Reducing dimensionality in remote homology detection using predicted contact maps

Abstract

In this paper, a new method for remote protein homology detection is presented. Most discriminative methods concatenate the values extracted from physicochemical properties to build a model that separates homolog and non-homolog examples. Each discriminative method uses a specific strategy to represent the information extracted from the protein sequence and a different number of indices. After the vector representation is achieved, support vector machines (SVM) are usually used. Most classification techniques are not suitable in remote homology detection because they do not address high dimensional datasets. In this paper, we propose a method that reduces the high dimensionality of the vector representation using models that are defined at the 3D level. Next, the models are mapped from the protein primary sequence. The new method, called remote-C3D, is presented and tested on the SCOP 1.53 and SCOP 1.55 datasets. The remote-C3D method achieves a higher accuracy than the composition-based methods and a comparable performance with profile-based methods.