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UID:pretalx-scipy-2026-RE9ETJ@pretalx.com
DTSTART;TZID=CST:20260715T160500
DTEND;TZID=CST:20260715T163500
DESCRIPTION:Engineering enzymes with improved catalytic activity remains a 
 central challenge in biotechnology. In this research\, we focus on enginee
 ring PETase\, a plastic-degrading enzyme\, as a testbed for developing a s
 imulation-informed machine learning workflow. We present a Python framewor
 k that integrates molecular simulations\, docking\, and structural analysi
 s with modern machine learning methods to predict enzyme activity from seq
 uence and structure. By combining simulation-derived descriptors—includi
 ng active-site geometry\, electrostatics\, stability metrics\, dynamics\, 
 and docking scores—with sequence embeddings\, we generate interpretable 
 predictions that guide rational mutation strategies. While developed for P
 ETase engineering\, the workflow is extensible to broader de novo enzyme d
 esign efforts.
DTSTAMP:20260715T022745Z
LOCATION:University Hall
SUMMARY:Simulation-Informed Machine Learning Workflows for PETase Engineeri
 ng - Sai Sanjana Prakash\, Charlie Hou\, Justin Kashi
URL:https://pretalx.com/scipy-2026/talk/RE9ETJ/
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