End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

Petti, Samantha, Bhattacharya, Nicholas, Rao, Roshan, Dauparas, Justas, Thomas, Neil, Zhou, Juannan, Rush, Alexander M, Koo, Peter K, Ovchinnikov, Sergey (October 2021) End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman. BioRxiv. (Unpublished)

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URL: https://www.biorxiv.org/content/10.1101/2021.10.23...
DOI: 10.1101/2021.10.23.465204


Multiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF mildly improves contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing the predicted confidence metric, we can learn MSAs that improve structure predictions over the initial MSAs. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > alignment > sequence alignment
bioinformatics > computational biology
CSHL Authors:
Communities: CSHL labs > Koo Lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 24 October 2021
Date Deposited: 03 Nov 2021 15:31
Last Modified: 03 Nov 2021 15:31
URI: https://repository.cshl.edu/id/eprint/40409

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