ArticlePLoS computational biology2017
Data driven flexible backbone protein design.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- Gate-based quantum computing for protein design.PLoS computational biology · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Mimetic Neural Networks: A Unified Framework for Protein Design and Folding.Frontiers in bioinformatics · 2022Article
- Insights Into the Properties, Biological Functions, and Regulation of USP21.Frontiers in pharmacology · 2022Review
- Step-by-step design of proteins for small molecule interaction: A review on recent milestones.Protein science : a publication of the Protein Society · 2021Review
- Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures. FlexiBaL-GP uses the machine learning method of Gaussian Process Latent Variable Models to learn a lower dimensional representation of the protein coordinates that best represent backbone movements. These learned backbone movements are used to explore alternative protein backbones, while engineering a protein within a parallel tempered MCMC framework. Using the human ubiquitin-USP21 complex as a model we demonstrate that our design strategy outperforms current strategies for the interface design task of identifying tight binding ubiquitin variants for USP21.
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Registered trials
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