Biochemistry, Physical Chemistry.
Dr. Cashman's background is in the realm of Computational and Medicinal Chemistry, specializing in the structure-based drug design and molecular simulations of proteins and nucleic acids. He has specific expertise in virtual screening, docking and scoring, molecular mechanics & dynamics, and Monte Carlo simulations. He is also particularly interested in studying protein flexibility and its implications in structure-based drug design and protein-protein interactions.
Since 2007, he has been engaged in NIH-funded research projects studying proteins involved in the bacterial chemotaxis pathway. The chemotaxis system of bacteria represents the best studied signal transduction pathway in biology today. This pathway allows bacteria to detect external stimuli via chemoreceptors in the cell membrane and to control the cell’s swimming behavior through phosphorylation of a response regulator protein by a histidine protein kinase. This pathway has been studied for many years, providing a wealth of structural, biochemical, and genetic information. However, many important questions about the system remain unanswered: how the signaling complex is assembled, for example: how signals are terminated, and how covalent modification of receptors contributes to adaptation. Understanding this pathway if of particular interest to medicinal chemists in that the results gained from modeling these proteins will be useful in identifying targets for new therapeutic agents against pathogenic bacteria.
Dr. Cashman's research conducted at the University of Pittsburgh focused on computational simulations of the E. coli Glucose-Galactose Chemosensory Receptor (GGBP), a 309-residue, 32 kDa, periplasmic binding protein consisting of two structural domains. This protein's fluctuations were studied with two computational simulation methods: all-atom molecular dynamics, as well as an extremely fast, "semi-atomistic" Library-Based Monte Carlo (LBMC) method which includes all backbone atoms but "implicit" side chains (open source software developed in the laboratory of Professor Daniel Zuckerman; http://www.ccbb.pitt.edu/). Both LBMC and MD simulations were performed using both the apo and glucose-bound forms of the protein, with LBMC exhibiting significantly larger fluctuations. The LBMC simulations are in general agreement with the disulfide trapping experiments of Careaga & Falke (J. Mol. Biol., 1992, Vol. 226, 1219-35), which indicate that distant residues in the crystal structure (i.e. beta carbons separated by 10 to 20 angstroms) form spontaneous transient contacts in solution. These simulations illustrate several possible “mechanisms” (configurational pathways) for these fluctuations.
From 2010-2013, he worked at the Oak Ridge National Laboratory, shifting focus from the periplasmic space of bacteria, to below the cell membrane, in modeling the structure of the core chemotaxis proteins cheA and cheW and how they interact with the methyl-accepting chemotaxis protein (MCP). The long term goal of this project is to understand how living cells detect, transmit, and adapt to various signals on a molecular level. It involves computational genomic as well as biophysical approaches to understanding three key steps of the bacterial chemotaxis signal transduction pathway: excitation, signal termination, and adaptation. Techniques used include creating a natural classification of chemotaxis proteins based on phylogenetic analysis, identification of conserved residues within evolutionarily related subgroups, co-variance analysis of co-evolving residues, prediction of protein-protein binding sites using computational approaches, and molecular docking simulations to test models of protein-protein interactions. Additionally, he is engaged in a research collaboration with Professor Barry Bruce's laboratory at the University of Tennessee - Knoxville, involving the computational modeling of proteins of Photosystem I.