ArticleBioinformatics (Oxford, England)2019
AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- AntiLipo: A Comprehensive Database, Deep Learning-Based Prediction Model, and Computational Study of Anti-Hyperlipidemic Peptides.Interdisciplinary sciences, computational life sciences · 2026Article
- HighFold4: extending AlphaFold3 to accurate cyclic peptide conformation prediction via custom chemical connectivity.Briefings in bioinformatics · 2026Article
- Exploring the material basis and potential mechanism of spleen-strengthening and damp-resolving prescriptions in the treatment of diabetes mellitus complicated with obesity based on data mining, UPLC-Q-TOF-MS/MS and network pharmacology.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- In Silico Structure-Guided Design of Peptide Candidates Targeting γ-Secretase Subunit Assembly.Proteins · 2026Article
- Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.Briefings in bioinformatics · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- A Review of Current Computational Tools for Peptide-Protein Docking.Journal of computational chemistry · 2026Review
- Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions.Acta pharmacologica Sinica · 2026Article
- PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.International journal of molecular sciences · 2026Article
- Bacteriocin-transport-inspired oral peptide-probiotic delivery ameliorates IBD complications via autophagy and gut homeostasis.Science advances · 2026Article
- Article
- A systematic exploration of gut microbiota-driven blood metabolites in sepsis: an integrated bioinformatics and genetic association study.Frontiers in genetics · 2026Article
- Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development.Frontiers in bioinformatics · 2026Review
- Phage Display Selection and In Silico Characterization of Peptides as Potential GroEL Modulators.Pharmaceutics · 2025Article
- Topological deep learning for enhancing peptide-protein complex prediction.Communications chemistry · 2025Article
- DiffPepDock: Efficient protein-peptide docking and binder screening via SE(3)-equivariant diffusion.Protein science : a publication of the Protein Society · 2025Article
- Identification of specific T-cell response and T-cell receptor targeting shared neoantigen for acute myeloid leukemia.Blood cancer journal · 2025Article
- Article
- Systematic Search for Blood-Brain Barrier Modulating Peptides Based on Exhaustive E-Cadherin Domain-Domain Docking.Journal of chemical information and modeling · 2025Article
34 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
motivationProtein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs.
resultsHere we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins. AVAILABILITY AND IMPLEMENTATION: ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.