Minimotif Miner (MnM) analyzes protein queries for the presence of short functional motifs that, in at least one protein, has been demonstrated to be involved in posttranslational modifications, binding to other proteins, nucleic acids, or small molecules, or proteins trafficking [Minimotif Miner: a tool for investigating protein function]. The low sequence complexity of motifs, suggest that “false positive” motifs may occur and any prediction made by MnM should be experimentally tested. To aid in the selection of motifs, MnM ranks motifs based on frequencies in proteomes, protein surface prediction, and evolutionary conservation. Using annotation of motifs in the Swiss-Prot database, we have found that higher scores are globally correlated with experimentally validated motifs when compared to a similar analysis using randomized motifs with the same amino acid composition. We suggest that the known biology of the protein of interest and of motifs be used in selecting motifs for experimental study.
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MnM Research Group
This project is led by Sanguthevar Rajasekaran (UCONN CSE Department) and Martin R. Schiller (UCONN Health Center) in collaboration with Chun-Hsi Huang (UCONN CSE Department), Mark Maciejewski (UCONN Health Center) and Michael R. Gryk (UCONN Health Center). The people responsible for the implementation of the web system are Sudha Balla, Vishal Thapar, Snigdha Verma, ThaiBinh Luong and Tanaz Faghri.
Referencing MnM (please refer this paper if you publish results obtained from MnM leads)
Sudha Balla, Vishal Thapar, Snigdha Verma, ThaiBinh Luong, Tanaz Faghri, Chun-Hsi Huang, Sanguthevar Rajasekaran, Jacob J del Campo, Jessica H Shinn, William A Mohler, Mark W Maciejewski, Michael R Gryk, Bryan Piccirillo, Stanley R Schiller, Martin R Schiller, Minimotif Miner: a tool for investigating protein function, Nature Methods, (March 2006, Vol. 3, No. 3), pp 175-177.