Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper presents a signal processing technique combining resonant recognition model (RRM) and Stockwell Transform (ST) for the identification of hotspots corresponding to a particular functionality. The characteristic frequency (CF) representing a specific biological function is determined using the RRM. Then the spectrum of the protein sequence is computed using ST. The CF is filtered from the ST spectrum using a time-frequency mask. The energy peaks in the filtered sequence represent the hotspots. The hotspots predicted by the proposed method are compared with the experimentally detected binding residues of Tubulin stabilizing drug Taxol and destabilizing drug Colchicine present in the Tubulin protein. Out of the 53 experimentally identified hotspots, 60% are predicted by the proposed method whereas around 20% are predicted by existing machine learning based methods. Additionally, the proposed method predicts some new hot spots, which may be investigated.
Sign-in or become an IEEE member to discover the full contents of the paper.