vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-25-113vavilov-4891Research ArticleСТРУКТУРНАЯ КОМПЬЮТЕРНАЯ БИОЛОГИЯSTRUCTURAL COMPUTATIONAL BIOLOGYПредсказание взаимодействий белка ORF3a SARS-CoV-2 с низкомолекулярными лигандами с использованием когнитивной платформы AND-System, графовых нейронных сетей и молекулярного моделированияPrediction of interactions between the SARS-CoV-2 ORF3a protein and small-molecule ligands using the AND-System cognitive platform, graph neural networks, and molecularhttps://orcid.org/0000-0002-0005-9155ИванисенкоТ. В.IvanisenkoT. V.

Новосибирск

Novosibirsk

itv@bionet.nsc.ruhttps://orcid.org/0000-0001-9433-8341ДеменковП. С.DemenkovP. S.

Новосибирск

Novosibirsk

https://orcid.org/0000-0002-7537-2525КлещевМ. А.KleshchevM. A.

Новосибирск

Novosibirsk

https://orcid.org/0000-0002-1859-4631ИванисенкоВ. А.IvanisenkoV. A.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian Federation20251212202529710841096Copyright © Иванисенко Т.В., Деменков П.С., Клещев М.А., Иванисенко В.А., 20252025Иванисенко Т.В., Деменков П.С., Клещев М.А., Иванисенко В.А.Ivanisenko T.V., Demenkov P.S., Kleshchev M.A., Ivanisenko V.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4891

   В последние годы методы искусственного интеллекта, основанные на анализе гетерогенных графов биомедицинских сетей, получили широкое распространение для предсказания молекулярных взаимодействий. В частности, графовые нейронные сети (graph neural networks, GNN) позволяют эффективно выявлять отсутствующие ребра в генных сетях, таких как сети белок-белковых взаимодействий, ген–заболевание, лекарство–мишень и др., и тем самым предсказывать новые биологические связи. Для реконструкции генных сетей часто применяют когнитивные системы автоматического анализа текстов научных публикаций и баз данных. Одна из таких платформ, базирующаяся на методах искусственного интеллекта,– AND-System, предназначенная для автоматического извлечения знаний о молекулярных взаимодействиях и на этой основе – реконструкции ассоциативных генных сетей. База знаний AND-System содержит сведения о более чем 100 млн взаимодействий между различными молекулярно-генетическими объектами (гены, белки, метаболиты, лекарства и др.). Взаимодействия представлены широким спектром типов: регуляторные связи, физические взаимодействия (белок–белок, белок–лиганд), каталитические и химические реакции, ассоциации между генами, фенотипами, заболеваниями и др. В настоящем исследовании мы применили графовые нейронные сети с механизмом внимания, обученные на графе знаний AND-System, для предсказания новых ребер между белками и лигандами и поиска потенциальных лигандов для белка ORF3a SARS-CoV-2. Вспомогательный белок ORF3a SARS-CoV-2 играет важную роль в патогенезе вируса за счет ион-канальной активности, индукции апоптоза и способности модулировать эндолизосомальные процессы и врожденный иммунитет хозяина. Несмотря на широкий спектр функций, ORF3a как фармакологическая мишень изучен значительно меньше, чем другие вирусные белки. Применение графовой нейронной сети позволило нам предсказать пять малых молекул разного происхождения (метаболиты и лекарство), потенциально взаимодействующих с ORF3a: N-ацетил-D-глюкозамин, 4-(бензоиламино)бензойная кислота, аустоцистин D, биктегравир и L-треонин. Молекулярный докинг и оценка аффинности методом MM/GBSA подтвердили потенциальную способность этих соединений образовывать комплексы с ORF3a. Анализ локализации показал, что сайты связывания биктегравира и 4-(бензоиламино)бензойной кислоты расположены в цитозольной поверхностной области белка, доступной растворителю; L-треонин связывается в межсубъединичной щели димера, а аустоцистин D и N-ацетил-D-глюкозамин – на границе между цитозольной поверхностью и трансмембранной областью. Доступность этих сайтов связывания может быть снижена из-за влияния липидного бислоя. Энергетические характеристики связывания у биктегравира по сравнению с 4-(бензоиламино)бензойной кислотой оказались более высокими (–7.37 ккал/моль в докинге; –14.71 ± 3.12 ккал/моль по MM/GBSA), что делает его перспективным кандидатом для репозиционирования как ингибитора ORF3a. Взаимодействие биктегравира с ORF3a может нарушать связывание ORF3а с белком хозяина VPS39 – субъединицей комплекса HOPS, участвующего в слиянии аутофагосом и поздних эндосом с лизосомами. Это, в свою очередь, может снимать индуцируемую ORF3a-блокаду данного процесса и тем самым способствовать восстановлению аутофагического потока и лизосомной деградации вирусных компонентов.

   In recent years, artificial intelligence methods based on the analysis of heterogeneous graphs of biomedical networks have become widely used for predicting molecular interactions. In particular, graph neural networks (GNNs) effectively identify missing edges in gene networks – such as protein–protein interaction, gene–disease, drug–target, and other networks – thereby enabling the prediction of new biological relationships. To reconstruct gene networks, cognitive systems for automatic text mining of scientific publications and databases are often employed. One such AI-driven platform, ANDSystem, is designed for automatic knowledge extraction of molecular interactions and, on this basis, the reconstruction of associative gene networks. The ANDSystem knowledge base contains information on more than 100 million interactions among diverse molecular genetic entities (genes, proteins, metabolites, drugs, etc.). The interactions span a wide range of types: regulatory relationships, physical interactions (protein–protein, protein–ligand), catalytic and chemical reactions, and associations among genes, phenotypes, diseases, and more. In the present study, we applied attention-based graph neural networks trained on the ANDSystem knowledge graph to predict new edges between proteins and ligands and to identify potential ligands for the SARS-CoV-2 ORF3a protein. The accessory protein ORF3a plays an important role in viral pathogenesis through ion-channel activity, induction of apoptosis, and the ability to modulate endolysosomal processes and the host innate immune response. Despite this broad functional spectrum, ORF3a has been explored far less as a pharmacological target than other viral proteins. Using a graph neural network, we predicted five small molecules of different origins (metabolites and a drug) that potentially interact with ORF3a: N-acetyl-D-glucosamine, 4-(benzoylamino)benzoic acid, austocystin D, bictegravirum, and L-threonine. Molecular docking and MM/GBSA affinity estimation indicate the potential ability of these compounds to form complexes with ORF3a. Localization analysis showed that the binding sites of bictegravir and 4-(benzoylamino)benzoic acid lie in a cytosolic surface pocket of the protein that is solvent-exposed; L-threonine binds within the intersubunit cleft of the dimer; and austocystin D and N-acetyl-D-glucosamine are positioned at the boundary between the cytosolic surface and the transmembrane region. The accessibility of these binding sites may be reduced by the influence of the lipid bilayer. The binding energetics for bictegravirum were more favorable than for 4-(benzoylamino)benzoic acid (docking score −7.37 kcal/mol; MM/GBSA ΔG −14.71 ± 3.12 kcal/mol), making bictegravirum a promising candidate for repurposing as an ORF3a inhibitor.

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