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González-Castañeda Y, Marrero-Ponce Y, Guerra JO, Echevarría-Díaz Y, Pérez N, Pérez-Giménez F, Simonet AM, Macías FA, Nogueiras CM, Olazabal E, Serrano H. Computational discovery of novel anthelmintic natural compounds from Agave Brittoniana trel. Spp. Brachypus. BIONATURA 2022. [DOI: 10.21931/rb/2022.07.04.53] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Helminth infections are a medical problem in the world nowadays. This report used bond-based 2D quadratic indices, a bond-level QuBiLs-MAS molecular descriptor family, and Linear Discriminant Analysis (LDA) to obtain a quantitative linear model that discriminates between anthelmintic and non-anthelmintic drug-like organic-compounds. The model obtained correctly classified 87.46% and 81.82% of the training and external data sets, respectively. The developed model was used in a virtual screening to predict the biological activity of all chemicals (19) previously obtained and chemically characterized by some authors of this report from Agave brittoniana Trel. spp. Brachypus. The model identified several metabolites (12) as possible anthelmintics, and a group of 5 novel natural products was tested in an in vitro assay against Fasciola hepatica (100% effectivity at 500 µg/mL). Finally, the two best hits were evaluated in vivo in bald/c mice and the same helminth parasite using a 25 mg/kg dose. Compound 8 (Karatavinoside A) showed an efficacy of 92.2% in vivo. It is important to remark that this natural compound exhibits similar-to-superior activity as triclabendazole, the best human fasciolicide available in the market against Fasciola hepatica, resulting in a novel lead scaffold with anti-helminthic activity.
Keywords: TOMOCOMD-CARDD Software; QuBiLs-MAS, nonstochastic and stochastic bond-based quadratic indices; LDA-based QSAR model; Computational Screening, Anthelmintic Agent; Agave brittoniana Trel. spp. Brachypus, Fasciola hepatica.
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Affiliation(s)
- Yeniel González-Castañeda
- Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA)
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain
| | - Jose O. Guerra
- Chemistry Department, Faculty of Chemistry-Pharmacy. Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Yunaimy Echevarría-Díaz
- Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE)
| | - Noel Pérez
- Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ), Quito, Ecuador
| | - Facundo Pérez-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain
| | - Ana M. Simonet
- Grupo de Alelopatía, Departamento de Química Orgánica, Facultad de Ciencias, Universidad de Cádiz
| | - Francisco A. Macías
- Grupo de Alelopatía, Departamento de Química Orgánica, Facultad de Ciencias, Universidad de Cádiz
| | - Clara M. Nogueiras
- Departamento de Química Orgánica, Facultad de Química, Universidad de La Habana
| | - Ervelio Olazabal
- Chemical Bioactive Center. Universidad Central “Marta Abreu” de Las Villas, Santa Clara
| | - Hector Serrano
- Chemical Bioactive Center. Universidad Central “Marta Abreu” de Las Villas, Santa Clara
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Ranjan P, Athar M, Jha PC, Krishna KV. Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling. Mol Divers 2018; 22:669-683. [PMID: 29611020 DOI: 10.1007/s11030-018-9825-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 03/16/2018] [Indexed: 12/30/2022]
Abstract
A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.
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Affiliation(s)
- Prabodh Ranjan
- CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India
| | - Mohd Athar
- CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India
| | - Prakash Chandra Jha
- CCG@CUG, Centre for Applied Chemistry, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India.
| | - Kari Vijaya Krishna
- Department of Chemistry, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, 632014, India
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Casañola-Martin GM, Pham-The H, Castillo-Garit JA, Le-Thi-Thu H. Atom based linear index descriptors in QSAR-machine learning classifiers for the prediction of ubiquitin-proteasome pathway activity. Med Chem Res 2018. [DOI: 10.1007/s00044-017-2091-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Valdés-Martiní JR, Marrero-Ponce Y, García-Jacas CR, Martinez-Mayorga K, Barigye SJ, Vaz d'Almeida YS, Pham-The H, Pérez-Giménez F, Morell CA. QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations. J Cheminform 2017; 9:35. [PMID: 29086120 PMCID: PMC5462671 DOI: 10.1186/s13321-017-0211-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 04/07/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0-2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations. RESULTS The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon's entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer's steroid dataset is carried out. CONCLUSIONS From these analyses, it is revealed that the QuBiLS-MAS approach for atom-pair relations yields similar-to-superior performance with regard to other QSAR methodologies reported in the literature. Therefore, the QuBiLS-MAS approach constitutes a useful tool for the diversity analysis of chemical compound datasets and high-throughput screening of structure-activity data.
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Affiliation(s)
- José R Valdés-Martiní
- StreelBridge Laboratories, SteelBridge Consulting Technology Solutions, Miami, FL, USA
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Ecuador. .,Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, 170157, Quito, Pichincha, Ecuador. .,Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador. .,Grupo de Investigación Ambiental (GIA), Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería de Procesos, Cartagena de Indias, Bolívar, Colombia. .,Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain.
| | - César R García-Jacas
- Instituto de Química, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México.,Escuela de Sistemas y Computación, Pontificia Universidad Católica del Ecuador Sede Esmeraldas (PUCESE), Esmeraldas, Ecuador.,Grupo de Investigación de Bioinformática, Universidad de las Ciencias Informáticas (UCI), Havana, Cuba
| | - Karina Martinez-Mayorga
- Instituto de Química, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México
| | - Stephen J Barigye
- Facultad de Medicina, Universidad de Las Américas, Quito, Pichincha, Ecuador
| | | | - Hai Pham-The
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Vietnam
| | - Facundo Pérez-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain
| | - Carlos A Morell
- Laboratorio de Inteligencia Artificial, Centro de Estudios de Informática (CEI), Facultad de Matemática, Física y Computación, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Villa Clara, Cuba
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Marrero-Ponce Y, Castañeda YG, Vivas-Reyes R, Vergara FM, Arán VJ, Castillo-Garit JA, Pérez-Giménez F, Torrens F, Le-Thi-Thu H, Pham-The H, Montenegro YV, Ibarra-Velarde F. Dry selection and wet evaluation for the rational discovery of new anthelmintics. Mol Phys 2017. [DOI: 10.1080/00268976.2017.1296194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Ecuador
- Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito, Ecuador
- Computer-Aided Molecular “Biosilico” Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Quito, Ecuador
- GIA (Grupo de Investigación Ambiental), Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería de Procesos, Cartagena de Indias, Bolívar, Colombia
| | - Yeniel González Castañeda
- Computer-Aided Molecular “Biosilico” Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Quito, Ecuador
| | - Ricardo Vivas-Reyes
- Grupo de Química Cuántica y Teórica, Facultad de Ciencias, Universidad de Cartagena, Cartagena de Indias, Bolívar, Colombia
- Grupo CipTec, Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería Industrial, Cartagena de Indias, Bolívar, Colombia
| | - Fredy Máximo Vergara
- Grupo de Química Cuántica y Teórica, Facultad de Ciencias, Universidad de Cartagena, Cartagena de Indias, Bolívar, Colombia
- Grupo CipTec, Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería Industrial, Cartagena de Indias, Bolívar, Colombia
| | | | - Juan A. Castillo-Garit
- Computer-Aided Molecular “Biosilico” Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Quito, Ecuador
- Unidad de Toxicología Experimental, Universidad de Ciencias Medicas de Villas Clara, Santa Clara, 50200, Cuba
| | - Facundo Pérez-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, València, Spain
| | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, València, Spain
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Hai Pham-The
- Pharmacy Department, Hanoi University of Pharmacy , 13-15 Le Thonh Tong, Hoan Kiem, Hanoi, Vietnam
| | - Yolanda Vera Montenegro
- Department of Parasitology, Faculty of Veterinarian Medicinal and Zootecnic, UNAM, Mexico, Mexico
| | - Froylán Ibarra-Velarde
- Department of Parasitology, Faculty of Veterinarian Medicinal and Zootecnic, UNAM, Mexico, Mexico
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Romero-Durán FJ, Alonso N, Yañez M, Caamaño O, García-Mera X, González-Díaz H. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology 2016; 103:270-8. [DOI: 10.1016/j.neuropharm.2015.12.019] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Revised: 11/22/2015] [Accepted: 12/18/2015] [Indexed: 01/22/2023]
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7
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Ruiz-Blanco YB, Paz W, Green J, Marrero-Ponce Y. ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins. BMC Bioinformatics 2015; 16:162. [PMID: 25982853 PMCID: PMC4432771 DOI: 10.1186/s12859-015-0586-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 04/22/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The exponential growth of protein structural and sequence databases is enabling multifaceted approaches to understanding the long sought sequence-structure-function relationship. Advances in computation now make it possible to apply well-established data mining and pattern recognition techniques to these data to learn models that effectively relate structure and function. However, extracting meaningful numerical descriptors of protein sequence and structure is a key issue that requires an efficient and widely available solution. RESULTS We here introduce ProtDCal, a new computational software suite capable of generating tens of thousands of features considering both sequence-based and 3D-structural descriptors. We demonstrate, by means of principle component analysis and Shannon entropy tests, how ProtDCal's sequence-based descriptors provide new and more relevant information not encoded by currently available servers for sequence-based protein feature generation. The wide diversity of the 3D-structure-based features generated by ProtDCal is shown to provide additional complementary information and effectively completes its general protein encoding capability. As demonstration of the utility of ProtDCal's features, prediction models of N-linked glycosylation sites are trained and evaluated. Classification performance compares favourably with that of contemporary predictors of N-linked glycosylation sites, in spite of not using domain-specific features as input information. CONCLUSIONS ProtDCal provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http://bioinf.sce.carleton.ca/ProtDCal/ . ProtDCal introduces local and group-based encoding which enhances the diversity of the information captured by the computed features. Furthermore, we have shown that adding structure-based descriptors contributes non-redundant additional information to the features-based characterization of polypeptide systems. This software is intended to provide a useful tool for general-purpose encoding of protein sequences and structures for applications is protein classification, similarity analyses and function prediction.
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Affiliation(s)
- Yasser B Ruiz-Blanco
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. .,Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - Waldo Paz
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. .,Centre of Informatics Studies (CEI), Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP:54830, Villa Clara, Cuba.
| | - James Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - Yovani Marrero-Ponce
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química y Farmacia, Universidad Central "Marta Abreu" de Las Villas, Road to Camajuani km 5 ½, Santa Clara, CP: 54830, Villa Clara, Cuba. .,Grupo de Investigación Microbiología y Ambiente (GIMA). Programa de Bacteriología, Facultad Ciencias de la Salud, Universidad de San Buenaventura, Calle Real de Ternera, Cartagena (Bolivar), Colombia.
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8
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Casañola-Martin GM, Le-Thi-Thu H, Pérez-Giménez F, Marrero-Ponce Y, Merino-Sanjuán M, Abad C, González-Díaz H. Multi-output model with Box–Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin–proteasome pathway. Mol Divers 2015; 19:347-56. [DOI: 10.1007/s11030-015-9571-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 02/14/2015] [Indexed: 12/29/2022]
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9
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Medina Marrero R, Marrero-Ponce Y, Barigye SJ, Echeverría Díaz Y, Acevedo-Barrios R, Casañola-Martín GM, García Bernal M, Torrens F, Pérez-Giménez F. QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:943-58. [PMID: 26567876 DOI: 10.1080/1062936x.2015.1104517] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections.
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Affiliation(s)
- R Medina Marrero
- a Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatic Research International Network (CAMD-BIR-IN) , Cartagena de Indias , Bolivar , Colombia
- b Department of Microbiology , Chemical Bioactive Center, Central University of Las Villas , Villa Clara , Cuba
| | - Y Marrero-Ponce
- a Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatic Research International Network (CAMD-BIR-IN) , Cartagena de Indias , Bolivar , Colombia
- c Grupo de Investigación en Estudios Químicos y Biológicos, Facultad de Ciencias Básicas , Universidad Tecnológica de Bolívar , Cartagena de Indias , Bolívar , Colombia
- d Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia , Universitat de València , Valencia , Spain
- h Grupo de Investigación Microbiología y Ambiente (GIMA) . Programa de Bacteriología, Facultad Ciencias de la Salud, Universidad de San Buenaventura , Calle Real de Ternera, 130010, Cartagena (Bolivar) , Colombia
| | - S J Barigye
- a Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatic Research International Network (CAMD-BIR-IN) , Cartagena de Indias , Bolivar , Colombia
- e Departamento de Química , Universidade Federal de Lavras , Lavras , MG , Brazil
| | - Y Echeverría Díaz
- a Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatic Research International Network (CAMD-BIR-IN) , Cartagena de Indias , Bolivar , Colombia
| | - R Acevedo-Barrios
- c Grupo de Investigación en Estudios Químicos y Biológicos, Facultad de Ciencias Básicas , Universidad Tecnológica de Bolívar , Cartagena de Indias , Bolívar , Colombia
| | - G M Casañola-Martín
- a Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatic Research International Network (CAMD-BIR-IN) , Cartagena de Indias , Bolivar , Colombia
- d Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia , Universitat de València , Valencia , Spain
- f Facultad de Ingeniería Ambiental , Universidad Estatal Amazónica , Puyo , Ecuador
| | - M García Bernal
- b Department of Microbiology , Chemical Bioactive Center, Central University of Las Villas , Villa Clara , Cuba
| | - F Torrens
- g Institut Universitari de Ciència Molecular, Universitat de València , Valencia , Spain
| | - F Pérez-Giménez
- d Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia , Universitat de València , Valencia , Spain
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Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int J Mol Sci 2014; 15:17035-64. [PMID: 25255029 PMCID: PMC4200850 DOI: 10.3390/ijms150917035] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 08/19/2014] [Accepted: 08/21/2014] [Indexed: 11/25/2022] Open
Abstract
In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.
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11
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García-Jacas CR, Marrero-Ponce Y, Acevedo-Martínez L, Barigye SJ, Valdés-Martiní JR, Contreras-Torres E. QuBiLS-MIDAS: a parallel free-software for molecular descriptors computation based on multilinear algebraic maps. J Comput Chem 2014; 35:1395-409. [PMID: 24889018 DOI: 10.1002/jcc.23640] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Revised: 04/22/2014] [Accepted: 04/23/2014] [Indexed: 11/12/2022]
Abstract
The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and employs the Chemical Development Kit library for the manipulation of the chemical structures and the calculation of the atomic properties. This software is composed by a desktop user-friendly interface and an Abstract Programming Interface library. The former was created to simplify the configuration of the different options of the MDs, whereas the library was designed to allow its easy integration to other software for chemoinformatics applications. This program provides functionalities for data cleaning tasks and for batch processing of the molecular indices. In addition, it offers parallel calculation of the MDs through the use of all available processors in current computers. The studies of complexity of the main algorithms demonstrate that these were efficiently implemented with respect to their trivial implementation. Lastly, the performance tests reveal that this software has a suitable behavior when the amount of processors is increased. Therefore, the QuBiLS-MIDAS software constitutes a useful application for the computation of the molecular indices based on N-linear algebraic maps and it can be used freely to perform chemoinformatics studies.
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Affiliation(s)
- César R García-Jacas
- Grupo de Investigación de Bioinformática, Centro de Estudio de Matemática Computacional, Universidad de las Ciencias Informáticas, La Habana, Cuba; Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central "Martha Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
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Martins Alho MA, Marrero-Ponce Y, Barigye SJ, Meneses-Marcel A, Machado Tugores Y, Montero-Torres A, Gómez-Barrio A, Nogal JJ, García-Sánchez RN, Vega MC, Rolón M, Martínez-Fernández AR, Escario JA, Pérez-Giménez F, Garcia-Domenech R, Rivera N, Mondragón R, Mondragón M, Ibarra-Velarde F, Lopez-Arencibia A, Martín-Navarro C, Lorenzo-Morales J, Cabrera-Serra MG, Piñero J, Tytgat J, Chicharro R, Arán VJ. Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones. Bioorg Med Chem 2014; 22:1568-85. [DOI: 10.1016/j.bmc.2014.01.036] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2012] [Revised: 01/13/2014] [Accepted: 01/21/2014] [Indexed: 12/20/2022]
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13
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Tenorio-Borroto E, Peñuelas-Rivas CG, Vásquez-Chagoyán JC, Castañedo N, Prado-Prado FJ, García-Mera X, González-Díaz H. Model for high-throughput screening of drug immunotoxicity – Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur J Med Chem 2014; 72:206-20. [DOI: 10.1016/j.ejmech.2013.08.035] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Revised: 08/29/2013] [Accepted: 08/31/2013] [Indexed: 10/26/2022]
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14
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Alonso N, Caamaño O, Romero-Duran FJ, Luan F, D. S. Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci 2013; 4:1393-403. [PMID: 23855599 DOI: 10.1021/cn400111n] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.
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Affiliation(s)
- Nerea Alonso
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Olga Caamaño
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Francisco J. Romero-Duran
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Feng Luan
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto,
4169-007, Porto, Portugal
- Department of Applied Chemistry, Yantai University, Yantai 264005, People’s Republic
of China
| | | | - Matilde Yañez
- Department of
Pharmacology,
Faculty of Pharmacy, USC, 15782, Santiago
de Compostela, Spain
| | - Humberto González-Díaz
- Departament
of Organic Chemistry
II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - Xerardo García-Mera
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
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Concepción RL, Froylán IV, Herminia I PM, Norberto MA, Héctor J SZ, Yeniel GC. In vitro assessment of the acaricidal activity of computer-selected analogues of carvacrol and salicylic acid on Rhipicephalus (Boophilus) microplus. EXPERIMENTAL & APPLIED ACAROLOGY 2013; 61:251-257. [PMID: 23543288 DOI: 10.1007/s10493-013-9688-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Accepted: 03/17/2013] [Indexed: 06/02/2023]
Abstract
Rhipicephalus (Boophilus) microplus is a tick that causes huge economic losses in cattle. The indiscriminate use of acaricides has generated resistance to most compounds present on the market. This makes further investigation on other potential acaricides necessary, the in silico assay being an alternative to the design of new compounds. In the present study a biosilico assay was performed using TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer-Aided Rational Drug Design) and WEKA (Waikato Environment for Knowledge Analysis) software. Two carvacrol and four salicylic acid derivatives, synthesized by conventional methods and evaluated with the larval packet test on larvae of R. (B.) microplus were selected. All evaluated compounds presented acaricidal activity; however, ethyl 2-methoxybenzoate (91.8 ± 1.7 % mortality) and ethyl 2,5-dihydroxybenzoate (89.1 ± 1.6 % mortality) showed greater activity than salicylic acid. With regard to the carvacrol analogues, carvacrol acetate (67.8 ± 2.1 % mortality) and carvacrol methyl ether (71.7 ± 1.6 % mortality) also showed greater activity than carvacrol (35.9 ± 3.2 % mortality). TOMOCOMD-CARDD and WEKA software were helpful tools in the search for alternative structures with potential acaricidal activity on R. (B.) microplus.
