ArticleProtein science : a publication of the Protein Society2024
Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Negative design enables cell-free expression and folding of designed transmembrane β-barrels.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Bioinformatic and computational biophysics tools for nanopore engineering: a review from standard approaches to machine learning advancements.Journal of nanobiotechnology · 2026Review
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.Protein science : a publication of the Protein Society · 2026Article
- Gas Vesicles and Acoustic Protein Nanostructures in Molecular Ultrasound Nanomedicine: Translational Archetypes, Biomaterial Design, and Barriers to Clinical Realization.International journal of nanomedicine · 2026Review
- Stable de novo protein design via joint conformational landscape and sequence optimization.Nature communications · 2025Article
- De Novo Design of α-helical Peptide Nanopores for Single-Molecule Detection Using Helix Packing Motifs.ACS nano · 2025Article
- Designing novel solenoid proteins with in silico evolution.Communications chemistry · 2025Article
- Article
- Efficient protein structure generation with sparse denoising models.Nature machine intelligence · 2025Article
- AlphaDesign: a de novo protein design framework based on AlphaFold.Molecular systems biology · 2025Article
- Assessing generative model coverage of protein structures with SHAPES.Cell systems · 2025Article
- Sculpting conducting nanopore size and shape through de novo protein design.Science (New York, N.Y.) · 2024Article
- Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting.Protein science : a publication of the Protein Society · 2024Article
- ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels.bioRxiv : the preprint server for biology · 2024Article
- Sculpting conducting nanopore size and shape throughbioRxiv : the preprint server for biology · 2023Article
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4 authors.
Funding
Abstract
In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.
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