Applying machine learning methods for finding significant amino acid properties in proteins

Abstract

There are several possibilities for definition and derivation of sequence patterns associated with structural motifs, in particular on the secondary structure level which may be used to predict these structure elements. Sequence patterns consist of a number of consecutive positions along the polypeptide chain from which a certain quantity is specified. One of the important factors in deriving sequence patterns in terms of amino acid properties is how to find the most characteristic properties to specify a certain position and thus to avoid redundant physical information. We have applied machine learning methods to select the most significant amino acid properties describing a structurally determined sequence position. Results are given for the beginning of α-helices. These methods may link the gap between amino acid patterns and property patterns and thus are valuable to improve protein structure prediction.