ArticleBioinformatics (Oxford, England)2023
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
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- EMERALD-UI: an interactive web application to unveil novel protein biology hidden in the alternative alignment space.Bioinformatics (Oxford, England) · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Large language models in bioinformatics: a comprehensive survey.Frontiers in genetics · 2026Review
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- An interpretable alphabet for local protein structure search based on amino acid neighborhoods.Bioinformatics (Oxford, England) · 2025Article
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.International journal of molecular sciences · 2024Article
- learnMSA2: deep protein multiple alignments with large language and hidden Markov models.Bioinformatics (Oxford, England) · 2024Article
- Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile.PLoS computational biology · 2024Article
- Article
- Differentiable partition function calculation for RNA.Nucleic acids research · 2024Article
- Rosetta Energy Analysis of AlphaFold2 models: Point Mutations and Conformational Ensembles.bioRxiv : the preprint server for biology · 2024Article
- Differentiable phylogeneticsBioinformatics advances · 2024Article
- PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.Scientific reports · 2023Article
- Alignment-based Protein Mutational Landscape Prediction: Doing More with Less.Genome biology and evolution · 2023Article
- Leveraging protein language models for accurate multiple sequence alignments.Genome research · 2023Article
- iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.PLoS computational biology · 2023Article
- Assessing structure and disorder prediction tools forF1000Research · 2023Article
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9 authors.
Funding
Abstract
motivationMultiple 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.
resultsHere, 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. AVAILABILITY AND IMPLEMENTATION: Our code and examples are available at: https://github.com/spetti/SMURF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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