ArticleStructure (London, England : 1993)2023
Benchmarking AlphaFold2 on peptide structure prediction.
Article in Structure (London, England : 1993), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it.
- High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An In Silico Approach.Probiotics and antimicrobial proteins · 2025Pooled it
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- Guanosine 10 methylation by TRMT11-TRMT112 optimizes human mitochondrial tRNAs for efficient translation.Nucleic acids research · 2026Article
- Virtual Screening of Bacteriocins From Lactic Acid Bacteria Against Monkeypox DNA Polymerase: Sakacin P and Mundticin KS Emerge as Promising Candidates.Food science & nutrition · 2026Article
- Evaluation of AlphaFold3 for Predicting Human Heme-Binding Protein Structures.International journal of molecular sciences · 2026Article
- The Ascent of Miniproteins.Biochemistry · 2026Review
- Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure-Activity Relationships, Molecular Targets and Mechanistic Insights.Molecules (Basel, Switzerland) · 2026Review
- Experiment-guided AlphaFold3 resolves measurement-consistent protein ensembles.Nature biotechnology · 2026Article
- Overcoming LPS-mediated resistance in gram-negative pathogens: a review of LL-37 analogs and computational design strategies.Archives of microbiology · 2026Review
- CLABP: a contrastive learning framework integrating protein language models and structural information for antibacterial peptide prediction.Briefings in bioinformatics · 2026Article
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- The Borderlands of Foldability: Lessons from Simplified Proteins.Trends in chemistry · 2026Article
- Hidden structural states of proteins revealed by conformer selection.Nature communications · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Simulation-guided design of peptide-metal coordination interfaces for next-generation metallo-immunotherapy.Nano convergence · 2026Review
- The rise and future of peptide-based antimicrobials.Journal of microbiology (Seoul, Korea) · 2026Review
- Comparative Analysis of Deep Learning-Based Algorithms for Peptide Structure Prediction.Proteins · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures.Antibiotics (Basel, Switzerland) · 2026Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.
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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.