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, Ovchinnikov, Sergey (November 2022) End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman. Bioinformatics. ISSN 1367-4803

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Abstract

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 learns MSAs that mildly improve 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 predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability</jats:title> <jats:p>Our code and examples are available at: https://github.com/spetti/SMURF.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>

Item Type:	Paper
Subjects:	bioinformatics > genomics and proteomics > alignment bioinformatics bioinformatics > genomics and proteomics bioinformatics > genomics and proteomics > alignment > sequence alignment bioinformatics > computational biology > algorithms bioinformatics > computational biology
CSHL Authors:	Koo, Peter K
Communities:	CSHL labs > Koo Lab CSHL Cancer Center Program CSHL Cancer Center Program > Gene Regulation and Inheritance Program
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	10 November 2022
Date Deposited:	14 Nov 2022 15:12
Last Modified:	09 Feb 2024 15:42
PMCID:	PMC9805565
Related URLs:	PubMed
URI:	https://repository.cshl.edu/id/eprint/40754

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