Refine your search
Collections
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Srivastava, Prachi
- Identification of Limiting Factors for the Optimum Growth of Fusarium oxysporum in Liquid Medium
Abstract Views :183 |
PDF Views:0
Authors
Affiliations
1 Amity Institute of Biotechnology, Amity University Uttar Pradesh (Lucknow Campus), Lucknow - 226 010, IN
2 Department of Biotechnology, Integral University, Lucknow - 201 126, Uttar Pradesh, IN
1 Amity Institute of Biotechnology, Amity University Uttar Pradesh (Lucknow Campus), Lucknow - 226 010, IN
2 Department of Biotechnology, Integral University, Lucknow - 201 126, Uttar Pradesh, IN
Source
Toxicology International (Formerly Indian Journal of Toxicology), Vol 18, No 2 (2011), Pagination: 111-116Abstract
Fusarium oxysporum is a highly ubiquitous species that infects a wide range of hosts causing various diseases such as vascular wilts, yellows, rots, and damping-off. Despite the immense economic significance of this phytopathogen, few workers have reported growth studies in this genus in submerged culture. In the present study, several parameters such as change in media pH, biomass, pattern of substrate utilization, viability of the fungal cells, and protein content were observed over a period of time. The fungal biomass increased at a slow rate for the initial 48 h and thereafter increased at an exponential rate. However, after about 8 days the rapid growth stabilized and the trend became more toward stationary phase. The concentration of glucose in the liquid media decreased rapidly up to the initial 4 days, followed by a slow decrease. The pH of the medium gradually decreased as the fungal growth progressed, the reduction being more pronounced in the initial 48 h. This study would be of immense importance for utilization of F. oxysporum for diverse applications because we can predict the growth pattern in the fungus and modulate its growth for human benefit.Keywords
Biomass production, Fusarium, glucose utilization, MTT assay, protein- Significant Analysis of Microarray (SAM) to Identify Synergistic Effect of RV and NGF in Repairing Damaged Neuronal Cells
Abstract Views :256 |
PDF Views:0
Authors
Affiliations
1 Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow - 226028, Uttar Pradesh, IN
1 Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow - 226028, Uttar Pradesh, IN
Source
Toxicology International (Formerly Indian Journal of Toxicology), Vol 25, No 1 (2018), Pagination: 26-39Abstract
Neurodevelopmental disorders include diseases that are related with genetic disorders and are caused due to stress condition or environmental toxins during pre or post natal condition. In recent years, there is considerable research in neurodevelopmental disorders and examining therapeutic role of resveratrol as potential antioxidant. There is less information about the genes that are mutated or altered during neurodevelopmental phase and cause neuronal developmental disorders. Current researches are evidentially showing the therapeutic potential of Resveratrol (RV) and Nerve Growth Factor (NGF) against neuronal diseases. In current study Microarray experiment was designed to identify the genes that are altered when Mesenchymal Stem Cells (MSC) were exposed to Monocrotophos (MCP). MSCs were also coexposed with resveratrol and nerve growth factor to study the synergistic effect of NGF with RV. Computational analysis of microarray data was carried out through different software’s and bioinformatics tools to identify genes that are expressed in different samples across microarray experiment. Statistical