Literature DB >> 18928200

COPid: composition based protein identification.

Manish Kumar1, Varun Thakur, Gajendra P S Raghava.   

Abstract

In the past, a large number of methods have been developed for predicting various characteristics of a protein from its composition. In order to exploit the full potential of protein composition, we developed the web-server COPid to assist the researchers in annotating the function of a protein from its composition using whole or part of the protein. COPid has three modules called search, composition and analysis. The search module allows searching of protein sequences in six different databases. Search results list database proteins in ascending order of Euclidian distance or descending order of compositional similarity with the query sequence. The composition module allows calculation of the composition of a sequence and average composition of a group of sequences. The composition module also allows computing composition of various types of amino acids (e.g. charge, polar, hydrophobic residues). The analysis module provides the following options; i) comparing composition of two classes of proteins, ii) creating a phylogenetic tree based on the composition and iii) generating input patterns for machine learning techniques. We have evaluated the performance of composition-based (or alignment-free) similarity search in the subcellular localization of proteins. It was found that the alignment free method performs reasonably well in predicting certain classes of proteins. The COPid web-server is available at http://www.imtech.res.in/raghava/copid/.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 18928200

Source DB:  PubMed          Journal:  In Silico Biol        ISSN: 1386-6338


  22 in total

1.  In silico characterization of thermostable lipases.

Authors:  Debamitra Chakravorty; Saravanan Parameswaran; Vikash Kumar Dubey; Sanjukta Patra
Journal:  Extremophiles       Date:  2010-12-12       Impact factor: 2.395

2.  PTPAMP: prediction tool for plant-derived antimicrobial peptides.

Authors:  Mohini Jaiswal; Ajeet Singh; Shailesh Kumar
Journal:  Amino Acids       Date:  2022-07-21       Impact factor: 3.789

3.  Ensemble-AHTPpred: A Robust Ensemble Machine Learning Model Integrated With a New Composite Feature for Identifying Antihypertensive Peptides.

Authors:  Supatcha Lertampaiporn; Apiradee Hongsthong; Warin Wattanapornprom; Chinae Thammarongtham
Journal:  Front Genet       Date:  2022-04-28       Impact factor: 4.772

4.  In silico design of a multi-epitope peptide construct as a potential vaccine candidate for Influenza A based on neuraminidase protein.

Authors:  Mandana Behbahani; Mohammad Moradi; Hassan Mohabatkar
Journal:  In Silico Pharmacol       Date:  2021-05-11

5.  A novel disulfide-rich protein motif from avian eggshell membranes.

Authors:  Vamsi K Kodali; Shawn A Gannon; Sivakumar Paramasivam; Sonali Raje; Tatyana Polenova; Colin Thorpe
Journal:  PLoS One       Date:  2011-03-30       Impact factor: 3.240

6.  Identification of mannose interacting residues using local composition.

Authors:  Sandhya Agarwal; Nitish Kumar Mishra; Harinder Singh; Gajendra P S Raghava
Journal:  PLoS One       Date:  2011-09-13       Impact factor: 3.240

7.  dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides.

Authors:  Arun Sharma; Pooja Gupta; Rakesh Kumar; Anshu Bhardwaj
Journal:  Sci Rep       Date:  2016-02-25       Impact factor: 4.379

8.  Developmental Testicular Expression, Cloning, and Characterization of Rat HDAC6 In Silico.

Authors:  Pratibha Verma; Omshree Shetty; Sweta Parab; Karen Menezes; Priyanka Parte
Journal:  Biomed Res Int       Date:  2017-10-19       Impact factor: 3.411

9.  Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods.

Authors:  Guillermin Agüero-Chapin; Reinaldo Molina-Ruiz; Emanuel Maldonado; Gustavo de la Riva; Aminael Sánchez-Rodríguez; Vitor Vasconcelos; Agostinho Antunes
Journal:  PLoS One       Date:  2013-07-16       Impact factor: 3.240

10.  Prediction of membrane transport proteins and their substrate specificities using primary sequence information.

Authors:  Nitish K Mishra; Junil Chang; Patrick X Zhao
Journal:  PLoS One       Date:  2014-06-26       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.