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Karasev DA, Savosina PI, Sobolev BN, Filimonov DA, Lagunin AA. [Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2017; 63:423-427. [PMID: 29080875 DOI: 10.18097/pbmc20176305423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
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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Affiliation(s)
- D A Karasev
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
| | - P I Savosina
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
| | - B N Sobolev
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - A A Lagunin
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
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In silico assessment of adverse drug reactions and associated mechanisms. Drug Discov Today 2015; 21:58-71. [PMID: 26272036 DOI: 10.1016/j.drudis.2015.07.018] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 07/15/2015] [Accepted: 07/31/2015] [Indexed: 12/31/2022]
Abstract
During recent years, various in silico approaches have been developed to estimate chemical and biological drug features, for example chemical fragments, protein targets, pathways, among others, that correlate with adverse drug reactions (ADRs) and explain the associated mechanisms. These features have also been used for the creation of predictive models that enable estimation of ADRs during the early stages of drug development. In this review, we discuss various in silico approaches to predict these features for a certain drug, estimate correlations with ADRs, establish causal relationships between selected features and ADR mechanisms and create corresponding predictive models.
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Corominas-Faja B, Santangelo E, Cuyàs E, Micol V, Joven J, Ariza X, Segura-Carretero A, García J, Menendez JA. Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins. Aging (Albany NY) 2015; 6:731-41. [PMID: 25324469 PMCID: PMC4221918 DOI: 10.18632/aging.100691] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Aging is associated with common conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. The type of multi-targeted pharmacological approach necessary to address a complex multifaceteddisease such as aging might take advantage of pleiotropic natural polyphenols affecting a wide variety of biological processes. We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute anew family of plant-produced gerosuppressant agents. This paper describes an analysis of the biological activity spectra (BAS) of OA and DOA using PASS (Prediction of Activity Spectra for Substances) software. PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities. Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of “mechanism-effect” and “effect-mechanism” relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.
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Affiliation(s)
- Bruna Corominas-Faja
- Metabolism and Cancer Group, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Girona, Spain. Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Elvira Santangelo
- Departament de Química Orgànica, Fac. de Química, Institut de Biomedicina de la UB (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - Elisabet Cuyàs
- Metabolism and Cancer Group, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Girona, Spain. Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Vicente Micol
- Instituto de Biología Molecular y Celular (IBMC), Universidad Miguel Hernández, Elche, Alicante, Spain
| | - Jorge Joven
- Campus of International Excellence Southern Catalonia, Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | - Xavier Ariza
- Departament de Química Orgànica, Fac. de Química, Institut de Biomedicina de la UB (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - Antonio Segura-Carretero
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Granada, Spain; Functional Food Research and Development Centre (CIDAF), PTS Granada, Granada, Spain
| | - Jordi García
- Departament de Química Orgànica, Fac. de Química, Institut de Biomedicina de la UB (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - Javier A Menendez
- Metabolism and Cancer Group, Translational Research Laboratory, Catalan Institute of Oncology (ICO), Girona, Spain. Girona Biomedical Research Institute (IDIBGI), Girona, Spain
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Lagunin A, Druzhilovsky D, Rudik A, Filimonov D, Gawande D, Suresh K, Goel R, Poroikov V. Computer evaluation of hidden potential of phytochemicals of medicinal plants of the traditional indian ayurvedic medicine. ACTA ACUST UNITED AC 2015; 61:286-97. [DOI: 10.18097/pbmc20156102286] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Applicability of our computer programs PASS and PharmaExpert to prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. The web-resource on phytochemicals of 50 medicinal plants used in Ayurveda was created for the study of hidden therapeutic potential of Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). It contains information on 50 medicinal plants, their using in TIM and their pharmacology activities, also as 1906 phytocomponents. PASS training set was updated by addition of information about 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the difference between the average accuracy of prediction obtained in leave-5%-out cross-validation (94,467%) and in leave-one-out cross-validation (94,605%) is very small. These results showed high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database are in good correspondence with the experimental data. Additional kinds of biological activity predicted with high probability provide the information about most promising directions of further studies. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of Passiflora incarnata extracts.
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Affiliation(s)
- A.A. Lagunin
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
| | | | - A.V. Rudik
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - D. Gawande
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala-147002 India
| | - K. Suresh
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala-147002 India
| | - R. Goel
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala-147002 India
| | - V.V. Poroikov
- Institute of Biomedical Chemistry, Moscow, Russia; Pirogov Russian National Research Medical University, Moscow, Russia
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Ivanov SM, Lagunin AA, Pogodin PV, Filimonov DA, Poroikov VV. Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes. Chem Res Toxicol 2014; 27:1263-81. [DOI: 10.1021/tx500147d] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Sergey M. Ivanov
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
| | - Alexey A. Lagunin
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
- Medico-biological
Faculty, Pirogov Russian National Research Medical University, 1,
Ostrovitianova str., 117997 Moscow, Russia
| | - Pavel V. Pogodin
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
- Medico-biological
Faculty, Pirogov Russian National Research Medical University, 1,
Ostrovitianova str., 117997 Moscow, Russia
| | - Dmitry A. Filimonov
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
| | - Vladimir V. Poroikov
- Orekhovich Institute
of Biomedical Chemistry of Russian Academy of Medical Sciences, 10, Pogodinskaya str., 119121 Moscow, Russia
- Medico-biological
Faculty, Pirogov Russian National Research Medical University, 1,
Ostrovitianova str., 117997 Moscow, Russia
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