ArticleNature methods2020
Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 45 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- A systematic review of nonsynonymous single nucleotide polymorphisms in the renin-angiotensin-aldosterone system.Cardiology journal · 2022Pooled it
- Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.Nature methods · 2021Pooled it
- Variant characterization in the intrinsically disordered human proteome.Nature structural & molecular biology · 2026Article
- Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Interpretable machine learning uncovers structural determinants of Wnt-Wntless binding specificity from atomistic simulations.Communications chemistry · 2026Article
- Recent Advances in Exploring Casein Peptide Regulation of Inflammatory Bowel Disease from an Intestinal Barrier Perspective: Correlations, Mechanisms, Challenges and Solutions.Foods (Basel, Switzerland) · 2026Review
- Targeting protein disorder: the next hurdle in drug discovery.Nature reviews. Drug discovery · 2025Review
- Learning Universal Representations of Intermolecular Interactions with ATOMICA.bioRxiv : the preprint server for biology · 2025Article
- Locality-aware pooling enhances protein language model performance across varied applications.Bioinformatics (Oxford, England) · 2025Article
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- TPepPro: a deep learning model for predicting peptide-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- How to build the virtual cell with artificial intelligence: Priorities and opportunities.Cell · 2024Article
- How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.ArXiv · 2024Article
- SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks.Nucleic acids research · 2024Article
- Tailored Viral-like Particles as Drivers of Medical Breakthroughs.International journal of molecular sciences · 2024Review
- Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.Nature communications · 2024Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 are known), their low binding affinities and the sensitivity of binding properties to minor sequence variation represent a substantial challenge to experimental and computational analysis of PBD specificity and the networks PBDs create. Here, we introduce a bespoke machine-learning approach, hierarchical statistical mechanical modeling (HSM), capable of accurately predicting the affinities of PBD-peptide interactions across multiple protein families. By synthesizing biophysical priors within a modern machine-learning framework, HSM outperforms existing computational methods and high-throughput experimental assays. HSM models are interpretable in familiar biophysical terms at three spatial scales: the energetics of protein-peptide binding, the multidentate organization of protein-protein interactions and the global architecture of signaling networks.
Indexed as
Identifiers
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.