ArticleJournal of molecular biology2017
BindProfX: Assessing Mutation-Induced Binding Affinity Change by Protein Interface Profiles with Pseudo-Counts.
Article in Journal of molecular biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
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Who cites it
85 citing papers in PubMed, 150 citations in OpenAlex.
- An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions.iScience · 2026Article
- Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies.Journal of computer-aided molecular design · 2026Article
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- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Bioinformatics (Oxford, England) · 2026Article
- Predictive Model for Single-Site Mutations That Change Intrinsically Disordered Protein Binding Energetics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.ACS omega · 2025Article
- Central Dysmyelination in SSADH-Deficient Humans and Mice.Annals of clinical and translational neurology · 2025Observational
- AlphaFold 3: an unprecedent opportunity for fundamental research and drug development.Precision clinical medicine · 2025Review
- Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering.Bioinformatics (Oxford, England) · 2025Article
- ProBASS-a language model with sequence and structural features for predicting the effect of mutations on binding affinity.Bioinformatics (Oxford, England) · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures.International journal of molecular sciences · 2025Article
- Article
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
- Multitask Learning-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Bioinformatic approach to identifying causative missense polymorphisms in animal genomes.BMC genomics · 2024Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
- SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.Bioinformatics (Oxford, England) · 2024Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.