TM-Vec: template modeling vectors for fast homology detection and alignment

Hamamsy, T.; Morton, J.; Berenberg, D.; Carriero, N.; Gligorijevic, V.; Blackwell, R.; Strauss, C.; Leman, J. K.; Cho, K.; Bonneau, R.

doi:10.1101/2022.07.25.501437

bioRxiv · 10.1101/2022.07.25.501437

TM-Vec: template modeling vectors for fast homology detection and alignment

Hamamsy, T.·Morton, J.·Berenberg, D.·Carriero, N.·Gligorijevic, V.·Blackwell, R.·Strauss, C.·Leman, J. K.·Cho, K.·Bonneau, R.

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Abstract

Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity [≤] 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.

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Hamamsy, T., Morton, J., Berenberg, D., Carriero, N., Gligorijevic, V., Blackwell, R., Strauss, C., Leman, J. K., Cho, K., Bonneau, R.. 2022-07-27. TM-Vec: template modeling vectors for fast homology detection and alignment. https://doi.org/10.1101/2022.07.25.501437

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