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.

中文翻译：

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共振识别模型与斯托克韦尔变换相结合的微管热点定位

微管蛋白是设计抗癌药物的一个很有前景的靶点。多功能微管蛋白中热点的鉴定为新药发现提供了见解。尽管机器学习技术在预测中显示出显着的结果，但它们无法识别与特定生物功能相对应的热点。本文提出了一种结合共振识别模型 (RRM) 和斯托克韦尔变换 (ST) 的信号处理技术，用于识别与特定功能相对应的热点。使用 RRM 确定代表特定生物功能的特征频率 (CF)。然后使用 ST 计算蛋白质序列的光谱。CF 是使用时频掩码从 ST 频谱中过滤出来的。过滤后的序列中的能量峰值代表热点。将所提出的方法预测的热点与实验检测到的微管蛋白稳定药物紫杉醇和微管蛋白中存在的去稳定药物秋水仙碱的结合残基进行比较。在 53 个实验确定的热点中，60% 是由所提出的方法预测的，而大约 20% 是由现有的基于机器学习的方法预测的。此外，所提出的方法预测了一些可以研究的新热点。