Dissecting AlphaFold2's capabilities with limited sequence information.

ABSTRACT:

Summary

Protein structure prediction aims to infer a protein's three-dimensional (3D) structure from its amino acid sequence. Protein structure is pivotal for elucidating protein functions, interactions, and driving biotechnological innovation. The deep learning model AlphaFold2, has revolutionized this field by leveraging phylogenetic information from multiple sequence alignments (MSAs) to achieve remarkable accuracy in protein structure prediction. However, a key question remains: how well does AlphaFold2 understand protein structures? This study investigates AlphaFold2's capabilities when relying primarily on high-quality template structures, without the additional information provided by MSAs. By designing experiments that probe local and global structural understanding, we aim

SUBMITTER: Gut JA 

PROVIDER: S-EPMC11751578 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature