Topsy-Turvy: integrating a global view into sequence-based PPI prediction.

ABSTRACT:

Summary

Computational methods to predict protein-protein interaction (PPI) typically segregate into sequence-based 'bottom-up' methods that infer properties from the characteristics of the individual protein sequences, or global 'top-down' methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. We thus introduce Topsy-Turvy, a method that newly synthesizes both views in a sequence-based, multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by incorporating patterns from both global and molecular-level

SUBMITTER: Singh R 

PROVIDER: S-EPMC9235477 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature