Protein inference based on peptides identified from tandem mass spectra

Protein inference is a critical computational step in the study of proteomics. It lays the foundation for further structural and functional analysis of proteins, based on which new medicine or technology can be developed. Today, mass spectrometry (MS) is the technique of choice for large-scale inference of proteins in proteomics. In MS-based protein inference, three levels of data are generated: (1) tandem mass spectra (MS/MS); (2) peptide sequences and their scores or probabilities; and (3) protein sequences and their scores or probabilities. Accordingly, the protein inference problem can be divided into three computational phases: (1) process MS/MS to improve the quality of the data and facilitate subsequent peptide identification; (2) postprocess peptide identification results from existing algorithms which match MS/MS to peptides; and (3) infer proteins by assembling identified peptides. The addressing of these computational problems consists of the main content of this thesis. The processing of MS/MS data mainly includes denoising, quality assessment, and charge state determination. Here, we discuss the determination of charge states from MS/MS data using low-resolution collision induced dissociation. Such spectra with multiple charges are usually searched multiple times by assuming each possible charge state. Not only does this strategy increase the overall database search time, but also yields more false positives. Hence, it is advantageous to determine the charge states of such spectra before the database search. A new approach is proposed to determine the charge states of low-resolution MS/MS. Four novel and discriminant features are adopted to describe each MS/MS and are used in Gaussian mixture model to distinguish doubly and triply charged peptides. The results have shown that this method can assign charge states to low-resolution MS/MS more accurately than existing methods. Many search engines are available for peptide identification. However, there is usually a high false positive rate (FPR) in the results. This can bring many false identifications to protein inference. As a result, it is necessary to postprocess peptide identification results. The most commonly used method is performing statistical analysis, which does not only make it possible to compare and combine the results from different search engines, but also facilitates subsequent protein inference. We proposed a new method to estimate the accuracy of peptide identification with logistic regression (LR) and exemplify it based on Sequest scores. Each peptide is characterized with the regularized Sequest scores ΔCn∗ and Xcorr∗. The score regularization is formulated as an optimization problem by applying two assumptions: the smoothing consistency between sibling peptides and the fitting consistency between original scores and new scores. The results have shown that the proposed method can robustly assign accurate probabilities to peptides and has a very high discrimination power, higher than that of PeptideProphet, to distinguish correctly and incorrectly identified peptides. Given identified peptides and their probabilities, protein inference is conducted by assembling these peptides. Existing methods to address this MS-based protein inference problem can be classified into two groups: twostage and one unified framework to identify peptides and infer proteins. In two-stage methods, protein inference is based on, but also separated from, peptide identification. Whereas in one unified framework, protein inference and peptide identification are integrated together. In this study, we proposed a unified framework for protein inference, and developed an iterative method accordingly to infer proteins based on Sequest peptide identification. The statistical analysis of peptide identification is performed with the LR previously introduced. Protein inference and peptide identification are iterated in one framework by adding a feedback from protein inference to peptide identification. The feedback information is a list of high-confidence proteins, which is used to update the adjacency matrix between peptides. The adjacency matrix is used in the regularization of peptide scores. The results have shown that the proposed method can infer more true positive proteins, while outputting less false positive proteins than ProteinProphet at the same FPR. The coverage of inferred proteins is also significantly increased due to the selection of multiple peptides for each MS/MS spectrum and the improvement of their scores by the feedback from the inferred proteins

Features-Based Deisotoping Method for Tandem Mass Spectra

For high-resolution tandem mass spectra, the determination of monoisotopic masses of fragment ions plays a key role in the subsequent peptide and protein identification. In this paper, we present a new algorithm for deisotoping the bottom-up spectra. Isotopic-cluster graphs are constructed to describe the relationship between all possible isotopic clusters. Based on the relationship in isotopic-cluster graphs, each possible isotopic cluster is assessed with a score function, which is built by combining nonintensity and intensity features of fragment ions. The non-intensity features are used to prevent fragment ions with low intensity from being removed. Dynamic programming is adopted to find the highest score path with the most reliable isotopic clusters. The experimental results have shown that the average Mascot scores and F-scores of identified peptides from spectra processed by our deisotoping method are greater than those by YADA and MS-Deconv software

Recent advances in non-optical microfluidic platforms for bioparticle detection

The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.</p

Design, Synthesis, and Pharmacological Evaluation of Haloperidol Derivatives as Novel Potent Calcium Channel Blockers with Vasodilator Activity

Several haloperidol derivatives with a piperidine scaffold that was decorated at the nitrogen atom with different alkyl, benzyl, or substituted benzyl moieties were synthesized at our laboratory to establish a library of compounds with vasodilator activity. Compounds were screened for vasodilatory activity on isolated thoracic aorta rings from rats, and their quantitative structure–activity relationships (QSAR) were examined. Based on the result of QSAR, N-4-tert-butyl benzyl haloperidol chloride (16c) was synthesized and showed the most potent vasodilatory activity of all designed compounds. 16c dose-dependently inhibited the contraction caused by the influx of extracellular Ca2+ in isolated thoracic aorta rings from rats. It concentration-dependently attenuated the calcium channel current and extracellular Ca2+ influx, without affecting the intracellular Ca2+ mobilization, in vascular smooth muscle cells from rats. 16c, possessing the N-4-tert-butyl benzyl piperidine structure, as a novel calcium antagonist, may be effective as a calcium channel blocker in cardiovascular disease

Genome-wide analysis and identification of stress-responsive genes of the CCCH zinc finger family in Capsicum annuum L.

The CCCH zinc finger gene family encodes a class of proteins that can bind to both DNA and RNA, and an increasing number of studies have demonstrated that the CCCH gene family plays a key role in growth and development and responses to environmental stress. Here, we identified 57 CCCH genes in the pepper (Capsicum annuum L.) genome and explored the evolution and function of the CCCH gene family in C. annuum. Substantial variation was observed in the structure of these CCCH genes, and the number of exons ranged from one to fourteen. Analysis of gene duplication events revealed that segmental duplication was the main driver of gene expansion in the CCCH gene family in pepper. We found that the expression of CCCH genes was significantly up-regulated during the response to biotic and abiotic stress, especially cold and heat stress, indicating that CCCH genes play key roles in stress responses. Our results provide new information on CCCH genes in pepper and will aid future studies of the evolution, inheritance, and function of CCCH zinc finger genes in pepper

Haemophilus parasuis molecular serotyping assay

Haemophilus parasuis causes Glässer's disease and pneumonia in pigs. Indirect hemagglutination (IHA) is typically used to serotype this bacterium, distinguishing 15 serovars with some nontypeable isolates. The capsule loci of the 15 reference strains have been annotated, and significant genetic variation was identified between serovars, with the exception of serovars 5 and 12. A capsule locus and in silico serovar were identified for all but two nontypeable isolates in our collection of >200 isolates. Here, we describe the development of a multiplex PCR, based on variation within the capsule loci of the 15 serovars of H. parasuis, for rapid molecular serotyping. The multiplex PCR (mPCR) distinguished between all previously described serovars except 5 and 12, which were detected by the same pair of primers. The detection limit of the mPCR was 4.29 × 10(5) ng/μl bacterial genomic DNA, and high specificity was indicated by the absence of reactivity against closely related commensal Pasteurellaceae and other bacterial pathogens of pigs. A subset of 150 isolates from a previously sequenced H. parasuis collection was used to validate the mPCR with 100% accuracy compared to the in silico results. In addition, the two in silico-nontypeable isolates were typeable using the mPCR. A further 84 isolates were analyzed by mPCR and compared to the IHA serotyping results with 90% concordance (excluding those that were nontypeable by IHA). The mPCR was faster, more sensitive, and more specific than IHA, enabling the differentiation of 14 of the 15 serovars of H. parasuis.This work was supported by a BPEX PhD studentship and a Longer and Larger (LoLa) grant from the Biotechnology and Biological Sciences Research Council (grant numbers BB/G020744/1, BB/G019177/1, BB/G019274/1 and BB/G003203/1), the UK Department for Environment, Food and Rural Affairs and Zoetis, awarded to the Bacterial Respiratory Diseases of Pigs-1 Technology (BRaDP1T) consortium. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the author accepted manuscript. The final version is available from American Society for Microbiology via http://dx.doi.org/10.1128/JCM.01991-1

Novel Aptamer-Nanoparticle Bioconjugates Enhances Delivery of Anticancer Drug to MUC1-Positive Cancer Cells In Vitro

MUC1 protein is an attractive target for anticancer drug delivery owing to its overexpression in most adenocarcinomas. In this study, a reported MUC1 protein aptamer is exploited as the targeting agent of a nanoparticle-based drug delivery system. Paclitaxel (PTX) loaded poly (lactic-co-glycolic-acid) (PLGA) nanoparticles were formulated by an emulsion/evaporation method, and MUC1 aptamers (Apt) were conjugated to the particle surface through a DNA spacer. The aptamer conjugated nanoparticles (Apt-NPs) are about 225.3 nm in size with a stable in vitro drug release profile. Using MCF-7 breast cancer cell as a MUC1-overexpressing model, the MUC1 aptamer increased the uptake of nanoparticles into the target cells as measured by flow cytometry. Moreover, the PTX loaded Apt-NPs enhanced in vitro drug delivery and cytotoxicity to MUC1+ cancer cells, as compared with non-targeted nanoparticles that lack the MUC1 aptamer (P<0.01). The behavior of this novel aptamer-nanoparticle bioconjugates suggests that MUC1 aptamers may have application potential in targeted drug delivery towards MUC1-overexpressing tumors