Computational protein profile similarity screening for quantitative mass spectrometry experiments

Motivation: The qualitative and quantitative characterization of protein abundance profiles over a series of time points or a set of environmental conditions is becoming increasingly important. Using isobaric mass tagging experiments, mass spectrometry-based quantitative proteomics deliver accurate peptide abundance profiles for relative quantitation. Associated data analysis workflows need to provide tailored statistical treatment that (i) takes the correlation structure of the normalized peptide abundance profiles into account and (ii) allows inference of protein-level similarity. We introduce a suitable distance measure for relative abundance profiles, derive a statistical test for equality and propose a protein-level representation of peptide-level measurements. This yields a workflow that delivers a similarity ranking of protein abundance profiles with respect to a defined reference. All procedures have in common that they operate based on the true correlation structure that underlies the measurements. This optimizes power and delivers more intuitive and efficient results than existing methods that do not take these circumstances into account. Results: We use protein profile similarity screening to identify candidate proteins whose abundances are post-transcriptionally controlled by the Anaphase Promoting Complex/Cyclosome (APC/C), a specific E3 ubiquitin ligase that is a master regulator of the cell cycle. Results are compared with an established protein correlation profiling method. The proposed procedure yields a 50.9-fold enrichment of co-regulated protein candidates and a 2.5-fold improvement over the previous method

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Lapatinib-binding protein kinases in the African trypanosome: identification of cellular targets for kinase-directed chemical scaffolds.

Human African trypanosomiasis is caused by the eukaryotic microbe Trypanosoma brucei. To discover new drugs against the disease, one may use drugs in the clinic for other indications whose chemical scaffolds can be optimized via a medicinal chemistry campaign to achieve greater potency against the trypanosome. Towards this goal, we tested inhibitors of human EGFR and/or VEGFR as possible anti-trypanosome compounds. The 4-anilinoquinazolines canertinib and lapatinib, and the pyrrolopyrimidine AEE788 killed bloodstream T. brucei in vitro with GI(50) in the low micromolar range. Curiously, the genome of T. brucei does not encode EGFR or VEGFR, indicating that the drugs recognize alternate proteins. To discover these novel targets, a trypanosome lysate was adsorbed to an ATP-sepharose matrix and washed with a high salt solution followed by nicotinamide adenine dinucleotide (NAD(+)). Proteins that remained bound to the column were eluted with drugs, and identified by mass spectrometry/bioinformatics. Lapatinib bound to Tb927.4.5180 (termed T. brucei lapatinib-binding protein kinase-1 (TbLBPK1)) while AEE788 bound Tb927.5.800 (TbLBPK2). When the NAD(+) wash was omitted from the protocol, AEE788, canertinib and lapatinib eluted TbLBPK1, TbLBPK2, and Tb927.3.1570 (TbLBPK3). In addition, both canertinib and lapatinib eluted Tb10.60.3140 (TbLBPK4), whereas only canertinib desorbed Tb10.61.1880 (TbCBPK1). Lapatinib binds to a unique conformation of protein kinases. To gain insight into the structural basis for lapatinib interaction with TbLBPKs, we constructed three-dimensional models of lapatinib•TbLBPK complexes, which confirmed that TbLBPKs can adopt lapatinib-compatible conformations. Further, lapatinib, AEE788, and canertinib were docked to TbLBPKs with favorable scores. Our studies (a) present novel targets of kinase-directed drugs in the trypanosome, and (b) offer the 4-anilinoquinazoline and pyrrolopyrimidines as scaffolds worthy of medicinal chemistry and structural biology campaigns to develop them into anti-trypanosome drugs

How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Selective glucocorticoid receptor properties of GSK866 analogs with cysteine reactive warheads

