Self-organizing tree growing network for classifying amino acids

Summary: A self-organizing tree growing neural network was applied to classify amino acids and amino acid exchange matrices. Availability: SOTA, is freely available by anonymous FTP or at http://www.cnb.uam.es/~bioinfo/Software/sota. Contact: carazo@cnb.uam.es Most protein sequence analysis tasks rely on a measure of similarity between different amino acids. There are at least 13 published scoring matrices for amino acids, based on genetic codes, physicochemical properties, observed frequency of mutations, secondary structural matching and structural properties (Johnson and Overington, 1993). Various attempts have been made to group amino acids based on these matrices, including dendrograms (Sneath, 1966; Doolittle, 1979; Johnson and Overington, 1993; Jones et al., 1994), Venn diagrams (Dickerson and Geis, 1969; Taylor, 1986; Taylor and Jones, 1993), principal components analysis (Johnson and Overington, 1993), multidimensional projection (Jones t al., 1992, 1994; Taylor and Jones, 1993) and Sammon’s non-linear mapping (Agrafiotis, 1997). In particular, Johnson and Overington (1993) used both hierarchical clustering by the KITSCH program of Felsenstein’s Phylogenetic Inference Package (PHYLIP) and the principal component projection to make an exhaustive examination of 13 amino acid exchange matrices. Here we apply a new artificial neural network (ANN)-based approach to examine these matrices and cluster the amino acids. The ANN method exploited here is our recently developed Self-Organizing Tree Algorithm (SOTA; Dopazo and Carazo, 1997), which is based on both Kohonen’s self-organizing mapping (Kohonen, 1990) and Fritzke’s growing cell structure (Fritzke, 1994). In this work, we have used a modified form of SOTA, which we will refer to as SOTA/DIST, which was originally designed to cluster protein sequences, based on their distance matrix, although its use here has been to cluster amino acids based on an amino acid exchange matrix. Thirteen such scoring matrices were extracted from Johnson’s collection of 15 amino acid exchange matrices (http://www.btk.utu.fi/molmol/ matrices.html), including those of Dayhoff PAM250, Doolittle, Fitch, Gonnet, Grantham, Henikoff, Johnson, Jones, Levin, McLachlan, Miyata, Rao and Risler. L 1 distances were calculated between pairs of columns in a particular matrix. For example, the distance between alanine and cysteine scoring distributions of a matrix, X, is calculated as follows: Dala,cys = |Xala→ala – Xcys→ala| + |Xala→cys – Xcys→cys| + |Xala→asp – Xcys→asp| + . + |Xala→tyr – Xcys→tyr | We further cluster the 13 matrices with SOTA/DIST, to examine relationships among them. The Euclidean distance between every two matrices ( X, Y) was calculated following the equation described by Johnson and Overington (1993): Dx,y = [(Xala→ala – Yala→ala) + (Xala→cys – Yala→cys) + (Xala→asp – Yala→asp) + . + (Xtyr→trp – Ytyr→trp) + (Xtyr→tyr – Ytyr→tyr)] When running SOTA/DIST to cluster amino acids using each of the 13 scoring matrices, we reached algorithmic convergence in all cases. Five of the 13 dendrograms corresponding to these classifications are shown in Figure 1a–e (the other eight dendrograms are not shown). Results based on nine of the 13 matrices (those of Dayhoff, Doolittle, Gonnet, Grantham, Henikoff, Johnson, Jones, McLachlan and Miyata) show that the amino acids are grouped into two main clusters: small, polar and charged side chains {A, G, S, T, P, D, E, N, Q, H, K, R}, and hydrophobic side chains {C, I, V, L, M, F, Y, W}. Within the first cluster, small amino acids and charged amino acids/acid amides re separated into two subclusters. Within the second cluster, {I, L, M, V} and aromatic {F, W, Y} are two subclusters, while the location of C is changed variously. In most cases, C and W are much distant from other amino acids. These results correspond to the well-known groupings of amino acids: volume, hydrophobicity, charges (N-acid, H-basic), acid amide and aromatic groups. The grouping by SOTA/DIST based on Dayhoff PAM250 is perfectly consistent with the original classification by George et al. (1990) (Figure 1a), followed by groupings based on the matrices of Grantham, Henikoff, Johnson and Jones. Groupings based on the matrices of Doolittle, Gonnet and McLachlan exhibit similar overall characteristics of physicochemical relationships. The matrix of Levin, as well as the three matrices based on two-dimensional or three-dimensional structures as well as residue volumes (Miyata, Rao and Risler), only "0/0