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Affiliation(s)
- Ramírez L Concepción
- Departamento de Parasitología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, Mexico city, Mexico.
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16
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Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods. PLoS One 2013; 8:e65926. [PMID: 23874386 PMCID: PMC3712989 DOI: 10.1371/journal.pone.0065926] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2012] [Accepted: 05/01/2013] [Indexed: 11/24/2022] Open
Abstract
The introduction of two-dimension (2D) graphs and their numerical characterization for comparative analyses of DNA/RNA and protein sequences without the need of sequence alignments is an active yet recent research topic in bioinformatics. Here, we used a 2D artificial representation (four-color maps) with a simple numerical characterization through topological indices (TIs) to aid the discovering of remote homologous of Adenylation domains (A-domains) from the Nonribosomal Peptide Synthetases (NRPS) class in the proteome of the cyanobacteria Microcystis aeruginosa. Cyanobacteria are a rich source of structurally diverse oligopeptides that are predominantly synthesized by NPRS. Several A-domains share amino acid identities lower than 20 % being a possible source of remote homologous. Therefore, A-domains cannot be easily retrieved by BLASTp searches using a single template. To cope with the sequence diversity of the A-domains we have combined homology-search methods with an alignment-free tool that uses protein four-color-maps. TI2BioP (Topological Indices toBioPolymers) version 2.0, available at http://ti2biop.sourceforge.net/ allowed the calculation of simple TIs from the protein sequences (four-color maps). Such TIs were used as input predictors for the statistical estimations required to build the alignment-free models. We concluded that the use of graphical/numerical approaches in cooperation with other sequence search methods, like multi-templates BLASTp and profile HMM, can give the most complete exploration of the repertoire of highly diverse protein families.
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Luan F, Cordeiro MND, Alonso N, García-Mera X, Caamaño O, Romero-Duran FJ, Yañez M, González-Díaz H. TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg Med Chem 2013; 21:1870-9. [DOI: 10.1016/j.bmc.2013.01.035] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Revised: 01/13/2013] [Accepted: 01/17/2013] [Indexed: 01/08/2023]
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18
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Quintero FA, Patel SJ, Muñoz F, Sam Mannan M. Review of Existing QSAR/QSPR Models Developed for Properties Used in Hazardous Chemicals Classification System. Ind Eng Chem Res 2012. [DOI: 10.1021/ie301079r] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Flor A. Quintero
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States
- Departamento de
Ingeniería Química, Universidad de los Andes, Cr.1 Este #19 A-40, Bogotá D.C.,
Colombia
| | - Suhani J. Patel
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States
| | - Felipe Muñoz
- Departamento de
Ingeniería Química, Universidad de los Andes, Cr.1 Este #19 A-40, Bogotá D.C.,
Colombia
| | - M. Sam Mannan
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States
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ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg Med Chem 2012; 20:6181-94. [DOI: 10.1016/j.bmc.2012.07.020] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Revised: 07/11/2012] [Accepted: 07/13/2012] [Indexed: 11/19/2022]
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20
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Castillo-Garit JA, del Toro-Cortés O, Kouznetsov VV, Puentes CO, Romero Bohórquez AR, Vega MC, Rolón M, Escario JA, Gómez-Barrio A, Marrero-Ponce Y, Torrens F, Abad C. Identification In Silico and In Vitro of Novel Trypanosomicidal Drug-Like Compounds. Chem Biol Drug Des 2012; 80:38-45. [DOI: 10.1111/j.1747-0285.2012.01378.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: The nitroindazolinone chemotype. Eur J Med Chem 2011; 46:5736-53. [DOI: 10.1016/j.ejmech.2011.07.053] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2011] [Revised: 07/28/2011] [Accepted: 07/29/2011] [Indexed: 11/15/2022]
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22
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Agüero-Chapin G, Sánchez-Rodríguez A, Hidalgo-Yanes PI, Pérez-Castillo Y, Molina-Ruiz R, Marchal K, Vasconcelos V, Antunes A. An alignment-free approach for eukaryotic ITS2 annotation and phylogenetic inference. PLoS One 2011; 6:e26638. [PMID: 22046320 PMCID: PMC3202569 DOI: 10.1371/journal.pone.0026638] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2011] [Accepted: 09/29/2011] [Indexed: 02/02/2023] Open
Abstract
The ITS2 gene class shows a high sequence divergence among its members that have complicated its annotation and its use for reconstructing phylogenies at a higher taxonomical level (beyond species and genus). Several alignment strategies have been implemented to improve the ITS2 annotation quality and its use for phylogenetic inferences. Although, alignment based methods have been exploited to the top of its complexity to tackle both issues, no alignment-free approaches have been able to successfully address both topics. By contrast, the use of simple alignment-free classifiers, like the topological indices (TIs) containing information about the sequence and structure of ITS2, may reveal to be a useful approach for the gene prediction and for assessing the phylogenetic relationships of the ITS2 class in eukaryotes. Thus, we used the TI2BioP (Topological Indices to BioPolymers) methodology [1], [2], freely available at http://ti2biop.sourceforge.net/ to calculate two different TIs. One class was derived from the ITS2 artificial 2D structures generated from DNA strings and the other from the secondary structure inferred from RNA folding algorithms. Two alignment-free models based on Artificial Neural Networks were developed for the ITS2 class prediction using the two classes of TIs referred above. Both models showed similar performances on the training and the test sets reaching values above 95% in the overall classification. Due to the importance of the ITS2 region for fungi identification, a novel ITS2 genomic sequence was isolated from Petrakia sp. This sequence and the test set were used to comparatively evaluate the conventional classification models based on multiple sequence alignments like Hidden Markov based approaches, revealing the success of our models to identify novel ITS2 members. The isolated sequence was assessed using traditional and alignment-free based techniques applied to phylogenetic inference to complement the taxonomy of the Petrakia sp. fungal isolate.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Porto, Portugal
