Paper ID: 2412.20329 • Published Dec 29, 2024
Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning
Giovanny Espitia, Yui Tik Pang, James C. Gumbart
TL;DR
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We address protein structure prediction in the 3D Hydrophobic-Polar lattice
model through two novel deep learning architectures. For proteins under 36
residues, our hybrid reservoir-based model combines fixed random projections
with trainable deep layers, achieving optimal conformations with 25% fewer
training episodes. For longer sequences, we employ a long short-term memory
network with multi-headed attention, matching best-known energy values. Both
architectures leverage a stabilized Deep Q-Learning framework with experience
replay and target networks, demonstrating consistent achievement of optimal
conformations while significantly improving training efficiency compared to
existing methods.