SavedAdd your journal

Journals / Türk Tarım - Gıda Bilim ve Teknoloji dergisi / 2017 / Cilt: 5 - Sayı: 1

Working with Proteins in silico: A Review of Online Available Tools for Basic Identification of Proteins

CANER YAVUZ
ZAHİDE NESLİHAN ÖZTÜRK GÖKÇE
Pages
65–70
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Annotation has included identification of motifs and domains, determination isoelectric point, molecular weight, subcellular localization, and post-translational modifications by focusing on the important points to be considered while selecting from online available tools.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Increase in online available bioinformatics tools for protein research creates an important opportunity for scientists to reveal characteristics of the protein of interest by only starting from the predicted or known amino acid sequence without fully depending on experimental approaches.
📊
Key Findings & Data:There are many sophisticated tools used for diverse purposes.
💡
Conclusion & Impact:Annotation has included identification of motifs and domains, determination isoelectric point, molecular weight, subcellular localization, and post-translational modifications by focusing on the important points to be considered while selecting from online available tools.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Increase in online available bioinformatics tools for protein research creates an important opportunity for scientists to reveal characteristics of the protein of interest by only starting from the predicted or known amino acid sequence without fully depending on experimental approaches. There are many sophisticated tools used for diverse purposes. However, there are not enough reviews covering the tips and tricks in selecting and using the correct tools as the literature mainly states the promotion of the new ones. In this review, with the aim of providing young scientists with no specific experience on protein work a reliable starting point for in silico analysis of the protein of interest, we summarized tools for annotation of proteins. Annotation has included identification of motifs and domains, determination isoelectric point, molecular weight, subcellular localization, and post-translational modifications by focusing on the important points to be considered while selecting from online available tools.

🔖

Cite This Article

YAVUZ, C., GÖKÇE, Z. N. Ö. (2017). Working with Proteins in silico: A Review of Online Available Tools for Basic Identification of Proteins. Türk Tarım - Gıda Bilim ve Teknoloji dergisi, Cilt: 5 - Sayı: 1, 65–70. https://ajindex.com/articles/332125

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Apweiler R, Bairoch A, Wu HC, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O’Donovan C, Redaschi N, Yeh LSL. 2004. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res, 32: D115-119. Apweiler R, Hermjakob H, Sharon N. 1999. On the frequency of protein glycosylation, as deduced from analysis of the SWISSPROT database. Biochim. Biophys. Acta, Gen. Subj., 1473: 4–8.
  2. Ashburner M, Ball AC, Blake AJ, Botstein D, Butler H, Cherry M, David PA, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. 2000. Gene Ontology: tool for the unification of biology, The Gene Ontology Consortium. Nat. Genet., 25(1): 25-29.
  3. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS. 2009. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res., 37: 202-208.
  4. Bailey TL, Williams N, Misleh C, Li WW. 2006. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res., 34: 369-373.
  5. Balla S, Thapar V, Verma S, Luong T, Faghri T, Huang CH, Rajasekaran S, del Campo JJ, Shinn JH, Mohler WA, Maciejewski MW, Gryk MR, Piccirillo B, Schiller SR, Schiller MR. 2006. Minimotif Miner: a tool for investigating protein function. Nat Methods. 3(3): 175-7.
  6. Bendtsen JD, Jensen LJ, Blom N, von Heijne G, Brunak S. 2004. Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng. Des. Sel., 17(4): 349–56.
  7. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Res, 28: 235-242.
  8. Bhasin M, Garg A, Raghava GP. 2005. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics, 21: 2522–2524.
  9. Binder JX, Pletscher-Frankild S, Tsafou K, Stolte C, O’Donoghue SI, Schneider R, Jensen LJ. 2014. COMPARTMENTS: unification and visualization of protein subcellular localization evidence. Database, 2014, 1-9.
  10. Bjellqvist B, Basse B, Olsen E, Celis JE. 1994. Reference points for comparisons of two-dimensional maps of proteins from different human cell types defined in a pH scale where isoelectric points correlate with polypeptide compositions. Electrophoresis. 15: 529-539.
  11. Bjellqvist B, Hughes G, JPasquali Ch, Paquet N, Ravier F, Sanchez JCh,., Frutiger S., Hochstrasser D.F. 1993. The focusing positions of polypeptides in immobilized pH gradients can be predicted from the