Assessing protein model quality based on deep graph coupled networks using protein language model.

ABSTRACT: Model quality evaluation is a crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain a challenge. More importantly, the quality assessment of multimer models is a hot topic for structure prediction. In this study, we propose GraphCPLMQA, a novel approach for evaluating residue-level model quality that combines graph coupled networks and embeddings from protein language models. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with

SUBMITTER: Liu D 

PROVIDER: S-EPMC10685403 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature