Physicochemical property distributions for accurate and rapid pairwise protein homology detection.

Background: The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.

Results: We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence.