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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–142 of 142
Liu, C., Li, M., Tan, Y., Gou, W., Fan, G., & Zhong, B. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf446 · Cited by 3
“These include AB645 (Wang et al. 2020) and S1131 (Xiong et al. 2017) consist of single-site mutations, and AB1101 (Wang et al. 2020) includes mutations across 1 to 7 residues, and the three datasets information are listed in Table 1, available as supplementary data at Bioinformatics online.”
InfluentialZhang, L., Zhou, X., Liang, L., & Lin, J. (2025) · ACS Omega · doi:10.1021/acsomega.5c08777 · Cited by 4
Computational design of protein complexes: influence of binding affinity
Ridha, F., Harini, K., Shanmugam, N. R. S., Nikam, R., & Gromiha, M. M. (2025) · Chemical Communications · doi:10.1039/d5cc04821d · Cited by 4
MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery
Shoshan, Y., Raboh, M., Ozery-Flato, M., Ratner, V., Golts, A., Weber, J. K., Barkan, E., Rabinovici‐Cohen, S., Polaczek, S., Amos, I., et al. (2026) · npj Drug Discovery. · doi:10.1038/s44386-026-00047-4 · Cited by 18
“…@TOKENIZER-TYPE=SMILES@MAX-LEN=10 〉〈 MOLECULAR ENTITY 〉 〈 MOLECULAR ENTITY SMALL MOLECULE 〉 CCC=CCC 〈 @TOKENIZER-TYPE=AA@MAXLEN=15 〉〈 MOLECULAR ENTITY 〉 〈 MOLECULAR ENTITY EPITOPE 〉 LMNPQRSTUVWY 〈 EOS 〉 Examples showing how a prompt is created for several downstream tasks can be found in Table S1 B”
Li, J., Chen, X., Huang, H., Zeng, M., Yu, J., Gong, X., & Ye, Q. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf120 · Cited by 4
Didiášová, M., Cesaro, S., Feldhoff, S., Bettin, I., Tiegel, N., Füssgen, V., Bertoldi, M., & Tikkanen, R. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25105237 · Cited by 3
“BindProfX [25] was used to predict changes in the binding affinity upon the amino acid substitutions in the form of ∆∆ G (change in free energy of binding) values.”
MethodRangel-Chávez, C. P., Galán-Vásquez, E., Pescador-Tapia, A., Delaye, L., & Martínez‐Antonio, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0239350 · Cited by 3
“With this strategy, we recreated single mutations along with the amino acids positively selected using the BindProfX server [54].”
MethodCrystal Structure of Abrin A chain (Recombinant) at 1.3 Angstroms
Bansia, H., Karande, A. A., & Ramakumar, S. (2018) · Worldwide Protein Data Bank · doi:10.2210/pdb5z37/pdb · Cited by 3
Protein–Protein Interactions and Genetic Disease
Brender, J., & Zhang, Y. (2017) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0026856 · Cited by 3
“The structural profile method gives results comparable to, and in many cases superior to, more involved physics-based calculations (Xiong et al., 2017; Brender and Zhang, 2015).”
Self-citationDeep Local Analysis estimates effects of mutations on protein-protein interactionsPreprint
Behbahani, Y. M., Laine, É., & Carbone, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.09.511484 · Cited by 3
“Several computational methods have been proposed for estimating mutation-induced binding affinity changes ( ∆∆ G bind ) by exploiting protein sequence and structural information [38, 15, 29, 31, 32, 36, 40, 23].”
MethodCentral Dysmyelination in SSADH‐Deficient Humans and Mice
Latzer, I. T., Lee, H. H. C., Yang, E., Alves, C., Bertoldi, M., Fung, C., Steele, S. V., Kule, M. E., Jin, Z., Rotenberg, A., et al. (2025) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.70148 · Cited by 2
“BindProfX [49] was utilized to understand whether there was impairment in the process by which monomers form dimers or tetramers according to differences in the type of ΔΔG values, calculated by an algo-rithm combining FoldX physics-based potentials with conservation scores from pairs of…”
Liu, C., Cai, S., Pan, T., Ogata, H., Song, J., & Akutsu, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00130 · Cited by 2
Peka, M., & Balatsky, V. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325023 · Cited by 2
Koirala, M., & Alexov, E. (2019) · Journal of Theoretical and Computational Chemistry · doi:10.1142/s0219633619300015 · Cited by 3
Ye, Q., li, J., Chen, X., Huang, H., Zeng, M., Yu, J., & Gong, X. (2024) · Research Square · doi:10.21203/rs.3.rs-4647798/v1 · Cited by 2
Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures
Chen, Z., He, S., Chi, X., & Bo, X. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26031343 · Cited by 1
“The SKEMPI 2.0 dataset and several subsets (e.g., S645 [16], S1131 [27], S4169 [11], M1707 [28]) are widely used in ∆∆ G prediction tasks.”
InfluentialMultitask Learning-Based Approaches for Protein Function Prediction
Bahmani, S., Chaudhari, M., Carrier, C., Garrett, S., Pratyush, P., & KC, D. B. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4662-5_4 · Cited by 1
Zamuner, S., Seno, F., & Trovato, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0254969 · Cited by 1
Peka, M., & Balatsky, V. (2023) · Veterinary and Animal Science · doi:10.1016/j.vas.2023.100303 · Cited by 1
Zhuang, J., Li, Z., Wang, S., Zheng, R., & Zhang, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.15.688659 · Cited by 1
Computation Resources for Molecular Biology: Special Issue 2018
Sternberg, M. J. E., & Yosef, N. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.05.041
Banerjee, S., Prasad, R., & Sen, P. (2019) · ACS Omega · doi:10.1021/acsomega.8b02647
Zamuner, S., Seno, F., & Trovato, A. (2021) · Research Square · doi:10.21203/rs.3.rs-267481/v1
Computation Resources for Molecular Biology: Special Issue 2017
Ostankovitch, M., & Sternberg, M. J. E. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.12.018
Effects of Mutations in Proteins on Their Interactions
Greß, A., & Kalinina, O. V. (2022) · Wiley eBooks · doi:10.1002/9783527830503.ch15
Computational Prediction Approaches for Predicting Mutation Impact on Protein-Protein Interactions
Ping, Y., Hoekstra, L., & Feenstra, K. A. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-28893-7_48
Geometric and topological AI for molecular sciences
Wee, J. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/165903
Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein–protein interfaces
Gurusinghe, S. N. S., & Shifman, J. M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05796-5
“While certain methods [27, 28] could predict changes in binding affinity due to mutations within seconds to minutes, these protocols require manual identification of the binding interface residues, manual specification of mutations and subsequent analysis of the produced data for cold spot…”
MethodSoftware for Predicting Binding Free Energy of Protein–Protein Complexes and Their Mutants
Jarończyk, M. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3985-6_9
Chandra, S., & Varadarajan, R. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4828-5_3
Gurusinghe, S. N. S., & Shifman, J. M. (2024) · Figshare · doi:10.6084/m9.figshare.26714440
SE3Bind: SE(3)-equivariant model for antibody-antigen binding affinity predictionPreprint
Subedy, A., Bhadra-Lobo, S., & Lamoureux, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.17.700115
“…[38] 1131 Random 0.85 TopNetTree [34] AB-Bind S645 [32] 618 AB-Bind S645 [32] 618 Random 0.68 TopNetTree [34, 35] SKEMPI 1131 [38] 1131 SKEMPI 1131 [38] 1131 Protein ECOD classes 0.38 PerSpect-EL (M3) [39] SKEMPI 1131 [38] 1131 SKEMPI 1131 [38] 1131 Random 0.85 PerSpect-EL (M3) [39] AB-Bind S645…”
InfluentialZhang, H., Liu, Y., Yu, L., Wang, Z., Liu, Y., & Guo, M. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356932
“PCC ↑ RMSE ↓ SAAMBE-SEQ [17] 0.624 - BindProfX [16] 0.738 - TopNetTree [8] 0.850 1.550 PerSpect-EL [9] 0.853 1.303 GeoPPI [10] 0.855 1.298 HCML [19] 0.857 1.279 PTA-SEL [20] 0.866 1.216 PIPR+S2F [21] 0.866 1.210 SFM-Net [11] 0.881 1.142 MFFN 0.911 1.112 The proposed MFFN framework has the best…”
InfluentialA Pretraining-Based Deep Learning Framework for Protein Mutation Effect Prediction
Zhang, P., Deng, J., Gu, M., & Liu, M. (2025) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1109/acai68217.2025.11406652
“To compare with more previous studies, we also employed a non-redundant interface single-point mutation subset of SKEM-PIv2, namely S1131, for model evaluation [14].”
Wu, S., Xu, N., Xin, X., Zhang, M., Liu, H., Zhu, H., Wei, Z., Zhao, C., Yu, L., & Feng, W. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag150
“S1131 comprises 1,131 non-redundant interface single-point mutations (Xiong et al. 2017); S4169 contains 4,169 single mutants from 319 complexes (Rodrigues et al. 2019); and adding their reverse mutations produces S8338.”
Behbahani, Y. M. (2022) · theses.fr (ABES) · doi:10.70675/bfdded7czb638z455azaedaz4ae9f8d0a13d
Zhou, H., Wang, Y., Shi, X., & Ma, Y. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00867-6
Li, X., Wen, Q., Zhang, Z., & Cui, F. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108985
Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences
Hu, X., Zhang, P., Wu, S., Sun, H., Li, M., Tsoka, S., Sheng, Z., & Deng, L. (2026) · Advanced Science · doi:10.1002/advs.202514899
Jiang, H., Bi, X., Ma, W., Huang, X., Nie, J., Wei, Z., & Zhang, S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114908
Mao, S., Meng, S., & Xiang, Y. (2026) · iScience · doi:10.1016/j.isci.2026.117693
“22 Those ΔΔ G predictors took use of the non-linear fitting ability from various machine-learning models.”
Utilizing Language Models to Forecast Antibody Activity against Influenza A
Sautto, G. (2026) · ImmPort · doi:10.21430/m3dz445dxw
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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
