A Structure Based Algorithm for Improving Motifs Prediction

• Published in: Lecture notes in computer science pp. 242 - 252
• Main Authors: Pathak, Sudipta, Kundeti, Vamsi Krishna, Schiller, Martin R., Rajasekaran, Sanguthevar
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2013
• Series: Lecture Notes in Computer Science
• Subjects: Eukaryotic Linear Motif; Nucleic Acid Research; Positive Occurrence; Protein Data Bank; Source Protein
• ISBN: 3642391583; 9783642391583
• ISSN: 0302-9743; 1611-3349; 1611-3349
• DOI: 10.1007/978-3-642-39159-0_22

Minimotifs are short contiguous peptide sequences in proteins that are known to have functions. There are many repositories for experimentally validated minimotifs. MnM is one of them. Predicting minimotifs (in unknown sequences) is a challenging and interesting problem in biology. Minimotifs stored in the MnM database range in length from 5 to 15. Any algorithm for predicting minimotifs in an unknown query sequence is likely to have many false positives owing to the short lengths of the motifs looked for. Our team has developed a series of algorithms (called filters) in the past to reduce the false positives and improve the prediction accuracy. All of these algorithms are based on sequence information. In a recent paper we have demonstrated the power of structural information in characterizing motifs. In this paper we present an algorithm that exploits structural information for reducing false positives in motifs prediction. We test the validity of our algorithm using the minimotifs stored in the MnM database. MnM is a web system for minimotif search that our team has built. It houses more than 300,000 minimotifs. Our new algorithm is a learning algorithm that will be trained in the first phase and in the second phase its accuracy will be measured. For any input query protein sequence, MnM identifies a list of putative minimotifs in the query sequence. We currently employ a series of sequence based algorithms to reduce the false positives in the predictions of MnM. For every minimotif stored in MnM, we also store a number of attributes pertinent to the motif. One such attribute is the source of the minimotif. The source is nothing but the protein in which the minimotif is present. For the analysis of our new algorithm we only employ those minimtofis that have multiple sources for positive control. Random data is used as negative data. The basic idea of our algorithm is the hypothesis that a putative minimotif is likely to be valid if its structure in the query sequence is very similar to its structure in its source protein. Another important feature of our algorithm is that it is specific to individual minimotifs. In other words, a unique set of parameters is learnt for every minimotif. We feel that this is a better approach than learning a common set of parameters for all the minimotifs together. Our findings reveal that in most of the cases the occurrences of the minimotifs in their source proteins are structurally similar. Also, typically, the occurrences of a minimotif in its source protein and a random protein are dissimilar. Our experimental results show that the parameters learnt by our algorithm can significantly reduce false positives.