SHPCP January Lunch & Learn

Date/Time
Date(s) - January 27, 2022
12:00 pm - 1:00 pm

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About the Event

Emerging hardware tailored for artificial intelligence (AI) and machine learning (ML) methods provide novel means to couple them with traditional high performance computing (HPC) workflows involving molecular dynamics (MD) simulations. We propose Stream-AI-MD, a novel instance of applying deep learning methods to drive adaptive MD simulation campaigns in a streaming manner. We leverage the ability to run ensemble MD simulations on GPU clusters, while the data from atomistic MD simulations are streamed continuously to AI/ML approaches to guide the conformational search in a biophysically meaningful manner on a wafer-scale AI accelerator. We demonstrate the efficacy of Stream-AI-MD simulations for two scientific use-cases: (1) folding a small prototypical protein, namely BBA FSD-EY and (2) understanding protein-protein interaction (PPI) within the SARS-CoV-2 proteome between two proteins, nsp16 and nsp10. We show that Stream-AI-MD simulations can improve time-to-solution by ~50X for BBA protein folding. In addition, we also demonstrate the use of Stream-AI-MD in running multi resolution simulations for understanding the SARS-CoV-2 replication transcription complex.

 

About the Speaker

Arvind Ramanathan, Ph.D.

Arvind Ramanathan is a computational biologist in the Data Science and Learning Division at Argonne National Laboratory and a senior scientist at the University of Chicago Consortium for Advanced Science and Engineering (CASE). His research interests are at the intersection of data science, high performance computing and biological/biomedical sciences. His research focuses on three areas focusing on scalable statistical inference techniques:
(1) for analysis and development of adaptive multi-scale molecular simulations for studying complex biological phenomena (such as how intrinsically disordered proteins self assemble, or how small molecules modulate disordered protein ensembles)
(2) to integrate complex data for public health dynamics
(3) for guiding design of CRISPR-Cas9 probes to modify microbial function(s).

Arvind obtained his Ph.D. in computational biology from Carnegie Mellon University, and was the team lead for integrative systems biology team within the Computational Science, Engineering and Division at Oak Ridge National Laboratory.  More information about his group and research interests can be found at http://ramanathanlab.org.

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