Computer search for molecular mechanisms of ulcerogenic action of nonsteroidal antiinflammatory drugs

Ivanov, Sergey; Lagunin, Alexey; Zakharov, Alexey; Филимонов, Д. А.; Poroikov, Vladimir

doi:10.18097/pbmc20146001007

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…We previously showed the possibility of revealing possible molecular mechanisms underlying the ulcerogenic action of nonsteroidal anti-inflammatory drugs based on PASS prediction results and identified 24 new mechanisms of drug action that are likely related with the development of peptic ulcers. 36 In the present work, we developed a special version of PASS with a training set based on information from the ChEMBLdb 16 database 37 and DrugBank 3.0. 38 This version predicts interactions with human targets present in the ChEMBLdb 16 database and DrugBank 3.0 without specifying the type of ligand−protein interaction (inhibition or stimulation).…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes

Ivanov

Lagunin

Pogodin

et al. 2014

Chem. Res. Toxicol.

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Drug-induced myocardial infarction (DIMI) is one of the most serious adverse drug effects that often lead to death. Therefore, the identification of DIMI at the early stages of drug development is essential. For this purpose, the in vitro testing and in silico prediction of interactions between drug-like substances and various off-target proteins associated with serious adverse drug reactions are performed. However, only a few DIMI-related protein targets are currently known. We developed a novel in silico approach for the identification of DIMI-related protein targets. This approach is based on the computational prediction of drug-target interaction profiles based on information from approximately 1738 human targets and 828 drugs, including 254 drugs that cause myocardial infarction. Through a statistical analysis, we revealed the 155 most significant associations between protein targets and DIMI. Because not all of the identified associations may lead to DIMI, an analysis of the biological functions of these proteins was performed. The Random Walk with Restart algorithm based on a functional linkage gene network was used to prioritize the revealed DIMI-related protein targets according to the functional similarity between their genes and known genes associated with myocardial infarction. The biological processes associated with the 155 selected protein targets were determined by gene ontology and pathway enrichment analysis. This analysis indicated that most of the processes leading to DIMI are associated with atherosclerosis. The revealed proteins were manually annotated with biological processes using functional and disease-related data extracted from the literature. Finally, the 155 protein targets were classified into three categories of confidence: (1) high (the protein targets are known to be involved in DIMI via atherosclerotic progression; 50 targets), (2) medium (the proteins are known to participate in biological processes related with DIMI; 65 targets), and (3) low (the proteins are indirectly involved in DIMI pathogenesis; 40 proteins).

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes

Ivanov

Lagunin

Pogodin

et al. 2014

Chem. Res. Toxicol.

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“…Этот способ описания успешно используется при прогнозе биологической активности низкомолекулярных химических соединений на протяжении ряда лет. При этом оценка принадлежности вещества к тому или иному виду биологической активности производится с помощью алгоритма PASS [9][10][11]. Одна из главных областей применения этой методики -поиск лигандов, специфичных к различным белкам-мишеням, что близко по смыслу к распознаванию пептидов, связываемых модифицирующим ферментом.…”

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Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences

et al. 2017

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Recognition of the phosphorylation sites in proteins is required for reconstruction of regulatory processes in living systems. This task is complicated because the phosphorylation motifs in amino acid sequences are considerably degenerated. To improve the prediction efficacy researchers often use additional descriptors, which should reflect physicochemical features of site-surrounding regions. We have evaluated the reasonability of this approach by applying molecular descriptors (MNA) for structural presentation of the peptide segments. Comparative testing was performed using the prognostic method PASS and two input data types: sets of the MNA descriptors represented peptides as chemical structures and amino acid sequences written using a one-letter code. Training sets were classified in accordance with the established types of the enzymes (protein kinases), modifying corresponding phosphorylation sites. The accuracy estimates obtained by prognosis validation for various classes of substrates were significantly different with both the letters and molecular descriptors. In case of the letter description, the prognosis accuracy demonstrated less dependence on the length of peptides in the training set, while in the case of structural descriptors the accuracy level was determined by the peptide size and descriptor characteristics (MNA levels). The maximal prognosis accuracy related to various kinase families was achieved at different sizes of molecular fragments covered by the MNA descriptors of corresponding levels. This obviously reflected structural differences in surroundings of phosphorylation sites modified by various protein kinases. The use of molecular descriptors provided the prognostic results comparable with the results obtained using traditional letter representation. The prognosis accuracy demonstrated less dependence on the method describing site-surrounding peptides at higher accuracy rates. Applying the MNA descriptors it is possible to achieve better accuracy in the cases when the letter description cannot provide acceptable accuracy.

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In silico assessment of adverse drug reactions and associated mechanisms

Ivanov

Lagunin

Poroikov

2016

Drug Discovery Today

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Computer search for molecular mechanisms of ulcerogenic action of nonsteroidal antiinflammatory drugs

Cited by 5 publications

References 18 publications

Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes

Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes

Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences

In silico assessment of adverse drug reactions and associated mechanisms

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