A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins

Abstract

Structural similarity among proteins is reflected in the distribution of hydropathicity along the amino acids in the protein sequence. Similarities in the hydropathy distributions are obvious for homologous proteins within a protein family. They also were observed for proteins with related structures, even when sequence similarities were undetectable. Here we present a novel method that employs the hydropathy distribution in proteins for identification of (sub)families in a set of (homologous) proteins. We represent proteins as points in a generalized hydropathy space, represented by vectors of specifically defined features. The features are derived from hydropathy of the individual amino acids. Projection of this space onto principal axes reveals groups of proteins with related hydropathy distributions. The groups identified correspond well to families of structurally and functionally related proteins. We found that this method accurately identifies protein families in a set of proteins, or subfamilies in a set of homologous proteins. Our results show that protein families can be identified by the analysis of hydropathy distribution, without the need for sequence alignment.

Prediction of protein subcellular multisite localization using a new feature extraction method

“…The 20 amino acids can be divided into four groups (Pánek et al, 2005), which use the letters L, B, W, and P, respectively, where L includes strong hydrophilic amino acids, B includes strong hydrophobic amino acids, W includes weak hydrophilic or hydrophobic amino acids, and P, G, and C have relatively unique properties, which are divided into a class using the letter P. The information for classification is listed in Table 2. For example, a sequence of protein {MERIKELRDLMSQG} can be converted to {BLLBLLBLLBBWLP} as shown in Table 3.…”

Classifying protein sequences using hydropathy blocks

“…Instead of dividing the protein sequences into segments and counting the number of hydropathy characters in each segment, the frequencies of the hydropathy blocks occurring in protein sequences are calculated and used to generate fixeddimensional vectors. Compared to the algorithm proposed by Panek et al [15], our technique is simpler and need not estimate the number of segments. Further, our method can be applied to protein sequences of any length.…”

“…It is shown that the hydropathy distributions in proteins are useful for recognizing the homologous proteins belonging to the same family. Nevertheless, the method developed by Panek et al [15] requires that: (1) the number of segments must be optimally estimated in advance, otherwise the sequel clustering algorithm will not work well; (2) the length of the protein sequence is not taken into account. Obviously, the longer sequences contain more hydropathy characters compared to the shorter ones.…”

“…Recently, Panek et al [15] have developed a new method for identification of protein families based on hydropathy distributions in proteins. According to the hydropathy characteristics, the 20 amino acids are divided into three sets: strongly hydrophilic (POL), strongly hydrophobic (HPO), and weakly hydrophilic or weakly hydrophobic (Ambi).…”

Identifying the Sulfate Ion Binding Residues in Proteins

“…Base on Panek's scale [15], G is a separate class, R, K, E, H are strongly hydrophilic, S, T are weak hydrophilicity. L and A are strong hydrophobic.…”