Synthetic glucocorticoids (GC) are the mainstay therapy for treatment of acute and chronic inflammatory disorders. Due to the high adverse effects associated with long-term use, GC pharmacology has focused since the nineties on more selective GC ligand-binding strategies, classified as selective glucocorticoid receptor (GR) agonists (SEGRAs) or selective glucocorticoid receptor modulators (SEGRMs). In the current study, GSK866 analogs with electrophilic covalent-binding warheads were developed with potential SEGRA properties to improve their clinical safety profile for long-lasting topical skin disease applications. Since the off-rate of a covalently binding drug is negligible compared to that of a non-covalent drug, its therapeutic effects can be prolonged and typically, smaller doses of the drug are necessary to reach the same level of therapeutic efficacy, thereby potentially reducing systemic side effects. Different analogs of SEGRA GSK866 coupled to cysteine reactive warheads were characterized for GR potency and selectivity in various biochemical and cellular assays. GR-and NF kappa B dependent reporter gene studies show favorable anti-inflammatory properties with reduced GR transactivation of two non-steroidal GSK866 analogs UAMC-1217 and UAMC-1218, whereas UAMC-1158 and UAMC-1159 compounds failed to modulate cellular GR activity. These results were further supported by GR immuno-localization and S211 phospho-GR western analysis, illustrating significant GR phosphoactivation and nuclear translocation upon treatment of GSK866, UAMC-1217, or UAMC-1218, but not in case of UAMC-1158 or UAMC-1159. Furthermore, mass spectrometry analysis of tryptic peptides of recombinant GR ligand-binding domain (LBD) bound to UAMC-1217 or UAMC-1218 confirmed covalent cysteine-dependent GR binding. Finally, molecular dynamics simulations, as well as glucocorticoid receptor ligand-binding domain (GR-LBD) coregulator interaction profiling of the GR-LBD bound to GSK866 or its covalently binding analogs UAMC-1217 or UAMC-1218 revealed subtle conformational differences that might underlie their SEGRA properties. Altogether, GSK866 analogs UAMC-1217 and UAMC-1218 hold promise as a novel class of covalent-binding SEGRA ligands for the treatment of topical inflammatory skin disorders

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Analiza peptida iz hrane

The aim of this review is to discuss the definition of food peptidomics and highlight the role of this approach in food and nutrition sciences. Similar to living organisms, food peptidome may be defined as the whole peptide pool present in a food product or raw material. This definition also covers peptides obtained during technological processes and/or storage. The area of interest of food peptidomics covers research concerning the origin of peptidome, its dynamic changes during processing and/or storage, the influence of its presence, the composition and changes in the pool of peptides on the properties of food products or raw materials as well as the methods applied in research into this group of compounds. The area of interests of food peptidomics would include biological activity, functional properties, allergenicity, sensory properties and information on the product or resource authenticity and origin as well as its history and relationships. Research methods applied in food peptidomics, with special emphasis on computational methods, are also summarized.Ovaj revijalni prikaz razmatra definiciju pojma „food peptidomics“, tj. istraživanje peptida iz hrane i njihovu ulogu u prehrambenoj tehnologiji i nutricionizmu. Slično živim organizmima, „peptidome“ hrane obuhvaća sve peptide u prehrambenom proizvodu ili sirovini, a i one proizvedene tijekom prerade i/ili njihova skladištenja. Ova grana znanosti obuhvaća istraživanje podrijetla peptida, dinamičnosti njihove promjene pri preradi i/ili skladištenju, utjecaja peptida, njihova sastava i promjene sastava na svojstva prehrambenih proizvoda i sirovina, te metode istraživanja. To uključuje ispitivanje njihove biološke aktivnosti, funkcionalnih, alergenskih i senzorskih svojstava, informacije o autentičnosti i porijeklu proizvoda ili izvora, te povijesni razvoj i odnose. U radu su opisane metode istraživanja peptida, s posebnim naglaskom na računske metode

Quantitative profiling of peptides from RNAs classified as noncoding.

Only a small fraction of the mammalian genome codes for messenger RNAs destined to be translated into proteins, and it is generally assumed that a large portion of transcribed sequences--including introns and several classes of noncoding RNAs (ncRNAs)--do not give rise to peptide products. A systematic examination of translation and physiological regulation of ncRNAs has not been conducted. Here we use computational methods to identify the products of non-canonical translation in mouse neurons by analysing unannotated transcripts in combination with proteomic data. This study supports the existence of non-canonical translation products from both intragenic and extragenic genomic regions, including peptides derived from antisense transcripts and introns. Moreover, the studied novel translation products exhibit temporal regulation similar to that of proteins known to be involved in neuronal activity processes. These observations highlight a potentially large and complex set of biologically regulated translational events from transcripts formerly thought to lack coding potential