ArticleFrontiers in bioinformatics2022
Machine learning methods for protein-protein binding affinity prediction in protein design.
Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 35 citations in OpenAlex.
- Learning Protein-Protein Binding Free Energies from Interface Graphs and Physicochemical Descriptors.ACS physical chemistry Au · 2026Article
- Machine-learning prediction of affinity and epistasis in the bovine pancreatic trypsin inhibitor-chymotrypsin complex.Protein science : a publication of the Protein Society · 2026Article
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Evaluation of Physics-Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities.Journal of computational chemistry · 2025Article
- Recent advances in antibody optimization based on deep learning methods.Journal of Zhejiang University. Science. B · 2025Review
- PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models.BMC bioinformatics · 2025Article
- Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.Journal of chemical information and modeling · 2025Article
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Structure-based computational design of antibody mimetics: challenges and perspectives.FEBS open bio · 2025Review
- The impact of curation errors in the PDBBind Database on machine learning predictions of protein-protein binding affinity.Database : the journal of biological databases and curation · 2025Article
- Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular Dynamics.Journal of chemical information and modeling · 2024Article
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Exploring Viral-Host Protein Interactions as Antiviral Therapies: A Computational Perspective.Microorganisms · 2024Review
- Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity.ACS omega · 2024Article
- Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development.Pharmaceuticals (Basel, Switzerland) · 2023Review
- DG-Affinity: predicting antigen-antibody affinity with language models from sequences.BMC bioinformatics · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.
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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.