Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.

ABSTRACT: The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries (i.e., sequence regions separating two domains) from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets (called DistDom) to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidd

SUBMITTER: Mahmud S 

PROVIDER: S-EPMC9295499 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature