Dereplicating nonribosomal peptides using an informatic search algorithm for natural products (iSNAP) discovery

Ibrahim, Ashraf; Yang, Lian; Johnston, Chad W.; Liu, Xiaowen; Ma, Bin; Magarvey, Nathan A.

doi:10.1073/pnas.1206376109

Cited by 62 publications

(52 citation statements)

References 29 publications

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“…During the golden age of antibiotic discovery, antistaphylococcal compounds emerged from random screens of bacterial extracts, particularly those from soil actinobacteria (Peláez 2006). Continued screening unfortunately leads to high rates of rediscovery, and although chemoinformatic dereplication tools may act to assist in diminishing this, new sources of microbes are needed that may produce small molecules with unique mechanisms of action (Ibrahim et al 2012;Kersten et al 2011;Mohimani et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Bioinformatic evaluation of the secondary metabolism of antistaphylococcal environmental bacterial isolates

et al. 2013

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The increasing occurrence of drug-resistant Staphylococcus aureus is exacerbated with a declining rate of antibiotic discovery, particularly those with new mechanisms of action. The decline in antibiotic discovery from traditional sources, such as soil actinobacteria, necessitates examination of lesser studied microbes. Here, we present a strategy to select for organisms that may have a propensity to result in new antistaphylococcal agents by using S. aureus as a bait organism, and selecting organisms that have a natural lytic activity towards it. We have isolated over 80 environmental isolates and typed these organisms using 16S rDNA sequence comparison and deployed bioinformatics to assess the secondary metabolic potential of the isolated antistaphylococcal bacteria using genomic sequences. Bioinformatic analysis highlights the enriched and unique suite of potential antibiotic polyketides and nonribosomal peptides and lantibiotic gene clusters from these organisms. Profiling organic microbial extracts further showed that many of the organisms from the 10 staphylolytic genera secrete agents with antistaphylococcal activity and may serve as new sources for future antistaphylococcal drug discovery.

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Section: Introductionmentioning

confidence: 99%

Bioinformatic evaluation of the secondary metabolism of antistaphylococcal environmental bacterial isolates

et al. 2013

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“…In principle, metabolomics databases that allow one to search based on an MS and/or MS/MS patterns such as Massbank 87 , Metlin 88 , NIST 89 , LIPIDMAPS 90 (Table 1) could be consulted, but these databases are rarely consulted because there are few NPs present in the databases. Emerging de novo informatics tools can predict fragmentation data from imported structures or partial structures and then match it to the experimental fragmentation spectra Such approaches are aiding in the dereplication of certain subclasses of ribosomally-synthesized and post-translationally-modified peptides NPs 91 , non-ribosomal peptides 91-94 , and lipids 95 (Table 2). …”

Section: Introductionmentioning

confidence: 99%

Mass spectrometry of natural products: current, emerging and future technologies

et al. 2014

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Although mass spectrometry is a century old technology, we are entering into an exciting time for the analysis of molecular information directly from complex biological systems. In this viewpoint article, we highlight emerging mass spectrometric methods and tools used by the natural product community and give a perspective of future directions where the mass spectrometry field is migrating towards over the next decade.

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“…Figure 7.2 illustrates the FDR estimation by the targetdecoy strategy, where the blue colors indicate the target hits and the orange colors indicate the decoy hits, the circles are the true hits, and squares are false hits. Furthermore, in the design of sensitive filtering criteria, researchers can be guided by decoy hits to precisely distinguish correct from incorrect PSMs [55].…”

Section: Target-decoy For Tandem Mass Spectrometrymentioning

confidence: 99%

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Liang

Lajoie

Zhang

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches.MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting intact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin.In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In the result validation, we propose two new target-decoy search strategies to estimate the false discovery rate (FDR). The experiments on simulated, authentic, and simulated authentic dataset demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.i

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Dereplicating nonribosomal peptides using an informatic search algorithm for natural products (iSNAP) discovery

Cited by 62 publications

References 29 publications

Bioinformatic evaluation of the secondary metabolism of antistaphylococcal environmental bacterial isolates

Bioinformatic evaluation of the secondary metabolism of antistaphylococcal environmental bacterial isolates

Mass spectrometry of natural products: current, emerging and future technologies

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

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