Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models.

TitlePrediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models.
Publication TypeJournal Article
Year of Publication2017
AuthorsZimmerman, MI, Hart, KM, Sibbald, CA, Frederick, TE, Jimah, JR, Knoverek, CR, Tolia, NH, Bowman, GR
JournalACS Cent Sci
Volume3
Issue12
Pagination1311-1321
Date Published2017/12/27
ISSN2374-7943
Abstract

Protein stabilization is fundamental to enzyme function and evolution, yet understanding the determinants of a protein's stability remains a challenge. This is largely due to a shortage of atomically detailed models for the ensemble of relevant protein conformations and their relative populations. For example, the M182T substitution in TEM β-lactamase, an enzyme that confers antibiotic resistance to bacteria, is stabilizing but the precise mechanism remains unclear. Here, we employ Markov state models (MSMs) to uncover how M182T shifts the distribution of different structures that TEM adopts. We find that M182T stabilizes a helix that is a key component of a domain interface. We then predict the effects of other mutations, including a novel stabilizing mutation, and experimentally test our predictions using a combination of stability measurements, crystallography, NMR, and measurements of bacterial fitness. We expect our insights and methodology to provide a valuable foundation for protein design.

DOI10.1021/acscentsci.7b00465
Alternate JournalACS Cent Sci
PubMed ID29296672
PubMed Central IDPMC5746865
Grant ListR01 GM124007 / GM / NIGMS NIH HHS / United States