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Deep-learning language models help to improve protein sequence alignment

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We trained DEDAL, an algorithm based on deep-learning language models, to generate pairwise alignments of protein sequences taking into account the sequence-specific context of amino acid substitutions or gaps. DEDAL improved the alignment correctness on remote homologs by up to threefold and the discrimination of remote homologs from evolutionarily unrelated sequences.

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Fig. 1: Example of pairwise alignment of two protein domain sequences.

References

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This is a summary of: Llinares-López, F., Berthet, Q., Blondel, M., Teboul, O. & Vert, J.-P. Deep embedding and alignment of protein sequences. Nat. Methods https://doi.org/10.1038/s41592-022-01700-2 (2022).

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Deep-learning language models help to improve protein sequence alignment. Nat Methods 20, 40–41 (2023). https://doi.org/10.1038/s41592-022-01707-9

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  • DOI: https://doi.org/10.1038/s41592-022-01707-9

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