Literature DB >> 11934756

BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms.

Harpreet Kaur1, G P S Raghava.   

Abstract

MOTIVATION: beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server.
RESULTS: This paper describes a web server called BetaTPred, developed for predicting beta-TURNS in a protein from its amino acid sequence. BetaTPred allows the user to predict turns in a protein using existing statistical algorithms. It also allows to predict different types of beta-TURNS e.g. type I, I', II, II', VI, VIII and non-specific. This server assists the users in predicting the consensus beta-TURNS in a protein. AVAILABILITY: The server is accessible from http://imtech.res.in/raghava/betatpred/

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Year:  2002        PMID: 11934756     DOI: 10.1093/bioinformatics/18.3.498

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  7 in total

1.  A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.

Authors:  Harpreet Kaur; G P S Raghava
Journal:  Protein Sci       Date:  2003-05       Impact factor: 6.725

2.  The basolateral targeting signal of CD147 (EMMPRIN) consists of a single leucine and is not recognized by retinal pigment epithelium.

Authors:  Ami A Deora; Diego Gravotta; Geri Kreitzer; Jane Hu; Dean Bok; Enrique Rodriguez-Boulan
Journal:  Mol Biol Cell       Date:  2004-06-23       Impact factor: 4.138

3.  A deep dense inception network for protein beta-turn prediction.

Authors:  Chao Fang; Yi Shang; Dong Xu
Journal:  Proteins       Date:  2019-07-23

4.  Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

Authors:  Petros Kountouris; Jonathan D Hirst
Journal:  BMC Bioinformatics       Date:  2010-07-31       Impact factor: 3.169

5.  Predicting turns in proteins with a unified model.

Authors:  Qi Song; Tonghua Li; Peisheng Cong; Jiangming Sun; Dapeng Li; Shengnan Tang
Journal:  PLoS One       Date:  2012-11-07       Impact factor: 3.240

6.  BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.

Authors:  Manish Kumar; Manoj Bhasin; Navjot K Natt; G P S Raghava
Journal:  Nucleic Acids Res       Date:  2005-07-01       Impact factor: 16.971

7.  Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks.

Authors:  Chao Fang; Yi Shang; Dong Xu
Journal:  Sci Rep       Date:  2018-10-24       Impact factor: 4.379

  7 in total

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