Literature DB >> 29255396

Fast protein classification by using the most significant pairs.

Essam Al-Daoud1.   

Abstract

This study introduces a new approach to speed up the protein classification process. The basic idea is rewriting the sequences of each family by using the most significant pairs, where the total number of the pairs that can be appeared in the protein sequences is 400 different pairs. The sequence length could be reduced to 0.86, 0.91 and 0.95 by using the most 100, 200 and 300 significant pairs, respectively. The average time reduction is 0.53 %, 0.33 % and 0.22 % for 100, 200, and 300 pairs, respectively. In the three cases the suggested procedure can be adopted to speed up the testing time. However to get identical classification rate to the previous profile HMM, 300 pairs at least must be used.

Entities:  

Keywords:  G-Protein Coupled Receptor; Hidden Markov; multi alignment; significant pairs

Year:  2010        PMID: 29255396      PMCID: PMC5698897     

Source DB:  PubMed          Journal:  EXCLI J        ISSN: 1611-2156            Impact factor:   4.068


  3 in total

1.  A novel method for GPCR recognition and family classification from sequence alone using signatures derived from profile hidden Markov models.

Authors:  P K Papasaikas; P G Bagos; Z I Litou; S J Hamodrakas
Journal:  SAR QSAR Environ Res       Date:  2003 Oct-Dec       Impact factor: 3.000

2.  Motif-based protein sequence classification using neural networks.

Authors:  Konstantinos Blekas; Dimitrios I Fotiadis; Aristidis Likas
Journal:  J Comput Biol       Date:  2005       Impact factor: 1.479

3.  Classification of G-protein coupled receptors at four levels.

Authors:  Qing-Bin Gao; Zheng-Zhi Wang
Journal:  Protein Eng Des Sel       Date:  2006-10-10       Impact factor: 1.650

  3 in total

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