Title: Advancing Gene Editing through Computational Methods and Deep Learning
Abstract:
Future medicines will increasingly rely on editing DNA and RNA to treat a wide range of diseases, with broader applications in agriculture and sustainability. Designing next-generation genome editing tools requires not only biological insight but also advanced computational approaches to probe their molecular mechanisms. I will present recent and unpublished advances that combine molecular simulations with deep learning to dissect and engineer CRISPR systems. Classical and ab-initio molecular dynamics, deep learning–based structure prediction, and Graph Neural Networks (GNNs) are used to capture the conformational dynamics and engineer these complex assemblies. In particular, deep learning combined with free-energy simulations reveals the dynamic behavior of large CRISPR complexes such as Cascade and informs the engineering of compact RNA-targeting Cas proteins. Linear Discriminant Analysis explains how conformational selection and induced fit enhance the efficiency of base-editing enzymes. Finally, Graph Attention Networks (GATs) coupled with free-energy simulations uncover the mechanism of DNA filament formation in CRISPR-associated transposons. Together, these findings underscore how cutting-edge computational methods drive new biological discoveries, paving the way for the rational design of improved genome editing technologies.
Bio:
Giulia Palermo is a computational biophysicist and a Professor at the University of California Riverside in the Department of Bioengineering and Chemistry. She is a native of Italy where she earned her Ph.D. in 2013 from the Italian Institute of Technology, studying in the lab of Dr. Marco De Vivo. During her doctoral studies, she was awarded an early career fellowship to join the group of Prof. Ursula Roethlisberger at the Swiss Federal Institute of Technology (EPFL). In 2016 she became a post-doc at the University of California San Diego (UCSD) working with Prof. J. Andrew McCammon, thanks to a Swiss National Science Foundation post-doctoral fellowship.
Her group is best known for pioneering computational studies of the CRISPR-Cas9 system. By applying and developing cutting-edge computer simulations and Artificial Intelligence (AI) approaches, the lab focuses on unraveling the mechanisms of action and engineering innovative genome editing systems that are revolutionizing the life sciences. She is a Camille Dreyfus Teacher-Scholar and has received several prestigious honors, including the Corwin Hansch Award for Outstanding Scientists Under 40, the NSF CAREER Award, and the Sloan Research Fellowship in Chemistry.
Giulia is an active educator passionate about teaching. Her lab strives to create research opportunities to inspire students to excel in their scientific careers.
Honors and Awards
2024 – Rosetta Briegel Barton Lecturer, University of Oklahoma, Norman, OK
2024 – Camille Dreyfus Teacher-Scholar
2023 – The Rising Star Award of the Women Chemists Committee – American Chemical Society
2023 – Sloan Research Fellow in Chemistry
2023 – Biophysical Society Theory and Computation Subgroup Early Career Award
2022 – DOE-NERSC Innovative Use of High-Performance Computing Award
2022 – ERC Starting Grant
2022 – Outstanding Doctoral Dissertation Advisor/Mentoring Award, UCR
2022 – NSF CAREER Award
2022 – ACS OpenEye Outstanding Junior Faculty Award in Computational Chemistry
2021 – Featured in the 2021 J. Am. Chem. Soc. Early Career Investigators Issue
2021 – First place prize at the 2021 RNA Society Arts and Music competition
2020 – Corwin Hansch Award for Outstanding Scientist Under 40
2018 – Featured in the 2018 J. Am. Chem. Soc. Early Career Investigators Issue
2018 – Nominated for the “Future of Biophysics Burroughs Wellcome Fund Symposium”
2017 – HPCwire Best Use of High-Performance Computing in Life Sciences
2017 – First place prize at the Art and Science image contest to the 61st Biophysical Society meeting, New Orleans.
Keywords: gene editing, molecular dynamics, artificial intelligence
Host: Prof. Xuhui Huang