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Zheng W. Predicting cryptic ligand binding sites based on normal modes guided conformational sampling. Proteins 2021; 89:416-426. [PMID: 33244830 DOI: 10.1002/prot.26027] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/26/2020] [Accepted: 11/21/2020] [Indexed: 12/22/2022]
Abstract
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse-grained elastic models followed by atomistic backbone refinement and side-chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine-learning protocols to optimize the combination of the sampling-enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1-2 hours for an average-size protein) and simpler (using only pocket scores), so it is suitable for high-throughput processing of large datasets of protein structures at the genome scale.
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Affiliation(s)
- Wenjun Zheng
- Department of Physics, University at Buffalo, Buffalo, New York, USA
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2
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Cirauqui Diaz N, Frezza E, Martin J. Using normal mode analysis on protein structural models. How far can we go on our predictions? Proteins 2020; 89:531-543. [PMID: 33349977 DOI: 10.1002/prot.26037] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/12/2020] [Indexed: 01/01/2023]
Abstract
Normal mode analysis (NMA) is a fast and inexpensive approach that is largely used to gain insight into functional protein motions, and more recently to create conformations for further computational studies. However, when the protein structure is unknown, the use of computational models is necessary. Here, we analyze the capacity of NMA in internal coordinate space to predict protein motion, its intrinsic flexibility, and atomic displacements, using protein models instead of native structures, and the possibility to use it for model refinement. Our results show that NMA is quite insensitive to modeling errors, but that calculations are strictly reliable only for very accurate models. Our study also suggests that internal NMA is a more suitable tool for the improvement of structural models, and for integrating them with experimental data or in other computational techniques, such as protein docking or more refined molecular dynamics simulations.
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Affiliation(s)
- Nuria Cirauqui Diaz
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, Université de Lyon, Lyon, France
| | - Elisa Frezza
- CiTCoM, CNRS, Université de Paris, Paris, France
| | - Juliette Martin
- CNRS, UMR 5086 Molecular Microbiology and Structural Biochemistry, Université de Lyon, Lyon, France
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3
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Jain R, Grover A. Maslinic acid differentially exploits the MAPK pathway in estrogen-positive and triple-negative breast cancer to induce mitochondrion-mediated, caspase-independent apoptosis. Apoptosis 2020; 25:817-834. [PMID: 32940876 DOI: 10.1007/s10495-020-01636-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2020] [Indexed: 12/14/2022]
Abstract
Breast cancer accounts for 1.4 million new cases every year. Triple-negative breast cancer (TNBC) is one the leading cause of mortality in developing countries and is associated with early age onset (under 40 years old). Chemotherapy has a poor success rate in patients with TNBC as compared to other types of breast cancers. It is due to the lack of expression of three validated molecular markers for breast cancer, the estrogen and progesterone receptors, and the amplification of HER-2/Neu. Therefore, a clear need exists for a greater understanding of TNBC at all levels and for the development of better therapies. We have studied the anti-tumor effects of a potential drug, maslinic acid, which can be extracted from olive oil industry waste. This natural product showed inhibitory effect at concentrations ranging from 30 to 50 µM within 24 h. It exhibited divergent effects in cell cycle progression for the MCF7 (estrogen positive) cell line when compared with TNBCs like MDA-MB-231 and MDA-MB-468. Also, maslinic acid treatment altered the mitochondrial membrane electrochemical potential and the reactive oxygen species (ROS) levels to cause a caspase-independent programmed cell death. In silico approaches and immunoblotting suggested the involvement of the MAPK pathway explaining the variability in cell cycle progression along with the apoptotic cell death caused by maslinic acid.
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Affiliation(s)
- R Jain
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - A Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
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Rallabandi HR, Ganesan P, Kim YJ. Targeting the C-Terminal Domain Small Phosphatase 1. Life (Basel) 2020; 10:life10050057. [PMID: 32397221 PMCID: PMC7281111 DOI: 10.3390/life10050057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/05/2020] [Accepted: 05/07/2020] [Indexed: 12/15/2022] Open
Abstract
The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.
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Cavasotto CN. Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization. Methods Mol Biol 2020; 2114:257-268. [PMID: 32016898 DOI: 10.1007/978-1-0716-0282-9_16] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.
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Affiliation(s)
- Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina. .,Austral Institute for Applied Artificial Intelligence, Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina. .,Facultad de Ciencias Biomédicas, Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina. .,Facultad de Ingeniería, Universidad Austral, Derqui-Pilar, Buenos Aires, Argentina.
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6
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Bajusz D, Rácz A, Héberger K. Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking. Molecules 2019; 24:E2690. [PMID: 31344902 PMCID: PMC6695709 DOI: 10.3390/molecules24152690] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/18/2019] [Accepted: 07/22/2019] [Indexed: 12/05/2022] Open
Abstract
Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
| | - Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary.
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
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7
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Soler D, Westermaier Y, Soliva R. Extensive benchmark of rDock as a peptide-protein docking tool. J Comput Aided Mol Des 2019; 33:613-626. [DOI: 10.1007/s10822-019-00212-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 06/19/2019] [Indexed: 12/11/2022]
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8
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Cavasotto CN, Adler NS, Aucar MG. Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization. Front Chem 2018; 6:188. [PMID: 29896472 PMCID: PMC5986912 DOI: 10.3389/fchem.2018.00188] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 05/09/2018] [Indexed: 12/05/2022] Open
Abstract
Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.
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Affiliation(s)
- Claudio N. Cavasotto
- Laboratory of Computational Chemistry and Drug Design, Instituto de Investigación en Biomedicina de Buenos Aires, CONICET, Partner Institute of the Max Planck Society, Buenos Aires, Argentina
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9
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Spyrakis F, Cavasotto CN. Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch Biochem Biophys 2015; 583:105-19. [DOI: 10.1016/j.abb.2015.08.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 08/03/2015] [Accepted: 08/03/2015] [Indexed: 01/05/2023]
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10
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Rossetti G, Dibenedetto D, Calandrini V, Giorgetti A, Carloni P. Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins. Arch Biochem Biophys 2015; 582:91-100. [DOI: 10.1016/j.abb.2015.03.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Revised: 03/11/2015] [Accepted: 03/12/2015] [Indexed: 01/05/2023]
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Abstract
It is widely accepted that protein receptors exist as an ensemble of conformations in solution. How best to incorporate receptor flexibility into virtual screening protocols used for drug discovery remains a significant challenge. Here, stepwise methodologies are described to generate and select relevant protein conformations for virtual screening in the context of the relaxed complex scheme (RCS), to design small molecule libraries for docking, and to perform statistical analyses on the virtual screening results. Methods include equidistant spacing, RMSD-based clustering, and QR factorization protocols for ensemble generation and ROC analysis for ensemble selection.
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12
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Hoi PM, Li S, Vong CT, Tseng HHL, Kwan YW, Lee SMY. Recent advances in structure-based drug design and virtual screening of VEGFR tyrosine kinase inhibitors. Methods 2014; 71:85-91. [PMID: 25239735 DOI: 10.1016/j.ymeth.2014.09.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Revised: 08/26/2014] [Accepted: 09/09/2014] [Indexed: 01/07/2023] Open
Abstract
During the past decade, developments in computational processing and X-ray crystallography have allowed virtual screening become integrated into drug discovery campaigns. This review focuses on the recent advancements in the drug discovery of VEGFR2 tyrosine kinase inhibitors (VEGFR2 TKIs) by using in silico methodologies. An introduction for the methodology framework of pharmacophore modeling, molecular docking and structure-based design are provided. We discuss the recent studies on the structures of VEGFR2 protein kinase in different binding modes, and the insights on molecular interactions gained from knowledge of the co-crystal structures complex with structurally diverse VEGFR2 inhibitors. We provide some aspects of model construction and molecular docking techniques. Several representative examples of successful applications on VEGFR2 virtual screening for hit discovery, lead optimization and structure-based design are also presented.
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Affiliation(s)
- Pui Man Hoi
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macao, China.
| | - Shang Li
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macao, China
| | - Chi Teng Vong
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macao, China
| | - Hisa Hui Ling Tseng
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macao, China
| | - Yiu Wa Kwan
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macao, China
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13
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Fayaz SM, Rajanikant GK. Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors. J Comput Aided Mol Des 2014; 28:779-94. [DOI: 10.1007/s10822-014-9771-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 06/23/2014] [Indexed: 12/29/2022]
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14
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Costanzi S. Modeling G protein-coupled receptors in complex with biased agonists. Trends Pharmacol Sci 2014; 35:277-83. [PMID: 24793542 DOI: 10.1016/j.tips.2014.04.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Revised: 03/01/2014] [Accepted: 04/03/2014] [Indexed: 01/09/2023]
Abstract
The biological response to the activation of G protein-coupled receptors (GPCRs) typically originates from the simultaneous modulation of various signaling pathways that lead to distinct biological consequences. Hence, 'biased agonists' (i.e., compounds that selectively activate one of the pathways while blocking the others) are highly sought-after molecules to provide fine-tuned pharmacological interventions. This review describes strategies that can be deployed to model the conformation of GPCRs in complex with ligands endowed with specific signaling profiles useful for the generation of hypotheses on the structural requirements for the activation of different signaling pathways or for rational computer-aided ligand discovery campaigns. In particular, it focuses on strategies potentially applicable to model the global or local conformational states of GPCRs stabilized by specific ligands.
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Affiliation(s)
- Stefano Costanzi
- Department of Chemistry, American University, Washington, DC 20016, USA; Center for Behavioral Neuroscience, American University, Washington, DC 20016, USA.
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15
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Du J, Bleylevens IWM, Bitorina AV, Wichapong K, Nicolaes GAF. Optimization of Compound Ranking for Structure-Based Virtual Ligand Screening Using an Established FRED-Surflex Consensus Approach. Chem Biol Drug Des 2013; 83:37-51. [DOI: 10.1111/cbdd.12202] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 07/11/2013] [Accepted: 07/18/2013] [Indexed: 11/27/2022]
Affiliation(s)
- Jiangfeng Du
- Department of Biochemistry; Cardiovascular Research Institute Maastricht; Maastricht University; P.O. Box 616, 6200 MD Maastricht The Netherlands
| | - Ivo W. M. Bleylevens
- Department of Population Genetics, Genomics and Bioinformatics; Maastricht University; P.O. Box 616, 6200 MD Maastricht The Netherlands
| | - Albert V. Bitorina
- Department of Biochemistry; Cardiovascular Research Institute Maastricht; Maastricht University; P.O. Box 616, 6200 MD Maastricht The Netherlands
| | - Kanin Wichapong
- Department of Biochemistry; Cardiovascular Research Institute Maastricht; Maastricht University; P.O. Box 616, 6200 MD Maastricht The Netherlands
| | - Gerry A. F. Nicolaes
- Department of Biochemistry; Cardiovascular Research Institute Maastricht; Maastricht University; P.O. Box 616, 6200 MD Maastricht The Netherlands
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16
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Modeling G protein-coupled receptors and their interactions with ligands. Curr Opin Struct Biol 2013; 23:185-90. [DOI: 10.1016/j.sbi.2013.01.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2012] [Revised: 01/08/2013] [Accepted: 01/22/2013] [Indexed: 12/20/2022]
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