Motivation Post-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called 'motifs' that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation. Results We describe a method for assigning statistical confidence estimates to PTM motifs, and we demonstrate that this method provides accurate P-values on both simulated and real data. Our methods are implemented in MoMo, a software tool for discovering motifs among sets of PTMs that we make available as a web server and as downloadable source code. MoMo re-implements the two most widely used PTM motif discovery algorithms-motif-x and MoDL-while offering many enhancements. Relative to motif-x, MoMo offers improved statistical confidence estimates and more accurate calculation of motif scores. The MoMo web server offers more proteome databases, more input formats, larger inputs and longer running times than the motif-x web server. Finally, our study demonstrates that the confidence estimates produced by motif-x are inaccurate. This inaccuracy stems in part from the common practice of drawing 'background' peptides from an unshuffled proteome database. Our results thus suggest that many of the papers that use motif-x to find motifs may be reporting results that lack statistical support. Availability and implementation The MoMo web server and source code are provided at http://meme-suite.org. Supplementary information Supplementary data are available at Bioinformatics online.

MoMo: discovery of statistically significant post-translational modification motifs.

Motivation: Post-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called "motifs" that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation. Results: We describe a method for assigning statistical confidence estimates to PTM motifs, and we demonstrate that this method provides accurate p -values on both simulated and real data. Our methods are implemented in MoMo, a software tool for discovering motifs among sets of PTMs that we make available as a web server and as downloadable source code. MoMo reimplements the two most widely used PTM motif discovery algorithms--motif-x and MoDL--while offering many enhancements. Relative to motif-x, MoMo offers improved statistical confidence estimates and more accurate calculation of motif scores. The MoMo web server offers more proteome databases, more input formats, larger inputs and longer running times than the motif-x web server. Finally, our study demonstrates that the confidence estimates produced by motif-x are inaccurate. This inaccuracy stems in part from the common practice of drawing "background" peptides from an unshuffled proteome database. Our results thus suggest that many of the hundreds of papers that use motif-x to find motifs may be reporting results that lack statistical support. Availability: http://meme-suite.org Contact: timothybailey@unr.edu

https://www.biorxiv.org/content/biorxiv/early/2018/09/06/410050.full.pdf

MoMo: Discovery of statistically significant post-translational modification motifs

Discriminative pattern mining is one of the most important techniques in data mining. This challenging task is concerned with finding a set of patterns that occur with disproportionate frequency in data sets with various class labels. Such patterns are of great value for group difference detection and classifier construction. Research on finding interesting discriminative patterns in class-labeled data evolves rapidly and lots of algorithms have been proposed to specifically address this problem. Discriminative pattern mining techniques have proven their considerable value in biological data analysis. The archetypical applications in bioinformatics include phosphorylation motif discovery, differentially expressed gene identification, discriminative genotype pattern detection, etc. In this article, we present an overview of discriminative pattern mining and the corresponding effective methods, and subsequently we illustrate their applications to tackling the bioinformatics problems. In the end, we give a general discussion of potential challenges and future work for this task.

Discriminative pattern mining and its applications in bioinformatics

Motivation: Protein phosphorylation, driven by specific recognition of substrates by kinases and phosphatases, plays central roles in a variety of important cellular processes such as signaling and enzyme activation. Mass spectrometry enables the determination of phosphorylated peptides (and thereby proteins) in scenarios ranging from targeted in vitro studies to in vivo cell lysates under particular conditions. The characterization of commonalities among identified phosphopeptides provides insights into the specificities of the kinases involved in a study. Several algorithms have been developed to uncover linear motifs representing position-specific amino acid patterns in sets of phosphopeptides. To more fully capture the available information, reduce sensitivity to both parameter choices and natural experimental variation, and develop more precise characterizations of kinase specificities, it is necessary to determine all statistically significant motifs represented in a dataset.

Results: We have developed MMFPh (Maximal Motif Finder for Phosphoproteomics datasets), which extends the approach of the popular phosphorylation motif software Motif-X (Schwartz and Gygi, 2005) to identify all statistically significant motifs and return the maximal ones (those not subsumed by motifs with more fixed amino acids). In tests with both synthetic and experimental data, we show that MMFPh finds important motifs missed by the greedy approach of Motif-X, while also finding more motifs that are more characteristic of the dataset relative to the background proteome. Thus MMFPh is in some sense both more sensitive and more specific in characterizing the involved kinases. We also show that MMFPh compares favorably to other recent methods for finding phosphorylation motifs. Furthermore, MMFPh is less dependent on parameter choices. We support this powerful new approach with a web interface so that it may become a useful tool for studies of kinase specificity and phosphorylation site prediction.

