Modelling of turns using Hidden Markov Model

Modelling of turns using Hidden Markov Model

Modelling of turns using Hidden Markov Model
Nivedita Rao
Ms Sunila Godara



Abstract One of the major tasks in predicting the secondary structure of a protein is to find the turns Functional and structural traits of a globular protein can be better understood by the turns as they play an important role in it turns play an important part in protein folding turns constitute on an average of 25 of the residues in all protein chains and are the most usual form of nonrepetitive structures It is already known that helices and sheets are among the most important keys in stabilizing the structures in proteins In this paper we have used hidden Markov model HMM in order to predict the turns in proteins based on amino acid composition and compared it with other existing methods

Keywords turns amino acid composition hidden Markov model residue

I Introduction

Bioinformatics has become a vital part of many areas of biology In molecular biology bioinformatics techniques such as signal processing or image processing allow mining of useful results from large volumes of raw data In the field ofgeneticsandgenomics it helps in sequencing and explaining genomes and their perceivedmutations It plays an important part in the analysis of protein expression gene expression and their regulation It also helps in literal mining of biological prose and the growth of biological and gene ontologies for organizing and querying biological data Bioinformatics tools aid in the contrast of genetic and genomic data and more commonly in the understanding of evolutionary facets of molecule based biology At a more confederated level bioinformatics helps in analyzing and categorizing the biological trails and networks that are an significant part of systems biology In structural biology bioinformatics helps in the understanding simulation and modelling of RNA DNA and protein structures as well as molecular bindings

The advancements in genome has increased radically over the recent years thus resulting in the explosive growth of biological data widening the gap between the number of protein sequences stored in the databases and the experimental annotation of their functions

There are many types of tight turns These turns may subject to the number of atoms form the turn 1 Among them is turn which is one of the important components of protein structure as it plays an important part in molecular structure and protein folding A turn invokes four consecutive residues where the polypeptide chain bends back on itself for about 180 degrees 2

Basically these chain reversals are the ones which provide a protein its globularity rather than linearity Even turns can be further classified into different types According to Venkatachalam 3 turns can be of 10 types based on phi psi angles and also some other Richardson4 suggested only 6 distinct typesIIIIIIVIa and VIb on the basis of phi psi ranges along with a new category IV Presently classification by Richardson is most widely used

Turns can be considered as an important part in globular proteins in respect to its structural and functional view Without the component of turns a polypeptide chain cannot fold itself into a compressed structure Also turns normally occur on the visible surface of proteins and therefore it possibly represents antigenic locations or involves molecular recognition Thus due to the above reasons the prediction of turns in proteins becomes an important element of secondary structure prediction

II RELATED WORK

A lot of work has been done for the prediction of turns To determine chain reversal regions of a globular protein Chou at al 5 used conformational parameters Chou at al 6 has given a residuecoupled model in order to predict the turns in proteins Chou at al 7 used sequence of tetra peptide Chou 8 again predicted tight turns and their types in protein using amino acid residues Guruprasad K at al 9 predicted turn and turn in proteins using a new set of amino acid and hydr

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