PRIMARY STRUCTURES OF PROTEINS AS SPACE-DEPENDENT SIGNALS

This minireview is devoted to a scarcely populated but potentially quite interesting eld of computational biochemistry: the use of signal analysis methods to describe protein sequences as mono-dimensional series. The protein sequences are described by means of a vector of numerical variables that summarize their autocorrelation structures. Thus, the simplest level of protein sequences description shifts from the pairwise alignment of structures to a self-consistent numerical description of the single sequence. The main steps of the method can be summarized as follows: a) use of hydrophobic code for primary structures; b) treatment of the hydrophobicity distribution along the sequence like a time series, with the corresponding use of nonlinear signal analysis techniques to underpin position-dependent properties of the hydrophobicity proles; c) adoption of a local approach for both inter-sequence (within homologous series of proteins) comparisons and intra-sequence (among short patches along the same sequence) analyses as a starting point for periodicity detection.