Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment
| Autor: | Nina N. Popova, Dmitry A. Suplatov, Vytas K. Švedas, Maksim V. Shegay, Vladimir V. Voevodin |
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| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Supplementary data Source code Computer science business.industry media_common.quotation_subject Structural alignment Machine learning computer.software_genre Biochemistry Computer Science Applications Computational Mathematics Tree (data structure) Protein sequencing Computational Theory and Mathematics Proof of concept Genetic algorithm Artificial intelligence business Molecular Biology computer media_common |
| Zdroj: | Bioinformatics. 38:985-989 |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | Motivation With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward 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 and implementation The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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