Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops

• URL: http://arxiv.org/abs/2409.18201v1
• Date: Thu, 26 Sep 2024 18:34:06 GMT
• Title: Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops
• Authors: Kevin Borisiak, Gian Marco Visani, Armita Nourmohammad,
• Abstract summary: Loop-Diffusion is an energy-based diffusion model that learns an energy function that generalizes to functional prediction tasks. We evaluate Loop-Diffusion's performance on scoring TCR-pMHC interfaces and demonstrate state-of-the-art results in recognizing binding-enhancing mutations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting protein functional characteristics from structure remains a central problem in protein science, with broad implications from understanding the mechanisms of disease to designing novel therapeutics. Unfortunately, current machine learning methods are limited by scarce and biased experimental data, and physics-based methods are either too slow to be useful, or too simplified to be accurate. In this work, we present Loop-Diffusion, an energy based diffusion model which leverages a dataset of general protein loops from the entire protein universe to learn an energy function that generalizes to functional prediction tasks. We evaluate Loop-Diffusion's performance on scoring TCR-pMHC interfaces and demonstrate state-of-the-art results in recognizing binding-enhancing mutations.

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