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Journal articles on the topic 'In-Silico identification'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Chen, Ping, Jun Duan, Liang Jiang, Qiong Liu, Ping Zhao, Qingyou Xia, and Huibi Xu. "In silico identification of silkworm selenoproteomes." Chinese Science Bulletin 51, no. 23 (December 2006): 2860–67. http://dx.doi.org/10.1007/s11434-006-2206-x.

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2

Reddy, Bandi Deepa, and Ch M. Kumari Chitturi. "Screening and Identification of Microbial Derivatives for Inhibiting Legumain: An In silico Approach." Journal of Pure and Applied Microbiology 12, no. 3 (September 30, 2018): 1623–30. http://dx.doi.org/10.22207/jpam.12.3.69.

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3

Moss, Alan, Stephen Madden, Padraic Mac Mathuna, and Peter Doran. "In silico gene identification in colonic neoplasia." Gastroenterology 124, no. 4 (April 2003): A110. http://dx.doi.org/10.1016/s0016-5085(03)80540-1.

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4

Esposito, C., L. Wiedmer, and A. Caflisch. "In Silico Identification of JMJD3 Demethylase Inhibitors." Journal of Chemical Information and Modeling 58, no. 10 (September 18, 2018): 2151–63. http://dx.doi.org/10.1021/acs.jcim.8b00539.

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5

Duckworth, D. Malcolm, and Philippe Sanseau. "In silico identification of novel therapeutic targets." Drug Discovery Today 7, no. 11 (May 2002): S64—S69. http://dx.doi.org/10.1016/s1359-6446(02)02282-1.

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6

Kaiser, Markus, and Christian Ottmann. "In Silico Identification of an Interferon Inhibitor." ChemMedChem 7, no. 4 (January 20, 2012): 555–57. http://dx.doi.org/10.1002/cmdc.201100579.

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7

Sen, Madhab Kumar, Kateřina Hamouzová, Sunil Kanti Mondal, and Josef Soukup. "Identification of the optimal codons for acetolactate synthase from weeds: an in-silico study." Plant, Soil and Environment 67, No. 6 (May 21, 2021): 331–36. http://dx.doi.org/10.17221/562/2020-pse.

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Although various studies of codon usage bias have been reported in a broad spectrum of organisms, no studies to date have examined codon usage bias for herbicide target genes. In this study, we analysed codon usage patterns for the acetolactate synthase (ALS) gene in eight monocot weeds and one model monocot. The base composition at the third codon position follows C3 > G3 > T3 > A3. The values of the effective number of codons (ENC or Nc) indicate low bias, and ENC or Nc vs. GC3 plot suggests that this low bias is due to mutational pressure. Low codon adaptation index and codon bias index values further supported the phenomenon of low bias. Additionally, the optimal codons, along with over- and under-represented codons, were identified. Gene design using optimal codons rather than overall abundant codons produce improved protein expression results. Our results can be used for further studies, including eliciting the mechanisms of herbicide resistance (occurring due to elevation of gene expression levels) and the development of new compounds, their efficiency and risk assessment for herbicide resistance evolution.

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8

Sharanee Kumar, Ilakiya, Nadiah Zaharin, and Kalaivani Nadarajah. "In silico Identification of Resistance and Defense Related Genes for Bacterial Leaf Blight (BLB) in Rice." Journal of Pure and Applied Microbiology 12, no. 4 (December 30, 2018): 1867–76. http://dx.doi.org/10.22207/jpam.12.4.22.

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9

Nimrod, G., F. Glaser, D. Steinberg, N. Ben-Tal, and T. Pupko. "In silico identification of functional regions in proteins." Bioinformatics 21, Suppl 1 (June 1, 2005): i328—i337. http://dx.doi.org/10.1093/bioinformatics/bti1023.

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10

Sun, Pingping, Sijia Guo, Jiahang Sun, Liming Tan, Chang Lu, and Zhiqiang Ma. "Advances in In-silico B-cell Epitope Prediction." Current Topics in Medicinal Chemistry 19, no. 2 (March 28, 2019): 105–15. http://dx.doi.org/10.2174/1568026619666181130111827.

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Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

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11

Gurav, Nitisha, Olivia J. S. Macleod, Paula MacGregor, and R. Ellen R. Nisbet. "In silico identification of Theileria parva surface proteins." Cell Surface 8 (December 2022): 100078. http://dx.doi.org/10.1016/j.tcsw.2022.100078.

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12

Jiang, Liang, Qiong Liu, and Jiazuan Ni. "In silico identification of the sea squirt selenoproteome." BMC Genomics 11, no. 1 (2010): 289. http://dx.doi.org/10.1186/1471-2164-11-289.

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13

Masignani, Vega, Enrico Balducci, Davide Serruto, Daniele Veggi, Beatrice Aricò, Maurizio Comanducci, Mariagrazia Pizza, and Rino Rappuoli. "In silico identification of novel bacterial ADP-ribosyltransferases." International Journal of Medical Microbiology 293, no. 7-8 (January 2004): 471–78. http://dx.doi.org/10.1078/1438-4221-00296.

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14

Padmanabhan, Kanchana, Kevin Wilson, Andrea M. Rocha, Kuangyu Wang, James R. Mihelcic, and Nagiza F. Samatova. "In-silico identification of phenotype-biased functional modules." Proteome Science 10, Suppl 1 (2012): S2. http://dx.doi.org/10.1186/1477-5956-10-s1-s2.

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15

Zhang, Michael Q. "Identification of Human Gene Core Promoters in Silico." Genome Research 8, no. 3 (March 1, 1998): 319–26. http://dx.doi.org/10.1101/gr.8.3.319.

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16

Guarneri, F., C. Guarneri, B. Guarneri, and S. Benvenga. "In silico identification of potential new latex allergens." Clinical Experimental Allergy 36, no. 7 (July 2006): 916–19. http://dx.doi.org/10.1111/j.1365-2222.2006.02516.x.

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17

Luo, Cheng, Peng Xie, and Ronen Marmorstein. "Identification of BRAF Inhibitors through In Silico Screening." Journal of Medicinal Chemistry 51, no. 19 (October 9, 2008): 6121–27. http://dx.doi.org/10.1021/jm800539g.

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18

RASPOR, PETER, JURE ZUPAN, and NEŽA ČADEŽ. "VALIDATION OF YEAST IDENTIFICATION BY IN SILICO RFLP." Journal of Rapid Methods and Automation in Microbiology 15, no. 3 (September 2007): 267–81. http://dx.doi.org/10.1111/j.1745-4581.2007.00097.x.

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19

Grishaeva, T. M., S. Ya Dadashev, and Yu F. Bogdanov. "Identification and Characterization in silico of Meiotic DNA." Russian Journal of Genetics 41, no. 5 (May 2005): 563–66. http://dx.doi.org/10.1007/s11177-005-0127-4.

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20

Thilakaraj, R., K. Raghunathan, S. Anishetty, and G. Pennathur. "In silico identification of putative metal binding motifs." Bioinformatics 23, no. 3 (December 5, 2006): 267–71. http://dx.doi.org/10.1093/bioinformatics/btl617.

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21

Smith, Jacqueline, David Speed, Andrew S. Law, Elizabeth J. Glass, and David W. Burt. "In-silico identification of chicken immune-related genes." Immunogenetics 56, no. 2 (May 1, 2004): 122–33. http://dx.doi.org/10.1007/s00251-004-0669-y.

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22

Wasserman, Wyeth W., and William Krivan. "In silico identification of metazoan transcriptional regulatory regions." Naturwissenschaften 90, no. 4 (March 27, 2003): 156–66. http://dx.doi.org/10.1007/s00114-003-0409-4.

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23

ZULKIPLY, NAWAL, MUHAMMAD EMIR RAMLI, and MOHD FAKHARUL ZAMAN RAJA YAHYA. "IN SILICO IDENTIFICATION OF ANTIGENIC PROTEINS IN Staphylococcus aureus." JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT 17, no. 2 (February 28, 2022): 18–26. http://dx.doi.org/10.46754/jssm.2022.02.002.

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24

Wedebye, E. B., and N. G. Nikolov. "Machine learning in silico models in chemical hazard identification." Toxicology Letters 350 (September 2021): S18. http://dx.doi.org/10.1016/s0378-4274(21)00275-7.

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25

Zhou, Chi, Chenyu Zhu, and Qi Liu. "Toward in silico Identification of Tumor Neoantigens in Immunotherapy." Trends in Molecular Medicine 25, no. 11 (November 2019): 980–92. http://dx.doi.org/10.1016/j.molmed.2019.08.001.

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26

Bhattacharyya, Malay, and Sanghamitra Bandyopadhyay. "In Silico Identification of OncomiRs in Different Cancer Types." Journal of The Institution of Engineers (India): Series B 93, no. 1 (March 2012): 15–23. http://dx.doi.org/10.1007/s40031-012-0003-2.

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27

Phi Bằng, Cao. "Identification and in silico analysis of DREB2 genes in Clementine oranges (Citrus clementina)." Journal of Science, Natural Science 60, no. 4 (2015): 127–31. http://dx.doi.org/10.18173/2354-1059.2015-0018.

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28

Fagerquist, Clifton K., Brandon R. Garbus, Katherine E. Williams, Anna H. Bates, Síobhán Boyle, and Leslie A. Harden. "Web-Based Software for Rapid Top-Down Proteomic Identification of Protein Biomarkers, with Implications for Bacterial Identification." Applied and Environmental Microbiology 75, no. 13 (May 1, 2009): 4341–53. http://dx.doi.org/10.1128/aem.00079-09.

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ABSTRACT We have developed web-based software for the rapid identification of protein biomarkers of bacterial microorganisms. Proteins from bacterial cell lysates were ionized by matrix-assisted laser desorption ionization (MALDI), mass isolated, and fragmented using a tandem time of flight (TOF-TOF) mass spectrometer. The sequence-specific fragment ions generated were compared to a database of in silico fragment ions derived from bacterial protein sequences whose molecular weights are the same as the nominal molecular weights of the protein biomarkers. A simple peak-matching and scoring algorithm was developed to compare tandem mass spectrometry (MS-MS) fragment ions to in silico fragment ions. In addition, a probability-based significance-testing algorithm (P value), developed previously by other researchers, was incorporated into the software for the purpose of comparison. The speed and accuracy of the software were tested by identification of 10 protein biomarkers from three Campylobacter strains that had been identified previously by bottom-up proteomics techniques. Protein biomarkers were identified using (i) their peak-matching scores and/or P values from a comparison of MS-MS fragment ions with all possible in silico N and C terminus fragment ions (i.e., ions a, b, b-18, y, y-17, and y-18), (ii) their peak-matching scores and/or P values from a comparison of MS-MS fragment ions to residue-specific in silico fragment ions (i.e., in silico fragment ions resulting from polypeptide backbone fragmentation adjacent to specific residues [aspartic acid, glutamic acid, proline, etc.]), and (iii) fragment ion error analysis, which distinguished the systematic fragment ion error of a correct identification (caused by calibration drift of the second TOF mass analyzer) from the random fragment ion error of an incorrect identification.

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29

Katoh, Masuko, and Masaru Katoh. "Identification and characterization of human CXXC10 gene in silico." International Journal of Oncology 25, no. 4 (October 1, 2004): 1193–202. http://dx.doi.org/10.3892/ijo.25.4.1193.

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30

Doytchinova, Irini A., and Darren R. Flower. "In Silico Identification of Supertypes for Class II MHCs." Journal of Immunology 174, no. 11 (May 19, 2005): 7085–95. http://dx.doi.org/10.4049/jimmunol.174.11.7085.

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31

Mehta, Aditya, Hemant Gupta, Rakesh Rawal, Archana Mankad, Tanushree Tiwari, Maulik Patel, and Arpita Ghosh. "In Silico MicroRNA Identification from Stevia rebaudiana Transcriptome Assembly." European Journal of Medicinal Plants 15, no. 2 (January 10, 2016): 1–14. http://dx.doi.org/10.9734/ejmp/2016/25221.

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32

Vu, Huyen-Trang, Phuong Huynh, Hoang-Dung Tran, and Ly Le. "In Silico Study on Molecular Sequences for Identification ofPaphiopedilumSpecies." Evolutionary Bioinformatics 14 (January 2018): 117693431877454. http://dx.doi.org/10.1177/1176934318774542.

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33

Wu, Emma, Priyanka Samanta, Ye Li, Le Shen, Fatemeh Khalili, and Christopher Weber. "P144 IN SILICO IDENTIFICATION OF PUTATITVE CLAUDIN CHANNEL BLOCKERS." Inflammatory Bowel Diseases 26, Supplement_1 (January 2020): S30. http://dx.doi.org/10.1093/ibd/zaa010.073.

