Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein-protein interactions.

ABSTRACT:

Background

Protein-protein interactions (PPIs) are heavily involved in many biological processes. Consequently, the identification of PPIs in the model plant Arabidopsis is of great significance to deeply understand plant growth and development, and then to promote the basic research of crop improvement. Although many experimental Arabidopsis PPIs have been determined currently, the known interactomic data of Arabidopsis is far from complete. In this context, developing effective machine learning models from existing PPI data to predict unknown Arabidopsis PPIs conveniently and rapidly is still urgently needed.

Results

We used a large-scale pre-trained protein language model (pLM) called ESM-1b to convert protein sequences into high-dimensional vectors and then used them as

SUBMITTER: Zhou K 

PROVIDER: S-EPMC10704805 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature