Department of Chemistry Master Calendar

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This calendar includes all events from the following individual calendars: Department of Chemistry Alumni EventsDepartment Events (events of general interest and/or relevant to all research areas), and events related to specific research areas and programs (Analytical Chemistry, Chemical Biology, Chemistry-Biology Interface Training Program, Inorganic Chemistry & Materials Chemistry, Organic Chemistry, Physical Chemistry), as well as Department of Chemical and Biomolecular Engineering Seminars & Events.


CHBE 565 Seminar, Prof. Jeffrey Gray, Johns Hopkins University, "Predicting Protein Complex Structures with Biophysical and Deep Learning Approaches" (host: Prof. Diwakar Shukla)

Event Type
Chemical & Biomolecular Engineering and International Paper Company
wifi event
Apr 8, 2021   2:00 pm  
Christy Bowser
Originating Calendar
Chemical & Biomolecular Engineering Seminars and Events

Biological function often arises from the interactions of proteins with other biomolecules, and these interactions are grounded in the three-dimensional structures of the complexes. My lab develops computational tools to study the atomic structure of protein interfaces to explain not only how biological and disease processes work but also how one might alter these processes at the molecular level.  Traditionally, creating computational tools first requires solving problems of basic science including (1) how to sample the myriad conformations available to proteins and (2) how to accurately calculate the energy of each conformation.  In this talk, I first will share our biophysical approaches to protein docking and the difficult challenge of capturing conformational change upon binding. We have made progress through novel sampling algorithms using libraries of protein conformations and through rapid, course-grained scoring schemes using large data sets. I will finish with recent work recent work exploiting deep learning approaches to predict antibody CDR H3 loop structure, the key feature for antibody-antigen binding, extensions to predict the entire Fv structure, and implications for design. I will discuss the philosophy of emerging deep learning approaches, the performance on a practical but general antibody problem, the capability to learn general features of protein structure, and the outlook for these approaches for molecular engineering in general.

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