ArticleScientific reports2019
Predicting protein-peptide interaction sites using distant protein complexes as structural templates.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- Whey Proteins and Bioactive Peptides: Advances in Production, Selection and Bioactivity Profiling.Biomedicines · 2025Review
- Sensory plasticity caused by up-down regulation encodes the information of short-term learning and memory.iScience · 2025Article
- PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model.iScience · 2024Article
- GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach.Briefings in bioinformatics · 2024Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- Peptriever: a Bi-Encoder approach for large-scale protein-peptide binding search.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Article
- Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function.Journal of computer-aided molecular design · 2023Article
- A Random Forest Model for Peptide Classification Based on Virtual Docking Data.International journal of molecular sciences · 2023Article
- In Silico and In Vivo Studies of a Tumor-Penetrating and Interfering Peptide with Antitumoral Effect on Xenograft Models of Breast Cancer.Pharmaceutics · 2023Article
- Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses.Proteins · 2022Article
- PepNN: a deep attention model for the identification of peptide binding sites.Communications biology · 2022Article
- Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.Journal of molecular liquids · 2022Article
- Strategies to overcome the main challenges of the use of CRISPR/Cas9 as a replacement for cancer therapy.Molecular cancer · 2022Review
- Harnessing protein folding neural networks for peptide-protein docking.Nature communications · 2022Article
- Modelling peptide-protein complexes: docking, simulations and machine learning.QRB discovery · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/ .
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