Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin
ABSTARCT :
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.
EXISTING SYSTEM :
? Based on previous works and existing data, the authors proposed a model of reversible branch migration in mobile 3WJs with trinucleotide repeats, which may help the treatment of diseases.
? Almost all function implementations involve multiple coexisting and mutable conformations.
? Some existing hot spots remained and new ones formed which ultimately polarizes the signaling vector towards the left and elicits a change in the cell velocity.
? We speculated that a stronger correlation would exist when cell direction was compared to PI3K signaling vectors with magnitudes defined by Models, which factor in the level of PI3K activity.
DISADVANTAGE :
? The problem of locating genes and exons is sometimes generally referred to as the gene-finding problem.
? This problem can be avoided by subtracting the average of the numerical sequence from the values of its samples before computing its DFT.
? A variety of algorithms can be used for the optimization problem under consideration such as algorithms of the quasi-Newton family which are both very efficient as well as robust.
? This would be an interesting research problem to pursue that may eventually lead to significant advancements in our understanding of protein hot spots.
? It would be interesting to investigate the applicability of digital filters for this as well as other similar problems.
PROPOSED SYSTEM :
• The purpose of clustering is to improve the arrangement of the data matrix by grouping data with ‘similar’ characteristics, in this case intensities and their proximities to one another.
• In an effort to recapitulate this process, we have adapted a published microfluidic design for generating tunable and stable PDGF gradients for the purpose of studying PI3K dynamics during directed fibroblast migration.
• Some of the bubbles come in contact with the fibroblasts attached in the channels and tear them from the surface, a phenomenon that is exploited in other microfluidic studies for the very purpose of detaching cells.
• The Whitesides group proposed a microfluidic design to overcome these limitations.
ADVANTAGE :
? The development, implementation, and performance evaluation of new techniques for the location of hot spots in proteins and exons in DNA using digital filters are presented.
? The performance of the techniques is then evaluated using metrics such as sensitivity, specificity, accuracy, precision, and computational efficiency.
? Extensive performance analysis of the filter-based hot-spot and exon location techniques is carried out using a number of evaluation metrics and the so-called receiver operating characteristic (ROC) technique.
? We investigate the application of digital filters towards this goal, and make contributions in the development, testing, and performance analysis of such techniques.
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