methods like T-test, SAM analysis and clustering techniques were performed between different samples using MeV software. Through Significant Analysis of Microarray (SAM) method we identified positive and negative significant genes with respect to current study. Clustering method was used to cluster genes associated with neuronal disease genes. Key genes that were predicted on the basis of t-test and SAM analysis are DNMT1, PGAP1, RDX and PEX26 genes, these genes have noticeable function in different classes of neuronal diseases like cerebellar ataxia, deafness, narcolepsy, mental retardation and zellweger spectrum disorder etc. Cluster analysis we identified genes such as ATP6V0D1, TESMIN, TRIM22, NAPEPLD, CDK7 and PKM, these genes have important function in cell growth, cell proliferation, protein synthesis, cell cycle regulation, and cell signaling. Study suggests that exposure of damaged neuronal cells to RV and NGF enhances the expression of neuronal repair genes, thus signifies the neuroprotectant and synergistic activity of RV and NGF. “The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus database and are accessible through GEO Series accession number GSE121261 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121261).”Keywords
Microarray Analysis, Nerve Growth Factor, Neuronal Damage, Neuronal Diseases, Resveratrol, SAM.References
- Levi-Montalcini R. The nerve growth factor 35 years later. Science. 1987; 237: 1154–62. https://doi.org/10.1126/science.3306916 PMid:3306916
- Schmidt CE, Leach JB. Neural tissue engineering: strategies for repair and regeneration. Annual Review of Biomedical Engineering. 2003; 5: 293–347. https://doi.org/10.1146/annurev.bioeng.5.011303.120731 PMid:14527315
- Bradshaw RA. Nerve growth factor. Annual Review of Biochemistry. 1978; 47: 191–216. https://doi.org/10.1146/ annurev.bi.47.070178.001203 PMid:79332
- Sofroniew MV, Charles LH, William CM. Nerve growth factor signaling, neuroprotection, and neural repair. Annual Review of Neuroscience. 2001; 24: 1217–81. https://doi.org/10.1146/annurev.neuro.24.1.1217 PMid:11520933
- Shigeno T, et al. Amelioration of delayed neuronal death in the hippocampus by nerve growth factor. Journal of Neuroscience. 1991; 11(9): 2914–9. https://doi.org/10.1523/JNEUROSCI.11-09-02914.1991 PMid:1880556
- Pfister LA, et al. Nerve conduits and growth factor delivery in peripheral nerve repair. Journal of the Peripheral Nervous System. 2007; 12(2): 65–82. https://doi.org/10.1111/j.1529-8027.2007.00125.x PMid:17565531
- Rich KM, et al. Nerve growth factor enhances regeneration through silicone chambers. Experimental Neurology. 1989; 105(2): 162–70. https://doi.org/10.1016/00144886(89)90115-5
- Schwab ME. Repairing the injured spinal cord. Science. 2002; 295(5557): 1029–31. https://doi.org/10.1126/science.1067840 PMid:11834824
- Fawcett JW, Richard AA. The glial scar and central nervous system repair. Brain Research Bulletin. 1999; 49: 377–91. https://doi.org/10.1016/S0361-9230(99)00072-6
- Hefti F, William JW. Nerve growth factor and Alzheimer’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society. 1986; 20: 275–81. https://doi.org/10.1002/ana.410200302 PMid:3532929
- Fahnestock M, et al. The precursor pro-nerve growth factor is the predominant form of nerve growth factor in brain and is increased in Alzheimer’s disease. Molecular and Cellular Neuroscience. 2001; 18: 210–20. https://doi.org/10.1006/mcne.2001.1016 PMid:11520181
- Siemann EH, Creasy LL. Concentration of the phytoalexin resveratrol in wine. American Journal of Enology and Viticulture. 1992; 43: 49–52.
- Baur JA, David AS. Therapeutic potential of resveratrol: the in vivo evidence. Nature Reviews Drug discovery. 2006; 5: 493. https://doi.org/10.1038/nrd2060 PMid:16732220
- Robb EL, Jeffrey AS. Trans-resveratrol as a neuroprotectant. Molecules. 2010; 15: 1196–212. https://doi.org/10.3390/molecules15031196 PMid:20335973 PMCid:PMC6257315
- Granzotto A, Zatta P. Resveratrol and Alzheimer’s disease: message in a bottle on red wine and cognition. Frontiers in Aging Neuroscience. 2014; 6: 95. https://doi.org/10.3389/fnagi.2014.00095 PMid:24860502 PMCid:PMC4030174
- Ding X-Z, Thomas EA. Resveratrol inhibits proliferation and induces apoptosis in human pancreatic cancer cells. Pancreas. 2002; 25: 71–6. https://doi.org/10.1097/00006676-200211000-00024
- Reddy NCS, Karnati PR. Protective effect of resveratrol against neuronal damage through oxidative stress in cerebral hemisphere of aluminum and fluoride treated rats. Interdisciplinary Toxicology. 2016; 9: 78–82. https://doi.org/10.1515/intox-2016-0009 PMid:28652849 PMCid:PMC5458107
- Baxter RA. Anti‐aging properties of resveratrol: review and report of a potent new antioxidant skin care formulation. Journal of Cosmetic dermatology. 2008; 7: 2–7. https://doi.org/10.1111/j.1473-2165.2008.00354.x PMid:18254804
- Zhang L-N, et al. Neuroprotective effect of resveratrol against glutamate-induced excitotoxicity. Adv Clin Exp Med. 2015; 24: 161–5.https://doi.org/10.17219/acem/38144 PMid:25923101
- Meftahi G, et al. Suppressive effects of resveratrol treatment on the intrinsic evoked excitability of CA1 pyramidal neurons. Cell Journal. 2015; 17: 532. PMid:26464825 PMCid:PMC4601874
- Saleh, MC, et al. Co-administration of resveratrol and lipoic acid, or their synthetic combination, enhances neuroprotection in a rat model of ischemia/reperfusion. PloS One. 2014; 9: 87865.https://doi.org/10.1371/journal.pone.0087865 PMid:24498217 PMCid:PMC3909267
- Frémont L. Biological effects of resveratrol. Life Sciences. 2000; 66: 663–73. https://doi.org/10.1016/S0024-3205(99)00410-5
- Siemann EH, Creasy LL. Concentration of the phytoalexin resveratrol in wine. American Journal of Enology and Viticulture. 1992; 43: 49–52.
- Mirnics K, et al. Analysis of complex brain disorders with gene expression microarrays: Schizophrenia as a disease of the synapse.Trends in Neurosciences. 2001; 24: 479–86. https://doi.org/10.1016/S0166-2236(00)01862-2
- Mirnics K, Pevsner J. Progress in the use of microarray technology to study the neurobiology of disease. Nature Neuroscience. 2004; 7: 434. https://doi.org/10.1038/nn1230 PMid:15114354
- Boccuto L, et al. Decreased tryptophan metabolism in patients with autism spectrum disorders. Molecular Autism. 2013; 4: 16. https://doi.org/10.1186/2040-2392-4-16 PMid:23731516 PMCid:PMC3680090
- Brazma A, et al. Array Express- a public repository for microarray gene expression data at the EBI. Nucleic Acids Research. 2003; 31: 68–71. https://doi.org/10.1093/nar/gkg091 PMid:12519949 PMCid:PMC165538
- Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research. 2002; 30: 207–10. https://doi.org/10.1093/nar/30.1.207PMid:11752295 PMCid:PMC99122
- Chahrour M, et al. MeCP2, a key contributor to neurological disease, activates and represses transcription. Science. 2008; 320: 1224–9. https://doi.org/10.1126/science.1153252 PMid:18511691 PMCid:PMC2443785
- Ishigaki S, et al. Differentially expressed genes in sporadic amyotrophic lateral sclerosis spinal cords–screening by molecular indexing and subsequent cDNA microarray analysis. FEBS Letters. 2002; 531: 354–8. https://doi.org/10.1016/S0014-5793(02)03546-9
- Mullighan CG. The molecular genetic makeup of acute lymphoblastic leukemia. ASH Education Program Book. 2012; 2012: 389–96.
- Freeman T. High throughput gene expression screening: its emerging role in drug discovery. Medicinal Research Reviews. 2000; 20: 197–202. https://doi.org/10.1002/(SICI)1098-1128(200005)20:3<197::AID-MED3>3.0.CO;2-1
- Yadav R, Srivastava P. Clustering, pathway enrichment, and protein-protein interaction analysis of gene expression in neurodevelopmental disorders. Advances in Pharmacological Sciences. 2018; 2018.
- Smyth GK, Yang YH, Speed T. Statistical issues in cDNA microarray data analysis. Functional Genomics. 2003; 224: 111036. https://doi.org/10.1385/1-59259-364-X:111 PMid:12710670
- Durinck S, et al. Bio Mart and Bioconductor: A powerful link between biological databases and microarray data analysis. Bioinformatics. 2005; 21: 3439–40. https://doi.org/10.1093/bioinformatics/bti525 PMid:16082012
- Saeed AI, et al. TM4 microarray software suite. Methods in Enzymology. 2006; 411: 134–93. https://doi.org/10.1016/S0076-6879(06)11009-5
- Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37: 13. https://doi.org/10.1093/nar/gkn923PMid:19033363 PMCid:PMC2615629
- Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics. 2001; 17: 509–19.https://doi.org/10.1093/bioinformatics/17.6.509 PMid:11395427
- Dinu I, et al. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics. 2007; 8: 242. https://doi.org/10.1186/1471-2105-8-242 PMid:17612399 PMCid:PMC1931607
- Cheadle C, et al. Analysis of microarray data using Z score transformation. The Journal of Molecular Diagnostics. 2003; 5: 73–81. https://doi.org/10.1016/S1525-1578(10)60455-2
- Cahan P, et al. Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization. Gene. 2007; 401: 12–8. https://doi.org/10.1016/j.gene.2007.06.016 PMid:17651921 PMCid:PMC2111172
- Butte A. The use and analysis of microarray data. Nature Reviews Drug discovery. 2002; 1: 951. https://doi.org/10.1038/nrd961 PMid:12461517
- Feng J, et al. Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons.Nature Neuroscience. 2010; 13: 423. https://doi.org/10.1038/nn.2514 PMid:20228804 PMCid:PMC3060772
- Liu L, et al. DNA methylation impacts on learning and memory in aging. Neurobiology of Aging. 2009; 30: 549–60. https://doi.org/10.1016/j.neurobiolaging.2007.07.020 PMid:17850924 PMCid:PMC2656583
- Novarino G, et al. Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders. Science. 2014; 343: 506–11. https://doi.org/10.1126/science.1247363 PMid:24482476 PMCid:PMC4157572
- Benitez-King G, et al. The neuronal cytoskeleton as a potential therapeutical target in neurodegenerative diseases and schizophrenia. Current Drug Targets-CNS and Neurological Disorders. 2004; 3: 515–33. https://doi.org/10.2174/1568007043336761 PMid:15581421
- Fujiki Y, Yagita Y, Matsuzaki T. Peroxisome biogenesis disorders: Molecular basis for impaired peroxisomal membrane assembly: in metabolic functions and biogenesis of peroxisomes in health and disease. Biochimicaet Biophysica Acta (BBA)-Molecular Basis of Disease. 2012; 1822: 42.
