Оценка качества прогноза анти-SARS-CoV-2 активности с помощью веб-сервиса D3Targets-2019-nCoV

The D3Targets-2019-nCoV web service predicting the interaction of chemical compounds with SARS-CoV-2 virus proteins and human proteins involved in the pathogenesis of COVID-19 by structural similarity and molecular docking was evaluated. The quality of the prediction was assessed as a balanced accuracy, which was calculated based on the results of the prediction for the structures of chemical compounds from the test set we compiled. The test set consisted of 35 active and 59 inactive molecules, including compounds with the experimetnaly confirmed absence of activity against the selected targets and compounds active against SARS-CoV-2 targets, not presented in the CoViLigands database. The authors of the analyzed web service did not indicate the thresholds for the values of the similarity score and the docking scoring function, using which it would be possible to reliably divide the compounds into active and inactive with respect to target proteins. Therefore, we assessed the balanced accuracy of the predictive methods D3Targets-2019-nCoV at various thresholds for cutting off active substances from inactive ones. Using our test set it was found that the highest value of balanced accuracy (0.59) was achieved when choosing active molecules based on the results of 2D similarity assessment (cutoff threshold was 46%). Assessment of 3D similarity did not allow achieving balanced accuracy values exceeding 0.5. It is shown that using the 2Dх3D integral similarity assessment recommended by the authors, the maximum value of the balanced accuracy 0.57 was achieved at a threshold of 31%. The calculated balanced accuracy for molecular docking results does not exceed 0.51. On the case study for the tideglusib, it was shown that the values of the scoring function for two target proteins, the activity against which was confirmed in the experiment (3CLpro and GSK3B), do not differ significantly from the values of the scoring function for the remaining 44 targets were not confirmed.Проведена оценка веб-сервиса D3Targets-2019-nCoV, позволяющего на основании структурного сходства и молекулярного докинга предсказывать взаимодействие химических соединений с белками вируса SARS-CoV-2 и белками человека, вовлеченными в патогенез COVID-19. Качество прогноза оценено как сбалансированная точность, которая была рассчитана по результатам прогноза для структур химических соединений из сформированной нами тестовой выборки. В тестовую выборку вошло 35 активных и 59 неактивных молекул, включающих в себя соединения с установленным отсутствием активности в отношении выбранных мишеней и соединения, активные по отношению к мишеням SARS-CoV-2, не представленным в базе данных CoViLigands. Авторами анализируемого веб-сервиса не указаны пороги значений оценки сходства и оценочной функции докинга, используя которые можно было бы достоверно разделить соединения на активные и неактивные по отношению к белкам-мишеням. Поэтому нами была проведена оценка сбалансированной точности прогностических методов D3Targets-2019-nCoV при различных порогах отсечения активных веществ от неактивных. С использованием сформированной нами выборки установлено, что наибольшее значение сбалансированной точности (0.59) достигается при выборе активных молекул по результатам оценки 2D сходства (порог отсечения равен 46%). Оценка 3D сходства не позволила достичь значений сбалансированной точности, превышающих 0.5. Показано, что при использовании рекомендуемой авторами интегральной оценки сходства 2Dх3D, максимальное значение сбалансированной точности (0.57) достигается при пороге, равном 31%. При расчёте сбалансированной точности для результатов молекулярного докинга показано, что она не превышает 0.51. На примере препарата тидеглусиб показано, что значения оценочной функции при докинге к двум белкам-мишеням, активность в отношении которых установлена в эксперименте (3CLpro и GSK3B), существенно не отличаются от значений оценочной функции докинга к остальным 44 белкам-мишеням, активность в отношении которых не подтверждена экспериментально

COMPUTER-AIDED PREDICTION OF MULTITARGET PROFILES: CASE STUDIES FOR CLOZAPINE AND DASATINIB

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Cyclobutane-Containing Alkaloids: Origin, Synthesis, and Biological Activities

