Biology subjects

Hays, J. M.

Publications and source records attributed to Hays, J. M..

4 recordsLinked to original sources

bioRxiv2022

p66Shc is an apoptotic rheostat whose targeted ROS inhibition improves MI outcomes

p66Shc is an oxidoreductase that responds to cell stress by translocating to mitochondria, where p66Shc produces pro-apoptotic reactive oxygen species (ROS). This study identifies ROS-active p66Shc as a monomer that produces superoxide anion independent of metal ions, inhibits cytochrome c peroxidase, and is regulated by environmental condition-induced structural changes. p66Shc anti-apoptotic functions, including: cytochrome c reduction, increased electron transport chain activity, and caspase cascade inhibition were also discovered. This study also demonstrates that p66Shc is a stress-dependent rheostat of apoptosis, regulated by p66Shc-mortalin complexes. These complexes decrease pro-apoptotic ROS production, without blocking p66Shc-mediated cytochrome c reduction. However, stress disrupts p66Shc-mortalin interactions, promoting apoptosis. Tipping p66Shcs apoptotic balance toward anti-apoptotic functions by genetic knockdown or p66Shc-selective ROS inhibition decreased pro-apoptotic effects and improved outcomes in zebrafish myocardial infarction models, representing a potential new myocardial infarction treatment with promising results.

biochemistry↗

bioRxiv2019

Hybrid refinement of heterogeneous conformational ensembles using spectroscopic data

Multi-structured biomolecular systems play crucial roles in a wide variety of cellular processes but have resisted traditional methods of structure determination which often resolve only a few low-energy states. High-resolution structure determination using experimental methods that yield distributional data remains extremely difficult, especially when the underlying conformational ensembles are quite heterogeneous. We have therefore developed a method to integrate sparse, multi-multimodal spectroscopic data to obtain high-resolution estimates of conformational ensembles. We have tested our method by incorporating double electron-electron resonance data on the SNARE protein syntaxin-1a into biased molecular dynamics simulations. We find that our method substantially outperforms existing state-of-the-art methods in capturing syntaxins open/closed conformational equilibrium and further yields new conformational states that are both consistent with experimental data and may help in understanding syntaxins function. Our improved methods for refining heterogeneous conformational ensembles from spectroscopic data will greatly accelerate the structural understanding of such systems.

biophysics↗

bioRxiv2018

Refinement of highly flexible protein structures using simulation-guided spectroscopy

Highly flexible proteins present a special challenge for structure determination because they are multi-structured yet not disordered, and the resulting conformational ensembles are essential for understanding function. Determining such ensembles is difficult because many measurements that capture multiple conformational populations provide sparse data. A powerful opportunity exists to leverage molecular simulations for spectroscopic experiment selection. We have developed an information-theoretic approach to guide experiments by identifying which measurements best refine the underlying conformational ensemble. We have tested this approach on three flexible bacterial proteins. For proteins where a clear mechanistic hypothesis drives label selection, our approach systematically identifies labels that would test this hypothesis. Furthermore, when available data do not yield an obvious mechanistically-guided label selection strategy, our approach guides label selection and produces conformational refinement that significantly outperforms standard structure-guided approaches. Our information-theoretic approach to label selection thus offers a particular advantage when refining challenging, underdetermined protein conformational ensembles.

bioinformatics↗

bioRxiv2018

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

SummaryMolecular 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 1) allows custom potential functions without modifying the simulation package code, 2) maintains the optimized performance of GROMACS, and 3) 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.\n\nAvailabilitySource and installation instructions are available at https://github.com/kassonlab/gmxapi.

biophysics↗