ArticlePLoS computational biology2020
Remote homology search with hidden Potts models.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution.bioRxiv : the preprint server for biology · 2026Article
- Harnessing deep learning for proteome-scale detection of amyloid signaling motifs.Bioinformatics (Oxford, England) · 2025Article
- Deep generative design of RNA family sequences.Nature methods · 2024Article
- DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors.Bioinformatics (Oxford, England) · 2023Article
- Review
- End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.Bioinformatics (Oxford, England) · 2023Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins.PLoS computational biology · 2022Article
- Evolutionary analyses of genes in Echinodermata offer insights towards the origin of metazoan phyla.Genomics · 2022Article
- Constructing benchmark test sets for biological sequence analysis using independent set algorithms.PLoS computational biology · 2022Article
- Efficient generative modeling of protein sequences using simple autoregressive models.Nature communications · 2021Article
- PPalign: optimal alignment of Potts models representing proteins with direct coupling information.BMC bioinformatics · 2021Article
- Alignments of biomolecular contact maps.Interface focus · 2021Article
- Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars.BMC bioinformatics · 2021Article
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2 authors.
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Abstract
Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA structure homology search benchmarks, where we can compare directly to exact alignment methods that capture nested RNA base-pairing correlations (stochastic context-free grammars). HPMs perform promisingly in these proof of principle experiments.
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