ArticleProceedings of the National Academy of Sciences of the United States of America2024
Learning the shape of protein microenvironments with a holographic convolutional neural network.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 14 citations in OpenAlex.
- USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Bioinformatics (Oxford, England) · 2026Article
- Probe-based identification of metal-binding sites using deep learning representations.Nature communications · 2026Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.bioRxiv : the preprint server for biology · 2026Article
- Interpretable Thermodynamic Score-based Classification of Relaxation Excursions.bioRxiv : the preprint server for biology · 2025Article
- T cell receptor specificity landscape revealed through de novo peptide design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- T-cell receptor specificity landscape revealed through de novo peptide design.bioRxiv : the preprint server for biology · 2025Article
- Article
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- Concepts and Methods for Predicting Viral Evolution.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Concepts and methods for predicting viral evolution.bioRxiv : the preprint server for biology · 2024Article
- Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.ArXiv · 2024Article
- Steering and controlling evolution - from bioengineering to fighting pathogens.Nature reviews. Genetics · 2023Review
- H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing.ArXiv · 2023Article
- AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design.JACS Au · 2023Review
- Understanding structure-guided variant effect predictions using 3D convolutional neural networks.Frontiers in molecular biosciences · 2023Article
- Holographic-(V)AE: An end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space.Physical review researchArticle
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.
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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.