ArticleProtein science : a publication of the Protein Society2023
Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.
Article in Protein science : a publication of the Protein Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- DeepIR-Pred: identification of insulin receptors using metaheuristic optimization of biologically informed multi-view features with deep recurrent learning.Briefings in bioinformatics · 2026Article
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Generative Protein Design: From Deep Learning Algorithms to Translational Applications.International journal of molecular sciences · 2026Review
- Self-assembling protein cages: from coiled-coil module to machine learning-drivenMaterials advances · 2026Review
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.bioRxiv : the preprint server for biology · 2026Article
- Design of Specific Peptide Inhibitors of Toxin-Antitoxin-Mediated Antiphage Defense.ACS synthetic biology · 2025Article
- Ensemble-conditioned protein sequence design with Caliby.bioRxiv : the preprint server for biology · 2025Article
- Design principles of the common Gly-X6-Gly membrane protein building block.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- STAT5B leukemic mutations, altering SH2 tyrosine 665, have opposing impacts on immune gene programs.Life science alliance · 2025Article
- Atomic context-conditioned protein sequence design using LigandMPNN.Nature methods · 2025Article
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
- Protein stability models fail to capture epistatic interactions of double point mutations.Protein science : a publication of the Protein Society · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Protein stability models fail to capture epistatic interactions of double point mutations.bioRxiv : the preprint server for biology · 2024Article
- Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.Protein science : a publication of the Protein Society · 2023Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
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6 authors.
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Abstract
Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science.
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