- Kroemer G. The proto-oncogene Bcl-2 and its role in regulating apoptosis. Nature Medicine. 1997; 3: 614. https://doi.org/10.1038/nm0897-934 PMid:9176486
- Myrie KA, et al. Mutation and expression analysis of human BUB1 and BUB1B in aneuploid breast cancer cell lines.Cancer Letters. 2000; 152: 193–9. https://doi.org/10.1016/S0304-3835(00)00340-2
- Meech R, et al. A novel function for UDP Glycosyltransferase 8 (UGT8): Galactosidation of bile acids. Molecular Pharmacology. 2015; 87(3): 442–50. https://doi.org/10.1124/mol.114.093823 PMid:25519837
- Cuevas R, et al. FGF-2 disrupts mitotic stability in prostate cancer through the intracellular trafficking protein CEP57. Cancer Research. 2013 Feb 15; 73(4): 1400–10. https://doi.org/10.1158/0008-5472.CAN-12-1857 PMid:23243019
- Moosmang S, et al. Cellular expression and functional characterization of four hyperpolarization‐activated pacemaker channels in cardiac and neuronal tissues. European Journal of Biochemistry. 2001; 268: 1646–52. https://doi.org/10.1046/j.1432-1327.2001.02036.x PMid:11248683
- Hoffmann K et al. Mutations in the gene encoding the lamin B receptor produces an altered nuclear morphology in granulocytes (Pelger–Huet anomaly). Nature Genetics. 2002; 31: 410. https://doi.org/10.1038/ng925PMid:12118250
- Marion V, et al. Exome sequencing identifies mutations in LZTFL1, a BBSome and smoothened trafficking regulator, in a family with Bardet–Biedl syndrome with situs inversus and insertional polydactyly. Journal of Medical Genetics. 2012; 49: 317–21. https://doi.org/10.1136/jmedgenet-2012-100737 PMid:22510444
- Liu N-P, et al. Mutations in corneal carbohydrate sulfotransferase 6 gene (CHST6) cause macular corneal dystrophy in Iceland. 2000 Dec 13; 6: 261–4.
- Flück CE, et al. Mutant P450 oxidoreductase causes disordered steroidogenesis with and without Antley-Bixler syndrome. Nature Genetics. 2004 Mar; 36(3): 228–30. https://doi.org/10.1038/ng1300PMid:14758361
- Tanaka K, et al. Analysis of a human DNA excision repair gene involved in group Axeroderma pigmentosum and containing a zinc-finger domain. Nature 1990; 348: 73. https://doi.org/10.1038/348073a0PMid:2234061
- Hallstrand TS, et al. Secreted phospholipase A2 group X overexpression in asthma and bronchial hyperresponsiveness. American Journal of Respiratory and Critical Care Medicine. 2007; 176: 1072–8. https://doi.org/10.1164/rccm.200707-1088OC PMid:17901411 PMCid:PMC2176098
- Zoncu R, et al. mTORC1 senses lysosomal amino acids through an inside-out mechanism that requires the vacuolar H+-ATPase. Science. 2011; 334: 678–83. https://doi.org/10.1126/science.1207056 PMid:22053050 PMCid:PMC3211112
- Yu S, et al. Identification of tripartite motif-containing 22 (TRIM22) as a novel NF-κB activator. Biochemical and Biophysical Research Communications. 2011; 410: 247–51. https://doi.org/10.1016/j.bbrc.2011.05.124 PMid:21651891
- Okamoto Y, et al. Molecular characterization of a phospholipase D generating an andamide and its congeners. Journal of Biological Chemistry. 2004; 279: 5298–305. https://doi.org/10.1074/jbc.M306642200 PMid:14634025
- Pendergast AM, et al. BCR-ABL-induced oncogenesis is mediated by direct interaction with the SH2 domain of the GRB-2 adaptor protein. Cell. 1993; 75: 175–85. https://doi.org/10.1016/S0092-8674(05)80094-7
- Wei Q, et al. Tumor-suppressive functions of leucine zipper transcription factor–like 1. Cancer Research. 2010; 0008: 5472. https://doi.org/10.1158/0008-5472.CAN-09-3826
- Bajorek, M, et al. Structural basis for ESCRT-III protein autoinhibition. Nature Structural and Molecular Biology. 2009; 16: 754.https://doi.org/10.1038/nsmb.1621PMid:19525971 PMCid:PMC2712734
- Chen S, Wang J, Siegelbaum SA. Properties of hyperpolarization-activated pacemaker current defined by coassembly of HCN1 and HCN2 subunits and basal modulation by cyclic nucleotide. The Journal of General Physiology. 2001; 117: 491–504. https://doi.org/10.1085/jgp.117.5.491 PMid:11331358 PMCid:PMC2233656
- Wu K, et al. Expression of neuronal protein synuclein gamma gene as a novel marker for breast cancer prognosis. Breast Cancer Research and Treatment. 2007; 101:259–67. https://doi.org/10.1007/s10549-006-9296-7 PMid:16821081
- In-silico Characterization and 3D Structure Prediction of MX Protein of Lates Calcarifer (Barramundi): A Major Threat to Aqua Industry
Abstract Views :325 |
PDF Views:0
Authors
Affiliations
1 Amity Institute of Biotechnology, Amity University, Lucknow - 226028, Uttar Pradesh, IN
2 ICAR- Central Institute of Fisheries Education, Versova, Mumbai - 400061, Maharashtra, IN
3 National Bureau of Fish Genetic Resources (Indian Council of Agricultural Research), Canal Ring Road, P.O. Dilkusha, Lucknow - 226002, Uttar Pradesh, IN
1 Amity Institute of Biotechnology, Amity University, Lucknow - 226028, Uttar Pradesh, IN
2 ICAR- Central Institute of Fisheries Education, Versova, Mumbai - 400061, Maharashtra, IN
3 National Bureau of Fish Genetic Resources (Indian Council of Agricultural Research), Canal Ring Road, P.O. Dilkusha, Lucknow - 226002, Uttar Pradesh, IN
Source
Toxicology International (Formerly Indian Journal of Toxicology), Vol 25, No 4 (2018), Pagination: 232-239Abstract
Betanoda virus is one of the most important and emerging groups of viruses known to infect around 40 species found to be worldwide in distribution. The most common and virulent target of infection for this virus is (Lates Calcarifer) (barramundi). It is found that the expression of MX protein is found to be the more susceptible reason for this viral infection. Considering this current study including characterization to structure prediction revolves around the MX protein as a target. The progression of this study describes the amino acid sequence of MX protein was retrieved from UniProt database in Fasta format and further primary structure analysis and characterization including nature of amino acids, instability index reading, GRAVY, determination of phosphorylation as well as signal peptide cleavage sites was done with the help of various tools. Secondary structure prediction has proceeded through SOPMA server analysis revealed that MX protein has mixed secondary structure, i.e., mostly alpha-helix and beta-turn. The progression of this work prediction of a 3D structure along with functional site prediction of MX protein of Fish (Lates Calcarifer) is done through standard modeling tools. The 3D structure of this protein of (Lates Calcarifer) as documented in this study may provide a valuable aid for designing an inhibitor or better ligand against viral nervous necrosis disease and could play a vital role in drug design.Keywords
Barramundi, Betanoda Virus, Ligand, MX Protein, Viral Nervous Necrosis.References
- Xavier Irz, James R. Stevenson, Arnold Tanoy, Portia Villarante, Pierre Morissens. The equity and poverty impacts of aquaculture: Insights from the Philippines, Development Policy Review. 2007; 25(4):495-516. https:// doi.org/10.1111/j.1467-7679.2007.00382.x.
- Ayalew Assefa, Fufa Abunna. Maintenance of fish health in aquaculture: Review of epidemiological approaches for prevention and control of infectious disease of fish, Veterinary Medicine International. 2018; 2018(5432497):10. https://doi.org/10.1155/2018/5432497. PMid: 29682272, PMCid: PMC5846361.
- Shetty M, Maiti B, Shiva Kumar, Santhosh K, Venugopal MN, Karunasagr I. Betanodavirus of marine and fresh water distribution, Indian J. Virol. 2012; 2:114-23. https:// doi.org/10.1007/s13337-012-0088-x. PMid: 23997435, PMCid: PMC3550751.
- Chi SC, Wu YC, Cheng TM. Persistent infection of betanodavirus in a novel cell line derived from the brain tissue of barramundi Lates Calcarifer, Dis. Aquat. Organ. 2005; 65:91-98. https://doi.org/10.3354/dao065091. PMid: 16060261.
- Wu YC, Chi SC. Cloning and analysis of antiviral activity of a barramundi (Lates calcarifer) Mx gene, Fish and Shellfish Immunol. 2006; 23:97-108. https://doi.org/10.1016/j.fsi.2006.09.008. PMid: 17097891.
