High-Throughput Reconstruction of Ancestral Protein Sequence, Structure, and Molecular Function.

Affinity prediction Ancestral sequence reconstruction Molecular evolution Protein evolution Protein function prediction Structural modeling

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:
2019
Historique:
entrez: 10 10 2018
pubmed: 10 10 2018
medline: 14 5 2019
Statut: ppublish

Résumé

Ancestral protein sequence reconstruction is a powerful technique for explicitly testing hypotheses about the evolution of molecular function, allowing researchers to meticulously dissect how historical changes in protein sequence impacted functional repertoire by altering the protein's 3D structure. These techniques have provided concrete, experimentally validated insights into ancient evolutionary processes and help illuminate the complex relationship between protein sequence, structure, and function. Inferring the protein family phylogenies on which ancestral sequence reconstruction depends and reconstructing the sequences, themselves, are amenable to high-throughput computational analysis. However, determining the structures of ancestral-reconstructed proteins and characterizing their functions typically rely on time-consuming and expensive laboratory analyses, limiting most current studies to examining a relatively small number of specific hypotheses. For this reason, we have little detailed, unbiased information about how molecular function evolves across large protein family phylogenies. Here we describe a generalized protocol that integrates ancestral sequence reconstruction with structural homology modeling and structure-based molecular affinity prediction to characterize historical changes in protein function across families with thousands of individual sequences. We highlight key steps in the analysis protocol requiring particularly careful attention to avoid introducing potential errors as well as steps for which computationally efficient subroutines can be substituted for more intensive approaches, allowing researchers to scale the analysis up or down, depending on available resources and requirements for reproducibility and scientific rigor. In our view, this approach provides a compelling compliment to more laboratory-intensive procedures, generating important contextual information that can help guide detailed experiments.

Identifiants

pubmed: 30298396
doi: 10.1007/978-1-4939-8736-8_8
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

135-170

Auteurs

Kelsey Aadland (K)

Department of Microbiology & Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA.

Charles Pugh (C)

Department of Microbiology & Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA.

Bryan Kolaczkowski (B)

Department of Microbiology & Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, USA. bryank@ufl.edu.
Genetics Institute, University of Florida, Gainesville, FL, USA. bryank@ufl.edu.

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Classifications MeSH