SPOT-Contact-LM: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model.

ABSTRACT:

Motivation

Accurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1b as an input along with a large training set and an ensemble of residual neural networks.

Results

We showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in t

SUBMITTER: Singh J 

PROVIDER: S-EPMC9113311 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature