Dr. Matthew McCoy joined ICBI as a new faculty member in November 2016. Interested in using computational methods and multi-scale modeling to study the underlying mechanisms of genetic disease, Dr. McCoy's current research aims to understand how genomic mutations alter protein function, and how functional changes impact the emergent behavior in biological systems.
Prior to joining Georgetown University, Dr. McCoy developed expertise in a number of bioinformatic and computational biology applications through various industry positions. Past projects have involved next generation sequence assembly for both genomic and transcriptomic analysis, applying classification and machine learning algorithms to the annotation of relevant sequence elements and association with phenotypic traits. Additionally, Dr. McCoy has applied structure analysis and molecular simulations to study the impact of mutations on the conformational dynamics and functional kinetics of proteins.
Ultimately, Dr. McCoy is interested in using the information gleaned through various high throughput technologies to parameterize physiologically realistic, multi-scale models of biological systems, with the ultimate goal of informing therapeutic decision making though personalized models of genetic disease.
Dr. McCoy holds a Ph.D. in Bioinformatics and Computational Biology from George Mason University, a M.Sc. in Bioinformatics from Johns Hopkins University, and a B.Sc in Chemical and Biological Engineering from the University of Colorado.
In March 2018, Dr. Matthew McCoy received the Marco Ramoni Distinguished Paper Award for work he presented at the AMIA 2018 Informatics Summit. The Marco Ramoni Award for Translational Bioinformatics is presented annually at the AMIA Joint Summits by the TBI Scientific Program Committee to an author that best exemplifies the spirit and scholarship of Marco Ramoni. In applying informatics methods, Dr. McCoy has helped illuminate basic molecular biology processes relevant to the conquest of human disease.
In Dr. McCoy’s research, protein structural modeling was used to predict the functional impact of somatic variation in Anaplastic Lymphoma Kinase by simulating the binding of targeted inhibitor molecules to known drug-resistant variants. His ultimate goal is to produce a predictive model that can inform the selection of second- and third-line interventions once resistance to an initial therapy has emerged.