ArticleNature communications2021
Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- A novel MYO6 variant identified in a Chinese family with autosomal dominant non-syndromic hearing loss.BMC medical genomics · 2026Article
- Decrypting cryptic pockets with physics-based simulations and artificial intelligence.Current opinion in structural biology · 2026Review
- Reinforced molecular dynamics: Physics-infused generative machine learning model simulates protein motion.PNAS nexus · 2026Article
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.Journal of molecular biology · 2025Review
- PathogenicThe journal of physical chemistry. B · 2025Article
- Using Machine Learning to Analyze Molecular Dynamics Simulations of Biomolecules.The journal of physical chemistry. B · 2025Article
- Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.Chemical science · 2025Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
- Systematic analysis of biomolecular conformational ensembles with PENSA.The Journal of chemical physics · 2025Article
- Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption.Genome research · 2024Article
- Complex state transitions of the bacterial cell division protein FtsZ.Molecular biology of the cell · 2024Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Research square · 2024Article
- ctGAN: combined transformation of gene expression and survival data with generative adversarial network.Briefings in bioinformatics · 2024Article
- Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants.Protein science : a publication of the Protein Society · 2024Review
- Homologous mutations in human β, embryonic, and perinatal muscle myosins have divergent effects on molecular power generation.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR.Journal of the American Chemical Society · 2024Article
- Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.
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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.