ArticleCommunications biology2022
PepNN: a deep attention model for the identification of peptide binding sites.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning.iScience · 2026Article
- Similarity-Enhanced Representation Learning of Non-Canonical Amino Acids for Therapeutic Peptide Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PEARL: novel protein targets recommendation model via heterogeneous graph representation learning.Briefings in bioinformatics · 2026Article
- Development of a Nanoscale Protein-Protein Mapping of PDE4 Interface-Disrupting Peptides.Nano letters · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- Target sequence-conditioned design of peptide binders using masked language modeling.Nature biotechnology · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- Structure-aware Multi-task Collaborative Learning: a multi-task collaborative learning framework for peptide-protein interaction prediction based on structure-aware protein language models.Briefings in bioinformatics · 2026Article
- A Review of Current Computational Tools for Peptide-Protein Docking.Journal of computational chemistry · 2026Review
- Approaches for accelerating microbial gene function discovery using artificial intelligence.Nature microbiology · 2026Review
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions.Acta pharmacologica Sinica · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- Peptide codes for organ-selective mRNA delivery.Nature materials · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.
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What Socratic holds
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 Socratic graph.