MOTIVATION: Modification of proteins via phosphorylation is a primary mechanism for signal transduction in cells. Phosphorylation sites on proteins are determined in part through particular patterns, or motifs, present in the amino acid sequence. RESULTS: We describe an algorithm that simultaneously discovers multiple motifs in a set of peptides that were phosphorylated by several different kinases. Such sets of peptides are routinely produced in proteomics experiments.Our motif-finding algorithm uses the principle of minimum description length to determine a mixture of sequence motifs that distinguish a foreground set of phosphopeptides from a background set of unphosphorylated peptides. We show that our algorithm outperforms existing motif-finding algorithms on synthetic datasets consisting of mixtures of known phosphorylation sites. We also derive a motif specificity score that quantifies whether or not the phosphoproteins containing an instance of a motif have a significant number of known interactions. Application of our motif-finding algorithm to recently published human and mouse proteomic studies recovers several known phosphorylation motifs and reveals a number of novel motifs that are enriched for interactions with a particular kinase or phosphatase. Our tools provide a new approach for uncovering the sequence specificities of uncharacterized kinases or phosphatases.
MOTIVATION: Modification of proteins via phosphorylation is a primary mechanism for signal transduction in cells. Phosphorylation sites on proteins are determined in part through particular patterns, or motifs, present in the amino acid sequence. RESULTS: We describe an algorithm that simultaneously discovers multiple motifs in a set of peptides that were phosphorylated by several different kinases. Such sets of peptides are routinely produced in proteomics experiments.Our motif-finding algorithm uses the principle of minimum description length to determine a mixture of sequence motifs that distinguish a foreground set of phosphopeptides from a background set of unphosphorylated peptides. We show that our algorithm outperforms existing motif-finding algorithms on synthetic datasets consisting of mixtures of known phosphorylation sites. We also derive a motif specificity score that quantifies whether or not the phosphoproteins containing an instance of a motif have a significant number of known interactions. Application of our motif-finding algorithm to recently published human and mouse proteomic studies recovers several known phosphorylation motifs and reveals a number of novel motifs that are enriched for interactions with a particular kinase or phosphatase. Our tools provide a new approach for uncovering the sequence specificities of uncharacterized kinases or phosphatases.
Authors: P J Coopman; M T Do; M Barth; E T Bowden; A J Hayes; E Basyuk; J K Blancato; P R Vezza; S W McLeskey; P H Mangeat; S C Mueller Journal: Nature Date: 2000-08-17 Impact factor: 49.962
Authors: Suraj Peri; J Daniel Navarro; Ramars Amanchy; Troels Z Kristiansen; Chandra Kiran Jonnalagadda; Vineeth Surendranath; Vidya Niranjan; Babylakshmi Muthusamy; T K B Gandhi; Mads Gronborg; Nieves Ibarrola; Nandan Deshpande; K Shanker; H N Shivashankar; B P Rashmi; M A Ramya; Zhixing Zhao; K N Chandrika; N Padma; H C Harsha; A J Yatish; M P Kavitha; Minal Menezes; Dipanwita Roy Choudhury; Shubha Suresh; Neelanjana Ghosh; R Saravana; Sreenath Chandran; Subhalakshmi Krishna; Mary Joy; Sanjeev K Anand; V Madavan; Ansamma Joseph; Guang W Wong; William P Schiemann; Stefan N Constantinescu; Lily Huang; Roya Khosravi-Far; Hanno Steen; Muneesh Tewari; Saghi Ghaffari; Gerard C Blobe; Chi V Dang; Joe G N Garcia; Jonathan Pevsner; Ole N Jensen; Peter Roepstorff; Krishna S Deshpande; Arul M Chinnaiyan; Ada Hamosh; Aravinda Chakravarti; Akhilesh Pandey Journal: Genome Res Date: 2003-10 Impact factor: 9.043
Authors: Fahad Saeed; Trairak Pisitkun; Jason D Hoffert; Guanghui Wang; Marjan Gucek; Mark A Knepper Journal: Proceedings (IEEE Int Conf Bioinformatics Biomed) Date: 2012-10-04