Domain Analysis and Visualization of NLRP10

Domain Analysis and Visualization of NLRP10

Автор: Sim-Hui Tee

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 9 Vol. 5, 2013 года.

Бесплатный доступ

NLRP10 is one of the members of NOD-like receptors (NLRs) family that is least characterized. It is a protein that takes part in pathogen sensing and responsible for the subsequent signaling propagation leading to immunologic response. In this study, computational tools such as algorithm, web server and database were used to investigate the domain of NLRP10 protein. The findings of this research may provide computational insights into the structure and functions of NLRP10, which in turn may foster better understanding of the role of NLRP10 in the immunologic defense.

Scientific Computing, Bioinformatics, Database, Algorithm, Visualization, Protein, Server

Короткий адрес: https://sciup.org/15011963

IDR: 15011963

Список литературы Domain Analysis and Visualization of NLRP10

  • T. Horváth, J. Ramon, and S. Wrobel, “Frequent subgraph mining in outerplanar graphs,” Data Mining and Knowledge Discovery, Vol. 21, pp. 472-508, 2010.
  • K.S. Kim, J.H. Seo, S.H. Ryu, M.H. Kim, and C.G. Song, “Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel,” Computer Methods and Programs in Biomedicine, Vol. 104, pp. 426-434, 2011.
  • Y. Teng and T. Zhang, “Three penalized EM-type algorithms for PET image reconstruction,” Computers in Biology and Medicine, Vol. 42, pp. 714-723, 2012.
  • R-P. Liang, S-Y. Huang, S-P. Shi, X-Y. Sun, S-B. Suo, and J-D. Qiu, “A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization,” Computers in Biology and Medicine, Vol. 42, pp. 180-187, 2012.
  • T. Liu, W. Zhao, L. Tian, R. Wu, “An algorithm for molecular dissection of tumor,” Journal of Mathematical Biology, Vol. 50, pp. 336-354, 2005.
  • C.E. Hann, P. Docherty, J.G. Chase, and G.M. Shaw, “A fast generalizable solution method for glucose control algorithms,” Mathematical Biosciences, Vol. 227, pp. 44-55, 2010.
  • T. Rausch, S. Koren, G. Denisov, D. Weese, A-K. Emde, A. Döring, and K. Reinert, “A consistency-based consensus algorithm for de novo and reference-guided sequence assembly of short reads,” Bioinformatics, Vol. 25, No. 9, pp. 1118-1124, 2009.
  • S. Gunewardena and Z. Zhang, “A hybrid model for robust detection of transcription factor binding sites,” Bioinformatics, Vol. 24, No. 4, pp. 484-491, 2008.
  • M.A Jonikas, R.J. Radmer, and R.B. Altman, “Knowledge-based instantiation of full atomic detail into coarse-grain RNA 3D structural models,” Bioinformatics, Vol. 25, No. 24, pp. 3259-3266, 2009.
  • S. Loriot and F. Cazals, “Modeling macro-molecular interfaces with Intervor,” Bioinformatics, Vol. 26, No. 7, pp. 964-965, 2010.
  • C-F. Huang, J. Kaur, A. Maguitman, and L.M. Rocha, “Agent-based model of genotype editing,” Evolutionary Computation, Vol. 15, pp. 253-289, 2007.
  • T. Müller, R. Spang, and M. Vingron, “Estimating amino acid substitution models: a comparison of Dayhoff’s estimator, the resolvent approach and a maximum likelihood method,” Molecular Biology and Evolution, Vol. 19, pp. 8-13, 2002.
  • D. Talavera, R.A. Laskowski, and J.M. Thornton, “WSsas: a web service for the annotation of functional residues through structural homologues,” Bioinformatics, Vol. 25, No. 9, pp. 1192-1194, 2009.
  • C. Yeats, J. Lees, P. Carter, I. Sillitoe, and C. Orengo, “The Gene3D web services: a platform for identifying, annotating and comparing structural domains in protein sequences,” Nucleic Acids Research, Vol. 39, pp. W546-W550, 2011.
  • F. Poitevin, H. Orland, S. Doniach, P. Koehl, and M. Delarue, “AquaSAXS: a web server for computation and fitting of SAXS profiles with non-uniformally hydrated atomic models,” Nucleic Acids Research, Vol. 39, pp. W184-W189, 2011.
  • E. Tjioe, K. Lasker, B. Webb, H.J. Wolfson, and A. Sali, “MultiFit: a web server for fitting multiple protein structures into their electron microscopy density map,” Nucleic Acids Research, Vol. 39, pp. W167-W170, 2011.
  • J-D. Qiu, S-B. Suo, X-Y. Sun, S-P. Shi, and R-P. Liang, “OligoPred: a web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou’s pseudo amino acid composition,” Journal of Molecular Graphics and Modelling, Vol. 30, pp. 129-134, 2011.
  • M. DiBernardo, R. Pottinger, and M. Wilkinson, “Semi-automatic web service composition for the life sciences using the BioMoby semantic web framework,” Journal of Biomedical Informatics, Vol. 41, pp. 837-847, 2008.
  • J.H. Gennari, M.L. Neal, M. Galdzicki, and D.L. Cook, “Multiple ontologies in action: composite annotations for biosimulation models,” Journal of Biomedical Informatics, Vol. 44, pp. 146-154, 2011.
  • G.D. O’Clock, Y.W. Lee, J. Lee, and W.J. Warwick, “A simulation tool to study high-frequency chest compression energy transfer mechanisms and waveforms for pulmonary disease applications,” IEEE Transactions on Biomedical Engineering, Vol. 57, No. 7, pp. 1539-1546, 2010.
  • M-D. Tsai and M-S. Hsieh, “Volume manipulations for simulating bone and joint surgery,” IEEE Transactions on Information Technology in Biomedicine, Vol. 9, No. 1, pp. 139-149,2005.
  • S. Costantini, G. Colonna, A.M. Facchiano, “Simulation of conformational changes occurring when a protein interacts with its receptor,” Computational Biology and Chemistry, Vol. 31, pp. 196-206, 2007.
  • H. Ogawa, M. Nakano, H. Watanabe, E. Starikov, S.M. Rothstein, and S. Tanaka, “Molecular dynamics simulation study on the structural stabilities of polyglutamine peptides,” Computational Biology and Chemistry, Vol. 32, pp. 102-110, 2008.
  • S. Moon, Y. Byun, and K. Han, “FSDB: a frameshift signal database,” Computational Biology and Chemistry, Vol. 31, pp. 298-302, 2007.
  • J-T. Horng, F.M. Lin, J.H. Lin, H.D. Huang, and B.J. Liu, “Database of repetitive elements in complete genomes and data mining using transcription factor binding sites,” IEEE Transactions on Information Technology in Biomedicine,” Vol. 7, No. 2, pp. 93-100, 2003.
  • D. Taylor, R.N.G. Naguib, and S. Boulton, “A dynamic clinical dental relational database,” IEEE Transactions on Information Technology in Biomedicine, Vol. 8, No. 3, pp. 298-305, 2004.
  • V.A. Gennarino, M. Sardiello, M. Mutarelli, G. Dharmalingam, V. Maselli, G. Lago, and S. Banfi, “HOCTAR database: a unique resource for microRNA target prediction,” Gene, Vol. 480, pp. 51-58, 2011.
  • F. Piva, M. Giulietti, L. Nocchi, and G. Principato, “SpliceAid: a database of experimental RNA target motifs bound by splicing proteins in humans,” Bioinformatics, Vol. 25, No. 9, pp. 1211-1213, 2009.
  • M. Pellegrini, M.E. Renda, and A. Vecchio, “TRStalker: an efficient heuristic for finding fuzzy tandem,” Bioinformatics, Vol. 26, pp. i358-i366, 2010.
  • S-Y. Ho, C-H. Hsieh, H-M. Chen, and H-L. Huang, “Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis,” BioSystems, Vol. 85, pp. 165-176, 2006.
  • G. Dudek, Z.J. Grzywna, and M.L. Willcox, “Classification of antituberculosis herbs for remedial purposes by using fuzzy sets,” BioSystems, Vol. 94, pp. 285-289, 2008.
  • R. Brette, “Generation of correlated spike trains,” Neural Computation, Vol. 21, pp. 188-215, 2009.
  • H. Chen, S. Saïghi, L. Buhry, and S. Renaud, “Real-time simulation of biologically realistic stochastic neurons in VLSI,” IEEE Transactions on Neural Networks, Vol. 21, No. 9, pp. 1511-1517, 2010.
