We may not be able to do any great thing, but if each of us will do something, however small it may be, a good deal will be accomplished.

Transcription

1 Chapter 4 Initial Experiments with Localization Methods We may not be able to do any great thing, but if each of us will do something, however small it may be, a good deal will be accomplished. D.L. Moody Bioinformatics use a wide range of computational methods to solve the mysteries of life. Computational subcellular localization prediction taps the potential of computer science for problem solving. In subcellular localization prediction, computational methods are applied for two main purposes, first to identify the biological features that can influence the subcellular localization and the second is to use the identified biological features to make the prediction with highest accuracy. This chapter reports a wide verity of experiments that was initially conducted for identification of biological features that impact subcellular localization. Though these altogether were inconclusive, they set the stage for the final investigations, which are reported in next two chapters. Mainly, K-means clustering, Shannon value and Hilbert Huang Transform were explored for achieving computational subcellular localization prediction. The main observation from each method are discussed in this chapter. 48

2 4.1 K-means Clustering K-means clustering is an unsupervised learning algorithm which partition data into k clusters in which each data belongs to the cluster with the closest mean. The procedure can be e:>"''plained in the following step. First define k centroids from the data, one for each cluster. The next step is the assignment step which associate each data item to the nearest centroid. This is followed by the update step, which recalculates k new centroids. Both the assignment and update steps are repeated till no change in centroids. The algorithm aims at minimizing an objective function, a squared error function. The objective function is (4.1) where Xl, X2, X3,... X n are n observation to be clustered into k clusters, Cj is the centroid of cluster j and Ilx~j) - Cjl12 is a chosen distance measure between a data point and the cluster centre. Application of K means Clustering in Subcellular Localization Prediction As part of this study, the amino acids were clustered based on different physiochemical parameters like hydrophobicity, charge etc. Ninety six physiochemical indices, downloaded from AAIndex Database were used for clustering. A list of these physiochemical parameters is given in the appendix. Clusters ranging from 2 to 19 were tried out as there are 20 amino acids. The idea behind applying clustering in the study originated from the observation that groups of similar (e.g. hydrophobic) amino acids defines the protein sorting signal present in the N-terminal of amino acid sequences. For example, in the case of signal peptides, which are the address signals of secretory proteins, has a regional structure with N-terminal domain that is positively charged, a hydrophobic central region (h-region; leucines are most common), and a cleavage site region with mostly small residues. In mtp sequences, which are the address signals of the mitochondria proteins, there is an over representation of Arg, Ala and Ser, while negatively charged residues (Asp, Glu) are rare [26]. The first step in applying clustering is the translation of 49

3 u>- ~ :> u -< II.. 0 ~ a EIIP Hydrophobicity D Molecular Weight a M composition Absolute Entropy a Average Non-bounded Energy a lsoelectrlc Point a Mean Polarity Size o CLUSTER Figure 4.1: Variation in prediction accuracies for different clusters of amino acids the original amino acid sequence into the clustered amino acid sequence. For this, a physiochemical parameter and a value for k was used for clustering the amino acids into k groups. The entire amino acid sequence is translated into a k-ietter string, where each amino acid is replaced by the representative alphabet of the cluster in which the amino acid is a member. These clustered amino acid sequences were used for many different investigations. For example, compositions of various clusters were explored using the covariant discriminant algorithm [134] proposed by K. C. Chou. A graph of prediction accuracies against various clusters using this method is given in Figure 4.1. The clustered amino acid sequences of proteins of each location were plotted for a visual representation and inspection for leads. The major observation from clustering is that, it has negative impact on protein subcellular localization prediction. In addition to K-means clustering, the widely accepted KooIman classification [135] is used to cluster the amino acids. The KooIman classification groups the amino acids based on its prominent characteristics as aliphatic, sulphur-containing, aromatic, neutral, acidic, basic and imino acid. The 50

4 amino acid composition of sequences, clustered based on KooIman classification were investigated for a better prediction accuracy. Support Vector Machines were used for this. The dipeptide, tripeptide amino acid composition of these sequences were also investigated. The optimized RBF kernels gave an accuracy of for tripeptide frequency and for dipeptide frequency. 4.2 Shannon Index The Shannon index, also known as Shannon-Wiener Index or the Shannon Weaver Index [136], is one of the diversity indices used to measure diversity in categorical data. It is widely used in measurement of biodiversity, by treating species as symbols and their relative population sizes as the probability. The index can be computed as s H' = -l:pi lnpi - [(8-1)/2N] i=l (4.2) where ni is the number ofindividuals in species i; S is the number ofspecies. N is the total number of all individuals. Pi is the relative abundance of each species and is calculated as the proportion of individuals of a given species to the total number of individuals in the community R Application of Shannon Index in Subcellular Localization Prediction Shannon index is selected for exploration because it is a measure of information entropy of the distribution. The order of amino acids with in the sequence (sequence order effect) was already proved to have impact on the prediction accuracy of subcellular localization [137]. So it would be good to explore the impact of entropy of distribution of amino acids on subcellular localization prediction. In the case of amino acid sequence, the amino acids were treated as symbols and their frequency as the probability. Shannon entropy for the full length sequences from various locations were calculated and plotted. But these shared a very close range, making the Shannon index 51

5 a poor parameter for classification based on protein locations. The Shannon index of different regions in the protein sequence like, N terminal, middle region and C-terminal were calculated and investigated. Support Vector machine were applied on this for making prediction. But the accuracy was only below 60 and not competitive to use for prediction. Both clustering and Shannon index were combined and investigated. The amino acids were clustered using different physiochemical parameters and clusters from 2 to 19. Shannon values of clustered sequence was calculated and used for subcellular localization prediction. The accuracy was poor for this method also. A sliding window of size 100 was used to study how Shannon index or the entropy of distribution changes over the sequence. This study also did not lead to a positive conclusion. 4.3 Hilbert Huang Transform In most of the physical applications, the data obtained will be both nonlinear and non stationary. Hilbert Huang Transform is an effective method to analyze this kind of data. The Hilbert-Huang transform (HHT), is proposed by Huang et al [138]. It has two parts, the empirical mode decomposition (EMD) and the Hilbert spectral analysis (HSA). The HHT uses the EMD method to decompose a signal into intrinsic mode function(imfs), and uses the Hilbert Spectral Analysis (HSA) method on these IMFs to obtain instantaneous frequency data. The EMD has been tested and validated exhaustively, but only empirically. An IMF is defined as a function that satisfies the two conditions (1) In the whole data set, the number of extrema and the number of zero-crossings must either be equal or differ at most by one. (2) At any point, the mean value of the upper envelope defined by the local maxima and the lower envelope defined by the local minima is zero. The IMFs are calculated as follows. First identify, the local maxima and mininma of the data series x(t) and connect them to form upper and lower envelops. Their mean ml is subtracted from the x(t) to get hi. ie x(t) - ml = hi' If hi doest not satisfy the condition for IMF, the steps are repeated, then hi - mll = h ll. This sifting procedure is repeated k times until hik is an IMF, that is hick-i) - mik = hik Hence the 52

6 first IMF is obtained. Then for finding the next IMF, the calculated IMF is subtracted from the x(t) and the entire procedure is repeated. The IMFs are related to the data as in the following equation. N x(t) = - 'L1MFi(t) + Tn(t) i=l (4.3) where Tn(t) is the left over from the final iteration, from which no more IMFs can be calculated. Application of Hilbert Huang Transform in Subcellular Localization Prediction Feng Shi, Qiu-Jian Chen and Na-na Li had earlier used HHT for subcellular localization prediction [139]. They had used hydrophobicity index to translate amino acid sequence into numeric sequence and used the energy value of IMFs for predicting the subcellular localization of Apoptosis proteins. In this work, for effectively utilising the Hilbert Huang Transform(HHT) in subcellular localization prediction, the amino acid composition of each protein was treated as the numerical sequences representing the protein. Thus a fixed length signal was generated from varying length amino acid sequence. The highest instantaneous amplitude from first four IMFs were selected and combined with amino acid composition. Support Vector Machine was applied over this data for subcellular localization prediction and an accuracy of was obtained. Even though this accuracy seems worth reporting, the support vector machines for amino acid composition alone gave better accuracy. This pointed out that the selected parameter reduced the prediction accuracy. Instead of highest instantaneous amplitude, many other parameters like position of highest instantaneous amplitude, highest instantaneous frequency and position of highest instantaneous frequency were also tried out. The HHT was even applied for amino acid composition of selective group of amino acids like acidic, aromatic, basic, ionizable, polar hydrophilic, nonpolar hydrophobic etc. The prediction accuracy was less than 75. The major observations from this work was that considering all twenty amino acids in 53

7 all its vividness gives better accuracy than selecting only a subgroup of them. This observation is in agreement with clustering, where the amino acid clustering reduces the subcellular localization prediction accuracy. 4.4 Digital Signal Processing Digital Signal Processing (DSP) is a popular method in engineering and is the basis of many areas of technology, from mobile phones to modems and multimedia PCs. DSP is the processing of signals by digital means. In DSP, the signals can be represented and studied in various domains like time domain, spatial domain, frequency domain, autocorrelation domain, and wavelet domains. The best representation is determined by the nature of information represented by the signal. Most of the signals, as observed from nature, is in time or spatial domain representation. A discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. The cross-correlation of the signal with itself over varying intervals of time or space constitute the autocorrelation. DSP can be applied to the biosequences. The character strings of biosequences can be translated to numerical signals using various mapping techniques. Upon these numerical signals, all DSP techniques can be applied and investigated. DSP has been successfully used in bioinformatics for gene finding, exon identification etc. [ ]. Application of Digital Signal Processing in Subcellular Localization Prediction A remarkable merit of using digital signal processing technique in subcellular localization is that many existing tools in mathematics and engineering can be straightforwardly used. Method in DSP such as Discrete Fourier Transform [112], Lyapunov index, Bessel function, Chebyshev filter [113] and Butterworth filter [114] were applied as techniques to bring forth the sequence order effect. In this study, the amino acid sequences were mapped to numerical sequences, mainly using the ninety six physiochemical parameters and k-means 54

8 clustering. Rather than deriving parameters to represent sequence properties, like in the earlier works, the entire signals were studied to bring out any hidden property that lead to identification of address signals present in the protein. The investigation was done by applying filters to enhance the property differences between the adjacent amino acids. This was tested by different physiochemical properties such as hydrophobicity, size. Differential filter was applied to find the impact of property differences in the amino acids on the subcellular location of protein. The filtered sequences were plotted for visual representation and investigation. The Fast Fourier Transforms (FFT) were applied to convert these preprocessed sequence into frequency domain. The FFTs were also plotted and investigated. Correlation also was applied upon the signals to detect the presence of sorting signal. Clustered indicator sequences were produced and investigated. For this, sequences were clustered using anyone of the ninety six physiochemical parameter and anyone of the cluster k ranging from 2 to 19. For K clusters, K indicator sequences can be generated. The K indicator sequences were studied by many approaches like finding the combined FFT. Even though many protein sequences are plotted using different DSP methods to bring out any hidden property of the address signal present in the protein sequences, none of them were able to reveal any significant information. 4.5 Conclusion This chapter presented various initial studies and investigations done on the biosequences for subcellular localization prediction. Even though the results were inconclusive, these investigations paved way for the successful prediction methods described in the next two chapters. It may be noted that the reported research [ ,139] show successful application of some of the methods tried, in different ways. For example, using parameters derived out of FFT and HHT had been effectively used for prediction. The many observations from the studies described in this chapter, lead to many new ideas. For example the decreasing prediction accuracy in clustering lead to the observation that including almost all biological features that can be significant will increase the prediction accuracy. This kindled ideas for experiments with full length sequences and considering the sequences as combination of domains. The ne>.."t chapter describes how these experiments and atomic composition were effectively used in subcellular localization prediction. 55