Cargando…
Rosetta FunFolDes – A general framework for the computational design of functional proteins
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277116/ https://www.ncbi.nlm.nih.gov/pubmed/30452434 http://dx.doi.org/10.1371/journal.pcbi.1006623 |
_version_ | 1783378103373922304 |
---|---|
author | Bonet, Jaume Wehrle, Sarah Schriever, Karen Yang, Che Billet, Anne Sesterhenn, Fabian Scheck, Andreas Sverrisson, Freyr Veselkova, Barbora Vollers, Sabrina Lourman, Roxanne Villard, Mélanie Rosset, Stéphane Krey, Thomas Correia, Bruno E. |
author_facet | Bonet, Jaume Wehrle, Sarah Schriever, Karen Yang, Che Billet, Anne Sesterhenn, Fabian Scheck, Andreas Sverrisson, Freyr Veselkova, Barbora Vollers, Sabrina Lourman, Roxanne Villard, Mélanie Rosset, Stéphane Krey, Thomas Correia, Bruno E. |
author_sort | Bonet, Jaume |
collection | PubMed |
description | The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are “designable”, meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the “designability” of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins—Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo “functionless” fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis. |
format | Online Article Text |
id | pubmed-6277116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62771162018-12-19 Rosetta FunFolDes – A general framework for the computational design of functional proteins Bonet, Jaume Wehrle, Sarah Schriever, Karen Yang, Che Billet, Anne Sesterhenn, Fabian Scheck, Andreas Sverrisson, Freyr Veselkova, Barbora Vollers, Sabrina Lourman, Roxanne Villard, Mélanie Rosset, Stéphane Krey, Thomas Correia, Bruno E. PLoS Comput Biol Research Article The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are “designable”, meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the “designability” of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins—Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo “functionless” fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis. Public Library of Science 2018-11-19 /pmc/articles/PMC6277116/ /pubmed/30452434 http://dx.doi.org/10.1371/journal.pcbi.1006623 Text en © 2018 Bonet et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bonet, Jaume Wehrle, Sarah Schriever, Karen Yang, Che Billet, Anne Sesterhenn, Fabian Scheck, Andreas Sverrisson, Freyr Veselkova, Barbora Vollers, Sabrina Lourman, Roxanne Villard, Mélanie Rosset, Stéphane Krey, Thomas Correia, Bruno E. Rosetta FunFolDes – A general framework for the computational design of functional proteins |
title | Rosetta FunFolDes – A general framework for the computational design of functional proteins |
title_full | Rosetta FunFolDes – A general framework for the computational design of functional proteins |
title_fullStr | Rosetta FunFolDes – A general framework for the computational design of functional proteins |
title_full_unstemmed | Rosetta FunFolDes – A general framework for the computational design of functional proteins |
title_short | Rosetta FunFolDes – A general framework for the computational design of functional proteins |
title_sort | rosetta funfoldes – a general framework for the computational design of functional proteins |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277116/ https://www.ncbi.nlm.nih.gov/pubmed/30452434 http://dx.doi.org/10.1371/journal.pcbi.1006623 |
work_keys_str_mv | AT bonetjaume rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT wehrlesarah rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT schrieverkaren rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT yangche rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT billetanne rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT sesterhennfabian rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT scheckandreas rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT sverrissonfreyr rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT veselkovabarbora rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT vollerssabrina rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT lourmanroxanne rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT villardmelanie rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT rossetstephane rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT kreythomas rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins AT correiabrunoe rosettafunfoldesageneralframeworkforthecomputationaldesignoffunctionalproteins |