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Barigye SJ, Gómez-Ganau S, Serrano-Candelas E, Gozalbes R. PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints. Proteins 2020; 89:174-184. [PMID: 32881068 DOI: 10.1002/prot.26003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 08/05/2020] [Accepted: 08/27/2020] [Indexed: 11/09/2022]
Abstract
We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.
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Affiliation(s)
- Stephen J Barigye
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, Valencia, Spain.,MolDrug AI Systems SL, Valencia, Spain
| | - Sergi Gómez-Ganau
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, Valencia, Spain.,Eurofins Agroscience Services Regulatory Spain SL, Valencia, Spain
| | - Eva Serrano-Candelas
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, Valencia, Spain
| | - Rafael Gozalbes
- ProtoQSAR SL, Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de Valencia, Valencia, Spain.,MolDrug AI Systems SL, Valencia, Spain
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2
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Undersampling: case studies of flaviviral inhibitory activities. J Comput Aided Mol Des 2019; 33:997-1008. [DOI: 10.1007/s10822-019-00255-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 11/19/2019] [Indexed: 12/22/2022]
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3
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When global and local molecular descriptors are more than the sum of its parts: Simple, But Not Simpler? Mol Divers 2019; 24:913-932. [PMID: 31659696 DOI: 10.1007/s11030-019-10002-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 10/09/2019] [Indexed: 01/29/2023]
Abstract
In this report, we introduce a set of aggregation operators (AOs) to calculate global and local (group and atom type) molecular descriptors (MDs) as a generalization of the classical approach of molecular encoding using the sum of the atomic (or fragment) contributions. These AOs are implemented in a new and free software denominated MD-LOVIs ( http://tomocomd.com/md-lovis ), which allows for the calculation of MDs from atomic weights vector and LOVIs (local vertex invariants). This software was developed in Java programming language and employed the Chemical Development Kit (CDK) library for handling chemical structures and the calculation of atomic weights. An analysis of the complexities of the algorithms presented herein demonstrates that these aspects were efficiently implemented. The calculation speed experiments show that the MD-LOVIs software has satisfactory behavior when compared to software such as Padel, CDKDescriptor, DRAGON and Bluecal software. Shannon's entropy (SE)-based variability studies demonstrate that MD-LOVIs yields indices with greater information content when compared to those of popular academic and commercial software. A principal component analysis reveals that our approach captures chemical information orthogonal to that codified by the DRAGON, Padel and Mold2 software, as a result of the several generalizations in MD-LOVIs not used in other programs. Lastly, three QSARs were built using multiple linear regression with genetic algorithms, and the statistical parameters of these models demonstrate that the MD-LOVIs indices obtained with AOs yield better performance than those obtained when the summation operator is used exclusively. Moreover, it is also revealed that the MD-LOVIs indices yield models with comparable to superior performance when compared to other QSAR methodologies reported in the literature, despite their simplicity. The studies performed herein collectively demonstrated that MD-LOVIs software generates indices as simple as possible, but not simpler and that use of AOs enhances the diversity of the chemical information codified, which consequently improves the performance of traditional MDs.
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4
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Steinberg L, Russo J, Frey J. A new topological descriptor for water network structure. J Cheminform 2019; 11:48. [PMID: 31292766 PMCID: PMC6617667 DOI: 10.1186/s13321-019-0369-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 07/02/2019] [Indexed: 11/10/2022] Open
Abstract
Bulk water molecular dynamics simulations based on a series of atomistic water potentials (TIP3P, TIP4P/Ew, SPC/E and OPC) are compared using new techniques from the field of topological data analysis. The topological invariants (the different degrees of homology) derived from each simulation frame are used to create a series of persistence diagrams from the atomic positions. These are averaged over the simulation time using the persistence image formalism, before being normalised by their total magnitude (the L1 norm) to ensure a size independent descriptor (L1NPI). We demonstrate that the L1NPI formalism is suitable for the analysis of systems where the number of molecules varies by at least a factor of 10. Using standard machine learning techniques, a basic linear SVM, it is shown that differences in water models are able to be isolated to different degrees of homology. In particular, whereas first degree homology is able to distinguish between all atomistic potentials studied, OPC is the only potential that differs in its second degree homology. The L1 normalised persistence images are then used in the comparison of a series of Stillinger-Weber potential simulations to the atomistic potentials and the effects of changing the strength of three-body interactions on the structures is easily evident in L1NPI space, with a reduction in variance of structures as interaction strength increases being the most obvious result. Furthermore, there is a clear tracking in L1NPI space of the λ parameter. The L1NPI formalism presents a useful new technique for the analysis of water and other materials. It is approximately size-independent, and has been shown to contain information as to real structures in the system. We finally present a perspective on the use of L1NPIs and other persistent homology techniques as a descriptor for water solubility.
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Affiliation(s)
- Lee Steinberg
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ UK
| | - John Russo
- School of Mathematics, University of Bristol, Bristol, UK
| | - Jeremy Frey
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ UK
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5
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García-Jacas CR, Marrero-Ponce Y, Cortés-Guzmán F, Suárez-Lezcano J, Martinez-Rios FO, García-González LA, Pupo-Meriño M, Martinez-Mayorga K. Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes. Chem Res Toxicol 2019; 32:1178-1192. [PMID: 31066547 DOI: 10.1021/acs.chemrestox.9b00011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with REPA-training ≥ 0.75 ( R = correlation coefficient) and MAEEPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the minimum operator and denoted as M19 and M22, as well as a consensus model based on the weighted average operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAEtest-AD = 0.4044, MAEProTox-AD = 0.4067 and MAET3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAEProTox-AD = 0.3992 and MAET3DB-AD = 0.2286, and MAEProTox-AD = 0.3773 and MAET3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).
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Affiliation(s)
- César R García-Jacas
- Departamento de Ciencias de la Computación , Centro de Investigación Científica y de Educación Superior de Ensenada , Ensenada , Baja California , México
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional, Colegio de Ciencias de la Salud , Escuela de Medicina, Edificio de Especialidades Médicas , Quito , Pichincha , Ecuador.,Grupo de Investigación Ambiental, Programas Ambientales, Facultad de Ingenierías , Fundacion Universitaria Tecnologico Comfenalco-Cartagena , Cr44 DN 30 A, 91 , Cartagena , Bolívar , Colombia
| | - Fernando Cortés-Guzmán
- Instituto de Química , Universidad Nacional Autónoma de México , Ciudad de México , México
| | - José Suárez-Lezcano
- Pontificia Universidad Católica del Ecuador Sede Esmeraldas , Esmeraldas , Ecuador
| | | | - Luis A García-González
- Grupo de Investigación de Bioinformática , Universidad de las Ciencias Informáticas , La Habana , Cuba
| | - Mario Pupo-Meriño
- Grupo de Investigación de Bioinformática , Universidad de las Ciencias Informáticas , La Habana , Cuba
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6
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Drug repositioning for novel antitrichomonas from known antiprotozoan drugs using hierarchical screening. Future Med Chem 2018; 10:863-878. [DOI: 10.4155/fmc-2016-0211] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Aim: Metronidazole is the most widely used drug in trichomoniasis therapy. However, the emergence of metronidazole-resistant Trichomonas vaginalis isolates calls for the search for new drugs to counter the pathogenicity of these parasites. Results: Classification models for predicting the antitrichomonas activity of molecules were built. These models were employed to screen antiprotozoal drugs, from which 20 were classified as active. The in vitro experiments showed moderate to high activity for 19 of the molecules at 10 μg/ml, while 3 compounds yielded higher activity than the reference at 1 μg/ml. The 11 most active chemicals were evaluated in vivo using Naval Medical Research Institute (NMRI) mice. Conclusion: Benznidazole showed similar results as metronidazole, and can thus be considered as a potential candidate in antitrichomonas therapy.
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Martínez-López Y, Barigye SJ, Martínez-Santiago O, Marrero-Ponce Y, Green J, Castillo-Garit JA. Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2017; 56:314-321. [PMID: 29091819 DOI: 10.1016/j.etap.2017.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 10/09/2017] [Accepted: 10/11/2017] [Indexed: 06/07/2023]
Abstract
Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)-1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q2=0.830 and R2=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.
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Affiliation(s)
- Yoan Martínez-López
- Department of Computer Sciences, Faculty of Informatics, Camaguey University, Camaguey City, 74650, Camaguey, 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
| | - Stephen J Barigye
- Departamento de Química, Universidade Federal de Lavras, CP 3037, 37200-000, Lavras, MG, Brazil
| | - Oscar Martínez-Santiago
- 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
| | - 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, Av. Interoceánica Km 12 ½, Cumbayá, Ecuador
| | - James Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Juan A Castillo-Garit
- 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; Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada; Unidad de Toxicologia Experimental, Universidad de Ciencias Médicas de Villa Clara Santa Clara, 50200, Villa Clara, Cuba.
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8
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Martínez-Santiago O, Marrero-Ponce Y, Vivas-Reyes R, Rivera-Borroto OM, Hurtado E, Treto-Suarez MA, Ramos Y, Vergara-Murillo F, Orozco-Ugarriza ME, Martínez-López Y. Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:367-389. [PMID: 28590848 DOI: 10.1080/1062936x.2017.1326403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 04/27/2017] [Indexed: 06/07/2023]
Abstract
Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q2ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q2ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.
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Affiliation(s)
- O Martínez-Santiago
- a Department of Chemical Sciences , Central University 'Martha Abreu' of Las Villas , Santa Clara , Cuba
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- c Group of Quantum and Theoretical Chemistry , University of Cartagena , Cartagena de Indias , Colombia
- d Facultad de Ingeniería , Grupo CipTec, Fundación Universitaria Tecnológico Comfenalco , Cartagena de Indias , Colombia
| | - Y Marrero-Ponce
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- e Escuela de Medicina, Edificio de Especialidades Médicas , Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA) , Av. Interoceánica Km 12 ½, Cumbayá , Ecuador
- f Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica , Quito , Ecuador
- g Grupo de Investigación Ambiental (GIA) , Fundación Universitaria Tecnológico de Comfenalco , Cartagena de Indias , Colombia
| | - R Vivas-Reyes
- c Group of Quantum and Theoretical Chemistry , University of Cartagena , Cartagena de Indias , Colombia
- d Facultad de Ingeniería , Grupo CipTec, Fundación Universitaria Tecnológico Comfenalco , Cartagena de Indias , Colombia
| | - O M Rivera-Borroto
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- h Departamento de Química Física Aplicada , Universidad Autónoma de Madrid (UAM) , Madrid , España
| | - E Hurtado
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
| | - M A Treto-Suarez
- i Center of Applied Nanosciences (CENAP), Andres Bello University , Chile
| | - Y Ramos
- j Department of Economic Sciences , University of Camagüey , Camagüey , Cuba
| | - F Vergara-Murillo
- c Group of Quantum and Theoretical Chemistry , University of Cartagena , Cartagena de Indias , Colombia
- d Facultad de Ingeniería , Grupo CipTec, Fundación Universitaria Tecnológico Comfenalco , Cartagena de Indias , Colombia
| | - M E Orozco-Ugarriza
- k Seccional Cartagena y Grupo de Investigación Traslacional en Biomedicina & Biotecnología - GITB&B , Universidad del Sinú - Elías Bechara Zainúm , Cartagena de Indias , Colombia
| | - Y Martínez-López
- b Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Quito , Ecuador
- l Grupo de Investigación de Inteligencia Artificial (AIRES) , Universidad de Camagüey , Camagüey , Cuba
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9
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Martínez-Santiago O, Marrero-Ponce Y, Barigye SJ, Le Thi Thu H, Torres FJ, Zambrano CH, Muñiz Olite JL, Cruz-Monteagudo M, Vivas-Reyes R, Vázquez Infante L, Artiles Martínez LM. Physico-Chemical and Structural Interpretation of Discrete Derivative Indices on N-Tuples Atoms. Int J Mol Sci 2016; 17:ijms17060812. [PMID: 27240357 PMCID: PMC4926346 DOI: 10.3390/ijms17060812] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 04/27/2016] [Accepted: 05/04/2016] [Indexed: 11/28/2022] Open
Abstract
This report examines the interpretation of the Graph Derivative Indices (GDIs) from three different perspectives (i.e., in structural, steric and electronic terms). It is found that the individual vertex frequencies may be expressed in terms of the geometrical and electronic reactivity of the atoms and bonds, respectively. On the other hand, it is demonstrated that the GDIs are sensitive to progressive structural modifications in terms of: size, ramifications, electronic richness, conjugation effects and molecular symmetry. Moreover, it is observed that the GDIs quantify the interaction capacity among molecules and codify information on the activation entropy. A structure property relationship study reveals that there exists a direct correspondence between the individual frequencies of atoms and Hückel’s Free Valence, as well as between the atomic GDIs and the chemical shift in NMR, which collectively validates the theory that these indices codify steric and electronic information of the atoms in a molecule. Taking in consideration the regularity and coherence found in experiments performed with the GDIs, it is possible to say that GDIs possess plausible interpretation in structural and physicochemical terms.
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Affiliation(s)
- Oscar Martínez-Santiago
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research International Network (CAMD-BIR IN), Cumbayá-Tumbaco, Quito 170184, Ecuador.
- Department of Chemical Science, Faculty of Chemistry-Pharmacy, Universidad Central "Martha Abreu" de Las Villas, Santa Clara 54830, Villa Clara, Cuba.
| | - Yovani Marrero-Ponce
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research International Network (CAMD-BIR IN), Cumbayá-Tumbaco, Quito 170184, Ecuador.
- 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, Hospital de los Valles, Av. Interoceánica Km 12 ½-Cumbayá, Quito 170157, Ecuador.
- Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador.
- Grupo de Investigación Microbiología y Ambiente (GIMA), Programa de Bacteriología, Facultad Ciencias de la Salud, Universidad de San Buenaventura, Calle Real de Ternera, Cartagena de Indias, Bolívar 130010, Colombia.
| | - Stephen J Barigye
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research International Network (CAMD-BIR IN), Cumbayá-Tumbaco, Quito 170184, Ecuador.
- Departamento de Química, Universidade Federal de Lavras (UFLA), Caixa Postal 3037, Lavras 37200-000, MG, Brazil.
| | - Huong Le Thi Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi 100000, Vietnam.
| | - F Javier Torres
- Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador.
- Universidad San Francisco de Quito (USFQ), Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Quito 170157, Ecuador.
| | - Cesar H Zambrano
- Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador.
- Universidad San Francisco de Quito (USFQ), Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Quito 170157, Ecuador.
| | - Jorge L Muñiz Olite
- Grupo de Investigación en Estudios Químicos y Biológicos, Facultad de Ciencias Básicas, Universidad Tecnológica de Bolívar (UTB), Parque Industrial y Tecnológico Carlos Vélez Pombo Km 1 vía Turbaco, Cartagena de Indias, Bolívar 130010, Colombia.
| | - Maykel Cruz-Monteagudo
- Instituto de Investigaciones Biomédicas (IIB), Universidad de Las Américas (UDLA), Quito 170513, Ecuador.
| | - Ricardo Vivas-Reyes
- Grupo de Química Cuántica y Teórica, Facultad de Ciencias, Universidad de Cartagena, Cartagena de Indias, Bolívar 130001, Colombia.
- Grupo CipTec, Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería de Procesos, Cartagena de Indias, Bolívar 130001, Colombia.
| | - Liliana Vázquez Infante
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research International Network (CAMD-BIR IN), Cumbayá-Tumbaco, Quito 170184, Ecuador.
- Department of Chemical Science, Faculty of Chemistry-Pharmacy, Universidad Central "Martha Abreu" de Las Villas, Santa Clara 54830, Villa Clara, Cuba.
| | - Luis M Artiles Martínez
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research International Network (CAMD-BIR IN), Cumbayá-Tumbaco, Quito 170184, Ecuador.
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10
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Barigye SJ, Freitas MP. Is molecular alignment an indispensable requirement in the MIA-QSAR method? J Comput Chem 2015; 36:1748-55. [DOI: 10.1002/jcc.23992] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2015] [Revised: 05/18/2015] [Accepted: 06/07/2015] [Indexed: 11/08/2022]
Affiliation(s)
- Stephen J. Barigye
- Department of Chemistry; Federal University of Lavras; P.O. Box 3037 Lavras, Minas Gerais 37200-000 Brazil
| | - Matheus P. Freitas
- Department of Chemistry; Federal University of Lavras; P.O. Box 3037 Lavras, Minas Gerais 37200-000 Brazil
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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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Speck-Planche A, Cordeiro MNDS. A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol Biol 2015; 1260:45-64. [PMID: 25502375 DOI: 10.1007/978-1-4939-2239-0_4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.
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Affiliation(s)
- A Speck-Planche
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
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A QSPR-like model for multilocus genotype networks of Fasciola hepatica in Northwest Spain. J Theor Biol 2014; 343:16-24. [DOI: 10.1016/j.jtbi.2013.11.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Revised: 11/08/2013] [Accepted: 11/11/2013] [Indexed: 11/23/2022]
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