Publications

Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data

Published in Journal of Physical Chemistry Letters, 2019

Highly flexible proteins present a special challenge for structure determination because they are multi‐structured yet not disordered, so their conformational ensembles are essential for understanding function. Because spectroscopic measurements of multiple conformational populations often provide sparse data, experiment selection is a limiting factor in conformational refinement. A molecular simulations‐ and information‐theory based approach to select which experiments best refine conformational ensembles has been developed. This approach was tested on three flexible proteins. For proteins where a clear mechanistic hypothesis exists, experiments that test this hypothesis were systematically identified. When available data did not yield such mechanistic hypotheses, experiments that significantly outperform structure‐guided approaches in conformational refinement were identified. This approach offers a particular advantage when refining challenging, underdetermined protein conformational ensembles.

Recommended citation: Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data. Jennifer M. Hays, David S. Cafiso, and Peter M. Kasson. The Journal of Physical Chemistry Letters 2019 10 (12), 3410-3414. https://doi.org/10.1021/acs.jpclett.9b01407

gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulation

Published in Bioinformatics, 2018

Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles.

Recommended citation: M Eric Irrgang, Jennifer M Hays, Peter M Kasson, gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations, Bioinformatics, Volume 34, Issue 22, 15 November 2018, Pages 3945–3947. https://doi.org/10.1093/bioinformatics/bty484

Conformational Intermediate That Controls KPC-2 Catalysis and Beta-Lactam Drug Resistance

Published in ACS Catalysis, 2018

The KPC-2 carbapenemase enzyme is responsible for drug resistance in the majority of carbapenem-resistant Gram-negative bacterial infections in the United States. A better understanding of what permits KPC-2 to hydrolyze carbapenem antibiotics and how this might be inhibited is thus of fundamental interest and great practical importance to development of better anti-infectives. By correlating molecular dynamics simulations with experimental enzyme kinetics, we identified conformational changes that control KPC-2’s ability to hydrolyze carbapenem antibiotics. Related beta-lactamase enzymes can interconvert between catalytically permissive and catalytically nonpermissive forms of an acylenzyme intermediate critical to drug hydrolysis. Using molecular dynamics simulations, we identify a similar equilibrium in KPC-2 and analyze the determinants of this conformational change. Because the conformational dynamics of KPC-2 are complex and sensitive to allosteric changes, we develop an information-theoretic approach to identify key determinants of this change. We measure unbiased estimators of the reaction coordinate between catalytically permissive and nonpermissive states, perform information-theoretic feature selection, and, using restrained molecular dynamics simulations, validate the protein conformational changes predicted to control catalytically permissive geometry. We identify two binding-pocket residues that control the conformational transitions between catalytically active and inactive forms of KPC-2. Mutations to one of these residues, Trp105, lower the stability of the catalytically permissive state in simulations and have reduced experimental kcat values that show a strong linear correlation with the simulated catalytically permissive state lifetimes. This understanding can be leveraged to predict the drug resistance of further KPC-2 mutants and help design inhibitors to combat extreme drug resistance.

Recommended citation: Conformational Intermediate That Controls KPC-2 Catalysis and Beta-Lactam Drug Resistance. George A. Cortina, Jennifer M. Hays, and Peter M. Kasson ACS Catalysis 2018 8 (4), 2741-2747. https://doi.org/10.1021/acscatal.7b03832

Refinement of Highly Flexible Protein Structures using Simulation‐Guided Spectroscopy

Published in Angewandte Chemie, 2018

Highly flexible proteins present a special challenge for structure determination because they are multi‐structured yet not disordered, so their conformational ensembles are essential for understanding function. Because spectroscopic measurements of multiple conformational populations often provide sparse data, experiment selection is a limiting factor in conformational refinement. A molecular simulations‐ and information‐theory based approach to select which experiments best refine conformational ensembles has been developed. This approach was tested on three flexible proteins. For proteins where a clear mechanistic hypothesis exists, experiments that test this hypothesis were systematically identified. When available data did not yield such mechanistic hypotheses, experiments that significantly outperform structure‐guided approaches in conformational refinement were identified. This approach offers a particular advantage when refining challenging, underdetermined protein conformational ensembles.

Recommended citation: J. M. Hays, M. K. Kieber, J. Z. Li, J. I. Han, L. Columbus, P. M. Kasson, Angew. Chem. Int. Ed. 2018, 57, 17110. https://doi.org/10.1002/anie.201810462