  • R.I. Ionasec, I. Voigt, B. Georgescu, Y. Wang, H. Houle, F. Vega-Higuera, N. Navab, and D. Comaniciu, “Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE,” IEEE Transactions on Medical Imaging, Vol. 29, No. 9, pp. 1636-1651, 2010.
  • J. Duan, S.L. Dixon, J.F. Lowrie, and W. Sherman, “Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods,” Journal of Molecular Graphics and Modelling, Vol. 29, pp. 157-170, 2010.
  • E. Yang, P. Liu, D.N. Rassokhin, and D.K. Agrafiotis, “Stochastic proximity embedding on graphics processing units: taking multidimensional scaling to a new scale,” Journal of Chemical Information and Modeling, Vol. 51, pp. 2852-2859, 2011.
  • R. Eslami and M. Jacob, “Robust reconstruction of MRSI data using a sparse spectral model and high resolution MRI priors,” IEEE Transactions on Medical Imaging, Vol. 29, No. 6, pp. 1297-1309, 2010.
  • F. Yang and T. Jiang, “Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model,” Journal of Biomedical Informatics, Vol. 34, pp. 67-73, 2001.
  • H.P. Benton, E.J. Want, and T. Ebbels, “Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data,” Bioinformatics, Vol. 26, No. 19, pp. 2488-2489, 2010.
  • Y. Kano, P. Dobson, M. Nakanishi, J. Tsujii, and S. Ananiadou, “Text mining meets workflow: linking U-Compare with Taverna,” Bioinformatics, Vol. 26, No. 19, pp. 2486-2487, 2010.
  • T.A. Peterson, A. Adadey, I. Santana-Cruz, Y. Sun, A. Winder, and M.G. Kann, “DMDM: domain mapping of disease mutations,” Bioinformatics, Vol. 26, No. 19, pp. 2458-2459, 2010.
  • A. Taneda, “Multi-objective pairwise RNA sequence alignment,” Bioinformatics, Vol. 26, No. 19, pp. 2383-2390, 2010.
  • H. Abel, E. Duncavage, N. Becker, J.R. Armstrong, V.J. Magrini, and J.D. Pfeifer, “SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data,” Bioinformatics, Vol. 26, No. 21, pp. 2684-2688, 2010.
  • X. Meng, Q. Lu, and J. Rinzel, “Control of firing patterns by two transient potassium currents: leading spike, latency, bistability,” Journal of Computational Neuroscience, Vol. 31, No. 1, pp. 117-136, 2011.
  • J.D. Drover, N.D. Schiff, and J.D. Victor, “Dynamics of coupled thalamocortical modules,” Journal of Computational Neuroscience, Vol. 28, pp. 605-616, 2010.
  • Y-K. Yu and S.F. Altschul, “The complexity of the Dirichlet model for multiple alignment data,” Journal of Computational Biology, Vol. 18, No. 8, pp. 925-939, 2011.
  • L-C. Wu, J-T. Horng, and Y-A. Chen, “A computation to integrate the analysis of genetic variations occurring within regulatory elements and their possible effects,” Journal of Computational Biology, Vol. 16, No. 12, pp. 1731-1747, 2009.
  • K. Krepkin and J. Costa, “Defining the role of cooperation in early tumor progression,” Journal of Theoretical Biology, Vol. 285, pp. 36-45, 2011.
  • J.K. Møller, K.R. Bergmann, L.E. Christiansen, H. Madsen, “Development of a restricted state space stochastic differential equation model for bacterial growth in rich media,” Journal of Theoretical Biology, Vol. 305, pp. 78-87, 2012.
  • M. Rueda, V. Katritch, E. Raush, and R. Abagyan, “SimiCon: a web tool for protein-ligand model comparison through calculation of equivalent atomic contacts,” Bioinformatics, Vol. 26, No. 21, pp. 2784-2785, 2010.
  • T. Nugent and D.T. Jones, “Membrane protein structural bioinformatics,” Journal of Structural Biology, Vol. 179, pp. 327-337, 2012.
  • M. Andronescu, A. Condon, H.H. Hoos, D.H. Mathews, and K. Murphy, “Computational approaches for RNA energy parameter estimation,” RNA, Vol. 16, pp. 2304-2318, 2010.
  • B.L. Howard, P.E. Thompson, and D.T. Manallack, “Active site similarity between human and Plasmodium falciparum phosphodiesterases: considerations for antimalarial drug design,” Journal of Computer-aided Molecular Design, Vol. 25, pp. 753-762, 2011.
