danДоклады Национальной академии наук БеларусиDoklady of the National Academy of Sciences of Belarus1561-83232524-2431The Republican Unitary Enterprise Publishing House "Belaruskaya Navuka"10.29235/1561-8323-2020-64-2-150-158dan-865Research ArticleИНФОРМАТИКАINFORMATICSАлгоритм предсказания структур белковых комплексов на основе генной онтологииStructure prediction algorithm for protein complexes based on gene ontologyХадаровичА. Ю.HadarovichA. Yu.

Хадарович Анна Юрьевна – науч. сотрудник.

ул. Сурганова, 6, 220012, Минск

Hadarovich Anna Yu. – Researcher.

6, Surganov Str., 220012, Minsk

ahadarovich@gmail.comАнищенкоИ. В.AnishchenkoI. V.

Анищенко Иван Владимирович – канд. техн. наук, науч. сотрудник.

Anishchanka Ivan V. – Ph. D. (Engineering), Researcher.

Seattle

aivan@uw.eduКундротасП.KundrotasP.

Кундротас Петрас – профессор.

66045, 2030 Бекер Драйв, Канзас

Kundrotas Petras – Assistant research professor.

66045, 2030 Becker Drive, Lawrence, Kansas

pkundro@ku.eduВаксерИ.VakserI.

Ваксер Илья – профессор, директор. Центр вычислительной биологии.

66045, 2030 Бекер Драйв, Канзас

Vakser Ilya – Professor, Director. Center for Computational Biology.

66045, 2030 Becker Drive, Lawrence, Kansas

vakser@ku.eduТузиковА. В. TuzikovA. V.

Тузиков Александр Васильевич – член-корреспондент, д-р физ.-мат. наук, профессор, генеральный директор.

ул. Сурганова, 6, 220012, Минск

Tuzikov Alexander V. – Corresponding Member, D. Sc. (Physics and Mathematics), Professor, General director.

6, Surganov Str., 220012, Minsk

tuzikov@newman.bas-net.byОбъединенный институт проблем информатики Национальной академии наук БеларусиUnited Institute of Informatics Problems of the National Academy of Sciences of BelarusУниверситет ВашингтонаUniversity of WashingtonУниверситет КанзасаUniversity of Kansas202017052020642150158Copyright © Хадарович А.Ю., Анищенко И.В., Кундротас П., Ваксер И., Тузиков А.В., 20202020Хадарович А.Ю., Анищенко И.В., Кундротас П., Ваксер И., Тузиков А.В.Hadarovich A.Y., Anishchenko I.V., Kundrotas P., Vakser I., Tuzikov A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://doklady.belnauka.by/jour/article/view/865

Предлагается алгоритм сравнения белок-белковых комплексов на основе их функциональных свойств в терминах генной онтологии. Мера функциональной схожести комплексов интегрируется со структурной мерой для нахождения шаблона для моделирования белковых комплексов. Приводятся результаты моделирования белковых комплексов с помощью предложенного алгоритма.

We propose an algorithm for comparing protein-protein complexes based on their functional properties in terms of Gene Ontology. The proposed measure of a functional similarity between complexes is combined with a structural measure to find templates for the template-based docking of protein complexes. We present the results on the modeling of protein complexes based on this algorithm.

белок-белковые взаимодействиямоделирование белковых комплексовтермины генной онтологииprotein-protein interactionsmodeling of protein complexesgene ontology termsReferencesGene Ontology Consortium: going forward / The Gene Ontology Consortium // Nucleic Acids Research. – 2015. – Vol. 43. – P. D1049–D1056. https://doi.org/10.1093/nar/gku1179The Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Research, 2015, vol. 43, pp. D1049–D1056. https://doi.org/10.1093/nar/gku1179Metrics for GO based protein semantic similarity: a systematic evaluation / C. Pesquita [et al.] // BMC Bioinformatics. – 2008. – Vol. 9. – P. S4. https://doi.org/10.1186/1471-2105-9-s5-s4Pesquita C., Faria D., Bastos H., Ferreira A. E. N., Falcão A. O., Couto F. M. Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinformatics, 2008, vol. 9, pp. S4. https://doi.org/10.1186/1471-2105-9-s5-s4Resnik, P. Using Information Content to Evaluate Semantic Similarity in a Taxonomy / P. Resnik // Proceedings of the 14th International Joint Conference on Artificial Intelligence. – 1995. – Vol. 1. – P. 448–453.Resnik P. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995, vol. 1, pp. 448–453.A new measure for functional similarity of gene products based on Gene Ontology / A. Schlicker [et al.] // BMC Bioinformatics. – 2006. – Vol. 7, N 1. – Art. 302. https://doi.org/10.1186/1471-2105-7-302Schlicker A., Domingues F. S., Rahnenführer J., Lengauer T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics, 2006, vol. 7, no. 1, art. 302. https://doi.org/10.1186/1471-2105-7-302Couto, F. M. Measuring semantic similarity between Gene Ontology terms / F. M. Couto, M. J. Silva, P. M. Coutinho // Data & Knowledge Engineering. – 2007. – Vol. 61, N 1. – P. 137–152. https://doi.org/10.1016/j.datak.2006.05.003Couto F. M., Silva M. J., Coutinho P. M. Measuring semantic similarity between Gene Ontology terms. Data & Knowledge Engineering, 2007, vol. 61, no. 1, pp. 137–152. https://doi.org/10.1016/j.datak.2006.05.003Zhang, Y. Scoring Function for Automated Assessment of Protein Structure Template Quality / Y. Zhang, J. Skolnick // PROTEINS: Structure, Function, and Bioinformatics. – 2004. – Vol. 57, N 4. – P. 702–710. https://doi.org/10.1002/prot.20264Zhang Y., Skolnick J. Scoring Function for Automated Assessment of Protein Structure Template Quality. Proteins: Structure, Function, and Bioinformatics, 2004, vol. 57, no. 4, pp. 702–710. https://doi.org/10.1002/prot.20264Zhang, Y. TM-align: a protein structure alignment algorithm based on the TM-score / Y. Zhang, J. Skolnick // Nucleic Acids Research. – 2005. – Vol. 33, N 7. – P. 2302–2309.Zhang Y., Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Research, 2005, vol. 33, no. 7, pp. 2302–2309. https://doi.org/10.1093/nar/gki524Negroni, J. Assessing the Applicability of Template-Based Protein Docking in the Twilight Zone / J. Negroni, R. Mosca, P. Aloy // Structure. – 2014. – Vol. 22, N 9. – P. 1356–1362. https://doi.org/10.1016/j.str.2014.07.009Negroni J., Mosca R., Aloy P. Assessing the Applicability of Template-Based Protein Docking in the Twilight Zone. Structure, 2014, vol. 22, no. 9, pp. 1356–1362. https://doi.org/10.1016/j.str.2014.07.009DOCKGROUND resource for studying protein-protein interfaces / D. Douguet [et al.] // Bioinformatics. – 2006. – Vol. 22, N 21. – P. 2612–2618. https://doi.org/10.1093/bioinformatics/btl447Douguet D., Chen H. C., Tovchigrechko A., Vakser I. A. DOCKGROUND resource for studying protein-protein interfaces. Bioinformatics, 2006, vol. 22, no. 21, pp. 2612–2618. https://doi.org/10.1093/bioinformatics/btl447DOCKGROUND: A comprehensive data resource for modeling of protein complexes / P. J. Kundrotas [et al.] // Protein Sci. – 2018. – Vol. 27, N 1. – P. 172–181. https://doi.org/10.1002/pro.3295Kundrotas P. J., Anishchenko I., Dauzhenka T., Kotthoff I., Mnevets D., Copeland M. M., Vakser I. A. DOCKGROUND: A comprehensive data resource for modeling of protein complexes. Protein Science, 2018, vol. 27, no. 1, pp. 172–181. https://doi.org/10.1002/pro.3295The Protein Data Bank / H. M. Berman [et al.] // Nucleic Acids Research. – 2000. – Vol. 28, N 1. – P. 235–242. https://doi.org/10.1093/nar/28.1.235Berman H. M., Westbrook J., Feng Z., Gilliland G., Bhat T. N., Weissig H., Shindyalov I. N., Bourne P. E. The Protein Data Bank. Nucleic Acids Research, 2000, vol. 28, no. 1, pp. 235–242. https://doi.org/10.1093/nar/28.1.235Structural templates for comparative protein docking / I. Anishchenko [et al.] // Proteins. – 2014. – Vol. 83, N 9. – P. 1563–1570. https://doi.org/10.1002/prot.24736Anishchenko I., Kundrotas P. J., Tuzikov A. V., Vakser I. A. Structural templates for comparative protein docking. Proteins: Structure, Function, and Bioinformatics, 2014, vol. 83, no. 9, pp. 1563–1570. https://doi.org/10.1002/prot.24736Das, S. Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives / S. Das, A. Abraham, A. Konar // Advances of Computational Intelligence in Industrial Systems. – 2008. – Vol. 116. – P. 1–38. https://doi.org/10.1007/978-3-540-78297-1_1Das S., Abraham A., Konar A. Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives. Advances of Computational Intelligence in Industrial Systems, 2008, vol. 116, pp. 1–38. https://doi.org/10.1007/978-3-540-78297-1_1Sinha, R. Docking by structural similarity at protein-protein interfaces / R. Sinha, P. J. Kundrotas, I. A. Vakser // Proteins. – 2010. – Vol. 78, N 15. – P. 3235–3241. https://doi.org/10.1002/prot.22812Sinha R., Kundrotas P. J., Vakser I. A. Docking by structural similarity at protein-protein interfaces. Proteins: Structure, Function, and Bioinformatics, 2010, vol. 78, no. 15, pp. 3235–3241. https://doi.org/10.1002/prot.22812Gene ontology improves template selection in comparative protein docking / A. Hadarovich [et al.] // Proteins. – 2019. – Vol. 87, N 3. – P. 245–253. https://doi.org/10.1002/prot.25645Hadarovich A., Anishchenko I., Kundrotas P. J., Tuzikov A. V., Vakser I. A. Gene ontology improves template selection in comparative protein docking. Proteins: Structure, Function, Bioinformatics, 2019, vol. 87, no. 3, pp. 245–253. https://doi.org/10.1002/prot.25645

The authors declare that there are no conflicts of interest present.