J. K. Meher al. / J. Biomedical Science and Engineering 4 (2011) 562-568 563

A

B

C

D

H

G

F

E

I

J

K

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Figure 1. Definition of transmembrane structures. [A;D] is a

transmembrane helix (TMH) and [B;C] represents the trans-

membrane segment (TMS)which is the embedded part of

TMH.

chemical value for each residue inside the frame is

summed up and assigned in the middle of the window.

Then the window slides across the sequence [9]. How-

ever, the relationship between segments and structure

does not always correspond. As extracting structural

information from amino acid sequences alone is difficult,

various prediction methods have been developed using

evolutionary information and neural network [10].

The span of window size is investigated in [11], which

reflected the interior and exterior portions of proteins.

Hydrophobicity profiles using the shortest window size

were noisy, and size less than seven residues produce

unsatisfactory result. On the other hand, long spans

tended to lose structural segments. Thus the optimum

choice between hydrophobic region and position of

amino acid residues was obtained with a nine for globu-

lar proteins. The problem with this technique is noisy in

the smoothed profiles, which makes it particularly diffi-

cult to find segments in case of globular proteins.

Recently signal processing methods play a major role

in predicting hydrophobic regions. Fourier analysis has

been applied to predict secondary structures from a se-

quential dotted hydrophobicity index [12]. The utilities

of the Fourier transform lie in its ability to analyze a

signal in the time domain for its frequency content. The

transform works by first translating a function in the

time domain into a function in the frequency domain.

The signal can then be analyzed for its frequency content

because the Fourier coefficients of the transformed func-

tion represent the contribution of each sine and cosine

function at each frequency. Although the Fourier analy-

sis is useful for acquiring structural information, this

method tends to cause positional error.

Wavelets are mathematical functions that divide data

into different frequency components. This approach has

advantages over traditional Fourier methods in analyzing

data where the signal contains discontinuities or high

frequency noise. Recently, the use of wavelet transform,

both continuous and discrete in the Bioinformatics field

is promising [13]. Continuous Wavelet Transform (CWT)

allows one-dimensional signal to be viewed in a more

discriminative two-dimensional time-scale representa-

tion. CWT is calculated by the continuous shifting of the

continuously scalable wavelet over the signal. In discrete

wavelet transform (DWT) a subset of scales and posi-

tions are chosen, in which the correlation between the

signal and the shifted and dilated waveforms are calcu-

lated. Consequently, the signal is decomposed into sev-

eral groups of coefficients, each containing signal fea-

tures corresponding to a group of frequencies. Small

scales refer to compressed wavelets, depicted by rapid

variations appropriate for extracting high frequency fea-

tures of the signal. An important attribute of wavelet

methods is that, due to the limited duration of every

wavelet, local variations of the signal are better extracted

and information on the location of these local features is

retained in the constituent waveforms. DWT has been

applied on hydrophobicity signals in order to predict

hydrophobic cores in proteins [14,15]. Protein sequence

similarity has also been studied using DWT of a signal

associated with the average energy states of all valence

electrons of each amino acid [16]. Wavelet transform

has been applied for transmembrane structure prediction

[17]. DWT has been used to decompose the amino acids

of TM proteins into a series of structures in different

layers, then predicting the location of TMHs according

to the information of the amino acids sequence in dif-

ferent scales [18]. A method based on discrete wavelet

transform has been developed to predict the number and

location of TMHs in membrane proteins [19].

The existing methods have their limitations in terms

of accuracy. As mentioned above, numerous attempts

have been made by researchers to define the relation

between interior and exterior regions directly from the

amino acid sequence. However, it is difficult to divide

interior and exterior position of amino acid residues by

assigning a hydrophobicity threshold, because of unac-

ceptable noise level. Hence there is a need to develop

advanced algorithm for faster and accurate prediction of

hydrophobic regions. This motivates to develop novel

approach based on digital filtering method to effectively

predict these regions in transmembrane α-helices.

The rest of the paper is organised as follows. Sec-

tion-2 deals with the proposed method for prediction of

hydrophobic regions in transmembrane α-helices. This

paper focuses on the development of signal processing

algorithms based on digital filter. Section-3 deals with

discussion of simulation results of proposed methods

using standard data set in terms of prediction measures.

Section-4 presents the conclusions of this paper.

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