Shotgun proteomics has grown rapidly in recent decades, but a large fraction of tandem mass spectrometry (MS/MS) data in shotgun proteomics are not successfully identified. We have developed a novel database search algorithm, Open-pFind, to efficiently identify peptides even in an ultra-large search space which takes into account unexpected modifications, amino acid mutations, semi-or non-specific digestion and co-eluting peptides. Tested on two metabolically labeled MS/MS datasets, Open-pFind reported 50.5-117.0% more peptide-spectrum matches (PSMs) than the seven other advanced algorithms. More importantly, the Open-pFind results were more credible judged by the verification experiments using stable isotopic labeling. Tested on four additional large-scale datasets, 70-85% of the spectra were confidently identified, and high-quality spectra were nearly completely interpreted by Open-pFind. Further, Open-pFind was over 40 times faster than the other three open search algorithms and 2-3 times faster than three restricted search algorithms. Re-analysis of an entire human proteome dataset consisting of ~25 million spectra using Open-pFind identified a total of 14,064 proteins encoded by 12,723 genes by requiring at least two uniquely identified peptides. In this search results, Open-pFind also excelled in an independent test for false positives based on the presence or absence of olfactory receptors. Thus, a practical use of the open search strategy has been realized by Open-pFind for the truly global-scale proteomics experiments of today and in the future..

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Chemical cross-linking of proteins coupled with mass spectrometry analysis (CXMS) is widely used to study protein-protein interactions (PPI), protein structures, and even protein dynamics. However, structural information provided by CXMS is still limited, partly because most CXMS experiments use lysine-lysine (K-K) cross-linkers. Although superb in selectivity and reactivity, they are ineffective for lysine deficient regions. Herein, we develop aromatic glyoxal cross-linkers (ArGOs) for arginine-arginine (R-R) cross-linking and the lysine-arginine (K-R) cross-linker KArGO. The R-R or K-R cross-links generated by ArGO or KArGO fit well with protein crystal structures and provide information not attainable by K-K cross-links. KArGO, in particular, is highly valuable for CXMS, with robust performance on a variety of samples including a kinase and two multi-protein complexes. In the case of the CNGP complex, KArGO cross-links covered as much of the PPI interface as R-R and K-K cross-links combined and improved the accuracy of Rosetta docking substantially.

The study of cell elasticity provides new insights into not only cell biology but also disease diagnosis based on cell mechanical state variation.

MS-based de novo peptide sequencing has been improved remarkably with significant development of mass-spectrometry and computational approaches but still lacks quality-control methods. Here we proposed a novel algorithm pSite to evaluate the confidence of each amino acid rather than the full-length peptides obtained by de novo peptide sequencing. A semi-supervised learning approach was used to discriminate correct amino acids from random one; then, an expectation-maximization algorithm was used to adaptively control the false amino-acid rate (FAR). On three test data sets, pSite recalled 86% more amino acids on average than PEAKS at the FAR of 5%. pSite also performed superiorly on the modification site localization problem, which is essentially a special case of amino acid confidence evaluation. On three phosphopeptide data sets, at the false localization rate of 1%, the average recall of pSite was 91% while those of Ascore and phosphoRS were 64 and 63%, respectively. pSite covered 98% of Ascore and phosphoRS results and contributed 21% more phosphorylation sites. Further analyses show that the use of distinct fragmentation features in high-resolution MS/MS spectra, such as neutral loss ions, played an important role in improving the precision of pSite. In summary, the effective and universal model together with the extensive use of spectral information makes pSite an excellent quality control tool for both de novo peptide sequencing and modification site localization.

Background: OTUB1 (ovarian tumor domain protease domain-containing ubiquitin aldehyde-binding proteins)mediated deubiquitination of FOXM1 (Forkhead box M1) participates in carcinogenesis of various tumors. We aim to investigate the effect and mechanism of OTUB1/FOXM1 on RCC (renal cell carcinoma) progression. Expression levels of OTUB1 in RCC tissues and cell lines were examined by qRT-PCR (quantitative real-time polymerase chain reaction) and immunohistochemistry. Cell proliferation was measured with CCK8 (Cell Counting Kit-8) and colony formation assays. Wound healing and transwell assays were used to determine cell migration and invasion, respectively. The effect of OTUB1 on FOXM1 ubiquitination was examined by Immunoprecipitation. Western blot was used to uncover the underlying mechanism. In vivo subcutaneous xenotransplanted tumor model combined with immunohistochemistry and western blot were used to examine the tumorigenic function of OTUB1. Results: OTUB1 was up-regulated in RCC tissues and cell lines, and was associated with poor prognosis of RCC patients. Knockdown of OTUB1 inhibited cell viability and proliferation, as well as migration and invasion of RCC cells. Mechanistically, knockdown of OTUB1 down-regulated FOXM1 expression by promoting its ubiquitination. Downregulation of FOXM1 inhibited ECT2 (epithelial cell transforming 2)-mediated Rho signaling. Moreover, the inhibition of RCC progression caused by OTUB1 knockdown was reversed by FOXM1 over-expression. In vivo subcutaneous xenotransplanted tumor model also revealed that knockdown of OTUB1 could suppress in vivo RCC growth via downregulation of FOXM1-mediated ECT2 expression. Conclusions: OTUB1-mediated deubiquitination of FOXM1 up-regulates ECT-2 to promote tumor progression in RCC, providing a new potential therapeutic target for RCC treatment.

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Objective. To systematically evaluate the efficacy and safety of XFZYD for coronary heart disease (CHD). Methods. A comprehensive literature search of randomized controlled trials using XFZYD for CHD was conducted in 10 electronic databases from their establishment to December 20, 2020. The researchers screened the relevant trials in NoteExpress, extracted the data in duplicate independently, assessed the risk of bias in the trials using the Cochrane collaboration tool, and then used Rev Man 5.3 for data analysis. Results. 30 trials with 3126 participants were included for meta-analysis. The results showed that the clinical effects of XFZYD and its combination with chemical drugs (CD) were 1.13 (RR; 1.13; 95% CI, 1.03 to 1.24) and 1.26 (RR; 1.26; 95% CI, 1.20 to 1.32) times those of CD, respectively. And, it could also improve electrocardiogram effect, which was 1.63 (RR; 1.63; 95% CI, 1.04 to 2.53) times that of CD. XFZYD could not only decrease duration of angina pectoris and improve vascular endothelial function but also obviously reduce the TCM syndrome score. When used in combination with CD, it could also lower AF, correct the dyslipidemia, and reduce the blood viscosity. Conclusion. These results demonstrated that XFZYD had great advantages in treating CHD with no obvious adverse reactions. Therefore, it is believed that XFZYD is more suitable for CHD patients with clinical indicators of dyslipidemia, high blood viscosity, or vascular endothelial dysfunction. This study is the first systematic review and meta-analysis with some unique ways, including its comprehensiveness, large-scale search, the novelty of findings, and transparent approach.