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Dhayalan A, Thillainathan N, Velramar B, Athiyappagounder P, Sundaramoorthy D, Pachiappan P. Pectinase from a Fish Gut Bacterium, Aeromonas guangheii (SS6): Production, Cloning and Characterization. Protein J 2022; 41:572-590. [PMID: 36208356 DOI: 10.1007/s10930-022-10077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2022] [Indexed: 11/28/2022]
Abstract
During the present research, 11 gut bacteria were isolated from the freshwater fish, Systomus sarana (General name: olive barb) and upon screening, the strains produced extracellular pectinase enzyme. Among them, the SS6 strain was found to produce a high quantity of 208.731 U/ml pectinase and through molecular characterization the SS6 strain was identified as Aeromonas guangheii. During the culture of SS6 strain, a set of parameters were optimized through the response surface methodology with a Box-Behnken design, for the production of the enzyme. The optimal conditions were found to be 2.11% of maltose, 2.20% of yeast extract, 6.5 of pH, and a temperature of 27.3 °C at 32-h incubation. Under the above conditions, the activity of pectinase production was enhanced to 371 U/ml. The purified pectinase's molecular weight was determined to be ~ 50 kDa (by 10% 2-D PAGE). Totally, nine peptides were identified from the purified pectinase enzyme through the MALDI-TOF-MS analysis and MASCOT tool was used to get the mass spectrum of the peak at 2211 of peptide that indicated the reference pectinase protein. The referenced gene primer (pectate lyases) was PCR amplified and its nucleotide sequence was analyzed. The exo-pelA gene was cloned in pREST vector, which was found to be over expressed in Escherichia coli BL21. The ORF encoded for a mature protein comprising of 425 amino acids (1236 nucleotides) with a predicted molecular weight of ~ 48.7 kDa. The present findings underline the potential of the fish-gut microbes as a source of biotechnologically important enzymes.
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Affiliation(s)
- Arul Dhayalan
- Department of Biotechnology, School of Biosciences, Periyar University, Salem, Tamil Nadu, 636011, India.,SRS of ICAR- National Dairy Research Institute, Adugodi, Bengaluru, Karnataka, 560030, India
| | - Natarajan Thillainathan
- Department of Biotechnology, School of Biosciences, Periyar University, Salem, Tamil Nadu, 636011, India.,Department of Biomedical Engineering, Central University of Rajasthan, Ajmer, Rajasthan, 305817, India
| | - Balasubramanian Velramar
- Department of Biotechnology, School of Biosciences, Periyar University, Salem, Tamil Nadu, 636011, India.,Amity Institute of Biotechnology, Amity University, Raipur, Chhattisgarh, 493225, India
| | - Palanisammi Athiyappagounder
- Veterinary College & Research Institute, Tamil Nadu Veterinary & Animal Science University, Tirunelveli, Tamil Nadu, 627358, India
| | - Dhanasundaram Sundaramoorthy
- Department of Marine Science, School of Marine Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India
| | - Perumal Pachiappan
- Department of Biotechnology, School of Biosciences, Periyar University, Salem, Tamil Nadu, 636011, India. .,Department of Marine Science, School of Marine Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India.
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2
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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.
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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
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3
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Agüero-Chapin G, Galpert D, Molina-Ruiz R, Ancede-Gallardo E, Pérez-Machado G, De la Riva GA, Antunes A. Graph Theory-Based Sequence Descriptors as Remote Homology Predictors. Biomolecules 2019; 10:E26. [PMID: 31878100 PMCID: PMC7022958 DOI: 10.3390/biom10010026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/16/2019] [Accepted: 12/18/2019] [Indexed: 12/23/2022] Open
Abstract
Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical-numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Deborah Galpert
- Departamento de Ciencia de la Computación. Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Reinaldo Molina-Ruiz
- Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Evys Ancede-Gallardo
- Programa de Doctorado en Fisicoquímica Molecular, Facultad de Ciencias Exactas, Universidad Andrés Bello, Av. República 239, Santiago 8370146, Chile;
| | - Gisselle Pérez-Machado
- EpiDisease S.L. Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Gustavo A. De la Riva
- Laboratorio de Biotecnología Aplicada S. de R.L. de C.V., GRECA Inc., Carretera La Piedad-Carapán, km 3.5, La Piedad, Michoacán 59300, Mexico;
- Tecnológico Nacional de México, Instituto Tecnológico de la Piedad, Av. Ricardo Guzmán Romero, Santa Fe, La Piedad de Cavadas, Michoacán 59370, Mexico
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
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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.
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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
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5
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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.
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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
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6
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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.
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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
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7
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Singh S. Alignment-Free Analyses of Nucleic Acid Sequences Using Graphical Representation (with Special Reference to Pandemic Bird Flu and Swine Flu). Synth Biol (Oxf) 2018. [PMCID: PMC7121243 DOI: 10.1007/978-981-10-8693-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The exponential growth in database of bio-molecular sequences have spawned many approaches towards storage, retrieval, classification and analyses requirements. Alignment-free techniques such as graphical representations and numerical characterisation (GRANCH) methods have enabled some detailed analyses of large sequences and found a number of different applications in the eukaryotic and prokaryotic domain. In particular, recalling the history of pandemic influenza in brief, we have followed the progress of viral infections such as bird flu of 1997 onwards and determined that the virus can spread conserved over space and time, that influenza virus can undergo fairly conspicuous recombination-like events in segmented genes, that certain segments of the neuraminidase and hemagglutinin surface proteins remain conserved and can be targeted for peptide vaccines. We recount in some detail a few of the representative GRANCH techniques to provide a glimpse of how these methods are used in formulating quantitative sequence descriptors to analyse DNA, RNA and protein sequences to derive meaningful results. Finally, we survey the surveillance techniques with a special reference to how the GRANCH techniques can be used for the purpose and recount the forecasts made of possible metamorphosis of pandemic bird flu to pandemic human infecting agents.
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Affiliation(s)
- Shailza Singh
- Department of Pathogenesis and Cellular Response, National Centre for Cell Science, Computational and Systems Biology Lab, Pune, Maharashtra India
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8
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Jalilvand A, Akbari B, Zare Mirakabad F. S-FLN: A sequence-based hierarchical approach for functional linkage network construction. J Theor Biol 2018; 437:149-162. [PMID: 29080781 DOI: 10.1016/j.jtbi.2017.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 07/27/2017] [Accepted: 10/18/2017] [Indexed: 11/24/2022]
Abstract
The functional linkage network (FLN) construction is a primary and important step in drug discovery and disease gene prioritization methods. In order to construct FLN, several methods have been introduced based on integration of various biological data. Although, there are impressive ideas behind these methods, they suffer from low quality of the biological data. In this paper, a hierarchical sequence-based approach is proposed to construct FLN. The proposed approach, denoted as S-FLN (Sequence-based Functional Linkage Network), uses the sequence of proteins as the primary data in three main steps. Firstly, the physicochemical properties of amino-acids are employed to describe the functionality of proteins. As the sequence of proteins is a more comprehensive and accurate primary data, more reliable relations are achieved. Secondly, seven different descriptor methods are used to extract feature vectors from the proteins sequences. Advantage of different descriptor methods lead to obtain diverse ensemble learners in the next step. Finally, a two-layer ensemble learning structure is proposed to calculated the score of protein pairs. The proposed approach has been evaluated using two biological datasets, S.Cerevisiae and H.Pylori, and resulted in 93.9% and 91.15% precision rates, respectively. The results of various experiments indicate the efficiency and validity of the proposed approach.
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Affiliation(s)
- A Jalilvand
- Department of Electronic and computer engineering,Tarbiat Modares University, Tehran, Iran
| | - B Akbari
- Department of Electronic and computer engineering,Tarbiat Modares University, Tehran, Iran.
| | - F Zare Mirakabad
- Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
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9
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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
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10
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MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:9183796. [PMID: 28744305 PMCID: PMC5514333 DOI: 10.1155/2017/9183796] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 05/29/2017] [Accepted: 06/04/2017] [Indexed: 02/05/2023]
Abstract
Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.
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Agüero-Chapin G, Pérez-Machado G, Sánchez-Rodríguez A, Santos MM, Antunes A. Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains. Methods Mol Biol 2016; 1401:253-272. [PMID: 26831713 DOI: 10.1007/978-1-4939-3375-4_16] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activities. However, that is challenging task due to the high sequence diversity of A-domains (~10-40 % of amino acid identity) and their selectivity for 50 different natural/unnatural amino acids. Altogether these characteristics make their detection and the prediction of their substrate specificity a real challenge when using traditional sequence alignment methods, e.g., BLAST searches. In this chapter we describe two workflows based on alignment-free methods intended for the identification and substrate specificity prediction of A-domains. To identify A-domains we introduce a graphical-numerical method, implemented in TI2BioP version 2.0 (topological indices to biopolymers), which in a first step uses protein four-color maps to represent A-domains. In a second step, simple topological indices (TIs), called spectral moments, are derived from the graphical representations of known A-domains (positive dataset) and of unrelated but well-characterized sequences (negative set). Spectral moments are then used as input predictors for statistical classification techniques to build alignment-free models. Finally, the resulting alignment-free models can be used to explore entire proteomes for unannotated A-domains. In addition, this graphical-numerical methodology works as a sequence-search method that can be ensemble with homology-based tools to deeply explore the A-domain signature and cope with the diversity of this class (Aguero-Chapin et al., PLoS One 8(7):e65926, 2013). The second workflow for the prediction of A-domain's substrate specificity is based on alignment-free models constructed by transductive support vector machines (TSVMs) that incorporate information of uncharacterized A-domains. The construction of the models was implemented in the NRPSpredictor and in a first step uses the physicochemical fingerprint of the 34 residues lining the active site of the phenylalanine-adenylation domain of gramicidin synthetase A [PDB ID 1 amu] to derive a feature vector. Homologous positions were extracted for A-domains with known and unknown substrate specificities and turned into feature vectors. At the same time, A-domains with known specificities towards similar substrates were clustered by physicochemical properties of amino acids (AA). In a second step, support vector machines (SVMs) were optimized from feature vectors of characterized A-domains in each of the resulting clusters. Later, SVMs were used in the variant of TSVMs that integrate a fraction of uncharacterized A-domains during training to predict unknown specificities. Finally, uncharacterized A-domains were scored by each of the constructed alignment-free models (TSVM) representing each substrate specificity resulting from the clustering. The model producing the largest score for the uncharacterized A-domain assigns the substrate specificity to it (Rausch et al., Nucleic Acids Res 33:5799-5808, 2005).
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, Porto, 4050-123, Portugal
- Centro de Bioactivos Químicos, Universidad Central "Marta Abreu" de Las Villas (UCLV), Santa Clara, 54830, Cuba
| | - Gisselle Pérez-Machado
- Centro de Bioactivos Químicos, Universidad Central "Marta Abreu" de Las Villas (UCLV), Santa Clara, 54830, Cuba
| | - Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, San Cayetano Alto, S/N, Loja, Ecuador
| | - Miguel Machado Santos
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, Porto, 4050-123, Portugal
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, Porto, 4169-007, Portugal
| | - Agostinho Antunes
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, Porto, 4050-123, Portugal.
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, Porto, 4169-007, Portugal.
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Surowka AD, Adamek D, Szczerbowska-Boruchowska M. The combination of artificial neural networks and synchrotron radiation-based infrared micro-spectroscopy for a study on the protein composition of human glial tumors. Analyst 2015; 140:2428-38. [DOI: 10.1039/c4an01867b] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Protein-related changes associated with the development of human brain gliomas are of increasing interest in modern neuro-oncology.
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Affiliation(s)
- A. D. Surowka
- AGH University of Science and Technology
- Faculty of Physics and Applied Computer Science
- 30-059 Krakow
- Poland
| | - D. Adamek
- Jagiellonian University
- Faculty of Medicine
- Department of Neuropathology
- Chair of Pathomorphology
- Krakow
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13
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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]
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Yao Y, Yan S, Han J, Dai Q, He PA. A novel descriptor of protein sequences and its application. J Theor Biol 2014; 347:109-17. [PMID: 24412564 DOI: 10.1016/j.jtbi.2014.01.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2013] [Revised: 11/10/2013] [Accepted: 01/01/2014] [Indexed: 02/05/2023]
Abstract
In this paper, a dynamic 3-D graphical representation of protein sequences is introduced based on three physical-chemical properties of amino acids. The coordinates of the graph have direct biological significance, which could reflect the innate structure of the proteins. The information of principal moments of inertia and range of axis coordinate are extracted as a novel mixed descriptor and proposed for the comparison of protein primary sequences. Meanwhile, the Euclidean distance of the normalized descriptor vectors which avoid the influence of the difference in length of protein sequences under consideration is employed as a quantitative measurement of the similarity of proteins. Finally, we take the nine ND5 (NADH dehydrogenase subunit 5) proteins for example and illustrate the effectiveness of our approach.
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Affiliation(s)
- Yuhua Yao
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
| | - Shoujiang Yan
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Jianning Han
- College of Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Qi Dai
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Ping-an He
- College of Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
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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
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Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods. PLoS One 2013; 8:e65926. [PMID: 23874386 PMCID: PMC3712989 DOI: 10.1371/journal.pone.0065926] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2012] [Accepted: 05/01/2013] [Indexed: 11/24/2022] Open
Abstract
The introduction of two-dimension (2D) graphs and their numerical characterization for comparative analyses of DNA/RNA and protein sequences without the need of sequence alignments is an active yet recent research topic in bioinformatics. Here, we used a 2D artificial representation (four-color maps) with a simple numerical characterization through topological indices (TIs) to aid the discovering of remote homologous of Adenylation domains (A-domains) from the Nonribosomal Peptide Synthetases (NRPS) class in the proteome of the cyanobacteria Microcystis aeruginosa. Cyanobacteria are a rich source of structurally diverse oligopeptides that are predominantly synthesized by NPRS. Several A-domains share amino acid identities lower than 20 % being a possible source of remote homologous. Therefore, A-domains cannot be easily retrieved by BLASTp searches using a single template. To cope with the sequence diversity of the A-domains we have combined homology-search methods with an alignment-free tool that uses protein four-color-maps. TI2BioP (Topological Indices toBioPolymers) version 2.0, available at http://ti2biop.sourceforge.net/ allowed the calculation of simple TIs from the protein sequences (four-color maps). Such TIs were used as input predictors for the statistical estimations required to build the alignment-free models. We concluded that the use of graphical/numerical approaches in cooperation with other sequence search methods, like multi-templates BLASTp and profile HMM, can give the most complete exploration of the repertoire of highly diverse protein families.
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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]
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Avram S, Milac AL, Mihailescu D. 3D-QSAR study indicates an enhancing effect of membrane ions on psychiatric drugs targeting serotonin receptor 5-HT1A. MOLECULAR BIOSYSTEMS 2012; 8:1418-25. [PMID: 22373544 DOI: 10.1039/c2mb00005a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Antidepressants and antipsychotics are psychiatric agents used for the treatment of various types of psychiatric diseases. Although currently among the most commonly prescribed drugs, their effectiveness and adverse effects are the topic of many studies and controversial claims. Here we generate QSAR models based on compounds series including 20 drugs recommended for two critical psychiatric diseases: depression and schizophrenia and we use these QSAR models to predict the biological activity of these 20 antidepressants and antipsychotics. We establish the membrane ions' contributions (sodium, potassium, calcium and iron) mediated by water to the antagonism of these drugs at the 5-HT1A receptor. The reliability of our QSAR models in predicting compounds activity is indicated by significant values for cross-validated correlation q² (0.60-0.76) and fitted correlation r² (0.96-0.98) coefficients. Our results indicate that potassium, calcium and iron play a key role for the antagonistic activity of drugs at the 5-HT1A receptor. Moreover, based on the established QSAR equations, we analysed 24 new escitalopram derivatives as possibly improved antidepressants targeting the 5-HT1A receptor. We identified that the presence of methyl groups and hydrogen atoms improves antidepressant activity while the simultaneous presence of ethyl, propyl or halogens decreased drastically antidepressant activity at the 5-HT1A site.
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Affiliation(s)
- Speranţa Avram
- University of Bucharest, Faculty of Biology, Dept. of Anatomy, Animal Physiology and Biophysics, 91-95th Spl. Independentei, Bucharest-076201, Romania
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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]
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20
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Agüero-Chapin G, Sánchez-Rodríguez A, Hidalgo-Yanes PI, Pérez-Castillo Y, Molina-Ruiz R, Marchal K, Vasconcelos V, Antunes A. An alignment-free approach for eukaryotic ITS2 annotation and phylogenetic inference. PLoS One 2011; 6:e26638. [PMID: 22046320 PMCID: PMC3202569 DOI: 10.1371/journal.pone.0026638] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2011] [Accepted: 09/29/2011] [Indexed: 02/02/2023] Open
Abstract
The ITS2 gene class shows a high sequence divergence among its members that have complicated its annotation and its use for reconstructing phylogenies at a higher taxonomical level (beyond species and genus). Several alignment strategies have been implemented to improve the ITS2 annotation quality and its use for phylogenetic inferences. Although, alignment based methods have been exploited to the top of its complexity to tackle both issues, no alignment-free approaches have been able to successfully address both topics. By contrast, the use of simple alignment-free classifiers, like the topological indices (TIs) containing information about the sequence and structure of ITS2, may reveal to be a useful approach for the gene prediction and for assessing the phylogenetic relationships of the ITS2 class in eukaryotes. Thus, we used the TI2BioP (Topological Indices to BioPolymers) methodology [1], [2], freely available at http://ti2biop.sourceforge.net/ to calculate two different TIs. One class was derived from the ITS2 artificial 2D structures generated from DNA strings and the other from the secondary structure inferred from RNA folding algorithms. Two alignment-free models based on Artificial Neural Networks were developed for the ITS2 class prediction using the two classes of TIs referred above. Both models showed similar performances on the training and the test sets reaching values above 95% in the overall classification. Due to the importance of the ITS2 region for fungi identification, a novel ITS2 genomic sequence was isolated from Petrakia sp. This sequence and the test set were used to comparatively evaluate the conventional classification models based on multiple sequence alignments like Hidden Markov based approaches, revealing the success of our models to identify novel ITS2 members. The isolated sequence was assessed using traditional and alignment-free based techniques applied to phylogenetic inference to complement the taxonomy of the Petrakia sp. fungal isolate.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Porto, Portugal
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | | | - Pedro I. Hidalgo-Yanes
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
- Area of Microbiology, University of León, León, Spain
| | - Yunierkis Pérez-Castillo
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
| | - Reinaldo Molina-Ruiz
- Molecular Simulation and Drug Design (CBQ), Universidad Central “Marta Abreu” de Las Villas (UCLV), Santa Clara, Cuba
| | - Kathleen Marchal
- CMPG, Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
| | - Vítor Vasconcelos
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Porto, Portugal
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | - Agostinho Antunes
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Porto, Portugal
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
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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.
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Affiliation(s)
- Wei Chen
- Department of Physics, College of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China.
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22
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García I, Fall Y, García-Mera X, Prado-Prado F. Theoretical study of GSK−3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors. Mol Divers 2011; 15:947-55. [DOI: 10.1007/s11030-011-9325-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2011] [Accepted: 06/20/2011] [Indexed: 10/18/2022]
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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]
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González-Díaz H, Muíño L, Anadón AM, Romaris F, Prado-Prado FJ, Munteanu CR, Dorado J, Sierra AP, Mezo M, González-Warleta M, Gárate T, Ubeira FM. MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens. MOLECULAR BIOSYSTEMS 2011; 7:1938-55. [PMID: 21468430 DOI: 10.1039/c1mb05069a] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.
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Affiliation(s)
- Humberto González-Díaz
- Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
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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.
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Ghosh A, Nandy A. Graphical representation and mathematical characterization of protein sequences and applications to viral proteins. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2011; 83:1-42. [PMID: 21570664 PMCID: PMC7150266 DOI: 10.1016/b978-0-12-381262-9.00001-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Graphical representation and numerical characterization (GRANCH) of nucleotide and protein sequences is a new field that is showing a lot of promise in analysis of such sequences. While formulation and applications of GRANCH techniques for DNA/RNA sequences started just over a decade ago, analyses of protein sequences by these techniques are of more recent origin. The emphasis is still on developing the underlying technique, but significant results have been achieved in using these methods for protein phylogeny, mass spectral data of proteins and protein serum profiles in parasites, toxicoproteomics, determination of different indices for use in QSAR studies, among others. We briefly mention these in this chapter, with some details on protein phylogeny and viral diseases. In particular, we cover a systematic method developed in GRANCH to determine conserved surface exposed peptide segments in selected viral proteins that can be used for drug and vaccine targeting. The new GRANCH techniques and applications for DNAs and proteins are covered briefly to provide an overview to this nascent field.
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Affiliation(s)
- Ambarnil Ghosh
- Physics Department, Jadavpur University, Jadavpur, Kolkata, India
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Agüero-Chapin G, de la Riva GA, Molina-Ruiz R, Sánchez-Rodríguez A, Pérez-Machado G, Vasconcelos V, Antunes A. Non-linear models based on simple topological indices to identify RNase III protein members. J Theor Biol 2010; 273:167-78. [PMID: 21192951 DOI: 10.1016/j.jtbi.2010.12.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2010] [Revised: 11/15/2010] [Accepted: 12/13/2010] [Indexed: 01/27/2023]
Abstract
Alignment-free classifiers are especially useful in the functional classification of protein classes with variable homology and different domain structures. Thus, the Topological Indices to BioPolymers (TI2BioP) methodology (Agüero-Chapin et al., 2010) inspired in both the TOPS-MODE and the MARCH-INSIDE methodologies allows the calculation of simple topological indices (TIs) as alignment-free classifiers. These indices were derived from the clustering of the amino acids into four classes of hydrophobicity and polarity revealing higher sequence-order information beyond the amino acid composition level. The predictability power of such TIs was evaluated for the first time on the RNase III family, due to the high diversity of its members (primary sequence and domain organization). Three non-linear models were developed for RNase III class prediction: Decision Tree Model (DTM), Artificial Neural Networks (ANN)-model and Hidden Markov Model (HMM). The first two are alignment-free approaches, using TIs as input predictors. Their performances were compared with a non-classical HMM, modified according to our amino acid clustering strategy. The alignment-free models showed similar performances on the training and the test sets reaching values above 90% in the overall classification. The non-classical HMM showed the highest rate in the classification with values above 95% in training and 100% in test. Although the higher accuracy of the HMM, the DTM showed simplicity for the RNase III classification with low computational cost. Such simplicity was evaluated in respect to HMM and ANN models for the functional annotation of a new bacterial RNase III class member, isolated and annotated by our group.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, 4050-123 Porto, Portugal
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Design, synthesis and biological evaluation of 1,3,4-oxadiazole derivatives. Eur J Med Chem 2010; 45:4963-7. [DOI: 10.1016/j.ejmech.2010.08.003] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2008] [Revised: 06/04/2010] [Accepted: 08/02/2010] [Indexed: 11/23/2022]
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Agüero-Chapin G, Pérez-Machado G, Molina-Ruiz R, Pérez-Castillo Y, Morales-Helguera A, Vasconcelos V, Antunes A. TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains. Amino Acids 2010; 40:431-42. [PMID: 20563611 DOI: 10.1007/s00726-010-0653-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2010] [Accepted: 06/02/2010] [Indexed: 02/04/2023]
Abstract
Bacteriocins are proteinaceous toxins produced and exported by both gram-negative and gram-positive bacteria as a defense mechanism. The bacteriocin protein family is highly diverse, which complicates the identification of bacteriocin-like sequences using alignment approaches. The use of topological indices (TIs) irrespective of sequence similarity can be a promising alternative to predict proteinaceous bacteriocins. Thus, we present Topological Indices to BioPolymers (TI2BioP) as an alignment-free approach inspired in both the Topological Substructural Molecular Design (TOPS-MODE) and Markov Chain Invariants for Network Selection and Design (MARCH-INSIDE) methodology. TI2BioP allows the calculation of the spectral moments as simple TIs to seek quantitative sequence-function relationships (QSFR) models. Since hydrophobicity and basicity are major criteria for the bactericide activity of bacteriocins, the spectral moments ((HP)μ(k)) were derived for the first time from protein artificial secondary structures based on amino acid clustering into a Cartesian system of hydrophobicity and polarity. Several orders of (HP)μ(k) characterized numerically 196 bacteriocin-like sequences and a control group made up of 200 representative CATH domains. Subsequently, they were used to develop an alignment-free QSFR model allowing a 76.92% discrimination of bacteriocin proteins from other domains, a relevant result considering the high sequence diversity among the members of both groups. The model showed a prediction overall performance of 72.16%, detecting specifically 66.7% of proteinaceous bacteriocins whereas the InterProScan retrieved just 60.2%. As a practical validation, the model also predicted successfully the cryptic bactericide function of the Cry 1Ab C-terminal domain from Bacillus thuringiensis's endotoxin, which has not been detected by classical alignment methods.
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Affiliation(s)
- Guillermín Agüero-Chapin
- CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, 4050-123, Porto, Portugal
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Ghosh A, Nandy A, Nandy P. Computational analysis and determination of a highly conserved surface exposed segment in H5N1 avian flu and H1N1 swine flu neuraminidase. BMC STRUCTURAL BIOLOGY 2010; 10:6. [PMID: 20170556 PMCID: PMC2836360 DOI: 10.1186/1472-6807-10-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2009] [Accepted: 02/22/2010] [Indexed: 01/30/2023]
Abstract
Background Catalytic activity of influenza neuraminidase (NA) facilitates elution of progeny virions from infected cells and prevents their self-aggregation mediated by the catalytic site located in the body region. Research on the active site of the molecule has led to development of effective inhibitors like oseltamivir, zanamivir etc, but the high rate of mutation and interspecies reassortment in viral sequences and the recent reports of oseltamivir resistant strains underlines the importance of determining additional target sites for developing future antiviral compounds. In a recent computational study of 173 H5N1 NA gene sequences we had identified a 50-base highly conserved region in 3'-terminal end of the NA gene. Results We extend the graphical and numerical analyses to a larger number of H5N1 NA sequences (514) and H1N1 swine flu sequences (425) accessed from GenBank. We use a 2D graphical representation model for the gene sequences and a Graphical Sliding Window Method (GSWM) for protein sequences scanning the sequences as a block of 16 amino acids at a time. Using a protein sequence descriptor defined in our model, the protein sliding scan method allowed us to compare the different strains for block level variability, which showed significant statistical correlation to average solvent accessibility of the residue blocks; single amino acid position variability results in no correlation, indicating the impact of stretch variability in chemical environment. Close to the C-terminal end the GSWM showed less descriptor-variability with increased average solvent accessibility (ASA) that is also supported by conserved predicted secondary structure of 3' terminal RNA and visual evidence from 3D crystallographic structure. Conclusion The identified terminal segment, strongly conserved in both RNA and protein sequences, is especially significant as it is surface exposed and structural chemistry reveals the probable role of this stretch in tetrameric stabilization. It could also participate in other biological processes associated with conserved surface residues. A RNA double hairpin secondary structure found in this segment in a majority of the H5N1 strains also supports this observation. In this paper we propose this conserved region as a probable site for designing inhibitors for broad-spectrum pandemic control of flu viruses with similar NA structure.
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Affiliation(s)
- Ambarnil Ghosh
- Physics Department, Jadavpur University, Kolkata, India.
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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]
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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
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Rinnan Å, Christensen NJ, Engelsen SB. How the energy evaluation method used in the geometry optimization step affect the quality of the subsequent QSAR/QSPR models. J Comput Aided Mol Des 2009; 24:17-22. [DOI: 10.1007/s10822-009-9308-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2009] [Accepted: 10/29/2009] [Indexed: 10/20/2022]
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Nandy A. Empirical relationship between intra-purine and intra-pyrimidine differences in conserved gene sequences. PLoS One 2009; 4:e6829. [PMID: 19714250 PMCID: PMC2730015 DOI: 10.1371/journal.pone.0006829] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2009] [Accepted: 07/24/2009] [Indexed: 11/18/2022] Open
Abstract
DNA sequences seen in the normal character-based representation appear to have a formidable mixing of the four nucleotides without any apparent order. Nucleotide frequencies and distributions in the sequences have been studied extensively, since the simple rule given by Chargaff almost a century ago that equates the total number of purines to the pyrimidines in a duplex DNA sequence. While it is difficult to trace any relationship between the bases from studies in the character representation of a DNA sequence, graphical representations may provide a clue. These novel representations of DNA sequences have been useful in providing an overview of base distribution and composition of the sequences and providing insights into many hidden structures. We report here our observation based on a graphical representation that the intra-purine and intra-pyrimidine differences in sequences of conserved genes generally follow a quadratic distribution relationship and show that this may have arisen from mutations in the sequences over evolutionary time scales. From this hitherto undescribed relationship for the gene sequences considered in this report we hypothesize that such relationships may be characteristic of these sequences and therefore could become a barrier to large scale sequence alterations that override such characteristics, perhaps through some monitoring process inbuilt in the DNA sequences. Such relationship also raises the possibility of intron sequences playing an important role in maintaining the characteristics and could be indicative of possible intron-late phenomena.
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Affiliation(s)
- Ashesh Nandy
- School of Environmental Studies, Jadavpur University, Kolkata, West Bengal, India.
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Protein functional class prediction using global encoding of amino acid sequence. J Theor Biol 2009; 261:290-3. [PMID: 19631664 DOI: 10.1016/j.jtbi.2009.07.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2009] [Revised: 07/16/2009] [Accepted: 07/17/2009] [Indexed: 11/21/2022]
Abstract
A key goal of the post-genomic era is to determine protein functions. In this paper, we proposed a global encoding method of protein sequence (GE) to descript global information of amino acid sequence, and then assign protein functional class using machine learning methods nearest neighbor algorithm (NNA). We predicted the function of 1818 Saccharomyces cerevisiae proteins which was used in Vazquez's global optimization method (GOM) except eight proteins which cannot get from the database now or whose sequence length is too short. Using our approach, the computed accuracy is better than Vazquez's global optimization method (GOM) in some cases. The experiment results show that our new method is efficient to predict functional class of unknown proteins.
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