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  April 1522, 3 pm CT AI-Enabled Deep Mutational Scanning of Interaction between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor Is Local Information Enough to Predict an Epidemic? Christian Borgs, Professor of Computer Science, University of California, BerkeleyDiwakar Shukla, Assistant Professor, Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign REGISTER FOR ZOOM WEBINAR The rapid and escalating spread of SARS-CoV-2 poses an immediate public health emergency. The viral spike protein S binds ACE2 on host cells to initiate molecular events that release the viral genome intracellularly. Soluble ACE2 inhibits entry of both SARS and SARS-2 coronaviruses by acting as a decoy for S binding sites, and is a candidate for therapeutic and prophylactic development. Deep mutational scans is one of the approaches that could provide such a detailed map of protein-protein interactions. However, this technique suffers from several issues such as experimental noise, expensive experimental protocol, and lack of techniques that could provide second or higher-order mutation effects. In this talk, we describe an approach that employs a recently developed platform, TLmutation, that could enable rapid investigation of sequence-structure-function relationship of proteins. In particular, we employ a transfer learning approach to generate high-fidelity scans from noisy experimental data and transfer the knowledge from single point mutation data to generate higher-order mutational scans from the single amino-acid substitution data. Using deep mutagenesis, variants of ACE2 will be identified with increased binding to the receptor binding domain of S at a cell surface. We plan to employ the information from the preliminary mutational landscape to generate the high order mutations in ACE2 that could enhance binding to S protein. We also aim to investigate this problem using distributed computing approaches to understand the underlying physics of the spike protein and ACE2 interaction. While simpler models of epidemics assume homogeneous mixing, it is clear that the structure of our social networks is important for the spread of an infection, with degree inhomogeneities and the related notion of super-spreaders being just the obvious reasons. This raises the question of whether knowledge of the local structure of a network is enough to predict the probability and size of an epidemic. More precisely, one might wonder if by having access to randomly sampled nodes in the network and their neighborhoods, we can predict the above quantities. It turns out that, in general, the answer to this question is negative, as the example of isolated, large communities show. However, under a suitable assumption on the global structure of the network, the size and probability of an outbreak can be determined from local graph features. This research is joint work with Yeganeh Alimohammadi and Amin Saberi from Stanford University. Christian Borgs is a professor of Computer Science in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley and a member of the Berkeley Artificial Intelligence Research (BAIR) Lab. He graduated in Physics at the University of Munich and holds a Ph.D. in Mathematical Physics from the University of Munich and the Max-Planck-Institute for Physics. In 1997, he joined Microsoft Research, where he co-founded the Theory Group and served as its manager until 2008, when he co-founded Microsoft Research New England in Cambridge, Massachusetts, until he joined UC Berkeley in 2020. A Fellow of both the American Mathematical Society and the American Association for the Advancement of Science, his current research focuses on responsible AI, from differential privacy to questions of bias in automatic decision makingDiwakar Shukla is the Blue Waters Assistant Professor, Department of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign. His research focuses on understanding the complex biological processes using novel physics-based models and techniques. He received his B.Tech and M.Tech. degrees from the Indian Institute of Technology in Bombay and his MS and PhD degrees from the Massachusetts Institute of Technology. His postdoctoral work was at Stanford University. He has received several awards for his research including the Peterson award from ACS, Innovation in Biotechnology award from AAPS, COMSEF Graduate student award from AIChE, Institute Silver Medal, and Manudhane Award from IIT Bombay.

  
  

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