ArticleProceedings of machine learning research2025
Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.
Article in Proceedings of machine learning research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Inverse FoldDir: Structure-conditioned Protein Sequence Design by Dirichlet Flow Matching.bioRxiv : the preprint server for biology · 2026Article
- Article
- ProteinDJ: A high-performance and modular protein design pipeline.Protein science : a publication of the Protein Society · 2026Article
- CoV-UniBind: a unified antibody binding database for SARS-CoV-2.Bioinformatics advances · 2026Article
- Ensemble-conditioned protein sequence design with Caliby.bioRxiv : the preprint server for biology · 2025Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- A combinatorial mutational map of active non-native protein kinases by deep learning guided sequence design.bioRxiv : the preprint server for biology · 2025Article
- Without safeguards, AI-Biology integration risks accelerating future pandemics.Frontiers in microbiology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Leading deep learning-based methods for fixed-backbone protein sequence design do not model protein sidechain conformation during sequence generation despite the large role the three-dimensional arrangement of sidechain atoms play in protein conformation, stability, and overall protein function. Instead, these models implicitly reason about crucial sidechain interactions based on backbone geometry and known amino acid sequence labels. To address this, we present FAMPNN (Full-Atom MPNN), a sequence design method that explicitly models both sequence identity and sidechain conformation for each residue, where the per-token distribution of a residue's discrete amino acid identity and its continuous sidechain conformation are learned with a combined categorical cross-entropy and diffusion loss objective. We demonstrate that learning these distributions jointly is a highly synergistic task that both improves sequence recovery while achieving state-of-the-art sidechain packing. Furthermore, benefits from full-atom modeling generalize from sequence recovery to practical protein design applications, such as zero-shot prediction of experimental binding and stability measurements.
Identifiers
41307002PMC12646570What 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.