Availability: A web server is at www.cs.dartmouth.edu/~cbk/

Contact: ude.htuomtrad.sc@kbc

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/mmfph-a-maximal-motif-finder-for-phosphoproteomics-datasets-4xn1ewpy0y.pdf

MMFPh: a maximal motif finder for phosphoproteomics datasets

Lytic gammaherpesvirus (GHV) replication facilitates the establishment of lifelong latent infection, which places the infected host at risk for numerous cancers. As obligate intracellular parasites, GHVs must control and usurp cellular signaling pathways in order to successfully replicate, disseminate to stable latency reservoirs in the host, and prevent immune-mediated clearance. To facilitate a systems-level understanding of phosphorylation-dependent signaling events directed by GHVs during lytic replication, we utilized label-free quantitative mass spectrometry to interrogate the lytic replication cycle of murine gammaherpesvirus-68 (MHV68). Compared to controls, MHV68 infection regulated by 2-fold or greater ca. 86% of identified phosphopeptides – a regulatory scale not previously observed in phosphoproteomic evaluations of discrete signal-inducing stimuli. Network analyses demonstrated that the infection-associated induction or repression of specific cellular proteins globally altered the flow of information through the host phosphoprotein network, yielding major changes to functional protein clusters and ontologically associated proteins. A series of orthogonal bioinformatics analyses revealed that MAPK and CDK-related signaling events were overrepresented in the infection-associated phosphoproteome and identified 155 host proteins, such as the transcription factor c-Jun, as putative downstream targets. Importantly, functional tests of bioinformatics-based predictions confirmed ERK1/2 and CDK1/2 as kinases that facilitate MHV68 replication and also demonstrated the importance of c-Jun. Finally, a transposon-mutant virus screen identified the MHV68 cyclin D ortholog as a viral protein that contributes to the prominent MAPK/CDK signature of the infection-associated phosphoproteome. Together, these analyses enhance an understanding of how GHVs reorganize and usurp intracellular signaling networks to facilitate infection and replication.

/pdf/phosphoproteomic-analyses-reveal-signaling-pathways-that-3ajrhhozyh.pdf

Phosphoproteomic analyses reveal signaling pathways that facilitate lytic gammaherpesvirus replication.

Background 
The need for efficient algorithms to uncover biologically relevant phosphorylation motifs has become very important with rapid expansion of the proteomic sequence database along with a plethora of new information on phosphorylation sites. Here we present a novel unsupervised method, called Motif Finder (in short, F-Motif) for identification of phosphorylation motifs. F-Motif uses clustering of sequence information represented by numerical features that exploit the statistical information hidden in some foreground data. Furthermore, these identified motifs are then filtered to find “actual” motifs with statistically significant motif scores.
Results and Discussion 
We have applied F-Motif to several new and existing data sets and compared its performance with two well known state-of-the-art methods. In almost all cases F-Motif could identify all statistically significant motifs extracted by the state-of-the-art methods. More importantly, in addition to this, F-Motif uncovers several novel motifs. We have demonstrated using clues from the literature that most of these new motifs discovered by F-Motif are indeed novel. We have also found some interesting phenomena. For example, for CK2 kinase, the conserved sites appear only on the right side of S. However, for CDK kinase, the adjacent site on the right of S is conserved with residue P. In addition, three different encoding methods, including a novel position contrast matrix (PCM) and the simplest binary coding, are used and the ability of F-motif to discover motifs remains quite robust with respect to encoding schemes.
Conclusions 
An iterative algorithm proposed here uses exploratory data analysis to discover motifs from phosphorylated data. The effectiveness of F-Motif has been demonstrated using several real data sets as well as using a synthetic data set. The method is quite general in nature and can be used to find other types of motifs also. We have also provided a server for F-Motif at http://f-motif.classcloud.org/, http://bio.classcloud.org/f-motif/ or http://ymu.classcloud.org/f-motif/.

/pdf/discovery-of-protein-phosphorylation-motifs-through-2b18jfdjbu.pdf

Yao-Tsung Wang

Papers

Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis