ArticleProceedings of the National Academy of Sciences of the United States of America2021
Protein sequence design by conformational landscape optimization.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed, 147 citations in OpenAlex.
- De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.Molecular diversity · 2026Article
- Identification of TMEM41B TM6 as a critical domain for coronavirus replicationJournal of virology · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- ProtSeqGen: a novel deep learning model for protein sequence design.BMC bioinformatics · 2026Article
- Review
- SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.Nature communications · 2026Article
- Stable de novo protein design via joint conformational landscape and sequence optimization.Nature communications · 2025Article
- Ensemble-conditioned protein sequence design with Caliby.bioRxiv : the preprint server for biology · 2025Article
- AlphaDesign: a de novo protein design framework based on AlphaFold.Molecular systems biology · 2025Article
- Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.Proceedings of machine learning research · 2025Article
- Designed miniproteins potently inhibit and protect against MERS-CoV.Cell reports · 2025Article
- Designed miniproteins potently inhibit and protect against MERS-CoV.bioRxiv : the preprint server for biology · 2024Article
- Expert-guided protein language models enable accurate and blazingly fast fitness prediction.Bioinformatics (Oxford, England) · 2024Article
- Scalable protein design using optimization in a relaxed sequence space.Science (New York, N.Y.) · 2024Article
- Decoding biology with massively parallel reporter assays and machine learning.Genes & development · 2024Review
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- Opportunities and challenges in design and optimization of protein function.Nature reviews. Molecular cell biology · 2024Review
- The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.Topics in current chemistry (Cham) · 2024Review
- Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.Cold Spring Harbor perspectives in biology · 2024Review
9 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure.
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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.