ArticleJournal of chemical information and modeling2022
Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 37 citations in OpenAlex.
- Evaluating AlphaFold Tools and Related Scoring Functions for Protein-peptide Complex Prediction.Genomics, proteomics & bioinformatics · 2026Article
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.Molecular diversity · 2026Article
- One Health tick bite exposure biomarkers against Ixodes ricinus salivary peptides.Research square · 2026Article
- Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.Journal of chemical information and modeling · 2026Article
- PpF: a density functional fine-tuned for noncovalent interactions of protein and peptide residues.Chemical science · 2026Article
- Targeted IL-27-based gene therapy in preventing SARS-CoV-2 entry.Molecular biology reports · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- A Review of Current Computational Tools for Peptide-Protein Docking.Journal of computational chemistry · 2026Review
- Topological deep learning for enhancing peptide-protein complex prediction.Communications chemistry · 2025Article
- Immunoinformatics investigation on pathogenic Escherichia coli proteome to develop an epitope-based peptide vaccine candidate.Molecular diversity · 2025Article
- Flexible Docking of Cyclic Peptides to Proteins Using CABS-dock.Journal of chemical theory and computation · 2025Article
- A Paired Database of Predicted and Experimental Protein Peptide Binding Information.Scientific data · 2025Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- AI-Assisted Protein-Peptide Complex Prediction in a Practical Setting.Journal of computational chemistry · 2025Article
- A claudin5-binding peptide enhances the permeability of the blood-brain barrier in vitro.Science advances · 2025Article
- In silico screening of protein-binding peptides with an application to developing peptide inhibitors against antibiotic resistance.PNAS nexus · 2024Article
- Current Computational Methods for Protein-peptide Complex Structure Prediction.Current medicinal chemistry · 2024Review
- Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction.Computational and structural biotechnology journal · 2023Article
- Matching protein surface structural patches for high-resolution blind peptide docking.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.
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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.