In Silico Discovery and Virtual Screening of Multi-Target Inhibitors for Proteins in Mycobacterium tuberculosis

Speck‐Planche, Alejandro; Kleandrova, Valeria V.; Luan, Feng; Cordeiro, M. Natália D. S.

doi:10.2174/138620712802650487

Cited by 41 publications

(18 citation statements)

References 54 publications

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“…Speck-Planche et al [61][62][63] introduced different multitarget/multiplexing QSAR models that incorporate this type of information based on MAs. The results obtained with the present model are excellent compared with other similar models in the literature useful for other problems including moving average models [64,65] or perturbation models [58]. Notably, this is also the first model combining both perturbation theory and MAs in a QSPR context.…”

Section: Resultssupporting

confidence: 64%

Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms

González-Dı́az

Pérez-Montoto

Ubeira

2014

Journal of Immunology Research

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Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

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Section: Resultssupporting

confidence: 64%

Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms

González-Dı́az

Pérez-Montoto

Ubeira

2014

Journal of Immunology Research

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“…More recently, González-Díaz et al [26,27] have used moving average operators to construct mt-QSAR models. See also the excellent works published by Speck-Planche and Cordeiro et al [28][29][30][31][32][33].…”

Section: Materials and Methods 21 Computational Methodsmentioning

confidence: 98%

Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

Prado

González-Dı́az

Sánchez

et al. 2014

Proceedings of the 18th International Electronic Conference on Synthetic Organic Chemistry

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Alzheimer's Disease (AD), Parkinson, and other neurodegenerative diseases are a major health problem nowadays. However, in many cases, current therapies are merely palliative and only temporarily slow cognitive decline. In this sense, the discovery of new drugs for the treatment of neurodegenerative diseases is a goal of the major importance. Public databases, like ChEMBL, contain a large amount of data about multiplexing assays of inhibitors of a group of enzymes with special relevance in central nervous system. Mono Amino Oxidases (MAOs), Acetyl Cholinesterase (AChE), Glycogen Synthase Kinase-3 (GSK-3), AChE (AChE), and 5α-reductases (5αRs). This data conform an important information source for the application of multi-target computational models. However, almost all the computational models known focus in only one target. In this work, we developed linear multi-target QSAR models (mt-QSAR) for inhibitors of 8 different enzymes promising in the treatment of different neurodegenerative diseases. In so doing, we combined by the first time the software DRAGON with Moving Average parameters with this objective. The best DRAGON model found predict with very high accuracy, specificity, and sensitivity >90% a very large data set >10000 cases in training and validation series.

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“…In these models, the average <Dij> = <Di(cj)>, used to calculate ΔDij values, is the average of the Di for different compounds and do not runs over a time domain but over a set of molecular descriptors that obey a given limit condition cj. (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). Last, we upload the input values in order in the Statistic or Machine Learning software to run different algorithms and seek different linear and non-linear models.…”

Section: Methodsmentioning

confidence: 99%

New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters

Aguirre-Crespo¹,

Arguello-Chan²,

Dominguez³

et al. 2015

Proceedings of the 19th International Electronic Conference on Synthetic Organic Chemistry

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According to Global Health in 2013, it was estimated that there were 508 000 women deaths in the world in the year 2011 caused by breast cancer. Even though cancer can be treated with different treatments for example: immunotherapy, radiotherapy and chemotherapy surgical operation, this disease continues being a severe medical problem. For that reason it has to be found another methods for cancer treatment. The discovery of new drugs with better activity and less toxicity for the treatment of Breast Cancer is a goal of the major importance. In this sense, theoretical models as QSAR can be useful to discover new anti-breast cancer drugs. For this reason, we developed a new multi-parameter-QSAR (mp-QSAR) model to discover new drugs. However, almost all the computational models known focus in only one target or receptor. In this work, Breast cancer type 1 susceptibility protein, ATP-binding cassette sub-family G member 2, Human breast cancer cell lines, Peroxisome proliferator-activated receptor gamma/nuclear receptor coactivator 3, nuclear receptor coactivator 3 and STE20-related kinase adapter protein alpha were used as receptor inputs in the model. A linear technique like Linear Discriminant Analysis (LDA) is our statistical analysis, and we compared with others models to seek alternative multi-target models for inhibitors of some of these receptors. In so doing, we used as input Topological Indices, in specific Wiener, Barabasi and Harary indices calculated by Dragon software. These operators quantify the deviations of the structure of one drug from the expected values for all drugs assayed in different boundary conditions or parameters (type of receptor, type of assay, type of target, target mapping). The best model correctly classifies as active compounds 84.00 % and non-active compounds (99.06 %) in the training series. Overall training performance was 95.91%. Validation of the model was carried out by means of external predicting series. Overall predictability performance was 95.52%. By the first time, the present work reports the attempts to calculate within unified framework probabilities of new anti-Breast cancer drugs.

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In Silico Discovery and Virtual Screening of Multi-Target Inhibitors for Proteins in Mycobacterium tuberculosis

Cited by 41 publications

References 54 publications

Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms

Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms

Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters

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