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | | | - Pedro I. Hidalgo-Yanes
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
- Area of Microbiology, University of León, León, Spain
| | - Yunierkis Pérez-Castillo
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
| | - Reinaldo Molina-Ruiz
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
| | - Kathleen Marchal
- CMPG, Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
| | - Vítor Vasconcelos
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Porto, Portugal
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | - Agostinho Antunes
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Porto, Portugal
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
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Ligand-based discovery of novel trypanosomicidal drug-like compounds: In silico identification and experimental support. Eur J Med Chem 2011; 46:3324-30. [DOI: 10.1016/j.ejmech.2011.04.057] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2010] [Revised: 04/26/2011] [Accepted: 04/26/2011] [Indexed: 01/08/2023]
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Amnerkar ND, Bhusari KP. Synthesis of some thiazolyl aminobenzothiazole derivatives as potential antibacterial, antifungal and anthelmintic agents. J Enzyme Inhib Med Chem 2011; 26:22-8. [DOI: 10.3109/14756360903555258] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Nikhil D. Amnerkar
- Department of Medicinal Chemistry, Sharad Pawar College of Pharmacy, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, Maharashtra, India
| | - Kishore P. Bhusari
- Department of Medicinal Chemistry, Sharad Pawar College of Pharmacy, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, Maharashtra, India
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Casañola-Martin GM, Marrero-Ponce Y, Khan MTH, Khan SB, Torrens F, Pérez-Jiménez F, Rescigno A, Abad C. Bond-based 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation. Chem Biol Drug Des 2010; 76:538-45. [PMID: 20964806 DOI: 10.1111/j.1747-0285.2010.01032.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In this report, we show the results of quantitative structure-activity relationship (QSAR) studies of tyrosinase inhibitory activity, by using the bond-based quadratic indices as molecular descriptors (MDs) and linear discriminant analysis (LDA), to generate discriminant functions to predict the anti-tyrosinase activity. The best two models [Eqs (6) and (12)] out of the total 12 QSAR models developed here show accuracies of 93.51% and 91.21%, as well as high Matthews correlation coefficients (C) of 0.86 and 0.82, respectively, in the training set. The validation external series depicts values of 90.00% and 89.44% for these best two equations (6) and (12), respectively. Afterwards, a second external prediction data are used to perform a virtual screening of compounds reported in the literature as active (tyrosinase inhibitors). In a final step, a series of lignans is analysed using the in silico-developed models, and in vitro corroboration of the activity is carried out. An issue of great importance to remark here is that all compounds present greater inhibition values than Kojic acid (standard tyrosinase inhibitor: IC₅₀ = 16.67 μm). The current obtained results could be used as a framework to increase the speed, in the biosilico discovery of leads for the treatment of skin disorders.
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Ortega-Broche SE, Marrero-Ponce Y, Díaz YE, Torrens F, Pérez-Giménez F. tomocomd-camps and protein bilinear indices - novel bio-macromolecular descriptors for protein research: I. Predicting protein stability effects of a complete set of alanine substitutions in the Arc repressor. FEBS J 2010; 277:3118-46. [DOI: 10.1111/j.1742-4658.2010.07711.x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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27
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Agüero-Chapin G, Pérez-Machado G, Molina-Ruiz R, Pérez-Castillo Y, Morales-Helguera A, Vasconcelos V, Antunes A. TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains. Amino Acids 2010; 40:431-42. [PMID: 20563611 DOI: 10.1007/s00726-010-0653-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2010] [Accepted: 06/02/2010] [Indexed: 02/04/2023]
Abstract
Bacteriocins are proteinaceous toxins produced and exported by both gram-negative and gram-positive bacteria as a defense mechanism. The bacteriocin protein family is highly diverse, which complicates the identification of bacteriocin-like sequences using alignment approaches. The use of topological indices (TIs) irrespective of sequence similarity can be a promising alternative to predict proteinaceous bacteriocins. Thus, we present Topological Indices to BioPolymers (TI2BioP) as an alignment-free approach inspired in both the Topological Substructural Molecular Design (TOPS-MODE) and Markov Chain Invariants for Network Selection and Design (MARCH-INSIDE) methodology. TI2BioP allows the calculation of the spectral moments as simple TIs to seek quantitative sequence-function relationships (QSFR) models. Since hydrophobicity and basicity are major criteria for the bactericide activity of bacteriocins, the spectral moments ((HP)μ(k)) were derived for the first time from protein artificial secondary structures based on amino acid clustering into a Cartesian system of hydrophobicity and polarity. Several orders of (HP)μ(k) characterized numerically 196 bacteriocin-like sequences and a control group made up of 200 representative CATH domains. Subsequently, they were used to develop an alignment-free QSFR model allowing a 76.92% discrimination of bacteriocin proteins from other domains, a relevant result considering the high sequence diversity among the members of both groups. The model showed a prediction overall performance of 72.16%, detecting specifically 66.7% of proteinaceous bacteriocins whereas the InterProScan retrieved just 60.2%. As a practical validation, the model also predicted successfully the cryptic bactericide function of the Cry 1Ab C-terminal domain from Bacillus thuringiensis's endotoxin, which has not been detected by classical alignment methods.
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Affiliation(s)
- Guillermín Agüero-Chapin
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, 4050-123, Porto, Portugal
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Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 2010; 18:2225-2231. [PMID: 20185316 DOI: 10.1016/j.bmc.2010.01.068] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2009] [Revised: 01/22/2010] [Accepted: 01/29/2010] [Indexed: 11/23/2022]
Abstract
There are many of pathogen parasite species with different susceptibility profile to antiparasitic drugs. Unfortunately, almost QSAR models predict the biological activity of drugs against only one parasite species. Consequently, predicting the probability with which a drug is active against different species with a single unify model is a goal of the major importance. In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a mt-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using spectral moments. The data was processed by linear discriminant analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 311 out of 358 active compounds (86.9%) and 2328 out of 2577 non-active compounds (90.3%) in training series. Overall training performance was 89.9%. Validation of the model was carried out by means of external predicting series. In these series the model classified correctly 157 out 190, 82.6% of antiparasitic compounds and 1151 out of 1277 non-active compounds (90.1%). Overall predictability performance was 89.2%. In addition we developed four types of non Linear Artificial neural networks (ANN) and we compared with the mt-QSAR model. The improved ANN model had an overall training performance was 87%. The present work report the first attempts to calculate within a unify framework probabilities of antiparasitic action of drugs against different parasite species based on spectral moment analysis.
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Prado-Prado FJ, Ubeira FM, Borges F, González-DÃaz H. Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J Comput Chem 2010; 31:164-73. [DOI: 10.1002/jcc.21292] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Marrero-Ponce Y, Martínez-Albelo ER, Casañola-Martín GM, Castillo-Garit JA, Echevería-Díaz Y, Zaldivar VR, Tygat J, Borges JER, García-Domenech R, Torrens F, Pérez-Giménez F. Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules. Mol Divers 2010; 14:731-53. [DOI: 10.1007/s11030-009-9201-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2009] [Accepted: 10/19/2009] [Indexed: 10/20/2022]
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Castillo-Garit J, Marrero-Ponce Y, Torrens F, García-Domenech R, Rodríguez-Borges J. Applications of Bond-Based 3D-Chiral Quadratic Indices in QSAR Studies Related to Central Chirality Codification. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200960085] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Castillo-Garit JA, Vega MC, Rolon M, Marrero-Ponce Y, Kouznetsov VV, Torres DFA, Gómez-Barrio A, Bello AA, Montero A, Torrens F, Pérez-Giménez F. Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis. Eur J Pharm Sci 2009; 39:30-6. [PMID: 19854271 DOI: 10.1016/j.ejps.2009.10.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2009] [Revised: 10/01/2009] [Accepted: 10/13/2009] [Indexed: 11/28/2022]
Abstract
Herein we present results of a quantitative structure-activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the present approach is finally satisfactorily applied to the virtual evaluation of 9 already synthesized in house compounds. The in vitro antitrypanosomal activity of this series against epimastigote forms of Trypanosoma cruzi is assayed. The model is able to predict correctly the behaviour for the majority of these compounds. Four compounds (FER16, FER32, FER33 and FER 132) showed more than 70% of epimastigote inhibition at a concentration of 100 microg/mL (86.74%, 78.12%, 88.85% and 72.10%, respectively) and two of these chemicals, FER16 (78.22% of AE) and FER33 (81.31% of AE), also showed good activity at a concentration of 10 microg/mL. At the same concentration, compound FER16 showed lower value of cytotoxicity (15.44%), and compound FER33 showed very low value of 1.37%. Taking into account all these results, we can say that these three compounds can be optimized in forthcoming works, but we consider that compound FER33 is the best candidate. Even though none of them resulted more active than Nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.
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Affiliation(s)
- Juan Alberto Castillo-Garit
- Applied Chemistry Research Center, Faculty of Chemistry-Pharmacy, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
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Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal Chim Acta 2009; 651:159-64. [PMID: 19782806 DOI: 10.1016/j.aca.2009.08.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2009] [Revised: 08/05/2009] [Accepted: 08/18/2009] [Indexed: 11/23/2022]
Abstract
The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.
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Nucleotide's bilinear indices: novel bio-macromolecular descriptors for bioinformatics studies of nucleic acids. I. Prediction of paromomycin's affinity constant with HIV-1 Psi-RNA packaging region. J Theor Biol 2009; 259:229-41. [PMID: 19272394 DOI: 10.1016/j.jtbi.2009.02.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2008] [Revised: 02/24/2009] [Accepted: 02/25/2009] [Indexed: 02/03/2023]
Abstract
A new set of nucleotide-based bio-macromolecular descriptors are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to nucleic acids quantitative structure-activity relationship (QSAR) studies. These bio-macromolecular indices are based on the calculus of bilinear maps on Re(n)[b(mk)(x (m),y (m)):Re(n) x Re(n)-->Re] in canonical basis. Nucleic acid's bilinear indices are calculated from kth power of non-stochastic and stochastic nucleotide's graph-theoretic electronic-contact matrices, M(m)(k) and (s)M(m)(k), respectively. That is to say, the kth non-stochastic and stochastic nucleic acid's bilinear indices are calculated using M(m)(k) and (s)M(m)(k) as matrix operators of bilinear transformations. Moreover, biochemical information is codified by using different pair combinations of nucleotide-base properties as weightings (experimental molar absorption coefficient epsilon(260) at 260 nm and pH=7.0, first (Delta E(1)) and second (Delta E(2)) single excitation energies in eV, and first (f(1)) and second (f(2)) oscillator strength values (of the first singlet excitation energies) of the nucleotide DNA-RNA bases. As example of this approach, an interaction study of the antibiotic paromomycin with the packaging region of the HIV-1 Psi-RNA have been performed and it have been obtained several linear models in order to predict the interaction strength. The best linear model obtained by using non-stochastic bilinear indices explains about 91% of the variance of the experimental Log K (R=0.95 and s=0.08 x 10(-4)M(-1)) as long as the best stochastic bilinear indices-based equation account for 93% of the Log K variance (R=0.97 and s=0.07 x 10(-4)M(-1)). The leave-one-out (LOO) press statistics, evidenced high predictive ability of both models (q(2)=0.86 and s(cv)=0.09 x 10(-4)M(-1) for non-stochastic and q(2)=0.91 and s(cv)=0.08 x 10(-4)M(-1) for stochastic bilinear indices). The nucleic acid's bilinear indices-based models compared favorably with other nucleic acid's indices-based approaches reported nowadays. These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k<or=3), middle-reaching (4<k<9), and far-reaching (k=10 or greater) nucleotide's bilinear indices. This situation points to electronic and topologic nucleotide's backbone interactions control of the stability profile of paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to theoretical-biology studies.
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García I, Munteanu CR, Fall Y, Gómez G, Uriarte E, González-Díaz H. QSAR and complex network study of the chiral HMGR inhibitor structural diversity. Bioorg Med Chem 2009; 17:165-75. [DOI: 10.1016/j.bmc.2008.11.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2008] [Revised: 10/31/2008] [Accepted: 11/06/2008] [Indexed: 10/21/2022]
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R, Romero-Zaldivar V. Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification. J Comput Chem 2008; 29:2500-12. [DOI: 10.1002/jcc.20964] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Rivera-Borroto O, Marrero-Ponce Y, Meneses-Marcel A, Escario J, Gómez Barrio A, Arán V, Martins Alho M, Montero Pereira D, Nogal J, Torrens F, Ibarra-Velarde F, Montenegro Y, Huesca-Guillén A, Rivera N, Vogel C. Discovery of Novel Trichomonacidals Using LDA-Driven QSAR Models and Bond-Based Bilinear Indices as Molecular Descriptors. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200610165] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Perez-Bello A, Munteanu CR, Ubeira FM, De Magalhães AL, Uriarte E, González-Díaz H. Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices. J Theor Biol 2008; 256:458-66. [PMID: 18992259 PMCID: PMC7126577 DOI: 10.1016/j.jtbi.2008.09.035] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2008] [Revised: 09/23/2008] [Accepted: 09/25/2008] [Indexed: 12/01/2022]
Abstract
The importance of the promoter sequences in the function regulation of several important mycobacterial pathogens creates the necessity to design simple and fast theoretical models that can predict them. This work proposes two DNA promoter QSAR models based on pseudo-folding lattice network (LN) and star-graphs (SG) topological indices. In addition, a comparative study with the previous RNA electrostatic parameters of thermodynamically-driven secondary structure folding representations has been carried out. The best model of this work was obtained with only two LN stochastic electrostatic potentials and it is characterized by accuracy, selectivity and specificity of 90.87%, 82.96% and 92.95%, respectively. In addition, we pointed out the SG result dependence on the DNA sequence codification and we proposed a QSAR model based on codons and only three SG spectral moments.
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Affiliation(s)
- Alcides Perez-Bello
- Department of Microbiology and Parasitology, University of Santiago de Compostela, Santiago de Compostela 15782, Spain.
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Castillo-Garit JA, Martinez-Santiago O, Marrero-Ponce Y, Casañola-Martín GM, Torrens F. Atom-based non-stochastic and stochastic bilinear indices: Application to QSPR/QSAR studies of organic compounds. Chem Phys Lett 2008. [DOI: 10.1016/j.cplett.2008.08.094] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Castillo-Garit JA, Marrero-Ponce Y, Escobar J, Torrens F, Rotondo R. A novel approach to predict aquatic toxicity from molecular structure. CHEMOSPHERE 2008; 73:415-427. [PMID: 18597811 DOI: 10.1016/j.chemosphere.2008.05.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2008] [Revised: 04/29/2008] [Accepted: 05/07/2008] [Indexed: 05/26/2023]
Abstract
The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respectively. In addition, a validation through an external test set was performed, which yields significant values of Rpred2 of 0.762 and 0.797. A brief study of the influence of the statistical outliers in QSAR's model development was also carried out. Finally, our method was compared with other approaches implemented in the Dragon software achieving better results. The non-stochastic and stochastic linear indices appear to provide an interesting alternative to costly and time-consuming experiments for determining toxicity.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
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Munteanu CR, González-Díaz H, Borges F, de Magalhães AL. Natural/random protein classification models based on star network topological indices. J Theor Biol 2008; 254:775-83. [PMID: 18692072 PMCID: PMC7094162 DOI: 10.1016/j.jtbi.2008.07.018] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2008] [Revised: 07/09/2008] [Accepted: 07/15/2008] [Indexed: 11/18/2022]
Abstract
The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6 Å and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set. A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau–Broto indices, Balaban distance connectivity index, Kier–Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type. In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis.
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Affiliation(s)
- Cristian Robert Munteanu
- REQUIMTE-University of Porto, Faculty of Science, Chemistry Department, University of Porto 4169-007, Portugal.
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Marrero-Ponce Y, Khan MTH, Casañola Martín GM, Ather A, Sultankhodzhaev MN, Torrens F, Rotondo R. Prediction of tyrosinase inhibition activity using atom-based bilinear indices. ChemMedChem 2008; 2:449-78. [PMID: 17366651 DOI: 10.1002/cmdc.200600186] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, M(k) and S(k), respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using M(k) and S(k) as matrix operators of bilinear transformations. Chemical information is coded by using different pair combinations of atomic weightings (mass, polarizability, vdW volume, and electronegativity). The results of QSAR studies of tyrosinase inhibitors using the new MDs and linear discriminant analysis (LDA) demonstrate the ability of the bilinear indices in testing biological properties. A database of 246 structurally diverse tyrosinase inhibitors was assembled. An inactive set of 412 drugs with other clinical uses was used; both active and inactive sets were processed by hierarchical and partitional cluster analyses to design training and predicting sets. Twelve LDA-based QSAR models were obtained, the first six using the nonstochastic total and local bilinear indices and the last six with the stochastic MDs. The discriminant models were applied; globally good classifications of 99.58 and 89.96 % were observed for the best nonstochastic and stochastic bilinear indices models in the training set along with high Matthews correlation coefficients (C) of 0.99 and 0.79, respectively, in the learning set. External prediction sets used to validate the models obtained were correctly classified, with accuracies of 100 and 87.78 %, respectively, yielding C values of 1.00 and 0.73. This subset contains 180 active and inactive compounds not considered to fit the models. A simulated virtual screen demonstrated this approach in searching tyrosinase inhibitors from compounds never considered in either training or predicting series. These fitted models permitted the selection of new cycloartane compounds isolated from herbal plants as new tyrosinase inhibitors. A good correspondence between theoretical and experimental inhibitory effects on tyrosinase was observed; compound CA6 (IC(50)=1.32 microM) showed higher activity than the reference compounds kojic acid (IC(50)=16.67 microM) and L-mimosine (IC(50)=3.68 microM).
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Affiliation(s)
- Yovani Marrero-Ponce
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou) P.O. Box 22085, 46071 Valencia, Spain.
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Dea-Ayuela MA, Pérez-Castillo Y, Meneses-Marcel A, Ubeira FM, Bolas-Fernández F, Chou KC, González-Díaz H. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg Med Chem 2008; 16:7770-6. [PMID: 18662882 DOI: 10.1016/j.bmc.2008.07.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2008] [Revised: 06/23/2008] [Accepted: 07/02/2008] [Indexed: 10/21/2022]
Abstract
The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools.
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Marrero-Ponce Y, Meneses-Marcel A, Rivera-Borroto OM, García-Domenech R, De Julián-Ortiz JV, Montero A, Escario JA, Barrio AG, Pereira DM, Nogal JJ, Grau R, Torrens F, Vogel C, Arán VJ. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds. J Comput Aided Mol Des 2008; 22:523-40. [DOI: 10.1007/s10822-008-9171-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2006] [Accepted: 01/05/2008] [Indexed: 10/22/2022]
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R. Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices. J Pharm Sci 2008; 97:1946-76. [PMID: 17724669 DOI: 10.1002/jps.21122] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The in vitro determination of the permeability through cultured Caco-2 cells is the most often-used in vitro model for drug absorption. In this report, we use the largest data set of measured P(Caco-2), consisting of 157 structurally diverse compounds. Linear discriminant analysis (LDA) was used to obtain quantitative models that discriminate higher absorption compounds from those with moderate-poorer absorption. The best LDA model has an accuracy of 90.58% and 84.21% for training and test set. The percentage of good correlation, in the virtual screening of 241 drugs with the reported values of the percentage of human intestinal absorption (HIA), was greater than 81%. In addition, multiple linear regression models were developed to predict Caco-2 permeability with determination coefficients of 0.71 and 0.72. Our method compares favorably with other approaches implemented in the Dragon software, as well as other methods from the international literature. These results suggest that the proposed method is a good tool for studying the oral absorption of drug candidates.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
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Prado-Prado FJ, González-Díaz H, de la Vega OM, Ubeira FM, Chou KC. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 2008; 16:5871-80. [PMID: 18485714 DOI: 10.1016/j.bmc.2008.04.068] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2008] [Revised: 04/22/2008] [Accepted: 04/25/2008] [Indexed: 10/22/2022]
Abstract
Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.
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GonzÁlez-DÍaz H, Prado-Prado FJ. Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals. J Comput Chem 2007; 29:656-67. [DOI: 10.1002/jcc.20826] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, Rotondo R. Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification. J Mol Graph Model 2007; 26:32-47. [PMID: 17110145 DOI: 10.1016/j.jmgm.2006.09.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2006] [Revised: 09/08/2006] [Accepted: 09/20/2006] [Indexed: 11/16/2022]
Abstract
Non-stochastic and stochastic 2D bilinear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R(2)=0.953 and s=0.238) and stochastic (R(2)=0.961 and s=0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q(2)=0.935 (s(cv)=0.259) and q(2)=0.946 (s(cv)=0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
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Ponce Y, Khan M, Martín G, Ather A, Sultankhodzhaev M, Torrens F, Rotondo R, Alvarado Y. Atom-Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors: Results ofIn Silico Studies Supported by Experimental Results. ACTA ACUST UNITED AC 2007. [DOI: 10.1002/qsar.200610156] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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González-Díaz H, Vilar S, Santana L, Podda G, Uriarte E. On the applicability of QSAR for recognition of miRNA bioorganic structures at early stages of organism and cell development: Embryo and stem cells. Bioorg Med Chem 2007; 15:2544-50. [PMID: 17300944 DOI: 10.1016/j.bmc.2007.01.050] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2006] [Revised: 01/24/2007] [Accepted: 01/31/2007] [Indexed: 11/18/2022]
Abstract
Quantitative structure-activity-relationship (QSAR) models have application in bioorganic chemistry mainly to the study of small sized molecules while applications to biopolymers remain not very developed. MicroRNAs (miRNAs), which are non-coding small RNAs, regulate a variety of biological processes and constitute good candidates to scale up the application of QSAR to biopolymers. The propensity of a small RNA sequence to act as miRNA depends on its secondary structure, which one can explain in terms of folding thermodynamic parameters. Then, thermodynamic QSAR can be used, for instance, for fast identification of miRNAs at early stages of development such as embryos and stem cells (called here esmiRNAs), and gain clarity inside cellular differentiation processes and diseases such as cancer. First, we calculated folding free energies (DeltaG), enthalpies (DeltaH), and entropies (DeltaS) as well as melting temperatures (T(m)) for 2623 small RNA sequences (including 623 esmiRNAs and 2000 negative control sequences). Next, we seek a QSAR classification model: esmiRNA=0.035 x T(m)-0.078 x DeltaS-8.748. The model correctly recognized 543 (87.2%) of esmiRNAs and 935 (93.5%) of non-esmiRNAs divided into both training and validation series. The model also recognized 908 out of 1000 additional negative control sequences. ROC curve analysis (area=0.93) demonstrated that the present model significantly differentiates from a random classifier. In addition, we map the influence of thermodynamic parameters over esmiRNA activity. Last, a double ordinate Cartesian plot of cross-validated residuals (first ordinate), standard residuals (second ordinate), and leverages (abscissa) defined the domain of applicability of the model as a squared area within +/-2 band for residuals and a leverage threshold of h=0.0074. The present is the first QSAR model for quickly accurate selection of new esmiRNAs with potential use in bioorganic and medicinal chemistry.
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Affiliation(s)
- Humberto González-Díaz
- Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela 15782, Spain.
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