Multiple protein structure alignment using time-frequency processing techniques

We propose a protein structure alignment method that exploits advances in time-frequency signal processing to increase the similarity measure accuracy between distantly-related proteins. The new method uses a waveform non-linear mapping technique and waveform transformations in time-frequency (TF) space. Specifically, protein amino acids are mapped to three-dimensional (3-D) linear frequency-modulated (LFM) Gaussian chirps that are translated and rotated to account for all possible protein structure matches. The protein structure directionality is changed by considering all possible chirp rate parameters. Furthermore, both local and global alignments can be identified between multiple protein structures due to the linear separability property of the Gaussian-type functions. Our results are successfully demonstrated using proteins structures from a known database without performing any pre-processing. The paper also introduces a web-based learning module Java-DSP that can be used to implement bioinformatics functions using signal processing methods.