  • M.A. Alam and P.K. Naik, “Molecular modelling evaluation of the cytotoxic activity of podophyllotoxin analogues,” Journal of Computer-aided Molecular Design, Vol. 23, pp. 209-225, 2009.
  • K-C. Shih, C-Y. Lin, H-C. Chi, C-S. Hwang, T-S. Chen, C-Y. Tang, and N-W. Hsiao, “Design of novel FLT-3 inhibitors based on dual-layer 3D-QSAR model and fragment-based compounds in silico,” Journal of Chemical Information and Modeling, Vol. 52, pp. 146-155, 2012.
  • R.O. Lindén, V-P. Eronen, and T. Aittokallio, “Quantitative maps of genetic interactions in yeast—comparative evaluation and integrative analysis,” BMC Systems Biology, Vol. 5: 45, 2011.
  • P. Wei and W. Pan, “Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model,” Bioinformatics, Vol. 24, No. 3, pp. 404-411, 2008.
  • K.Y. Yip, P. Patel, P.M. Kim, D.M. Engelman, D. McDermott, and M. Gerstein, “An integrated system for studying residue coevolution in proteins,” Bioinformatics, Vol. 24, No. 2, pp. 290-292, 2008.
  • C. Huang, R. Zhang, Z. Chen, Y. Jiang, Z. Shang, P. Sun, X. Zhang, and X. Li, “Predict potential drug targets from the ion channel proteins based on SVM,” Journal of Theoretical Biology, Vol. 262, pp. 750-756, 2010.
  • R. Wallace, “Protein folding disorders: toward a basic biological paradigm,” Journal of Theoretical Biology, Vol. 267, pp. 582-594, 2010.
  • R. Xu and Y. Xiao, “A common sequence-associated physicochemical feature for proteins of beta-trefoil family,” Computational Biology and Chemistry, Vol. 29, pp. 79-82, 2005.
  • L. Yao, Y. Zhang, Y. Li, P. Sanseau, and P. Agarwal, “Electronic health records: implications for drug discovery,” Drug Discovery Today, Vol. 16, pp. 594-599, 2011.
  • L. Xie, L. Xie, S.L. Kinnings, and P.E. Bourne, “Novel computational approaches to polypharmacology as a means to define responses to individual drugs,” Annual Review of Pharmacology and Toxicology, Vol. 52, pp. 361-379, 2012.
  • B.H. Havsteen, “Dynamic analysis of the atomic vibrations of proteins, as exemplified by the binding of myristic acid to human serum albumin,” European Biophysics Journal, Vol. 38, pp. 1029-1034, 2009.
  • B.M. Mills and L.T. Chong, “Molecular simulations of mutually exclusive folding in a two-domain protein switch,” Biophysical Journal, Vol. 100, pp. 756-764, 2011.
  • G. Heinzelmann, T. Baştuğ, and S. Kuyucak, “Free energy simulations of ligand binding to the aspartate transporter GltPh,” Biophysical Journal, Vol. 101, pp. 2380-2388, 2011.
  • E.E. Schadt, M.D. Linderman, J. Sorenson, L. Lee, and G.P. Nolan, “Computational solutions to large-scale data management and analysis,” Nature Reviews Genetics, Vol. 11, pp. 647-657, 2010.
  • K. Schroder and J. Tschopp, “The inflammasomes,” Cell, Vol. 140, pp. 821-832, 2010.
  • L.M. Rehaume, T. Jouault, and M. Chamaillard, “Lessons from the inflammasome: a molecular sentry linking Candida and Crohn’s disease,” Trends in Immunology, Vol. 31, No. 5, pp. 171-175, 2010.
  • M.H. Shaw, T. Reimer, Y-G. Kim, and G. Nuñez, “NOD-like receptors (NLRs): bona fide intracellular microbial sensors,” Current Opinion in Immunology, Vol. 20, pp. 377-382, 2008.
  • L. O’Neill, “How toll-like receptors signal: what we know and what we don’t know,” Current Opinion in Immunology, Vol. 18, pp. 3-9, 2006.
  • G.N. Barber, “Innate immune DNA sensing pathways: STING, AIMII and the regulation of interferon production and inflammatory responses,” Current Opinion in Immunology, Vol. 23, pp. 10-20, 2011.
  • F. van de Veerdonk, M.G. Netea, C.A. Dinarello, and L. Joosten, “Inflammasome activation and IL-1β and IL-18 processing during infection,” Trends in Immunology, Vol. 32, No. 3, pp. 110-116, 2011.
  • I.K. Pang and A. Iwasaki, “Inflammasomes as mediators of immunity against influenza virus,” Trends in Immunology, Vol. 32, No. 1, pp. 34-41, 2011.
  • Y. Qiao, P. Wang, J. Qi, L. Zhang, and C. Gao, “TLR-induced NF-ĸB activation regulates NLRP3 expression in murine macrophages,” FEBS Letters, Vol. 586, pp. 1022-1026, 2012.
  • S.H. Park, M.S. Kyeong, Y. Hwang, S.Y. Ryu, S-B. Han, and Y. Kim, “Inhibition of LPS binding to MD-2 co-receptor for suppressing TLR4-mediated expression of inflammatory cytokine by 1-dehydro-10-gingerdione from dietary ginger,” Biochemical and Biophysical Research Communications, Vol. 419, pp. 735-740, 2012.
  • W. Hugo, F. Song, Z. Aung, S-K. Ng, and W-K. Sung, “SLiM on diet: finding short linear motifs on domain interaction interfaces in protein data bank,” Bioinformatics, Vol. 26, No. 8, pp. 1036-1042, 2010.
  • K.O. Kopec, V. Alva, and A.N. Lupas, “Homology of SMP domains to the TULIP superfamily of lipid-binding proteins provides a structural basis for lipid exchange between ER and mitochondria,” Bioinformatics, Vol. 26, No. 16, pp. 1927-1931, 2010.
  • M. Sarver, C.L. Zirbel, J. Stombaugh, A. Mokdad, and N.B. Leontis, “FR3D: finding local and composite recurrent structural motifs in RNA 3D structures,” Journal of Mathematical Biology, Vol. 56, pp. 215-252, 2008.
  • G. Li, T-M. Chan, K-S. Leung, and K-H. Lee, “A cluster refinement algorithm for motif discovery,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 7, No. 4, pp. 654-668, 2010.
  • Z. Qin, L. McCue, W. Thompson, L. Mayerhofer, C. Lawrence, and J. Liu, “Identification of co-regulated genes through Bayesian clustering of predicted regulatory binding sites,” Nature Biotechnology, Vol. 21, pp. 435-439, 2003.
  • T. Przytycka, G. Davis, N. Song, and D. Durand, “Graph theoretical insights into evolution of multidomain proteins,” Journal of Computational Biology, Vol. 13, No. 2, pp. 351-363, 2006.
  • N. Song, R.D. Sedgewick, and D. Durand, “Domain architecture comparison for multidomain homology identification,” Journal of Computational Biology, Vol. 14, No. 4, pp. 496-516, 2007.
  • J.F. Doreleijers, A.J. Nederveen, W. Vranken, J. Lin, A.M. Bonvin, R. Kaptein, J.L. Markley, and E.L. Ulrich, “BioMagResBank databases DOCR and FRED with converted and filtered sets of experimental NMR restraints and coordinates from over 500 protein PDB structures,” Journal of Biomolecular NMR, Vol. 32, pp. 1-12, 2005.
  • J. Lundstrom, L. Rychlewski, J. Bujnicki, and A. Elofsson, “Pcons: a neural-network-based consensus predictor that improves fold recognition,” Protein Science, Vol. 10, pp. 2354-2362, 2001.
  • J.F. Doreleijers, W.F. Vranken, C. Schulte, J.L. Markley, E.L. Ulrich, G. Vriend, and G.W. Vuister, “NRG-CING: integrated validation reports of remediated experimental biomolecular NMR data and coordinates in wwPDB,” Nucleic Acids Research, Vol. 40, pp. D519-D524, 2012.
  • J.R. Quine, T.A. Cross, M.S. Chapman, and R. Bertram, “Mathematical aspects of protein structure determination with NMR orientational restraints,” Bulletin of Mathematical Biology, Vol. 66, pp. 1705-1730, 2004.
  • S. Kumar and R. Nussinov, “Relationship between ion pair geometries and electrostatic strengths in proteins,” Biophysical Journal, Vol. 83, pp. 1595-1612, 2002.
  • G. Ramachandran, C. Ramakrishnan, and V. Sasisekharan, “Stereochemistry of polypeptide chain configurations,” Journal of Molecular Biology, Vol. 7, pp. 95-99, 1963.
  • B.X. Yan and Y.Q. Sun, “Glycine residues provide flexibility for enzyme active sites,” Journal of Biological Chemistry, Vol. 272, pp. 3190-3194, 1997.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp