ArticleBioData mining2020
ISLAND: in-silico proteins binding affinity prediction using sequence information.
Article in BioData mining, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Learning Protein-Protein Binding Free Energies from Interface Graphs and Physicochemical Descriptors.ACS physical chemistry Au · 2026Article
- DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.Chemical science · 2026Article
- Binding Affinity Prediction of Membrane Protein-Protein Complexes Using MPA-Pred.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Analysing open-source protein folding models for nanobody binding prediction.Frontiers in bioinformatics · 2026Article
- Multimodal Learning of Protein-Protein Interactions for Accurate Binding Affinity Prediction.ACS omega · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Computational approaches for toxicology and Pharmacokinetic properties prediction.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- MVSF-AB: accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning.Bioinformatics (Oxford, England) · 2025Article
- PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models.BMC bioinformatics · 2025Article
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength.Computational and structural biotechnology journal · 2024Article
- Cysteine protease I29 propeptide from Calotropis procera R. Br. As a potent cathepsin L inhibitor and its suppressive activity in breast cancer metastasis.Scientific reports · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 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
- EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations.Journal of molecular modeling · 2023Article
- A source of resistance against yellow mosaic disease in soybeans correlates with a novel mutation in a resistance gene.Frontiers in plant science · 2023Article
- CC16 drives VLA-2-dependent SPLUNC1 expression.Frontiers in immunology · 2023Article
- Prediction of antibody binding to SARS-CoV-2 RBDs.Bioinformatics advances · 2023Article
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundDetermining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning.
methodWe have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity.
resultsWe present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software .
conclusionThis paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.
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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.