Ancestral Sequence Reconstruction and Alternate Amino Acid States Guide Protein Library Design for Directed Evolution.

Ancestral sequence reconstruction Directed evolution Library design Phylogenetic analysis Rational design

Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2022
Historique:
entrez: 28 4 2022
pubmed: 29 4 2022
medline: 3 5 2022
Statut: ppublish

Résumé

Engineered proteins possess nearly limitless possibilities in medical and industrial applications but finding a precise amino acid sequence for these applications is challenging. A robust approach for discovering protein sequences with a desired functionality uses a library design method in which combinations of mutations are applied to a robust starting point. Determining useful mutations can be tortuous, yet rewarding; in this chapter, we present a novel library design method that uses information provided by ancestral sequence reconstruction (ASR) to create a library likely to have stable proteins with diverse function. ASR computational tools use a multi-sequence alignment of homologous proteins and an evolutionary model to estimate the protein sequences of the numerous common ancestors. For all ancestors, these tools calculate the probability of every amino acid occurring at each position within the sequence alignment. The alternate amino acid states at individual positions corelate to a region of stability in sequence space around the ancestral sequence which can inform site-wise diversification within a combinatorial library. The method presented in this chapter balances the quality of results, the computational resources needed, and ease of use.

Identifiants

pubmed: 35482185
doi: 10.1007/978-1-0716-2285-8_4
doi:

Substances chimiques

Amino Acids 0
Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

75-86

Informations de copyright

© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

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Auteurs

James VanAntwerp (J)

Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA.
Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.

Patrick Finneran (P)

Menten AI, Palo Alto, CA, USA.

Benedikt Dolgikh (B)

Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA.
Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.

Daniel Woldring (D)

Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA. woldring@egr.msu.edu.
Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA. woldring@egr.msu.edu.

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