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

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          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 in vivo measurements of bacterial fitness. We expect our insights and methodology to provide a valuable foundation for protein design.

          Abstract

          Markov state models guide experiments to determine the mechanism of stabilization for a clinical mutant of TEM-1 β-lactamase, which leads to the prediction of a new stabilizing mutant.

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          Most cited references27

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          Is Open Access

          Canonical sampling through velocity-rescaling

          We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. We illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent on the thermostat parameter also with regard to the dynamic properties.
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            Amino acid preferences for specific locations at the ends of alpha helices.

            A definition based on alpha-carbon positions and a sample of 215 alpha helices from 45 different globular protein structures were used to tabulate amino acid preferences for 16 individual positions relative to the helix ends. The interface residue, which is half in and half out of the helix, is called the N-cap or C-cap, whichever is appropriate. The results confirm earlier observations, such as asymmetrical charge distributions in the first and last helical turn, but several new, sharp preferences are found as well. The most striking of these are a 3.5:1 preference for Asn at the N-cap position, and a preference of 2.6:1 for Pro at N-cap + 1. The C-cap position is overwhelmingly dominated by Gly, which ends 34 percent of the helices. Hydrophobic residues peak at positions N-cap + 4 and C-cap - 4.
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              MSMBuilder2: Modeling Conformational Dynamics at the Picosecond to Millisecond Scale.

              Markov State Models provide a framework for understanding the fundamental states and rates in the conformational dynamics of biomolecules. We describe an improved protocol for constructing Markov State Models from molecular dynamics simulations. The new protocol includes advances in clustering, data preparation, and model estimation; these improvements lead to significant increases in model accuracy, as assessed by the ability to recapitulate equilibrium and kinetic properties of reference systems. A high-performance implementation of this protocol, provided in MSMBuilder2, is validated on dynamics ranging from picoseconds to milliseconds.
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                Author and article information

                Journal
                ACS Cent Sci
                ACS Cent Sci
                oc
                acscii
                ACS Central Science
                American Chemical Society
                2374-7943
                2374-7951
                21 November 2017
                27 December 2017
                : 3
                : 12
                : 1311-1321
                Affiliations
                []Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine , 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
                []Department of Molecular Microbiology, Washington University School of Medicine , 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
                []Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis , One Brookings Drive, St. Louis, Missouri 63130, United States
                Author notes
                Article
                10.1021/acscentsci.7b00465
                5746865
                29296672
                5d70a058-9e32-416c-9f5e-3f1dc8757178
                Copyright © 2017 American Chemical Society

                This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

                History
                : 02 October 2017
                Categories
                Research Article
                Custom metadata
                oc7b00465
                oc-2017-004659

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