SOURCE-LINKED INTELLIGENCE

Simulation-based Protein Engineering with Transferable Implicit Transfer Operators

CORDIS · observation · Publication date unknown

molecules would revolutionize biology and enable rational, physics-informed bioengineering applications. SPETITO outlines the methodological foundations for such a framework. Recent advances in deep generative AI (GenAI) have enabled surrogate methods for efficient molecular dynamics (MD) simulations, tackling the challenge of sampling rare events and conformational transitions. The PI co-developed groundbreaking methods—Boltzmann Generators (BG) and Implicit Transfer Operators (ITO)— that enable the study of conformational changes, protein folding, and prediction of experiments five orders of magnitude faster than MD. However, these methods cannot generalize across molecular systems nor scale to larger molecules, severely limiting their utility. To address this, we propose developing and disseminating transformative technologies through widely accessible software: • A transferable ITO (TransITO) model that captures universal protein physics, enabling rapid sampling with microsecond-time-steps for any protein system without requiring system-specific training data. • A ‘protein sequ

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recordType

award

status

SIGNED

region

EU

value

1999163

unit

EUR