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      Mining the Yucatan Coastal Microbiome for the Identification of Non-Ribosomal Peptides Synthetase (NRPS) Genes

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          Abstract

          Prokaryotes represent a source of both biotechnological and pharmaceutical molecules of importance, such as nonribosomal peptides (NRPs). NRPs are secondary metabolites which their synthesis is independent of ribosomes. Traditionally, obtaining NRPs had focused on organisms from terrestrial environments, but in recent years marine and coastal environments have emerged as an important source for the search and obtaining of nonribosomal compounds. In this study, we carried out a metataxonomic analysis of sediment of the coast of Yucatan in order to evaluate the potential of the microbial communities to contain bacteria involved in the synthesis of NRPs in two sites: one contaminated and the other conserved. As well as a metatranscriptomic analysis to discover nonribosomal peptide synthetases (NRPSs) genes. We found that the phyla with the highest representation of NRPs producing organisms were the Proteobacteria and Firmicutes present in the sediments of the conserved site. Similarly, the metatranscriptomic analysis showed that 52% of the sequences identified as catalytic domains of NRPSs were found in the conserved site sample, mostly (82%) belonging to Proteobacteria and Firmicutes; while the representation of Actinobacteria traditionally described as the major producers of secondary metabolites was low. It is important to highlight the prediction of metabolic pathways for siderophores production, as well as the identification of NRPS’s condensation domain in organisms of the Archaea domain. Because this opens the possibility to the search for new nonribosomal structures in these organisms. This is the first mining study using high throughput sequencing technologies conducted in the sediments of the Yucatan coast to search for bacteria producing NRPs, and genes that encode NRPSs enzymes.

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          A new generation of homology search tools based on probabilistic inference.

          Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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            antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers

            Microbial secondary metabolites are a potent source of antibiotics and other pharmaceuticals. Genome mining of their biosynthetic gene clusters has become a key method to accelerate their identification and characterization. In 2011, we developed antiSMASH, a web-based analysis platform that automates this process. Here, we present the highly improved antiSMASH 2.0 release, available at http://antismash.secondarymetabolites.org/. For the new version, antiSMASH was entirely re-designed using a plug-and-play concept that allows easy integration of novel predictor or output modules. antiSMASH 2.0 now supports input of multiple related sequences simultaneously (multi-FASTA/GenBank/EMBL), which allows the analysis of draft genomes comprising multiple contigs. Moreover, direct analysis of protein sequences is now possible. antiSMASH 2.0 has also been equipped with the capacity to detect additional classes of secondary metabolites, including oligosaccharide antibiotics, phenazines, thiopeptides, homo-serine lactones, phosphonates and furans. The algorithm for predicting the core structure of the cluster end product is now also covering lantipeptides, in addition to polyketides and non-ribosomal peptides. The antiSMASH ClusterBlast functionality has been extended to identify sub-clusters involved in the biosynthesis of specific chemical building blocks. The new features currently make antiSMASH 2.0 the most comprehensive resource for identifying and analyzing novel secondary metabolite biosynthetic pathways in microorganisms.
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              The evolution of genome mining in microbes - a review.

              Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.
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                Author and article information

                Journal
                Toxins (Basel)
                Toxins (Basel)
                toxins
                Toxins
                MDPI
                2072-6651
                26 May 2020
                June 2020
                : 12
                : 6
                : 349
                Affiliations
                UMDI-Sisal, Facultad de Ciencias, Universidad Nacional Autónoma de México, Puerto de Abrigo s/n, Sisal, Yucatán CP 97355, Mexico
                Author notes
                [* ]Correspondence: mamn@ 123456ciencias.unam.mx (M.A.M.-N.); zuemy.rodriguez@ 123456gmail.com (Z.R.-E.); Tel.: +52-999-341-0860 (ext. 7631) (M.A.M.-N.)
                Author information
                https://orcid.org/0000-0001-8907-2542
                https://orcid.org/0000-0003-2977-9364
                Article
                toxins-12-00349
                10.3390/toxins12060349
                7354552
                32466531
                bb0a42f9-0729-41c3-b6ee-8302811b937f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 February 2020
                : 16 April 2020
                Categories
                Article

                Molecular medicine
                nonribosomal peptides,metataxonomic,metatranscriptomic,yucatan coast
                Molecular medicine
                nonribosomal peptides, metataxonomic, metatranscriptomic, yucatan coast

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