ArticleNature communications2022
Harnessing protein folding neural networks for peptide-protein docking.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers.
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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
161 citing papers in PubMed.
- Article
- Resnet-Driven In Silico Identification of Lead Peptides from the Venom Gland Transcriptome ofPharmaceuticals (Basel, Switzerland) · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- Computationally Evidence-Grounded Sequence-First Design of Peptide Binders.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.Journal of chemical information and modeling · 2026Article
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Variant characterization in the intrinsically disordered human proteome.Nature structural & molecular biology · 2026Article
- PpF: a density functional fine-tuned for noncovalent interactions of protein and peptide residues.Chemical science · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure-Activity Relationships, Molecular Targets and Mechanistic Insights.Molecules (Basel, Switzerland) · 2026Review
- Peptide design through binding interface mimicry with PepMimic.Nature biomedical engineering · 2026Article
- Peptide design through binding interface mimicry with PepMimic.Nature biomedical engineering · 2026Article
- AI-validated fusion proteins for local inhibition of interleukin-17A.Journal of controlled release : official journal of the Controlled Release Society · 2026Article
- Design of permeability-optimized target-binding macrocyclesChemical science · 2026Article
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- Efficient exploration of peptide libraries using active learning with AlphaFold-based screening.bioRxiv : the preprint server for biology · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.Nature methods · 2026Article
- Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions.Acta pharmacologica Sinica · 2026Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.
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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.