Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation.

ABSTRACT:

Background

Locating small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many drug-discovery scenarios. Since it is not always easy to find such binding sites using conventional methods, different deep learning methods to predict binding sites out of protein structures have been developed in recent years. The existing deep learning based methods have several limitations, including (1) the inefficiency of the CNN-only architecture, (2) loss of information due to excessive post-processing, and (3) the under-utilization of available data sources.

Methods

We present a new model architecture and training method that resolves the aforementioned problems. First, by layering geometric self-attention units on top of residue-le

SUBMITTER: Lee D 

PROVIDER: S-EPMC11416008 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature