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LUŽAIĆ TZ, GRAHOVAC NL, HLADNI NT, ROMANIĆ RS. Evaluation of oxidative stability of new cold-pressed sunflower oils during accelerated thermal stability tests. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.67320] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Affiliation(s)
| | | | - Nada T. HLADNI
- Institute of Field and Vegetable Crops, Republic of Serbia
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LUŽAIĆ TZ, GRAHOVAC NL, HLADNI NT, ROMANIĆ RS. Evaluation of oxidative stability of new cold-pressed sunflower oils during accelerated thermal stability tests. FOOD SCIENCE AND TECHNOLOGY 2022; 42. [DOI: https:/doi.org/10.1590/fst.67320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Affiliation(s)
| | | | - Nada T. HLADNI
- Institute of Field and Vegetable Crops, Republic of Serbia
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Lužaić T, Romanić R, Grahovac N, Jocić S, Cvejić S, Hladni N, Pezo L. Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:5827-5833. [PMID: 33792064 DOI: 10.1002/jsfa.11234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/17/2021] [Accepted: 03/31/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Sunflower seeds are in the top five most abundant oilseeds in the world, as well as sunflower oil in the edible oils group. Recently, increasing attention has been paid to cold-pressed sunflower oil because less processing is involved and no solvent is used. The present study was carried out to investigate dimensions (length, width, thickness), firmness, general (moisture content and hull content, mass of 1000 seeds), gravimetric (true and bulk density, porosity) and geometric characteristics (equivalent diameter, surface area, seed volume, sphericity) of 20 new sunflower hybrid seeds. Steps to determine most of these parameters are quite simple and easy since the process does not require long time or special equipment. RESULTS Principal component analysis and cluster analysis confirmed differences in the mentioned characteristics between oily and confectionary sunflower hybrid seeds. One of the major differences between two groups of samples was in extraction oil yield. Mechanical extraction oil yield of the oily hybrid seeds was significantly (P ˂ 0.05) higher (from 68.72 ± 4.21% to 75.61 ± 1.99%) compared to confectionary hybrids (from 20.10 ± 2.82% to 39.91 ± 6.23%). Extraction oil yield values are known only after oil extraction. CONCLUSION Knowledge of the extraction oil yield value before the mechanical extraction enables better management of the process. By application of the artificial neural network approach, an optimal neural network model was developed. The developed model showed a good generalization capability to predict the mechanical extraction oil yield of new sunflower hybrids based on the experimental data, which was a main goal of this paper. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Tanja Lužaić
- Department of Food Preservation Engineering, Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Republic of Serbia
| | - Ranko Romanić
- Department of Food Preservation Engineering, Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Republic of Serbia
| | - Nada Grahovac
- Sunflower Department, Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Republic of Serbia
| | - Siniša Jocić
- Sunflower Department, Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Republic of Serbia
| | - Sandra Cvejić
- Sunflower Department, Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Republic of Serbia
| | - Nada Hladni
- Sunflower Department, Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Republic of Serbia
| | - Lato Pezo
- Institute of General and Physical Chemistry, University of Belgrade, Studentski trg 12/V, 11000 Belgrade, Republic of Serbia
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Karadžić MŽ, Jevrić LR, Mandić AI, Markov SL, Podunavac-Kuzmanović SO, Kovačević SZ, Nikolić AR, Oklješa AM, Sakač MN, Penov-Gaši KM. Chemometrics approach based on chromatographic behavior, in silico characterization and molecular docking study of steroid analogs with biomedical importance. Eur J Pharm Sci 2017; 105:71-81. [DOI: 10.1016/j.ejps.2017.05.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 04/23/2017] [Accepted: 05/03/2017] [Indexed: 01/05/2023]
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5
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Markov mean properties for cell death-related protein classification. J Theor Biol 2014; 349:12-21. [DOI: 10.1016/j.jtbi.2014.01.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 01/21/2014] [Accepted: 01/24/2014] [Indexed: 11/18/2022]
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6
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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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Fernandez-Lozano C, Fernández-Blanco E, Dave K, Pedreira N, Gestal M, Dorado J, Munteanu CR. Improving enzyme regulatory protein classification by means of SVM-RFE feature selection. MOLECULAR BIOSYSTEMS 2014; 10:1063-71. [DOI: 10.1039/c3mb70489k] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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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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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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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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Randić M, Zupan J, Balaban AT, Vikić-Topić D, Plavšić D. Graphical Representation of Proteins. Chem Rev 2010; 111:790-862. [PMID: 20939561 DOI: 10.1021/cr800198j] [Citation(s) in RCA: 93] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Milan Randić
- National Institute of Chemistry, P.O. Box 3430, 1001 Ljubljana, Slovenia; NMR Center, Ruđer Bošković Institute, P.O. Box 180, HR-10002 Zagreb, Croatia; and Texas A&M University at Galveston, Galveston, Texas 77553
| | - Jure Zupan
- National Institute of Chemistry, P.O. Box 3430, 1001 Ljubljana, Slovenia; NMR Center, Ruđer Bošković Institute, P.O. Box 180, HR-10002 Zagreb, Croatia; and Texas A&M University at Galveston, Galveston, Texas 77553
| | - Alexandru T. Balaban
- National Institute of Chemistry, P.O. Box 3430, 1001 Ljubljana, Slovenia; NMR Center, Ruđer Bošković Institute, P.O. Box 180, HR-10002 Zagreb, Croatia; and Texas A&M University at Galveston, Galveston, Texas 77553
| | - Dražen Vikić-Topić
- National Institute of Chemistry, P.O. Box 3430, 1001 Ljubljana, Slovenia; NMR Center, Ruđer Bošković Institute, P.O. Box 180, HR-10002 Zagreb, Croatia; and Texas A&M University at Galveston, Galveston, Texas 77553
| | - Dejan Plavšić
- National Institute of Chemistry, P.O. Box 3430, 1001 Ljubljana, Slovenia; NMR Center, Ruđer Bošković Institute, P.O. Box 180, HR-10002 Zagreb, Croatia; and Texas A&M University at Galveston, Galveston, Texas 77553
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Lokwani D, Bhandari S, Pujari R, Shastri P, shelke G, Pawar V. Use of Quantitative Structure–Activity Relationship (QSAR) and ADMET prediction studies as screening methods for design of benzyl urea derivatives for anti-cancer activity. J Enzyme Inhib Med Chem 2010; 26:319-31. [DOI: 10.3109/14756366.2010.506437] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Affiliation(s)
- Deepak Lokwani
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, India
| | - Shashikant Bhandari
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, India
| | - Radha Pujari
- National Centre for Cell Science (NCCS), Ganeshkhind, Pune, India
| | - Padma Shastri
- National Centre for Cell Science (NCCS), Ganeshkhind, Pune, India
| | - Ganesh shelke
- National Centre for Cell Science (NCCS), Ganeshkhind, Pune, India
| | - Vidya Pawar
- Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, India
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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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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
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Huang RB, Du QS, Wang CH, Liao SM, Chou KC. A fast and accurate method for predicting pKa of residues in proteins. Protein Eng Des Sel 2010; 23:35-42. [PMID: 19926592 DOI: 10.1093/protein/gzp067] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Predicting the pH-activities of residues in proteins is an important problem in enzyme engineering and protein design. A novel predictor called 'Pred-pK(a)' was developed based on the physicochemical properties of amino acids and protein 3D structure. The Pred-pK(a) approach considers the influence of all other residues of the protein to predict the pK(a) value of an ionizable residue. An empirical equation was formulated, in which the pK(a) value was a distance-dependent function of physicochemical parameters of 20 amino acid types, describing their electrostatic and van der Waals interaction, as well as the effects of hydrogen bonds and solvation. Two sets of coefficients, {a(alpha)} and {b(l)}, were used in the predictor: {a(alpha)} is the weight factors of 20 amino acid types and {b(l)} is the weight factors of physicochemical properties of amino acids. An iterative double least square procedure was proposed to solve the two sets of weight factors alternately and iteratively in a training set. The two coefficient sets {a(alpha)} and {b(l)} thus obtained were used to predict the pK(a) values of residues in a protein. The average predictive error is +/-0.6 pH in less than a minute in common personal computer.
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Affiliation(s)
- Ri-Bo Huang
- Guangxi Academy of Sciences, 98 Daling Road, Nanning, Guangxi 530004, People's Republic of China
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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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3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2009; 1794:1784-94. [DOI: 10.1016/j.bbapap.2009.08.020] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2009] [Revised: 08/07/2009] [Accepted: 08/17/2009] [Indexed: 11/21/2022]
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20
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Sapre NS, Gupta S, Pancholi N, Sapre N. A group center overlap based approach for “3D QSAR” studies on TIBO derivatives. J Comput Chem 2009; 30:922-33. [DOI: 10.1002/jcc.21114] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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21
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Shen HB, Chou KC. QuatIdent: A Web Server for Identifying Protein Quaternary Structural Attribute by Fusing Functional Domain and Sequential Evolution Information. J Proteome Res 2009; 8:1577-84. [DOI: 10.1021/pr800957q] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hong-Bin Shen
- Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China, and Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, California 92130
| | - Kuo-Chen Chou
- Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China, and Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, California 92130
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Vilar S, González-Díaz H, Santana L, Uriarte E. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 2008; 29:2613-22. [PMID: 18478581 DOI: 10.1002/jcc.21016] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure-activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sjöblom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (xi(k)) was calculated to describe each protein numerically. The use of xi(k) as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers.
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Affiliation(s)
- Santiago Vilar
- Unit of Bioinformatics and Connectivity Analysis, Institute of Industrial Pharmacy, and Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela 15782, Spain
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23
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R, Romero-Zaldivar V. Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification. J Comput Chem 2008; 29:2500-12. [DOI: 10.1002/jcc.20964] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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24
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Cruz-Monteagudo M, Munteanu CR, Borges F, Cordeiro MND, Uriarte E, Chou KC, González-Díaz H. Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case. POLYMER 2008. [DOI: 10.1016/j.polymer.2008.09.070] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Quantitative Proteome–Property Relationships (QPPRs). Part 1: Finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra. Bioorg Med Chem 2008; 16:9684-93. [DOI: 10.1016/j.bmc.2008.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2008] [Revised: 09/29/2008] [Accepted: 10/02/2008] [Indexed: 11/22/2022]
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26
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Lin ZH, Long HX, Bo Z, Wang YQ, Wu YZ. New descriptors of amino acids and their application to peptide QSAR study. Peptides 2008; 29:1798-805. [PMID: 18606203 DOI: 10.1016/j.peptides.2008.06.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2008] [Revised: 06/09/2008] [Accepted: 06/10/2008] [Indexed: 11/18/2022]
Abstract
A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values (Q(2)) were 0.921, 0.943 and 0.773. The multiple correlation coefficients (R(2)) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.
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Affiliation(s)
- Zhi-hua Lin
- College of Bioengineering, Chongqing Institute of Technology, Chongqing 400050, People's Republic of China.
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27
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Liang G, Yang L, Kang L, Mei H, Li Z. Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides. Amino Acids 2008; 37:583-91. [PMID: 18821054 DOI: 10.1007/s00726-008-0177-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2008] [Accepted: 08/25/2008] [Indexed: 10/21/2022]
Abstract
On the basis of exploratory factor analysis, six multidimensional patterns of 516 amino acid attributes, namely, factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, are proposed to represent structures of 48 bitter-tasting dipeptides and 58 angiotensin-converting enzyme inhibitors. Characteristic parameters related to bioactivities of the peptides studied are selected by genetic algorithm, and quantitative structure-activity relationship (QSAR) models are constructed by partial least square (PLS). Our results by a leave-one-out cross validation are compared with the previously known structure representation method and are shown to give slightly superior or comparative performance. Further, two data sets are divided into training sets and test sets to validate the characterization repertoire of FASGAI. Performance of the PLS models developed by training samples by a leave-one-out cross validation and external validation for test samples are satisfying. These results demonstrate that FASGAI is an effective representation technique of peptide structures, and that FASGAI vectors have many preponderant characteristics such as straightforward physicochemical information, high characterization competence and easy manipulation. They can be further applied to investigate the relationship between structures and functions of various peptides, even proteins.
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Affiliation(s)
- G Liang
- College of Bioengineering, Chongqing University, 400030, Chongqing, China.
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28
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Dea-Ayuela MA, Pérez-Castillo Y, Meneses-Marcel A, Ubeira FM, Bolas-Fernández F, Chou KC, González-Díaz H. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg Med Chem 2008; 16:7770-6. [PMID: 18662882 DOI: 10.1016/j.bmc.2008.07.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2008] [Revised: 06/23/2008] [Accepted: 07/02/2008] [Indexed: 10/21/2022]
Abstract
The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools.
