Finger Base - An Algorithm to Predict the Incidence of Zinc Finger Motif from Uncharacterized Proteins

Background: One of the basic problems in the insilico approach is to identify zinc finger motif from uncharacterized proteins. Existing algorithms such as Zif Base, Zifibi and ZiFiT can identify the presence of zinc finger motifs only in characterized proteins. Objective: This paper focuses on developing a solution to overcome the existing limitation and to identify zinc finger motif from uncharacterized proteins. Method: This tool consists of two algorithms PATTERN and FINGER. The PATTERN algorithm generates templates for all the characterized proteins that are available in various databases. Then the FINGER algorithm compares the query sequence of an uncharacterized protein with that of templates identified through PATTERN algorithm. Results: If there is a presence of template in that query sequence, the tool infers that the query sequence has a transcriptional role. Moreover, the veracity of the algorithm is validated by comparing the result with the result of characterized data derived from the experimental methods. Conclusion: The precision and recall of the algorithm were predicted as 86% and 89% respectively. Furthermore, this algorithm determines with higher accuracy compared to any other prevailing computational approaches.