1
|
Chen L, Zhang C, Xu J. PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes. BMC Bioinformatics 2024; 25:50. [PMID: 38291384 PMCID: PMC10829269 DOI: 10.1186/s12859-024-05665-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Enzymes play an irreplaceable and important role in maintaining the lives of living organisms. The Enzyme Commission (EC) number of an enzyme indicates its essential functions. Correct identification of the first digit (family class) of the EC number for a given enzyme is a hot topic in the past twenty years. Several previous methods adopted functional domain composition to represent enzymes. However, it would lead to dimension disaster, thereby reducing the efficiency of the methods. On the other hand, most previous methods can only deal with enzymes belonging to one family class. In fact, several enzymes belong to two or more family classes. RESULTS In this study, a fast and efficient multi-label classifier, named PredictEFC, was designed. To construct this classifier, a novel feature extraction scheme was designed for processing functional domain information of enzymes, which counting the distribution of each functional domain entry across seven family classes in the training dataset. Based on this scheme, each training or test enzyme was encoded into a 7-dimenion vector by fusing its functional domain information and above statistical results. Random k-labelsets (RAKEL) was adopted to build the classifier, where random forest was selected as the base classification algorithm. The two tenfold cross-validation results on the training dataset shown that the accuracy of PredictEFC can reach 0.8493 and 0.8370. The independent test on two datasets indicated the accuracy values of 0.9118 and 0.8777. CONCLUSION The performance of PredictEFC was slightly lower than the classifier directly using functional domain composition. However, its efficiency was sharply improved. The running time was less than one-tenth of the time of the classifier directly using functional domain composition. In additional, the utility of PredictEFC was superior to the classifiers using traditional dimensionality reduction methods and some previous methods, and this classifier can be transplanted for predicting enzyme family classes of other species. Finally, a web-server available at http://124.221.158.221/ was set up for easy usage.
Collapse
Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China.
| | - Chenyu Zhang
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China
| | - Jing Xu
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China
| |
Collapse
|
2
|
MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions. Molecules 2023; 28:molecules28031182. [PMID: 36770857 PMCID: PMC9921108 DOI: 10.3390/molecules28031182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/09/2023] [Accepted: 01/16/2023] [Indexed: 01/27/2023] Open
Abstract
Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.
Collapse
|
3
|
Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H. Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). ACS Chem Neurosci 2021; 12:203-215. [PMID: 33347281 DOI: 10.1021/acschemneuro.0c00687] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.
Collapse
Affiliation(s)
- Ivo E. Sampaio-Dias
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - José E. Rodríguez-Borges
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Víctor Yáñez-Pérez
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Sonia Arrasate
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Javier Llorente
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Pharmacology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - José M. Brea
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Harbil Bediaga
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Physical Chemistry, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Dolores Viña
- Dept. of Pharmacology, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - María Isabel Loza
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Olga Caamaño
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Xerardo García-Mera
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Humberto González-Díaz
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Basque Center for Biophysics (CSIC UPV/EHU), University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| |
Collapse
|
4
|
Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9235920. [PMID: 32596396 PMCID: PMC7273372 DOI: 10.1155/2020/9235920] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 04/22/2020] [Indexed: 11/17/2022]
Abstract
Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (CKSAAP), and selected informative amino acid pairs through the use of a feature selection technique. A training dataset including 1117 human enzymes and 2099 nonenzymes and a test dataset including 684 human enzymes and 1270 nonenzymes were constructed to train and test the proposed model. The results of jackknife cross-validation showed that the overall accuracy was 76.46% for the training set and 76.21% for the test set, which are higher than the 72.6% achieved in previous research. Furthermore, various feature extraction methods and mainstream classifiers were compared in this task, and informative feature parameters of k-spaced amino acid pairs were selected and compared. The results suggest that our classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs.
Collapse
|
5
|
Tan JX, Lv H, Wang F, Dao FY, Chen W, Ding H. A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods. Curr Drug Targets 2020; 20:540-550. [PMID: 30277150 DOI: 10.2174/1389450119666181002143355] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 08/17/2018] [Accepted: 09/04/2018] [Indexed: 12/13/2022]
Abstract
Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.
Collapse
Affiliation(s)
- Jiu-Xin Tan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.,Gordon Life Science Institute, Boston, MA 02478, United States
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
6
|
Concu R, Cordeiro MNDS. Alignment-Free Method to Predict Enzyme Classes and Subclasses. Int J Mol Sci 2019; 20:ijms20215389. [PMID: 31671806 PMCID: PMC6862210 DOI: 10.3390/ijms20215389] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/21/2019] [Accepted: 10/23/2019] [Indexed: 01/03/2023] Open
Abstract
The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.
Collapse
Affiliation(s)
- Riccardo Concu
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| |
Collapse
|
7
|
Munteanu CR, Gestal M, Martínez-Acevedo YG, Pedreira N, Pazos A, Dorado J. Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning. Int J Mol Sci 2019; 20:ijms20184362. [PMID: 31491969 PMCID: PMC6770149 DOI: 10.3390/ijms20184362] [Citation(s) in RCA: 4] [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: 07/31/2019] [Revised: 08/26/2019] [Accepted: 08/30/2019] [Indexed: 01/27/2023] Open
Abstract
In this work, we improved a previous model used for the prediction of proteomes as new B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its sequence, a known reference epitope sequence under specific experimental conditions. The peptide sequences were transformed into molecular descriptors of sequence recurrence networks and were mixed under experimental conditions. The new models were generated using 709,100 instances of pair descriptors for query and reference peptide sequences. Using perturbations of the initial descriptors under sequence or assay conditions, 10 transformed features were used as inputs for seven Machine Learning methods. The best model was obtained with random forest classifiers with an Area Under the Receiver Operating Characteristics (AUROC) of 0.981 ± 0.0005 for the external validation series (five-fold cross-validation). The database included information about 83,683 peptides sequences, 1448 epitope organisms, 323 host organisms, 15 types of in vivo processes, 28 experimental techniques, and 505 adjuvant additives. The current model could improve the in silico predictions of epitopes for vaccine design. The script and results are available as a free repository.
Collapse
Affiliation(s)
- Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Marcos Gestal
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain.
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), Campus de Elviña s/n, 15071 A Coruña, Spain.
| | - Yunuen G Martínez-Acevedo
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
- Unidad Profesional Interdisciplinaria de Biotecnología, National Polytechnic Institute (IPN), Ticoman, 07340 Mexico City, Mexico
| | - Nieves Pedreira
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
| | - Julián Dorado
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), Campus de Elviña s/n, 15071 A Coruña, Spain
| |
Collapse
|
8
|
Vásquez-Domínguez E, Armijos-Jaramillo VD, Tejera E, González-Díaz H. Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol Pharm 2019; 16:4200-4212. [PMID: 31426639 DOI: 10.1021/acs.molpharmaceut.9b00538] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c0), 55 protein accessions (c1), 83 cell lines (c2), 64 organisms of assay (c3), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.
Collapse
Affiliation(s)
- Emilia Vásquez-Domínguez
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Vinicio Danilo Armijos-Jaramillo
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Eduardo Tejera
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Spain
| |
Collapse
|
9
|
Concu R, D. S. Cordeiro MN, Munteanu CR, González-Díaz H. PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms. J Proteome Res 2019; 18:2735-2746. [DOI: 10.1021/acs.jproteome.8b00949] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Riccardo Concu
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - M. Natália. D. S. Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Cristian R. Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain
- INIBIC Biomedical Research Institute of Coruña, CHUAC University Hospital, 15006 A Coruña, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| |
Collapse
|
10
|
Ferreira da Costa J, Silva D, Caamaño O, Brea JM, Loza MI, Munteanu CR, Pazos A, García-Mera X, González-Díaz H. Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics. ACS Chem Neurosci 2018; 9:2572-2587. [PMID: 29791132 DOI: 10.1021/acschemneuro.8b00083] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC50, EC50, Ki, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.
Collapse
Affiliation(s)
- Joana Ferreira da Costa
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - David Silva
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Olga Caamaño
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - José M. Brea
- CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Maria Isabel Loza
- CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Department of Pharmacology, Pharmacy and Pharmaceutical Technology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Cristian R. Munteanu
- Instituto de Investigacion Biomedica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, 15006, Spain
| | - Alejandro Pazos
- Instituto de Investigacion Biomedica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, 15006, Spain
- Computer Science Department, Faculty of Computer Science, University of A Coruna, 15071 A Coruña, Spain
| | - Xerardo García-Mera
- Department of Organic Chemistry, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| |
Collapse
|
11
|
Bediaga H, Arrasate S, González-Díaz H. PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer. ACS COMBINATORIAL SCIENCE 2018; 20:621-632. [PMID: 30240186 DOI: 10.1021/acscombsci.8b00090] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c j formed from >70 different biological activity parameters ( c0), >300 different drug targets ( c1), >230 cell lines ( c2), and 5 organisms of assay ( c3) or organisms of the target ( c4). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.
Collapse
Affiliation(s)
- Harbil Bediaga
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| |
Collapse
|
12
|
Martínez-Arzate SG, Tenorio-Borroto E, Barbabosa Pliego A, Díaz-Albiter HM, Vázquez-Chagoyán JC, González-Díaz H. PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical–Experimental Study of Bm86 Protein Sequences from Colima, Mexico. J Proteome Res 2017; 16:4093-4103. [DOI: 10.1021/acs.jproteome.7b00477] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Saúl G. Martínez-Arzate
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Esvieta Tenorio-Borroto
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Alberto Barbabosa Pliego
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Héctor M. Díaz-Albiter
- Laboratory
of Biochemistry and Physiology of Insects, Oswaldo Cruz Institute, FIOCRUZ, 4365 Rio de Janeiro, Brazil
- Wellcome
Trust Centre for Molecular Parasitology, University of Glasgow, University Place, Glasgow G12 8TA, United Kingdom
| | - Juan C. Vázquez-Chagoyán
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Humbert González-Díaz
- Department
of Organic Chemistry II, University of the Basque Country (UPV/EHU), Bilbao, 48940 Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48011 Biscay, Spain
| |
Collapse
|
13
|
Akbar S, Hayat M, Iqbal M, Jan MA. iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space. Artif Intell Med 2017; 79:62-70. [PMID: 28655440 DOI: 10.1016/j.artmed.2017.06.008] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 06/12/2017] [Accepted: 06/16/2017] [Indexed: 01/10/2023]
Abstract
Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.
Collapse
Affiliation(s)
- Shahid Akbar
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Muhammad Iqbal
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| | - Mian Ahmad Jan
- Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
| |
Collapse
|
14
|
Amidi S, Amidi A, Vlachakis D, Paragios N, Zacharaki EI. Automatic single- and multi-label enzymatic function prediction by machine learning. PeerJ 2017; 5:e3095. [PMID: 28367366 PMCID: PMC5374972 DOI: 10.7717/peerj.3095] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 02/15/2017] [Indexed: 11/25/2022] Open
Abstract
The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level) and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC) code (six main classes) on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss) of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available at https://figshare.com/s/a63e0bafa9b71fc7cbd7.
Collapse
Affiliation(s)
- Shervine Amidi
- Department of Applied Mathematics, Center for Visual Computing, Ecole Centrale de Paris (CentraleSupélec), Châtenay-Malabry, France
| | - Afshine Amidi
- Department of Applied Mathematics, Center for Visual Computing, Ecole Centrale de Paris (CentraleSupélec), Châtenay-Malabry, France
| | - Dimitrios Vlachakis
- MDAKM Group, Department of Computer Engineering and Informatics, University of Patras, Patras, Greece
| | - Nikos Paragios
- Department of Applied Mathematics, Center for Visual Computing, Ecole Centrale de Paris (CentraleSupélec), Châtenay-Malabry, France
- Equipe GALEN, INRIA Saclay, Orsay, France
| | - Evangelia I. Zacharaki
- Department of Applied Mathematics, Center for Visual Computing, Ecole Centrale de Paris (CentraleSupélec), Châtenay-Malabry, France
- Equipe GALEN, INRIA Saclay, Orsay, France
| |
Collapse
|
15
|
Speck-Planche A, Kleandrova VV, Ruso JM, Cordeiro MNDS. First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens. J Chem Inf Model 2016; 56:588-98. [PMID: 26960000 DOI: 10.1021/acs.jcim.5b00630] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.
