Literature DB >> 21364804

ProCoS: Protein composition server.

Lavanya Rishishwar, Neha Mishra, Bhasker Pant, Kumud Pant, Kamal Raj Pardasani.   

Abstract

ProCoS is a free online tool for computing different combinations of peptide compositions. It is developed as an applet and a server with a capability to handle multiple FASTA sequences. The generalized algorithm for computing poly-amino acid composition forms the core of ProCoS. It produces output in different formats for easy visualization of results. It also allows composition analysis of sequences in full or in specific parts. Thus, ProCoS is user-friendly, flexible and unique.

Entities:  

Year:  2010        PMID: 21364804      PMCID: PMC3040505          DOI: 10.6026/97320630005227

Source DB:  PubMed          Journal:  Bioinformation        ISSN: 0973-2063


Background

Sequences in databases like GenBank are usually not fully annotated with functional information. Therefore, it is important to annotate such sequences with useful functional information using prediction tools and techniques. A number of such tools are already available in the public domain. The parameters that have been widely used are amino acid composition [1 ][2], pseudo amino acid composition [3][4], amphiphilic pseudo amino acid composition [5] [6], di-peptide composition [7], and even combination of few of these [8]. These parameters have been applied in the prediction of sub cellular localization of proteins, functionally characterizing proteins and identification of proteins from 2D gels [9]. Calculation of these features is usually non-trivial in the context of functional inference. Here, we describe ProCoS: PROtein COmposition Server (v 2.0) as a freely available tool at http://www.bifmanit.org/procos.

Input/Output

ProCoS can be accessed both as an applet and as a server. Both the versions provide users with an extensive and interactive GUI enabling full command on the system. It requires input: (1) peptide sequence(s) in FASTA format; (2) calculation type (single or multiple); (3) output in FOUR formats (JTable; text table; text list; feature value vector); (4) description and (5) composition degree; (6) break mode [automated; manual; disabled]

Features and caveats

ProCoS eliminates data migration one tool to another. The two versions are provided so as to help user's need. The applet is designed in Java and the server works on Perl-PHP backbone. The applet version is best suited for small input data with less memory computational requirement. It can work in offline mode with a JRE installed in client machine. However, this is less interactive.

Future developments

Facility for the calculation of numerous plots will be added in the next version
  7 in total

1.  Solving the protein sequence metric problem.

Authors:  William R Atchley; Jieping Zhao; Andrew D Fernandes; Tanja Drüke
Journal:  Proc Natl Acad Sci U S A       Date:  2005-04-25       Impact factor: 11.205

2.  Amino acid composition and physiochemical characterization of chondroitinase from Arthrobacter aurescens.

Authors:  K Hiyama; S Okada
Journal:  J Biochem       Date:  1975-12       Impact factor: 3.387

3.  Protein identification with N and C-terminal sequence tags in proteome projects.

Authors:  M R Wilkins; E Gasteiger; L Tonella; K Ou; M Tyler; J C Sanchez; A A Gooley; B J Walsh; A Bairoch; R D Appel; K L Williams; D F Hochstrasser
Journal:  J Mol Biol       Date:  1998-05-08       Impact factor: 5.469

4.  Predicting enzyme subclass by functional domain composition and pseudo amino acid composition.

Authors:  Yu-Dong Cai; Kuo-Chen Chou
Journal:  J Proteome Res       Date:  2005 May-Jun       Impact factor: 4.466

5.  Using pseudo-amino acid composition and support vector machine to predict protein structural class.

Authors:  Chao Chen; Yuan-Xin Tian; Xiao-Yong Zou; Pei-Xiang Cai; Jin-Yuan Mo
Journal:  J Theor Biol       Date:  2006-07-01       Impact factor: 2.691

6.  Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.

Authors:  Kuo-Chen Chou
Journal:  Bioinformatics       Date:  2004-08-12       Impact factor: 6.937

7.  TMB-Hunt: an amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins.

Authors:  Andrew G Garrow; Alison Agnew; David R Westhead
Journal:  BMC Bioinformatics       Date:  2005-03-15       Impact factor: 3.169

  7 in total
  1 in total

1.  Classifying nitrilases as aliphatic and aromatic using machine learning technique.

Authors:  Nikhil Sharma; Ruchi Verma; Tek Chand Bhalla
Journal:  3 Biotech       Date:  2018-01-12       Impact factor: 2.406

  1 in total

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