framework for estimating dispensable PPI content and for estimating the overall fitness effect for different mutation edgotypes is robust to different choices of methods for predicting ΔΔ G upon mutation [20,21], including the FoldX method [75], BindProfX [84], mCSM-PPI2 [85] and DynaMut2 [86].”
Changing the Apoptosis Pathway through Evolutionary Protein Design
Shultis, D., Mitra, P., Huang, X., Johnson, J. S., Khattak, N. A., Gray, F., 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
“The binding potentials can be either physical [20] or evolutionary based [21,22].”
BackgroundSelf-citationBæk, K. T., & Kepp, K. P. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.26810 · Cited by 17
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
Estimating dispensable content in the human interactome
Ghadie, M. A., & Xia, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-11180-2 · Cited by 16
Gurusinghe, S. N. S., Wu, Y., DeGrado, W. F., & Shifman, J. M. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf270 · Cited by 15
“Incorporation of advanced machine learning techniques into the model building (Pires et al. 2014; Cang and Wei 2017; Xiong et al. 2017; Pahari et al. 2020; Zhang et al. 2020) improved prediction accuracy achieving correlations of � 0.7 for single mutations.”
InfluentialViricel, C., de Givry, S., Schiex, T., & Barbe, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty092 · Cited by 16
Montioli, R., Janson, G., Paiardini, A., Bertoldi, M., & Voltattorni, C. B. (2018) · IUBMB Life · doi:10.1002/iub.1718 · Cited by 14
“Moreover, to quantitatively predict the impact of the R160W mutation on the dimer stability, we used the BindProfX server (24).”
MethodPeka, M., & Balatsky, V. (2023) · Future Virology · doi:10.2217/fvl-2022-0152 · Cited by 13
“…a set of statistical potentials) [31] ; BindProfX (predicts G single and G multiple ; takes into account statistical and physics-based potentials) [32] ; Mutabind2 (predicts G single and G multiple ; is based on an accounting of changes in physicochemical features and evolutionary conservation)…”
BackgroundMuToN 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
Wang, B., Su, Z., & Wu, Y. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2021.03.004 · Cited by 11
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
“:3) mol (cid:4) 1 ) by the BindProfX program [48] (https://zhanggroup.org/BindProfX/help.html).”
MethodInfluentialRana, M. M., & Nguyen, D. D. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c02679 · Cited by 11
Verkhivker, G. M., Agajanian, S., Öztaş, D. Y., & Gupta, G. (2021) · ACS Omega · doi:10.1021/acsomega.1c03558 · Cited by 11
On the linkage of thermodynamics and pathogenicity
Pandey, P., Ghimire, S., Wu, B., & Alexov, E. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102572 · Cited by 11
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
“Calculations on the change in the binding energy of the proteinprotein complex upon mutations in the residues on interaction interface were performed by BindProfX (Xiong et al., 2017).”
MethodTowards a General Intermolecular Binding Affinity CalculatorPreprint
Li, W. (2022) · Preprints.org · doi:10.20944/preprints202208.0213.v1 · Cited by 10
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 12
Abdollahi, S., Lin, P., & Chiang, J.-H. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3093441 · Cited by 10
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 11
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
“Performance of FoldX4 (the current release) differs somewhat from that of FoldX3 (Xiong et al., 2017), and does not appear to benefit from ZEMu.”
BackgroundMethodInfluentialRaisinghani, 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
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
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
Self-citationSu, Z., Wang, B., Almo, S. C., & Wu, Y. (2020) · iScience · doi:10.1016/j.isci.2020.101835 · Cited by 9
Lombardi, G., & Carbone, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.24.609515 · Cited by 10
“0 dataset (Jankauskait et al. , 2018) has significantly enhanced the precision and efficiency of methods like iSEE (Geng et al. , 2019b), mCSM-PPI2 (Rodrigues et al. , 2019), BindProfX (Xiong et al. , 2017), MuPIPR (Zhou et al. , 2020), MutaBind2 (Zhang et al. , 2020), GeoPPI (Liu et al. , 2021), and DLA-Mutations (Mo…”
MethodBioinformatic 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 10
Rimal, P., Panday, S. K., Wang, X., Peng, Y., & Alexov, E. G. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae544 · Cited by 8
“…the vast majority of the methods for predicting the change of the binding free energy caused by mutation for soluble proteins (Dehouck et al. 2013, Xiong et al. 2017, Geng et al. 2019, Rodrigues et al. 2019, Jemimah et al. 2020, Pahari et al. 2020, Li et al. 2021) are structure-based, i.e. they…”
Chen, J., & Wu, Y. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.02.013 · Cited by 11
“Given the structure of protein complexes, computational efforts were mainly focused on predicting the relative changes of binding affinity due to mutations 52 .”
BackgroundThe 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
Floriani, F., Voltattorni, C. B., Cellini, B., & Montioli, R. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043369 · Cited by 8
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 9
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
“…electrostatic energies, hydrogen bonds, solvation energy, etc. Based on the contacts at atomic, residual, or coarse-grained levels, statistical-energy-based models have been proposed, such as BindProfX [216], BeAtMuSiC [61], contact potentials [148], Profile-score [184], and Dcomplex [127].”
BackgroundEngineered 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
“The interactive surface of HuscFvs and PIM2 was identify by using BindProfX [54].”
MethodStructural 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
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 7
Nikam, R., Jemimah, S., & Gromiha, M. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae309 · Cited by 7
“Structure-based methods such as FoldX (Guerois et al. 2002, Schymkowitz et al. 2005), MutaBind (Li et al. 2016), BindProfX (Xiong et al. 2017), mCSM-PPI2 (Rodrigues et al. 2019), iSee (Geng et al. 2019), MutaBind2 (Zhang et al. 2020), SAAMBE-3D (Pahari et al. 2020), and DLA-Mutation (Behbahani et…”
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
Multiscale simulation unravel the kinetic mechanisms of inflammasome assembly
Su, Z., & Wu, Y. (2019) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2019.118612 · Cited by 6
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
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202405.1739.v1 · Cited by 6
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
“Other knowledge-based scoring functions [41,42] offer alternative means of estimating binding affinities, but with limited accuracy and validity.”
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
Dhusia, K., Madrid, C., Su, Z., & Wu, Y. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00612 · Cited by 4
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
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
“Their details are shown in Table I. 1) SKP1400m: This is a dataset produced by Xiong et al. [11].”
MethodKhor, 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
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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