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Investigations on the role of CH…O interactions and its impact on stability and specificity of penicillin binding proteins. Comput Biol Med 2015; 65:85-92. [DOI: 10.1016/j.compbiomed.2015.07.028] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Revised: 07/09/2015] [Accepted: 07/31/2015] [Indexed: 11/22/2022]
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2
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Shi X, Zhao Q, Huang J, Xie Y, Ma S. Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics 2015; 31:3977-83. [PMID: 26342102 DOI: 10.1093/bioinformatics/btv518] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 07/20/2015] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation. RESULTS A sparse double Laplacian shrinkage method is developed. It jointly models the effects of multiple CNAs on multiple GEs. Penalization is adopted to achieve sparsity and identify the regulation relationships. Network adjacency is computed to describe the interconnections among GEs and among CNAs. Two Laplacian shrinkage penalties are imposed to accommodate the network adjacency measures. Simulation shows that the proposed method outperforms the competing alternatives with more accurate marker identification. The Cancer Genome Atlas data are analysed to further demonstrate advantages of the proposed method. AVAILABILITY AND IMPLEMENTATION R code is available at http://works.bepress.com/shuangge/49/.
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Affiliation(s)
- Xingjie Shi
- Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Qing Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Jian Huang
- Department of Statistics and Actuarial Science, University of Iowa, Iowa, IA, USA
| | - Yang Xie
- Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, TX, USA and
| | - Shuangge Ma
- Department of Biostatistics, Yale University, New Haven, CT, USA, VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA
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Lavanya P, Ramaiah S, Anbarasu A. Binding site residues in β-lactamases: role in non-classical interactions and metal binding. J COORD CHEM 2014. [DOI: 10.1080/00958972.2014.956661] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- P. Lavanya
- Medical & Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore, India
| | - Sudha Ramaiah
- Medical & Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore, India
| | - Anand Anbarasu
- Medical & Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore, India
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Lavanya P, Ramaiah S, Anbarasu A. Computational analysis of N–H⋯π interactions and its impact on the structural stability of β-lactamases. Comput Biol Med 2014; 46:22-8. [DOI: 10.1016/j.compbiomed.2013.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2013] [Revised: 12/12/2013] [Accepted: 12/15/2013] [Indexed: 10/25/2022]
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5
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Huang IK, Pei J, Grishin NV. Defining and predicting structurally conserved regions in protein superfamilies. ACTA ACUST UNITED AC 2012. [PMID: 23193223 DOI: 10.1093/bioinformatics/bts682] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. RESULTS Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. AVAILABILITY The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. CONTACT 91huangi@gmail.com or grishin@chop.swmed.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics Online.
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Affiliation(s)
- Ivan K Huang
- Department of Mathematics, Rice University, Houston, TX 77005, USA.
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Redondo M, Palomo V, Brea J, Pérez DI, Martín-Álvarez R, Pérez C, Paúl-Fernández N, Conde S, Cadavid MI, Loza MI, Mengod G, Martínez A, Gil C, Campillo NE. Identification in silico and experimental validation of novel phosphodiesterase 7 inhibitors with efficacy in experimental autoimmune encephalomyelitis mice. ACS Chem Neurosci 2012; 3:793-803. [PMID: 23077723 DOI: 10.1021/cn300105c] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 08/08/2012] [Indexed: 12/27/2022] Open
Abstract
A neural network model has been developed to predict the inhibitory capacity of any chemical structure to be a phosphodiesterase 7 (PDE7) inhibitor, a new promising kind of drugs for the treatment of neurological disorders. The numerical definition of the structures was achieved using CODES program. Through the validation of this neural network model, a novel family of 5-imino-1,2,4-thiadiazoles (ITDZs) has been identified as inhibitors of PDE7. Experimental extensive biological studies have demonstrated the ability of ITDZs to inhibit PDE7 and to increase intracellular levels of cAMP. Among them, the derivative 15 showed a high in vitro potency with desirable pharmacokinetic profile (safe genotoxicity and blood brain barrier penetration). Administration of ITDZ 15 in an experimental autoimmune encephalomyelitis (EAE) mouse model results in a significant attenuation of clinical symptoms, showing the potential of ITDZs, especially compound 15, for the effective treatment of multiple sclerosis.
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Affiliation(s)
- Miriam Redondo
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - Valle Palomo
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - José Brea
- Instituto de Farmacia
Industrial,
Facultad de Farmacia, Universidad de Santiago de Compostela, Campus Universitario Sur s/n, 15782 Santiago de Compostela, Spain
| | - Daniel I. Pérez
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - Rocío Martín-Álvarez
- Instituto de Investigaciones Biomédicas de Barcelona (CSIC, IDIBAPS, CIBERNED),
Rosselló 161, 08036 Barcelona, Spain
| | - Concepción Pérez
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - Nuria Paúl-Fernández
- Instituto de Investigaciones Biomédicas de Barcelona (CSIC, IDIBAPS, CIBERNED),
Rosselló 161, 08036 Barcelona, Spain
| | - Santiago Conde
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - María Isabel Cadavid
- Instituto de Farmacia
Industrial,
Facultad de Farmacia, Universidad de Santiago de Compostela, Campus Universitario Sur s/n, 15782 Santiago de Compostela, Spain
| | - María Isabel Loza
- Instituto de Farmacia
Industrial,
Facultad de Farmacia, Universidad de Santiago de Compostela, Campus Universitario Sur s/n, 15782 Santiago de Compostela, Spain
| | - Guadalupe Mengod
- Instituto de Investigaciones Biomédicas de Barcelona (CSIC, IDIBAPS, CIBERNED),
Rosselló 161, 08036 Barcelona, Spain
| | - Ana Martínez
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - Carmen Gil
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
| | - Nuria E. Campillo
- Instituto de Química Médica (CSIC), Juan de la Cierva 3, 28006 Madrid,
Spain
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Pugalenthi G, Kandaswamy KK, Suganthan PN, Sowdhamini R, Martinetz T, Kolatkar PR. SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information. J Biomol Struct Dyn 2011; 28:405-14. [PMID: 20919755 DOI: 10.1080/07391102.2010.10507369] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SMpred.htm.
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Affiliation(s)
- Ganesan Pugalenthi
- Laboratory of Structural Biochemistry, Genome Institute of Singapore, 60 Biopolis Street, Singapore 138672
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