Od sekvencije DNA do kemijske strukture – pretraživanje mikrobnih genomskih i metagenomskih skupova podataka radi pronalaženja novih prirodnih spojeva

Rapid mining of large genomic and metagenomic data sets for modular polyketide synthases, non-ribosomal peptide synthetases and hybrid polyketide synthase/non-ribosomal peptide synthetase biosynthetic gene clusters has been achieved using the generic computer program packages ClustScan and CompGen. These program packages perform the annotation with the hierarchical structuring into polypeptides, modules and domains, as well as storage and graphical presentations of the data. This aims to achieve the most accurate predictions of the activities and specificities of catalytically active domains that can be made with present knowledge, leading to a prediction of the most likely chemical structures produced by these enzymes. The program packages also allow generation of novel clusters by homologous recombination of the annotated genes in silico. ClustScan and CompGen were used to construct a custom database of known compounds (CSDB) and of predicted entirely novel recombinant products (r-CSDB) that can be used for in silico screening with computer aided drug design technology. The use of these programs has been exemplified by analysing genomic sequences from terrestrial prokaryotes and eukaryotic microorganisms, a marine metagenomic data set and a newly discovered example of a \u27shared metabolic pathway\u27 in marine-microbial endosymbiosis.Brzo pretraživanje genomskih i metagenomskih skupova podataka, modularnih biosintetskih genskih nakupina poliketid sintaza i sintetaza neribosomalno sintetiziranih peptida, postignuto je primjenom generičkih računalnih programskih paketa ClustScan i CompGen. Ti programski paketi provode anotaciju hijerarhijskim strukturiranjem podataka na polipeptide, module i domene, te pohranu i grafičku prezentaciju tih podataka. Na temelju dosadašnjih spoznaja, nastoji se postići najtočnije moguće predviđanje aktivnosti i specifičnosti katalitički aktivnih domena, što vodi prema predviđanju najvjerojatnijih kemijskih struktura koje ti enzimi mogu sintetizirati. Programski paketi ClustScan i CompGen omogućuju generiranje novih genskih nakupina homolognom rekombinacijom anotiranih gena u uvjetima in silico, a upotrijebljeni su i za konstrukciju vlastitih baza podataka poznatih poliketidnih i peptidnih supstancija (CSDB) te potpuno novih poliketidnih i peptidnih supstancija produkata rekombinacije (r-CSDB). Ti će se produkti rekombinacije moći upotrijebiti za izbor supstancija s potencijalnom biološkom aktivnošću pomoću računalom vođenog dizajna lijekova u uvjetima in silico. Primjenjivost programskih paketa ClustScan i CompGen dokazana je u analizi genomskih sekvencija prokariotskih i eukariotskih mikroorganizama što žive u tlu, analizi metagenomske skupine podataka u uzorku iz morske vode, a i na nedavno opisanom primjeru \u27zajedničkog metaboličkoga puta\u27 u mikrobnog endosimbionta morske životinje

Bioprospecting for Genes Encoding Hydrocarbon-Degrading Enzymes from Metagenomic Samples Isolated from Northern Adriatic Sea Sediments

Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM-profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library

Novi pristup konstrukciji industrijskih mikroorganizama pomoću sintetičke biologije

The recent achievement of synthesising a functioning bacterial chromosome marks a coming of age for engineering living organisms. In the future this should allow the construction of novel organisms to help solve the problems facing the human race, including health care, food, energy and environmental protection. In this minireview, the current state of the field is described and the role of synthetic biology in biotechnology in the short and medium term is discussed. It is particularly aimed at the needs of food technologists, nutritionists and other biotechnologists, who might not be aware of the potential significance of synthetic biology to the research and development in their fields. The potential of synthetic biology to produce interesting new polyketide compounds is discussed in detail.Razvojem područja sinteze funkcionalnog bakterijskog kromosoma obilježen je početak novog doba genetičkog inženjerstva. Konstrukcijom novih organizama mogli bi se riješiti neki problemi vezani uz zdravstvo, proizvodnju hrane i energenata te zaštitu okoliša. U ovom su kratkom revijalnom prikazu opisana sadašnja dostignuća na području sintetičke biologije, a raspravlja se i o njezinoj ulozi u razvoju biotehnologije. Prikaz je posebno namijenjen prehrambenim tehnolozima, nutricionistima i ostalim biotehnolozima koji možda nisu svjesni značaja što bi sintetička biologija mogla imati za njihova istraživanja. Detaljno se raspravlja o mogućem utjecaju sintetičke biologije u formiranju potpuno novih poliketida, koji se mogu upotrijebiti za proizvodnju lijekova

MEGGASENSE – pretraživač (meta)genomski anotiranih sekvencija pomoću govornog jezika – platforma za izradu bioloških skladišta podataka

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya. The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel ‘functional’ assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.Platforma MEGGASENSE služi za izradu relacijskih baza podataka koje sadržavaju nukleotidne ili proteinske sekvencije. Osnovna funkcionalna analiza zasniva se na primjeni 14 106 profila skrivenih Markovljevih modela (HMM), temeljenih na sekvencijama dostupnim u bazi podataka KEGG. Pomoću tražilice Solr mogu se zadati napredni upiti u sprezi s implementiranom pretragom BLAST. Osnovne funkcionalnosti platforme omogućile su izradu baze podataka SCATT, temeljene na predviđenom proteomu bakterije Streptomyces cattleya. U radu je opisana implementacija specijalizirane metagenomske baze podataka (AMYLOMICS) za „bioprospecting“ enzima koji modificiraju ugljikohidrate. Uz standardno slaganje očitanih kratkih sljedova DNA, razvijen je funkcionalni postupak pretraživanja HMM profila u očitanim slijedovima DNA prije slaganja. Baza podataka AMYLOMICS sadržava i dodatne HMM profile enzima za modifikaciju ugljikohidrata. U radu je prikazano kako se kombinacijom analiza HMM i BLAST mogu identificirati ciljani geni. Platforma MEGGASENSE upotrijebljena je za izradu raznih proteomskih i metagenomskih baza podataka

With food to health : proceedings of the 10th International scientific and professional conference

Proceedings contains 13 original scientific papers, 10 professional papers and 2 review papers which were presented at "10th International Scientific and Professional Conference WITH FOOD TO HEALTH", organised in following sections: Nutrition, Dietetics and diet therapy, Functional food and food supplemnents, Food safety, Food analysis, Production of safe food and food with added nutritional value

Benefits of selective peptide derivatization with sulfonating reagent at acidic pH for facile matrix-assisted laser desorption/ionization de novo sequencing

RATIONALE: One of the most challenging tasks of proteomics is peptide de novo sequencing. 4-Sulfophenyl isothiocyanate (SPITC) peptide derivatization enables acquisition of high-quality tandem mass spectra (MS/MS) for de novo sequencing, but unwanted non-specific reactions and reduced mass spectra (MS) signal intensities still represent the obstacles in highthroughput de novo sequencing. METHODS: We developed a SPITC peptide derivatization procedure under acidic conditions (pH LT = 5). Derivatized peptides were analyzed by matrix-assisted laser desorption/ionization (MALDI-MS) in negative ion mode followed by MS/MS in positive ion mode. A de novo sequencing tool, named DUST, adjusted to SPITC chemistry, was designed for successful high-throughput peptide de novo sequencing. This high-throughput peptide de novo sequencing was tested on Fusarium delphinoides, an organism with an uncharacterized genome. RESULTS: The SPITC derivatization procedure under acidic conditions produced a significantly improved MS dataset in comparison to commonly used derivatization under basic conditions. Signal intensities were 6 to 10 times greater and the over-sulfonation effect measured on lysine-containing peptides was significantly decreased. Furthermore, development of a novel DUST algorithm enabled automated de novo sequencing with the calculated accuracy of 70.6%. CONCLUSIONS: The SPITC derivatization and de novo sequencing approach outlined here provides a reliable method for high-throughput peptide de novo sequencing. High-throughput peptide de novo sequencing enabled protein mutation identification and identification of proteins from organisms with non-sequenced genomes. Copyright (C) 2016 John Wiley and Sons, Ltd

Predicting substrate specificity of adenylation domains of nonribosomal peptide synthetases and other protein properties by latent semantic indexing

Abstract Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are &amp;gt;500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.</jats:p

Evolutionary concepts in natural products discovery:what actinomycetes have taught us

Abstract Actinomycetes are a very important source of natural products for the pharmaceutical industry and other applications. Most of the strains belong to Streptomyces or related genera, partly because they are particularly amenable to growth in the laboratory and industrial fermenters. It is unlikely that chemical synthesis can fulfil the needs of the pharmaceutical industry for novel compounds so there is a continuing need to find novel natural products. An evolutionary perspective can help this process in several ways. Genome mining attempts to identify secondary metabolite biosynthetic clusters in DNA sequences, which are likely to produce interesting chemical entities. There are often technical problems in assembling the DNA sequences of large modular clusters in genome and metagenome projects, which can be overcome partially using information about the evolution of the domain sequences. Understanding the evolutionary mechanisms of modular clusters should allow simulation of evolutionary pathways in the laboratory to generate novel compounds.</jats:p