Present review describes research on novel natural cyclobutane-containing alkaloids isolated from terrestrial and marine species. More than 60 biological active compounds have been confirmed to have antimicrobial, antibacterial, antitumor, and other activities. The structures, synthesis, origins, and biological activities of a selection of cyclobutane-containing alkaloids are reviewed. With the computer program PASS some additional biological activities are also predicted, which point toward new possible applications of these compounds. This review emphasizes the role of cyclobutane-containing alkaloids as an important source of leads for drug discovery

RHIVDB: A Freely Accessible Database of HIV Amino Acid Sequences and Clinical Data of Infected Patients

Human immunodeficiency virus (HIV) infection remains one of the most severe problems for humanity, particularly due to the development of HIV resistance. To evaluate an association between viral sequence data and drug combinations and to estimate an effect of a particular drug combination on the treatment results, collection of the most representative drug combinations used to cure HIV and the biological data on amino acid sequences of HIV proteins is essential. We have created a new, freely available web database containing 1,651 amino acid sequences of HIV structural proteins [reverse transcriptase (RT), protease (PR), integrase (IN), and envelope protein (ENV)], treatment history information, and CD4+ cell count and viral load data available by the user’s query. Additionally, the biological data on new HIV sequences and treatment data can be stored in the database by any user followed by an expert’s verification. The database is available on the web at http://www.way2drug.com/rhivdb

HOW SHOULD ONE USE AVAILABLE DATA TO BE SUCCESSFUL IN COMPUTER-AIDED SEARCH FOR NOVEL KINASE INHIBITORS?

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PREDICTION OF THE METABOLIC NETWORK FOR XENOBIOTICS IN THE HUMAN ORGANISM

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OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

<p>Abstract</p> <p>Background</p> <p>The OpenTox Framework, developed by the partners in the OpenTox project (<url>http://www.opentox.org</url>), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.</p> <p>Results</p> <p>The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in <it>in vivo</it> studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.</p> <p>OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.</p> <p>The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).</p> <p>Availability</p> <p>The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page <url>http://www.opentox.org/dev/ontology</url>; the OpenTox ontology is available as OWL at <url>http://opentox.org/api/1 1/opentox.owl</url>, the ToxML - OWL conversion utility is an open source resource available at <url>http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/</url></p

Transcriptome-based analysis of human peripheral blood reveals regulators of immune response in different viral infections

IntroductionThere are difficulties in creating direct antiviral drugs for all viruses, including new, suddenly arising infections, such as COVID-19. Therefore, pathogenesis-directed therapy is often necessary to treat severe viral infections and comorbidities associated with them. Despite significant differences in the etiopathogenesis of viral diseases, in general, they are associated with significant dysfunction of the immune system. Study of common mechanisms of immune dysfunction caused by different viral infections can help develop novel therapeutic strategies to combat infections and associated comorbidities.MethodsTo identify common mechanisms of immune functions disruption during infection by nine different viruses (cytomegalovirus, Ebstein-Barr virus, human T-cell leukemia virus type 1, Hepatitis B and C viruses, human immunodeficiency virus, Dengue virus, SARS-CoV, and SARS-CoV-2), we analyzed the corresponding transcription profiles from peripheral blood mononuclear cells (PBMC) using the originally developed pipeline that include transcriptome data collection, processing, normalization, analysis and search for master regulators of several viral infections. The ten datasets containing transcription data from patients infected by nine viruses and healthy people were obtained from Gene Expression Omnibus. The analysis of the data was performed by Genome Enhancer pipeline.ResultsWe revealed common pathways, cellular processes, and master regulators for studied viral infections. We found that all nine viral infections cause immune activation, exhaustion, cell proliferation disruption, and increased susceptibility to apoptosis. Using network analysis, we identified PBMC receptors, representing proteins at the top of signaling pathways that may be responsible for the observed transcriptional changes and maintain the current functional state of cells.DiscussionThe identified relationships between some of them and virus-induced alteration of immune functions are new and have not been found earlier, e.g., receptors for autocrine motility factor, insulin, prolactin, angiotensin II, and immunoglobulin epsilon. Modulation of the identified receptors can be investigated as one of therapeutic strategies for the treatment of severe viral infections

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets