Artificial Intelligence Tools for Antibody Engineering and Protein Docking
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Bio
“Artificial Intelligence Tools for
Antibody Engineering and Protein Docking”
Jeffrey J. Gray is Professor and Vice Chair for Research in the Department of Chemical and Biomolecular Engineering (ChemBE) at the Johns Hopkins University, with joint appointments in the Program in Molecular Biophysics and the Sidney Kimmel Comprehensive Cancer Center (Oncology). He earned his B.S.E. in chemical engineering at the University of Michigan and his Ph.D. in chemical engineering at the University of Texas at Austin, and he completed post-doctoral training at the University of Washington. His research focuses on computational protein structure prediction and design, particularly protein-protein docking, antibody engineering, membrane proteins, protein-carbohydrate interactions, and recently deep learning.
Gray is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), and his awards include the AIChE’s David Himmelblau Award, the Beckman Young Investigator Award, the Johns Hopkins Alumni Association Excellence in Teaching Award, and the Capers and Marion McDonald Award for Excellence in Mentoring and Advising. He served on the editorial board of Proteins, and he is the Director of the Rosetta Commons, a 100-lab international research collaboration. He is the Director of the NSF-supported Rosetta Commons Summer Intern (REU) Program and the Rosetta Commons Post-baccalaureate Program (RaMP). At Johns Hopkins he has been a member of the Diversity Leadership Council, a co-founder of the Homewood Council on Inclusive Excellence, and the ChemBE departmental diversity champion, through which he has worked to create more inclusive and equitable scientific environments. He coauthored the 2024 Community Statement on the Responsible Development of AI for Protein Design (responsiblebiodesign.ai).
Further information about Prof. Gray and his research group is available at http://graylab.jhu.edu.
Date/Time: Tuesday, October 1st at 4:00PM EST
Location: Homewood Campus: Clark 110
Live Webcast: https://wse.zoom.us/j/92636200923
Abstract
“Artificial Intelligence Tools for
Antibody Engineering and Protein Docking”
In a blind prediction challenge in 2020, AlphaFold2 calculated highly accurate three-dimensional structures of hundreds of proteins from their sequences, thus “solving…one of the biggest problems in biology”.1 This achievement and others based on artificial intelligence (AI) algorithms have unlocked incredible possibilities for biomolecular engineering. In this talk, I will share advances from my lab in antibody engineering and protein-protein docking based on AI. Our neural network models (CNNs and multi-track transformer networks) outperform physical models for antibody structure prediction. Generative language models offer multiple promising routes for design of antibody therapeutics, and they produce repertoire distributions different than those produced with heuristic, gene-recombination and somatic-mutation models. Docking methods reveal biological mechanisms and allow for screening of potential therapeutics. I will use the docking case to show how AI methods differ from physics-based approaches, suggesting ways to benefit from their combination.
Date/Time: Tuesday,October 1st at 4:00PM EST
Location: Homewood Campus: Clark 110
Live Webcast: https://wse.zoom.us/j/92636200923
