Our interdisciplinary group focuses on research at the intersection of biology, artificial intelligence/ machine learning and high-performance computing. The core interests of our group are:
How do intrinsically disordered proteins (IDP) mediate signaling in the cell? IDPs are a class of proteins that possess the remarkable ability to morph their structure in response to specific binding partners. This ability to morph and adapt their binding surface precisely to their binding partner allows them to effectively multiplex and propagate signals within a cell, in a context dependent manner. For e.g., depending on the phosphorylation states of some amino-acid residues, an IDP like p27 can activate the cell cycle promoting normal growth.
Not surprisingly, IDPs are found as effective mediators of complex signaling mechanisms in the cell. Up to 35% of eukaryotic proteins are believed to be disordered — and mutations in over 65% of IDPs are implicated in a variety of diseases including cancer, diabetes, cardiovascular and neurological disorders.
Our research focuses on understanding the biophysical mechanisms of how IDPs mediate such signaling activities. We use a combination of experimental techniques, scalable molecular dynamics simulations, and machine learning to probe the function of IDPs. Specific projects are described in the research section.
Ongoing collaborations: (1) Christopher B. Stanley, Debsindhu Bhowmik – Oak Ridge National Laboratory, (2) Richard Kriwacki – St. Jude Children’s Hospital.
How to build scalable statistical inference and machine learning tools on emerging supercomputer architectures?
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