Integration of pre-trained protein language models into geometric deep learning networks.

ABSTRACT: Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Several preceding studies consider combining these different protein modalities to promote the representation power of geometric neural networks but fail to present a comprehensive understanding of their benefits. In this work, we integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks and evaluate a variety of

SUBMITTER: Wu F 

PROVIDER: S-EPMC10457366 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature