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- W2949632023 abstract "Abstract Motivation Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments. Results In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure’s accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods. Availability and implementation https://github.com/shuichiro-makigaki/exmachina. Supplementary information Supplementary data are available at Bioinformatics online." @default.
- W2949632023 created "2019-06-27" @default.
- W2949632023 creator A5004756730 @default.
- W2949632023 creator A5027016084 @default.
- W2949632023 date "2019-06-14" @default.
- W2949632023 modified "2023-09-30" @default.
- W2949632023 title "Sequence alignment using machine learning for accurate template-based protein structure prediction" @default.
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- W2949632023 doi "https://doi.org/10.1093/bioinformatics/btz483" @default.
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