Collapse
Affiliation(s)
- Alejandro Speck-Planche
- Department of Applied Physics, University of Santiago de Compostela (USC) , 15782 Santiago de Compostela, Spain.,REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
| | - Valeria V Kleandrova
- Faculty of Technology and Production Management, Moscow State University of Food Production , Volokolamskoe shosse 11, 125080 Moscow, Russia
| | - Juan M Ruso
- Department of Applied Physics, University of Santiago de Compostela (USC) , 15782 Santiago de Compostela, Spain
| | - M N D S Cordeiro
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
| |
Collapse
|
16
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model. Nanomedicine (Lond) 2015; 10:193-204. [PMID: 25600965 DOI: 10.2217/nnm.14.96] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
AIMS We introduce the first quantitative structure-activity relationship (QSAR) perturbation model for probing multiple antibacterial profiles of nanoparticles (NPs) under diverse experimental conditions. MATERIALS & METHODS The dataset is based on 300 nanoparticles containing dissimilar chemical compositions, sizes, shapes and surface coatings. In general terms, the NPs were tested against different bacteria, by considering several measures of antibacterial activity and diverse assay times. The QSAR perturbation model was created from 69,231 nanoparticle-nanoparticle (NP-NP) pairs, which were randomly generated using a recently reported perturbation theory approach. RESULTS The model displayed an accuracy rate of approximately 98% for classifying NPs as active or inactive, and a new copper-silver nanoalloy was correctly predicted by this model with consensus accuracy of 77.73%. CONCLUSION Our QSAR perturbation model can be used as an efficacious tool for the virtual screening of antibacterial nanomaterials.
Collapse
Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | | | | | | |
Collapse
|
17
|
Munteanu CR, Pedreira N, Dorado J, Pazos A, Pérez-Montoto LG, Ubeira FM, González-Díaz H. LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design. Mol Inform 2014; 33:276-85. [DOI: 10.1002/minf.201300027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 12/11/2014] [Indexed: 01/05/2023]
|
18
|
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]
|
19
|
Duardo-Sánchez A, Munteanu CR, Riera-Fernández P, López-Díaz A, Pazos A, González-Díaz H. Modeling Complex Metabolic Reactions, Ecological Systems, and Financial and Legal Networks with MIANN Models Based on Markov-Wiener Node Descriptors. J Chem Inf Model 2013; 54:16-29. [DOI: 10.1021/ci400280n] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Aliuska Duardo-Sánchez
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
- Department of Special Public Law, Financial
and Tributary Law Area, Faculty of Law, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, A Coruña, Spain
| | - Cristian R. Munteanu
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
| | - Pablo Riera-Fernández
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
| | - Antonio López-Díaz
- Department of Special Public Law, Financial
and Tributary Law Area, Faculty of Law, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, A Coruña, Spain
| | - Alejandro Pazos
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940, Leioa, Bizkaia, Spain
- IKERBASQUE, Basque
Foundation for Science, 48011, Bilbao, Biscay, Spain
| |
Collapse
|
20
|
Ziarek JJ, Liu Y, Smith E, Zhang G, Peterson FC, Chen J, Yu Y, Chen Y, Volkman BF, Li R. Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction. Curr Top Med Chem 2013; 12:2727-40. [PMID: 23368099 DOI: 10.2174/1568026611212240003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2012] [Revised: 10/08/2012] [Accepted: 11/03/2012] [Indexed: 12/21/2022]
Abstract
The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.
Collapse
Affiliation(s)
- Joshua J Ziarek
- Department of Biochemistry, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
| | | | | | | | | | | | | | | | | | | |
Collapse
|
21
|
The Rücker–Markov invariants of complex Bio-Systems: Applications in Parasitology and Neuroinformatics. Biosystems 2013; 111:199-207. [DOI: 10.1016/j.biosystems.2013.02.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Accepted: 02/11/2013] [Indexed: 11/23/2022]
|
22
|
Mor S, Pahal P, Narasimhan B. Synthesis, characterization, biological evaluation and QSAR studies of 11-p-substituted phenyl-12-phenyl-11a,12-dihydro-11H-indeno[2,1-c][1,5]benzothiazepines as potential antimicrobial agents. Eur J Med Chem 2012; 57:196-210. [DOI: 10.1016/j.ejmech.2012.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Revised: 08/31/2012] [Accepted: 09/03/2012] [Indexed: 10/27/2022]
|
23
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg Med Chem 2012; 20:4848-55. [DOI: 10.1016/j.bmc.2012.05.071] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 05/21/2012] [Accepted: 05/25/2012] [Indexed: 12/23/2022]
|
24
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur J Pharm Sci 2012; 47:273-9. [DOI: 10.1016/j.ejps.2012.04.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Revised: 03/22/2012] [Accepted: 04/09/2012] [Indexed: 12/25/2022]
|
25
|
He PA, Li D, Zhang Y, Wang X, Yao Y. A 3D graphical representation of protein sequences based on the Gray code. J Theor Biol 2012; 304:81-7. [PMID: 22554947 DOI: 10.1016/j.jtbi.2012.03.023] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2011] [Revised: 02/17/2012] [Accepted: 03/17/2012] [Indexed: 11/18/2022]
Abstract
Based on the order of 6-bit binary Gray code, a cyclic order of 20 amino acids is introduced. A novel 3D graphical representation of protein sequences is proposed according to the CGR of DNA sequences. Furthermore, the mathematical descriptor is suggested to characterize the graphical representation curve. The efficiency of our approach can be illustrated by performing the comparison of similarities/dissimilarities among sequences of the ND5 proteins of nine different species. With the correlation and significance analysis, the comparisons of both our results and results of other graphical representation with the ClustalW's results can show the utility of our approach.
Collapse
Affiliation(s)
- Ping-an He
- College of Science, Zhejiang Sci-Tech University, Hangzhou 310018, PR China.
| | | | | | | | | |
Collapse
|
26
|
HE PENGCHENG, LIU YANFENG, ZHANG MEI, WANG XIAONING, WANG HUAIYU, XI JIEYING, WEI KAIHUA, WANG HONGLI, ZHAO JING. Establishment of two-dimensional gel electrophoresis profiles of the human acute promyelocytic leukemia cell line NB4. Mol Med Rep 2012; 6:570-4. [DOI: 10.3892/mmr.2012.963] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Accepted: 06/11/2012] [Indexed: 11/06/2022] Open
|
27
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection. MOLECULAR BIOSYSTEMS 2012; 8:2188-96. [PMID: 22688327 DOI: 10.1039/c2mb25093d] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.
Collapse
Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
| | | | | | | |
Collapse
|
28
|
Ma Y, Wang SQ, Xu WR, Wang RL, Chou KC. Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach. PLoS One 2012; 7:e38546. [PMID: 22685582 PMCID: PMC3369836 DOI: 10.1371/journal.pone.0038546] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Accepted: 05/07/2012] [Indexed: 12/02/2022] Open
Abstract
Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses.
Collapse
Affiliation(s)
- Ying Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
| | - Shu-Qing Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
- Gordon Life Science Institute, San Diego, California, United States of America
- * E-mail: (SQW); (RLW)
| | - Wei-Ren Xu
- Tianjin Institute of Pharmaceutical Research (TIPR), Tianjin, China
| | - Run-Ling Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China
- * E-mail: (SQW); (RLW)
| | - Kuo-Chen Chou
- Gordon Life Science Institute, San Diego, California, United States of America
| |
Collapse
|
29
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 80:308-313. [PMID: 22521812 DOI: 10.1016/j.ecoenv.2012.03.018] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Revised: 03/22/2012] [Accepted: 03/23/2012] [Indexed: 05/31/2023]
Abstract
Agriculture is needed to deal with crop losses caused by biotic stresses like pests. The use of pesticides has played a vital role, contributing to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health. An important group of these pesticides is fungicides. However, the use of these agrochemical fungicides is an important source of contamination, damaging the ecosystems. Several studies have been realized for the assessment of the toxicity in agrochemical fungicides, but the principal limitation is the use of structurally related compounds against usually one indicator species. In order to overcome this problem, we explore the quantitative structure-toxicity relationships (QSTR) in agrochemical fungicides. Here, we developed the first multi-species (ms) chemoinformatic approach for the prediction multiple ecotoxicological profiles of fungicides against 20 indicators species and their classifications in toxic or nontoxic. The ms-QSTR discriminant model was based on substructural descriptors and a heterogeneous database of compounds. The percentages of correct classification were higher than 90% for both, training and prediction series. Also, substructural alerts responsible for the toxicity/no toxicity in fungicides respect all ecotoxicological profiles, were extracted and analyzed.
Collapse
Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
| | | | | | | |
Collapse
|
30
|
González-Díaz H, Munteanu CR, Postelnicu L, Prado-Prado F, Gestal M, Pazos A. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria. MOLECULAR BIOSYSTEMS 2012; 8:851-62. [DOI: 10.1039/c2mb05432a] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
31
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads. Eur J Med Chem 2011; 46:5910-6. [DOI: 10.1016/j.ejmech.2011.09.055] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2011] [Revised: 09/24/2011] [Accepted: 09/29/2011] [Indexed: 12/17/2022]
|
32
|
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Multi-target drug discovery in anti-cancer therapy: Fragment-based approach toward the design of potent and versatile anti-prostate cancer agents. Bioorg Med Chem 2011; 19:6239-44. [DOI: 10.1016/j.bmc.2011.09.015] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2011] [Revised: 07/24/2011] [Accepted: 09/08/2011] [Indexed: 11/25/2022]
|
33
|
Chen W, Feng P, Lin H. Prediction of ketoacyl synthase family using reduced amino acid alphabets. J Ind Microbiol Biotechnol 2011; 39:579-84. [PMID: 22042516 DOI: 10.1007/s10295-011-1047-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2011] [Accepted: 10/04/2011] [Indexed: 11/28/2022]
Abstract
Ketoacyl synthases are enzymes involved in fatty acid synthesis and can be classified into five families based on primary sequence similarity. Different families have different catalytic mechanisms. Developing cost-effective computational models to identify the family of ketoacyl synthases will be helpful for enzyme engineering and in knowing individual enzymes' catalytic mechanisms. In this work, a support vector machine-based method was developed to predict ketoacyl synthase family using the n-peptide composition of reduced amino acid alphabets. In jackknife cross-validation, the model based on the 2-peptide composition of a reduced amino acid alphabet of size 13 yielded the best overall accuracy of 96.44% with average accuracy of 93.36%, which is superior to other state-of-the-art methods. This result suggests that the information provided by n-peptide compositions of reduced amino acid alphabets provides efficient means for enzyme family classification and that the proposed model can be efficiently used for ketoacyl synthase family annotation.
Collapse
Affiliation(s)
- Wei Chen
- Department of Physics, College of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China.
| | | | | |
Collapse
|
34
|
In silico design of multi-target inhibitors for C–C chemokine receptors using substructural descriptors. Mol Divers 2011; 16:183-91. [DOI: 10.1007/s11030-011-9337-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2011] [Accepted: 10/07/2011] [Indexed: 10/16/2022]
|
35
|
Speck-Planche A, Kleandrova VV, Rojas-Vargas JA. QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol Divers 2011; 15:901-9. [DOI: 10.1007/s11030-011-9320-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2011] [Accepted: 05/17/2011] [Indexed: 11/27/2022]
|
36
|
González-Díaz H, Prado-Prado F, Sobarzo-Sánchez E, Haddad M, Maurel Chevalley S, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Muños M, Munteanu CR, José Torres-Labandeira J, García-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: A web server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol 2011; 276:229-49. [DOI: 10.1016/j.jtbi.2011.01.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 12/02/2010] [Accepted: 01/10/2011] [Indexed: 10/18/2022]
|
37
|
Prado-Prado F, García-Mera X, Abeijón P, Alonso N, Caamaño O, Yáñez M, Gárate T, Mezo M, González-Warleta M, Muiño L, Ubeira FM, González-Díaz H. Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. Eur J Med Chem 2011; 46:1074-94. [PMID: 21315497 DOI: 10.1016/j.ejmech.2011.01.023] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2010] [Revised: 01/10/2011] [Accepted: 01/13/2011] [Indexed: 12/11/2022]
Abstract
There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.
Collapse
|
38
|
Mohammed A, Guda C. Computational Approaches for Automated Classification of Enzyme Sequences. ACTA ACUST UNITED AC 2011; 4:147-152. [PMID: 22114367 DOI: 10.4172/jpb.1000183] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Determining the functional role(s) of enzymes is very important to build the metabolic blueprint of an organism and to identify the potential roles enzymes may play in metabolic and disease pathways. With exponential growth in gene and protein sequence data, it is not feasible to experimentally characterize the function(s) of all enzymes. Alternatively, computational methods can be used to annotate the enormous amount of unannotated enzyme sequences. For function prediction and classification of enzymes, features based on amino acid composition, sequence and structural properties, domain composition and specific peptide information have been widely used by different computational approaches. Each feature space has its own merits and limitations on the overall prediction accuracy. Prediction accuracy improves when machine-learning methods are used to classify enzymes. Given the incomplete and unbalanced nature of annotations in biological databases, ensemble methods or methods that bank on a combination of orthogonal feature are more desirable for achieving higher accuracy and coverage in enzyme classification. In this review article, we systematically describe all the features and methods used thus far for enzyme class prediction. To the authors' knowledge, this review represents the most exhaustive description of methods used for computational prediction of enzyme classes.
Collapse
Affiliation(s)
- Akram Mohammed
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, NE, USA
| | | |
Collapse
|
39
|
García I, Fall Y, Gómez G, González-Díaz H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol Divers 2010; 15:561-7. [PMID: 20931280 DOI: 10.1007/s11030-010-9280-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2010] [Accepted: 09/13/2010] [Indexed: 10/19/2022]
Abstract
In the work described here, we developed the first multi-target quantitative structure-activity relationship (QSAR) model able to predict the results of 42 different experimental tests for GSK-3 inhibitors with heterogeneous structural patterns. GSK-3β inhibitors are interesting candidates for developing anti-Alzheimer compounds. GSK-3β are also of interest as anti-parasitic compounds active against Plasmodium falciparum, Trypanosoma brucei, and Leishmania donovani; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The MARCH-INSIDE technique was used to quickly calculate total and local polarizability, n-octanol/water partition coefficients, refractivity, van der Waals area and electronegativity values to 4,508 active/non-active compounds as well as the average values of these indexes for active compounds in 42 different biological assays. Both the individual molecular descriptors and the average values for each test were used as input for a linear discriminant analysis (LDA). We discovered a classification function which used in training series correctly classifies 873 out of 1,218 GSK-3 cases of inhibitors (97.4%) and 2,140 out of 2,163 cases of non-active compounds (86.1%) in the 42 different tests. In addition, the model correctly classifies 285 out of 406 GSK-3 inhibitors (96.3%) and 710 out of 721 cases of non-active compounds (85.4%) in external validation series. The result is important because, for the first time, we can use a single equation to predict the results of heterogeneous series of organic compounds in 42 different experimental tests instead of developing, validating, and using 42 different QSAR models. Lastly, 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 = 0.0044.
Collapse
Affiliation(s)
- Isela García
- Department of Organic Chemistry, University of Vigo, Vigo, Spain.
| | | | | | | |
Collapse
|
40
|
García I, Fall Y, Gómez G. Using topological indices to predict anti-Alzheimer and anti-parasitic GSK-3 inhibitors by multi-target QSAR in silico screening. Molecules 2010; 15:5408-22. [PMID: 20714305 PMCID: PMC6257681 DOI: 10.3390/molecules15085408] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2010] [Revised: 07/27/2010] [Accepted: 08/02/2010] [Indexed: 01/20/2023] Open
Abstract
Plasmodium falciparum, Leishmania, Trypanosomes, are the causers of diseases such as malaria, leishmaniasis and African trypanosomiasis that nowadays are the most serious parasitic health problems worldwide. The great number of deaths and the few drugs available against these parasites, make necessary the search for new drugs. Some of these antiparasitic drugs also are GSK-3 inhibitors. GSKI-3 are candidates to develop drugs for the treatment of Alzheimer's disease. In this work topological descriptors for a large series of 3,370 active/non-active compounds were initially calculated with the ModesLab software. Linear Discriminant Analysis was used to fit the classification function and it predicts heterogeneous series of compounds like paullones, indirubins, meridians, etc. This study thus provided a general evaluation of these types of molecules.
Collapse
Affiliation(s)
- Isela García
- Department of Organic Chemistry, Faculty of Chemistry, University of Vigo, Spain.
| | | | | |
Collapse
|
41
|
Wang JF, Chou KC. Insights from studying the mutation-induced allostery in the M2 proton channel by molecular dynamics. Protein Eng Des Sel 2010; 23:663-6. [PMID: 20571121 DOI: 10.1093/protein/gzq040] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
As an essential component of the viral envelope, M2 proton channel plays a central role in the virus replications and has been a key target for drug design against the influenza A viruses. The adamantadine-based drugs, such as amantadine and rimantadine, were developed for blocking the channel so as to suppress the replication of viruses. However, patients, especially those infected by the H1N1 influenza A viruses, are increasingly suffering from the drug-resistance problem. According to the findings revealed recently by the high-resolution NMR studies, the drug-resistance problem is due to the structural allostery caused by some mutations, such as L26F, V27A and S31N, in the four-helix bundle of the channel. In this study, we are to address this problem from a dynamic point of view by conducting molecular dynamics (MD) simulations on both the open and the closed states of the wild-type (WT) and S31N mutant M2 channels in the presence of rimantadine. It was observed from the MD simulated structures that the mutant channel could still keep open even if binding with rimantadine, but the WT channel could not. This was because the mutation would destabilize the helix bundle and trigger it from a compact packing state to a loose one. It is anticipated that the findings may provide useful insights for in-depth understanding the action mechanism of the M2 channel and developing more-effective drugs against influenza A viruses.
Collapse
Affiliation(s)
- Jing-Fang Wang
- Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, 800 Dongchuan, Shanghai 200240, China.
| | | |
Collapse
|
42
|
Fernandez M, Ahmad S, Sarai A. Proteochemometric Recognition of Stable Kinase Inhibition Complexes Using Topological Autocorrelation and Support Vector Machines. J Chem Inf Model 2010; 50:1179-88. [DOI: 10.1021/ci1000532] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Michael Fernandez
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, 820-8502 Japan, and National Institute of Biomedical Innovation, 7-6-8, Saito-Asagi, Ibaraki-shi, Osaka 5670085, Japan
| | - Shandar Ahmad
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, 820-8502 Japan, and National Institute of Biomedical Innovation, 7-6-8, Saito-Asagi, Ibaraki-shi, Osaka 5670085, Japan
| | - Akinori Sarai
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology (KIT), 680-4 Kawazu, Iizuka, 820-8502 Japan, and National Institute of Biomedical Innovation, 7-6-8, Saito-Asagi, Ibaraki-shi, Osaka 5670085, Japan
| |
Collapse
|
43
|
Rodriguez-Soca Y, Munteanu CR, Dorado J, Rabuñal J, Pazos A, González-Díaz H. Plasmod-PPI: A web-server predicting complex biopolymer targets in plasmodium with entropy measures of protein–protein interactions. POLYMER 2010. [DOI: 10.1016/j.polymer.2009.11.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
44
|
Rodriguez-Soca Y, Munteanu CR, Dorado J, Pazos A, Prado-Prado FJ, González-Díaz H. Trypano-PPI: A Web Server for Prediction of Unique Targets in Trypanosome Proteome by using Electrostatic Parameters of Protein−protein Interactions. J Proteome Res 2009; 9:1182-90. [DOI: 10.1021/pr900827b] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Yamilet Rodriguez-Soca
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Cristian R. Munteanu
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Julián Dorado
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Alejandro Pazos
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Francisco J. Prado-Prado
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain, and Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain
| |
Collapse
|