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Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. K...

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Autores principales: Engelberger, Felipe, Zakary, Jonathan D., Künze, Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204868/
https://www.ncbi.nlm.nih.gov/pubmed/37228581
http://dx.doi.org/10.3389/fmolb.2023.1178035
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author Engelberger, Felipe
Zakary, Jonathan D.
Künze, Georg
author_facet Engelberger, Felipe
Zakary, Jonathan D.
Künze, Georg
author_sort Engelberger, Felipe
collection PubMed
description Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.
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spelling pubmed-102048682023-05-24 Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application Engelberger, Felipe Zakary, Jonathan D. Künze, Georg Front Mol Biosci Molecular Biosciences Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org. Frontiers Media S.A. 2023-05-09 /pmc/articles/PMC10204868/ /pubmed/37228581 http://dx.doi.org/10.3389/fmolb.2023.1178035 Text en Copyright © 2023 Engelberger, Zakary and Künze. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Engelberger, Felipe
Zakary, Jonathan D.
Künze, Georg
Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
title Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
title_full Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
title_fullStr Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
title_full_unstemmed Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
title_short Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
title_sort guiding protein design choices by per-residue energy breakdown analysis with an interactive web application
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204868/
https://www.ncbi.nlm.nih.gov/pubmed/37228581
http://dx.doi.org/10.3389/fmolb.2023.1178035
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