Improving MHC‐I Ligand Identifications from LC‐MS/MS Data by Incorporating Allelic Peptide Motifs

MHC class I (MHC‐I)‐bound ligands play a pivotal role in CD8 T cell immunity and are hence of major interest in understanding and designing immunotherapies. One of the most commonly utilized approaches for detecting MHC ligands is LC‐MS/MS. Unfortunately, the effectiveness of current algorithms to identify MHC ligands from LC‐MS/MS data is limited because the search algorithms used were originally developed for proteomics approaches detecting tryptic peptides. Consequently, the analysis often results in inflated false discovery rate (FDR) statistics and an overall decrease in the number of peptides that pass FDR filters. Andreatta et al. describe a new scoring tool (MS‐rescue) for peptides from MHC‐I immunopeptidome datasets. MS‐rescue incorporates the existence of MHC‐I peptide motifs to rescore peptides from ligandome data. The tool is demonstrated here using peptides assigned from LC‐MS/MS data with PEAKs software but can be deployed on data from any search algorithm. This new approach increased the number of peptides identified by up to 20–30% and promises to aid the discovery of novel MHC‐I ligands with immunotherapeutic potential.     

Predicting protein post-translational modifications using meta-analysis of proteome scale data sets

Protein post-translational modifications are an important biological regulatory mechanism, and the rate of their discovery using high throughput techniques is rapidly increasingly. To make use of this wealth of sequence data, we introduce a new general strategy designed to predict a variety of post-translational modifications in several organisms. We used the motif-x program to determine phosphorylation motifs in yeast, fly, mouse, and man and lysine acetylation motifs in man. These motifs were then scanned against proteomic sequence data using a newly developed tool called scan-x to globally predict other potential modification sites within these organisms. 10-fold cross-validation was used to determine the sensitivity and minimum specificity for each set of predictions, all of which showed improvement over other available tools for phosphoprediction. New motif discovery is a byproduct of this approach, and the phosphorylation motif analyses provide strong evidence of evolutionary conservation of both known and novel kinase motifs.     

GlobPlot: Exploring protein sequences for globularity and disorder

A major challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Non-globular sequence segments often contain short linear peptide motifs (e.g. SH3-binding sites) which are important for protein function. We present here a new tool for discovery of such unstructured, or disordered regions within proteins. GlobPlot (http://globplot.embl.de) is a web service that allows the user to plot the tendency within the query protein for order/globularity and disorder. We show examples with known proteins where it successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognised domain. GlobPlot may be useful in domain hunting efforts. The plots indicate that instances of known domains may often contain additional N- or C-terminal segments that appear ordered. Thus GlobPlot may be of use in the design of constructs corresponding to globular proteins, as needed for many biochemical studies, particularly structural biology. GlobPlot has a pipeline interface--GlobPipe--for the advanced user to do whole proteome analysis. GlobPlot can also be used as a generic infrastructure package for graphical displaying of any possible propensity.     

Recognition of LD motifs by the focal adhesion targeting domains of focal adhesion kinase and proline‐rich tyrosine kinase 2‐beta: Insights from molecular dynamics simulations

The focal adhesion kinase (FAK) and the proline‐rich tyrosine kinase 2‐beta (PYK2) are implicated in cancer progression and metastasis and represent promising biomarkers and targets for cancer therapy. FAK and PYK2 are recruited to focal adhesions (FAs) via interactions between their FA targeting (FAT) domains and conserved segments (LD motifs) on the proteins Paxillin, Leupaxin, and Hic‐5. A promising new approach for the inhibition of FAK and PYK2 targets interactions of the FAK domains with proteins that promote localization at FAs. Advances toward this goal include the development of surface plasmon resonance, heteronuclear single quantum coherence nuclear magnetic resonance (HSQC‐NMR) and fluorescence polarization assays for the identification of fragments or compounds interfering with the FAK‐Paxillin interaction. We have recently validated this strategy, showing that Paxillin mimicking polypeptides with 2 to 3 LD motifs displace FAK from FAs and block kinase‐dependent and independent functions of FAK, including downstream integrin signaling and FA localization of the protein p130Cas. In the present work we study by all‐atom molecular dynamics simulations the recognition of peptides with the Paxillin and Leupaxin LD motifs by the FAK‐FAT and PYK2‐FAT domains. Our simulations and free‐energy analysis interpret experimental data on binding of Paxillin and Leupaxin LD motifs at FAK‐FAT and PYK2‐FAT binding sites, and assess the roles of consensus LD regions and flanking residues. Our results can assist in the design of effective inhibitory peptides of the FAK‐FAT: Paxillin and PYK2‐FAT:Leupaxin complexes and the construction of pharmacophore models for the discovery of potential small‐molecule inhibitors of the FAK‐FAT and PYK2‐FAT focal adhesion based functions.     

The evolution,impact and properties of exonic splice enhancers

Background

In humans, much of the information specifying splice sites is not at the splice site. Exonic splice enhancers are one of the principle non-splice site motifs. Four high-throughput studies have provided a compendium of motifs that function as exonic splice enhancers, but only one, RESCUE-ESE, has been generally employed to examine the properties of enhancers. Here we consider these four datasets to ask whether there is any consensus on the properties and impacts of exonic splice enhancers.

Results

While only about 1% of all the identified hexamer motifs are common to all analyses we can define reasonably sized sets that are found in most datasets. These consensus intersection datasets we presume reflect the true properties of exonic splice enhancers. Given prior evidence for the properties of enhancers and splice-associated mutations, we ask for all datasets whether the exonic splice enhancers considered are purine enriched; enriched near exon boundaries; able to predict trends in relative codon usage; slow evolving at synonymous sites; rare in SNPs; associated with weak splice sites; and enriched near longer introns. While the intersect datasets match expectations, only one original dataset, RESCUE-ESE, does. Unexpectedly, a fully experimental dataset identifies motifs that commonly behave opposite to the consensus, for example, being enriched in exon cores where splice-associated mutations are rare.

Conclusions

Prior analyses that used the RESCUE-ESE set of hexamers captured the properties of consensus exonic splice enhancers. We estimate that at least 4% of synonymous mutations are deleterious owing to an effect on enhancer functioning.     

Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources

Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (available at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.     

A generic motif discovery algorithm for sequential data

MOTIVATION: Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems. RESULTS: Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemoda's output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures. AVAILABILITY: Gemoda is freely available at http://web.mit.edu/bamel/gemoda     

Transcription factor binding site identification using the self-organizing map

MOTIVATION: The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. In this paper, we propose a self-organizing map of position weight matrices as an alternative method for motif discovery. The advantage of this approach is that it can be used to simultaneously characterize every feature present in the dataset, thus lessening the chance that weaker signals will be missed. Features identified are ranked in terms of over-representation relative to a background model. RESULTS: We present an implementation of this approach, named SOMBRERO (self-organizing map for biological regulatory element recognition and ordering), which is capable of discovering multiple distinct motifs present in a single dataset. Demonstrated here are the advantages of our approach on various datasets and SOMBRERO's improved performance over two popular motif-finding programs, MEME and AlignACE. AVAILABILITY: SOMBRERO is available free of charge from http://bioinf.nuigalway.ie/sombrero SUPPLEMENTARY INFORMATION: http://bioinf.nuigalway.ie/sombrero/additional.