- Yu-Chi Wu, Yi-Fan Lu, Shau-Chi Chi. Anti-viral mechanism of barramundi Mx against betanodavirus involves the inhibition of viral RNA synthesis through the interference of RdRp, Fish and Shellfish Immunology. 2010; 28:467-75. https://doi.org/10.1016/j.fsi.2009.12.008. PMid: 20034570.
- Wu YC, Chi SC. Persistence of betanodavirus in Barramundi Brain (BB) cell line involves the induction of Interferon response, Fish and Shellfish Immunology. 2006; 21(5):5407. https://doi.org/10.1016/j.fsi.2006.03.002. PMid: 16698284.
- Chi SC, Shieh JR, Lin SJ. Genetic and antigenic analysis of betanodaviruses isolated from aquatic organisms in Taiwan, Dis. Aquat. Organ. 2003; 55:221-28. https://doi.org/10.3354/dao055221. PMid: 13677508.
- Anshul Tiwari, Prachi Srivastava. In silico characterization of retinal s antigen and retinol binding protein, Journal of Ocular Biology, Diseases and Informatics. 2012; 5(2):40-43.
- Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook; 2005. p. 571-607. https://doi.org/10.1385/1-59259-890-0:571.
- Gill SC, VonHippel PH. Calculation of protein extinction coefficients from amino acid sequence data, Anal. Biochem. 1989; 182(2):319-26. https://doi.org/10.1016/00032697(89)90602-7.
- Geourjon C, G. Deleage. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments, Comput. Appl. Biosci. 1995; 11:681-84. https://doi.org/10.1093/bioinformatics/11.6.681.
- PirovanoW, Heringa J. Protein secondary structure prediction, Methods Mol. Biol. 2010; 609:327-48. https:// doi.org/10.1007/978-1-60327-241-4_19. PMid:20221928.
- Blom N, Gammel S, Brunak S. Sequence and structure based prediction of eukaryotic protein phosphorylation sites, Journal of Molecular Biology. 1999; 294:1351-62. https://doi.org/10.1006/jmbi.1999.3310. PMid: 10600390.
- Petersen TN, Brunak S, Heijne G, Nielsen H. Signal P 4.0: Discriminating signal peptides from transmembrane regions, Nature Methods. 2011; 8:785-86. https://doi.org/10.1038/nmeth.1701. PMid: 21959131.
- Eswar N, Eramian D, Webb B, Shen MY, Sali A. Protein structure modeling with MODELLER, Current Protocols in Bioinformatics John Wiley & Sons, Inc. 2006; 15:5.6.15.6.30. https://doi.org/10.1002/0471250953.bi0506s15. PMid: 18428767, PMCid: PMC4186674.
- Andres Aszodi, William R. Taylor. Homology modelling by distance geometry: Division of Mathematical Biology, National Institute for medical Research, Folding and Design. 1996; 1:325-34. https://doi.org/10.1016/S1359-0278(96)00048-X.
- Browne WJ, North ACT, Phillips DC, Brew K, Vanaman TC, Hill RL. A possible three-dimensional structure of bovine alpha-lactalbumin based on that of hens eggwhite lysozyme, J. Mol. Biol. 1969; 42:65-86. https://doi.org/10.1016/0022-2836(69)90487-2.
- Sali A. Modelling mutations and homologous proteins, Current Opinion Biotechnology. 1995; 6:437-51. https:// doi.org/10.1016/0958-1669(95)80074-3
- Dmitrii M, Nikolaev, Andrey A. Shtyrov, Maxim S, Panov, Adeel Jamal, Oleg B. Chakchir, Vladimir A, Kochemirovsky, Massimo Olivucci, Mikhail N. Ryazantsev. A comparative study of modern homology modeling algorithms for rhodopsin structure prediction, ACS Omega. 2018; 3:7555−66. https://doi.org/10.1021/acsomega.8b00721. PMid: 30087916, PMCid: PMC6068592.
