ArticlePLoS computational biology2015
Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 90 citations in OpenAlex.
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- A look beyond the QR code of SNARE proteins.Protein science : a publication of the Protein Society · 2024Article
- Efficacious human metapneumovirus vaccine based on AI-guided engineering of a closed prefusion trimer.Nature communications · 2024Article
- A curated rotamer library for common post-translational modifications of proteins.Bioinformatics (Oxford, England) · 2024Article
- Article
- Review
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- Protein engineering in the computational age: An open source framework for exploring mutational landscapesEngineering biology · 2023Article
- An end-to-end deep learning method for protein side-chain packing and inverse folding.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A computationally designed ACE2 decoy has broad efficacy against SARS-CoV-2 omicron variants and related viruses in vitro and in vivo.Communications biology · 2023Article
- Rationally Guided Improvement of NOV1 Dioxygenase for the Conversion of Lignin-Derived Isoeugenol to Vanillin.Biochemistry · 2023Article
- Designing peptides predicted to bind to the omicron variant better than ACE2 via computational protein design and molecular dynamics.PloS one · 2023Article
- Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin.ACS catalysis · 2022Article
- Structure- and computational-aided engineering of an oxidase to produce isoeugenol from a lignin-derived compound.Nature communications · 2022Article
- A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions.BMC bioinformatics · 2022Article
- On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations.Antibodies (Basel, Switzerland) · 2022Article
- Protein design via deep learning.Briefings in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein-ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art "fixed backbone" design methods perform poorly on these tests, we develop a new "coupled moves" design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein-ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.
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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.