ArticleBio-protocol2020
Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction.
Article in Bio-protocol, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq.BMC bioinformatics · 2024Article
- Protein structure prediction based on particle swarm optimization and tabu search strategy.BMC bioinformatics · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful when good templates can be available. Indeed, modern homology detection methods can find remote homologs with high sensitivity. However, the accuracy of template-based models generated from the homology-detection-based alignments is often lower than that from ideal alignments. In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. Our method trains a machine learning model using the structural alignment of known homologs. When calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model.
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Registered trials
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