- Dassault Systems Biovia. Discovery studio modelling environment. Release 2017, San Diego: Dassault Systèmes, 2016.
- Ikai AJ. Thermo Stability and aliphatic index of globular proteins, J. Biochem. 1980; 88:1895-98.
- Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 1982; 157:105−32. https://doi.org/10.1016/0022-2836(82)90515-0.
- Jiangning Song, Huilinwang, Jiaweiwang, Andre, Tatiana, Bingjiao, Ziding, Tatsuya, Geoffrey, Roger. Phosphodirect: A bioinformatics tool for predicting for prediction of human kinase-specific phosphorylation substrates and sites by integrating hetreogenous feature selection, Scientific Reports. 2017; 7:6862. https://doi.org/10.1038/s41598-01707199-4. PMid: 28761071, PMCid: PMC5537252.
- Duan G, Walther D. The roles of post-translational modifications in the context of protein interaction networks, Comput. Biol. 2015; 11(2):1004049. https:// doi.org/10.1371/journal.pcbi.1004049. PMid: 25692714, PMCid: PMC4333291.
- Huang HD, Lee TY, Tzeng SW, Horng JT. Kinase Phos: A web tool for identifying protein kinase-specific phosphorylation sites, Nucleic. Acids. Res. 2005; 33:226−29. https:// doi.org/10.1093/nar/gki471. PMid: 15980458, PMCid: PMC1160232.
- Johnson LN. The regulation of protein phosphorylation, Biochem. Soc. Trans. 2009; 37:627−41. https://doi.org/10.1042/BST0370627. PMid: 19614568.
- Mundla Sri Latha, Madhu Sudhana Saddala. Molecular docking based screening of a simulated HIF-1 protein model for potential inhibitors, Biomedical Informatics. 2017; 13(11):388−93. https://doi.org/10.6026/97320630013388. PMid: 29225432, PMCid: PMC5712784.
- Shen, Min-yi; Sali, Andrej. Statistical potential for assessment and prediction of protein structures, Protein Science. 2006; 15(11):2507−24. https://doi.org/10.1110/ps.062416606. PMid: 17075131, PMCid: PMC2242414.
- Azad IS, Shekhar MS, Thirunavukkarasu AR, Poornima M, Kailasam M, Rajan JJ, Ali SA, Abraham M, Ravichandran P. Nodavirus infection causes mortalities in hatchery produced larvae of Lates calcarifer: First report from India, Dis. Aquat. Organ. 2005; 63:113−18. https://doi.org/10.3354/dao063113. PMid: 15819426.
- Thiery R, Cozien J, Cabon J, Lamour F, Baud M, Schneeman A. Induction of a protective immune response against viral nervous necrosis in the European sea bass dicentrarchus labrax by using betanodavirus-like particles, Journal of Virology. 2006; 80(20):10201−07. https://doi.org/10.1128/ JVI.01098-06. PMid: 17005697, PMCid: PMC1617310.
- Anderson ED, Mourich DV, Fahrenkrug SC, La Patra S, Shepherd J, Leong A. Genetic immunization of rainbow trout (Oncorhynchusmykiss) against infectious hematopoietic necrosis virus, Mol. Mar. Biol. Biotechnol. 1996; 5:114−22.
- Castric J, Thiéry R, Jeffroy J, Kinkelin P, Raymond JC. Sea bream Sparusaurata, an asymptomatic contagious fish host for nodavirus, Dis. Aquat. Organ. 2001; 47:33−38. https:// doi.org/10.3354/dao047033. PMid: 11797913.
- Chi SC, Lo BJ, Lin SC. Characterization of Grouper Nervous Necrosis Virus (GNNV). J. Fish Dis. 2001; 24:3−14. https:// doi.org/10.1046/j.1365-2761.2001.00256.x.
- Review of OIE international aquatic animal health code, International Journal of Parasitology. 2002; 32(4):487−88.