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      Automated Genome Mining of Ribosomal Peptide Natural Products

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          Abstract

          Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity.1 In light of exponentially increasing microbial genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computational tools that connect natural product genotypes predicted from microbial genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based genome mining using algorithmic tools rather than manual inspection of mass spectrometry data and genetic information. The presented tool is available at cyclo.ucsd.edu.

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          Most cited references22

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          Telomere length in early life predicts lifespan.

          The attrition of telomeres, the ends of eukaryote chromosomes, is thought to play an important role in cell deterioration with advancing age. The observed variation in telomere length among individuals of the same age is therefore thought to be related to variation in potential longevity. Studies of this relationship are hampered by the time scale over which individuals need to be followed, particularly in long-lived species where lifespan variation is greatest. So far, data are based either on simple comparisons of telomere length among different age classes or on individuals whose telomere length is measured at most twice and whose subsequent survival is monitored for only a short proportion of the typical lifespan. Both approaches are subject to bias. Key studies, in which telomere length is tracked from early in life, and actual lifespan recorded, have been lacking. We measured telomere length in zebra finches (n = 99) from the nestling stage and at various points thereafter, and recorded their natural lifespan (which varied from less than 1 to almost 9 y). We found telomere length at 25 d to be a very strong predictor of realized lifespan (P < 0.001); those individuals living longest had relatively long telomeres at all points at which they were measured. Reproduction increased adult telomere loss, but this effect appeared transient and did not influence survival. Our results provide the strongest evidence available of the relationship between telomere length and lifespan and emphasize the importance of understanding factors that determine early life telomere length.
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            BAGEL3: automated identification of genes encoding bacteriocins and (non-)bactericidal posttranslationally modified peptides

            Identifying genes encoding bacteriocins and ribosomally synthesized and posttranslationally modified peptides (RiPPs) can be a challenging task. Especially those peptides that do not have strong homology to previously identified peptides can easily be overlooked. Extensive use of BAGEL2 and user feedback has led us to develop BAGEL3. BAGEL3 features genome mining of prokaryotes, which is largely independent of open reading frame (ORF) predictions and has been extended to cover more (novel) classes of posttranslationally modified peptides. BAGEL3 uses an identification approach that combines direct mining for the gene and indirect mining via context genes. Especially for heavily modified peptides like lanthipeptides, sactipeptides, glycocins and others, this genetic context harbors valuable information that is used for mining purposes. The bacteriocin and context protein databases have been updated and it is now easy for users to submit novel bacteriocins or RiPPs. The output has been simplified to allow user-friendly analysis of the results, in particular for large (meta-genomic) datasets. The genetic context of identified candidate genes is fully annotated. As input, BAGEL3 uses FASTA DNA sequences or folders containing multiple FASTA formatted files. BAGEL3 is freely accessible at http://bagel.molgenrug.nl.
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              Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases.

              A key problem in computational proteomics is distinguishing between correct and false peptide identifications. We argue that evaluating the error rates of peptide identifications is not unlike computing generating functions in combinatorics. We show that the generating functions and their derivatives ( spectral energy and spectral probability) represent new features of tandem mass spectra that, similarly to Delta-scores, significantly improve peptide identifications. Furthermore, the spectral probability provides a rigorous solution to the problem of computing statistical significance of spectral identifications. The spectral energy/probability approach improves the sensitivity-specificity tradeoff of existing MS/MS search tools, addresses the notoriously difficult problem of "one-hit-wonders" in mass spectrometry, and often eliminates the need for decoy database searches. We therefore argue that the generating function approach has the potential to increase the number of peptide identifications in MS/MS searches.
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                Author and article information

                Journal
                ACS Chem Biol
                ACS Chem. Biol
                cb
                acbcct
                ACS Chemical Biology
                American Chemical Society
                1554-8929
                1554-8937
                06 May 2015
                06 May 2014
                18 July 2014
                : 9
                : 7
                : 1545-1551
                Affiliations
                []Department of Electrical and Computer Engineering, University of California San Diego , La Jolla, California 92093, United States
                []Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego , La Jolla, California 92093, United States
                [§ ]Department of Chemistry and Biochemistry, University of California San Diego , La Jolla, California 92093, United States
                []Department of Computer Science and Engineering, University of California San Diego , La Jolla, California 92093, United States
                []Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland, Washington 99354, United States
                [# ]Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego , La Jolla, California 92093, United States
                Author notes
                Article
                10.1021/cb500199h
                4215869
                24802639
                ba16da74-c854-497c-8441-560eb16f14df
                Copyright © 2014 American Chemical Society

                Terms of Use

                History
                : 15 March 2014
                : 06 May 2014
                Funding
                National Institutes of Health, United States
                Categories
                Articles
                Custom metadata
                cb500199h
                cb-2014-00199h

                Biochemistry
                Biochemistry

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