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End-to-end learning of multiple sequence alignmentswith differentiable Smith-Waterman
Samantha Petti · Nicholas Bhattacharya · Roshan Rao · Justas Dauparas · Neil Thomas · Juannan Zhou · Alexander Rush · Peter Koo · Sergey Ovchinnikov

Mon Dec 13 11:30 AM -- 11:40 AM (PST) @
in Machine Learning in Structural Biology »

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. In another application, we demonstrate that connecting our differentiable alignment module to AlphaFold2 and optimizing the learnable alignments leads to improved structure predictions with a higher confidence. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment.

Author Information

Samantha Petti (Harvard University)
Nicholas Bhattacharya (UC Berkeley)
Roshan Rao (UC Berkeley)
Justas Dauparas (University of Washington)
Neil Thomas (UC Berkeley)
Juannan Zhou (University of Florida)
Alexander Rush (Cornell University)
Peter Koo (Cold Spring Harbor Laboratory)

I am an Assistant Professor in the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. My lab's research explores how representations of biological sequences learned by deep neural networks can provide novel insights into biological processes.

Sergey Ovchinnikov (Harvard)

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