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29
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Pérez-Garrido A, González MP, Escudero AG. Halogenated derivatives QSAR model using spectral moments to predict haloacetic acids (HAA) mutagenicity. Bioorg Med Chem 2008; 16:5720-32. [DOI: 10.1016/j.bmc.2008.03.070] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2007] [Revised: 02/29/2008] [Accepted: 03/25/2008] [Indexed: 10/22/2022]
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30
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Prado-Prado FJ, González-Díaz H, de la Vega OM, Ubeira FM, Chou KC. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 2008; 16:5871-80. [PMID: 18485714 DOI: 10.1016/j.bmc.2008.04.068] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2008] [Revised: 04/22/2008] [Accepted: 04/25/2008] [Indexed: 10/22/2022]
Abstract
Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.
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31
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González-Díaz H, González-Díaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics 2008; 8:750-78. [DOI: 10.1002/pmic.200700638] [Citation(s) in RCA: 170] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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32
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Cruz-Monteagudo M, González-Díaz H, Borges F, Dominguez ER, Cordeiro MNDS. 3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy. Chem Res Toxicol 2008; 21:619-32. [PMID: 18257557 DOI: 10.1021/tx700296t] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- Physico-Chemical Molecular Research Unit, Department of Organic Chemistry, Faculty of Pharmacy, University of Porto, 4150-047 Porto, Portugal
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33
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Fernández M, Fernández L, Abreu JI, Garriga M. Classification of voltage-gated K(+) ion channels from 3D pseudo-folding graph representation of protein sequences using genetic algorithm-optimized support vector machines. J Mol Graph Model 2008; 26:1306-14. [PMID: 18289899 DOI: 10.1016/j.jmgm.2008.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2007] [Revised: 01/03/2008] [Accepted: 01/03/2008] [Indexed: 11/26/2022]
Abstract
Voltage-gated K(+) ion channels (VKCs) are membrane proteins that regulate the passage of potassium ions through membranes. This work reports a classification scheme of VKCs according to the signs of three electrophysiological variables: activation threshold voltage (V(t)), half-activation voltage (V(a50)) and half-inactivation voltage (V(h50)). A novel 3D pseudo-folding graph representation of protein sequences encoded the VKC sequences. Amino acid pseudo-folding 3D distances count (AAp3DC) descriptors, calculated from the Euclidean distances matrices (EDMs) were tested for building the classifiers. Genetic algorithm (GA)-optimized support vector machines (SVMs) with a radial basis function (RBF) kernel well discriminated between VKCs having negative and positive/zero V(t), V(a50) and V(h50) values with overall accuracies about 80, 90 and 86%, respectively, in crossvalidation test. We found contributions of the "pseudo-core" and "pseudo-surface" of the 3D pseudo-folded proteins to the discrimination between VKCs according to the three electrophysiological variables.
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Affiliation(s)
- Michael Fernández
- Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, 44740 Matanzas, Cuba.
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34
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GonzÁlez-DÍaz H, Prado-Prado FJ. Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals. J Comput Chem 2007; 29:656-67. [DOI: 10.1002/jcc.20826] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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35
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Caballero J, Fernández M, Saavedra M, González-Nilo FD. 2D Autocorrelation, CoMFA, and CoMSIA modeling of protein tyrosine kinases' inhibition by substituted pyrido[2,3-d]pyrimidine derivatives. Bioorg Med Chem 2007; 16:810-21. [PMID: 17964795 DOI: 10.1016/j.bmc.2007.10.024] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2007] [Revised: 09/21/2007] [Accepted: 10/10/2007] [Indexed: 10/22/2022]
Abstract
2D Autocorrelation, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of substituted pyrido[2,3-d]pyrimidine derivatives to correlate platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and c-Src tyrosine kinases' inhibition with 2D and 3D structural properties of 22 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling each protein tyrosine kinase (PTK) inhibitory activity were selected by genetic algorithm (GA) and multiple linear regression (MLR) approach. The 2D autocorrelation space brings different descriptors for each PTK inhibition and suggests the atomic properties relevant for the inhibitors to interact with each PTK active site. CoMFA and CoMSIA were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the hydrophobic and H-bond donor fields around them.
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Affiliation(s)
- Julio Caballero
- Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile.
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