Add TM-Vec and DeepBLAST from "Protein remote homology detection and structural alignment using deep learning"

Description of feature

https://www.nature.com/articles/s41587-023-01917-2?fromPaywallRec=false

Exploiting sequence–structure–function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure–structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

The issue provides a research paper describing TM-Vec and DeepBLAST but names no repository files, tests, or implementation entry points. Start by reviewing the paper and the existing pipeline structure to determine the integration scope; done should include both methods running as part of the protein annotation workflow with coverage defined by maintainer guidance.