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Abstract Compromised epithelial barrier function is known to be associated with inflammatory bowel disease (IBD) and may contribute to disease development. One mechanism of barrier dysfunction is increased expression of paracellular tight junction ion and water channels formed by claudins. Claudin-2 and -15 are two such channels. We hypothesize that blocking these channels could be a viable therapeutic approach to treat diarrhea in IBD. In an effort to develop blockers of these channels, we turn to our previously developed and validated in silico models of claudin-15 (Samanta et al. 2018). We reasoned that molecules that can bind with the interior of claudin pores can limit paracellular water and ion flux. Thus, we used docking algorithms to search for putative drugs that bind in the claudin-15 pore. AutoDock Vina (Scripps Research Institute) was initially used to assess rigid docking using small molecule ligand databases. The ligands were analyzed based on binding affinity to the pore and visualized using VMD (University of Illinois at Urbana-Champaign) for their potential blockage of the channel. Overall, a total of eight candidate ligands from the databases were identified: three from the UICentre database of 10000 ligands, one chemically similar structure identified in another online database (Chemspider), and four which are modifications on the chemical structure generated using ChemDraw. The analysis revealed that the eight ligands were docked in two predominant positions. In the first position, the ligands with more rings docked in an almost linear fashion and interacted with both D55 and D64 pore residues. In the second position of binding, the ligands were more flexible and could hence fold to interact only with D55 residues, thus biding predominantly in the center of the pores. To further evaluate these ligands, we will now turn to 1) flexible claudin-15 docking studies, 2) molecular dynamic simulations and, 3) in vitro measurements using monolayers induced to express claudin -15 and claudin-15 mutants. We also developed a claudin-2 homology model on which we will perform docking studies and in vitro measurements, which we expect will result in similar candidate ligands for blocking claudin-2. Finally, other databases will be analyzed for potential ligand blockers of claudin-2 and -15.

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34

Hyun-Jung Lee, Chloe, and Hashem Koohy. "In silico identification of vaccine targets for 2019-nCoV." F1000Research 9 (February 25, 2020): 145. http://dx.doi.org/10.12688/f1000research.22507.1.

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Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

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35

Lee, Chloe H., and Hashem Koohy. "In silico identification of vaccine targets for 2019-nCoV." F1000Research 9 (April 14, 2020): 145. http://dx.doi.org/10.12688/f1000research.22507.2.

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Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

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36

Daggupati, Trinath, Rishika Pamanji, and Suneetha Yeguvapalli. "In silico screening and identification of potential GSK3β inhibitors." Journal of Receptors and Signal Transduction 38, no. 4 (June 27, 2018): 279–89. http://dx.doi.org/10.1080/10799893.2018.1478854.

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37

Wu, Emma, Priyanka Samanta, Ye Li, Le Shen, Fatemeh Khalili, and Christopher Weber. "P144 IN SILICO IDENTIFICATION OF PUTATITVE CLAUDIN CHANNEL BLOCKERS." Gastroenterology 158, no. 3 (February 2020): S48. http://dx.doi.org/10.1053/j.gastro.2019.11.137.

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38

Moreno-Castillo, Elena, Daniel P. Ramírez-Echemendía, Giselle Hernández-Campoalegre, Dayana Mesa-Tejeda, Francisco Coll-Manchado, and Yamilet Coll-García. "In silico identification of new potentially active brassinosteroid analogues." Steroids 138 (October 2018): 35–42. http://dx.doi.org/10.1016/j.steroids.2018.06.009.

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39

Zhong, Hai-Jing, Sheng Lin, I. Lam Tam, Lihua Lu, Daniel Shiu-Hin Chan, Dik-Lung Ma, and Chung-Hang Leung. "In silico identification of natural product inhibitors of JAK2." Methods 71 (January 2015): 21–25. http://dx.doi.org/10.1016/j.ymeth.2014.07.003.

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40

Porto, William F., Valéria A. Souza, Diego O. Nolasco, and Octávio L. Franco. "In silico identification of novel hevein-like peptide precursors." Peptides 38, no. 1 (November 2012): 127–36. http://dx.doi.org/10.1016/j.peptides.2012.07.025.

