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Guide Tree Optimization with Genetic Algorithm to Improve Multiple Protein 3D-structure Alignmentстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Shegay Maksim, Svedas Vytas, Voevodin Vladimir, Suplatov Dmitry, Popova Nina
- Журнал: Bioinformatics
- Год издания: 2021
- Издательство: Oxford University Press
- Местоположение издательства: United Kingdom
- DOI: 10.1093/bioinformatics/btab798
- Аннотация: Motivation: With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments towards more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. Results: We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms leading to a statistically significant improvement of the TM-score quality indicator by up to 220–1523% on “SABmark Superfamilies” (in 49–77% of cases) and “SABmark Twilight” (in 59–80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. Availability: The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps.
- Добавил в систему: Суплатов Дмитрий Андреевич