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41

Dakshanamurthy, Sivanesan, Min Kim, Milton L. Brown, and Stephen W. Byers. "In-silico fragment-based identification of novel angiogenesis inhibitors." Bioorganic & Medicinal Chemistry Letters 17, no. 16 (August 2007): 4551–56. http://dx.doi.org/10.1016/j.bmcl.2007.05.104.

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42

de la Caridad Addine Ramírez, Bárbara, Reynel Marrón, Rommel Calero, Mayelin Mirabal, Juan Ramírez, María E. Sarmiento, Mohd Nor Norazmi, and Armando Acosta. "In silico identification of common epitopes from pathogenic mycobacteria." BMC Immunology 14, Suppl 1 (2013): S6. http://dx.doi.org/10.1186/1471-2172-14-s1-s6.

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43

Talukdar, Sandipan, Udeshna Bayan, and Kandarpa Kr Saikia. "In silico identification of vaccine candidates against Klebsiella oxytoca." Computational Biology and Chemistry 69 (August 2017): 48–54. http://dx.doi.org/10.1016/j.compbiolchem.2017.05.003.

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44

Liu, Yukun, Yan Zhou, Lixia Liu, Liping Sun, and Dequan Li. "In Silico Identification and Evolutionary Analysis of Plant MAPKK6s." Plant Molecular Biology Reporter 29, no. 4 (March 8, 2011): 859–65. http://dx.doi.org/10.1007/s11105-011-0295-4.

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45

Wu, Yifei, Kuan Y. Chang, Lei Lou, Lorette G. Edwards, Bly K. Doma, and Zhong-Ru Xie. "In silico identification of drug candidates against COVID-19." Informatics in Medicine Unlocked 21 (2020): 100461. http://dx.doi.org/10.1016/j.imu.2020.100461.

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46

Ojha, Hina, Gaurang Mahajan, Shekhar Mande, and Arvind Sahu. "In silico identification of CCP sequence motifs allow identification of novel complement regulators." Immunobiology 221, no. 10 (October 2016): 1166. http://dx.doi.org/10.1016/j.imbio.2016.06.095.

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47

Kah, Leong Liew, Meng Jee Jap, and Chen Yong Voon. "In silico approaches in the identification of Cryptococcus neoformans chemoreceptors." African Journal of Biotechnology 11, no. 46 (June 7, 2012): 10469–72. http://dx.doi.org/10.5897/ajb11.042.

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48

Gonçales, Relber A., Ayda LM Salamanca, Luiz RB Júnior, Kleber SF e Silva, Elton JR de Vasconcelos, Thaila F. dos Reis, Ricardo C. Castro, et al. "In silico identification of glycosylphosphatidylinositol-anchored proteins in Paracoccidioides spp." Future Microbiology 16, no. 8 (May 2021): 589–606. http://dx.doi.org/10.2217/fmb-2020-0282.

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Aim: To predict glycosylphosphatidylinositol (GPI)-anchored proteins in the genome of Paracoccidioides brasiliensis and Paracoccidioides lutzii. Materials & methods: Five different bioinformatics tools were used for predicting GPI-anchored proteins; we considered as GPI-anchored proteins those detected by at least two in silico analysis methods. We also performed the proteomic analysis of P. brasiliensis cell wall by mass spectrometry. Results: Hundred GPI-anchored proteins were predicted in P. brasiliensis and P. lutzii genomes. A series of 57 proteins were classified in functional categories and 43 conserved proteins were reported with unknown functions. Four proteins identified by in silico analyses were also identified in the cell wall proteome. Conclusion: The data obtained in this study are important resources for future research of GPI-anchored proteins in Paracoccidioides spp. to identify targets for new diagnostic tools, drugs and immunological tests.

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49

Sheikh, Ishfaq A., Adeel Malik, Sameera F. M. AlBasri, and Mohd A. Beg. "In silico identification of genes involved in chronic metabolic acidosis." Life Sciences 192 (January 2018): 246–52. http://dx.doi.org/10.1016/j.lfs.2017.11.014.

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50

Potocnakova, Lenka, Mangesh Bhide, and Lucia Borszekova Pulzova. "An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction." Journal of Immunology Research 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6760830.

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Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

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