Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…
Kahsay, B. N.; Ziegler, J.; Imming, P.; Gebre-Mariam, T.; Neubert, R. H. H.; Moeller, L.;Free amino acid contents of selected Ethiopian plant and fungi species: a search for alternative natural free amino acid sources for cosmeceutical applicationsAmino Acids531105-1122(2021)DOI: 10.1007/s00726-021-03008-5
Free amino acids (FAAs), the major constituents of the natural moisturizing factor (NMF), are very important for maintaining the moisture balance of human skin and their deficiency results in dry skin conditions. There is a great interest in the identification and use of nature-based sources of these molecules for such cosmeceutical applications. The objective of the present study was, therefore, to investigate the FAA contents of selected Ethiopian plant and fungi species; and select the best sources so as to use them for the stated purpose. About 59 different plant species and oyster mushroom were included in the study and the concentrations of 27 FAAs were analyzed. Each sample was collected, lyophilized, extracted using aqueous solvent, derivatized with Fluorenylmethoxycarbonyl chloride (Fmoc-Cl) prior to solid-phase extraction and quantified using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC-ESI–MS/MS) system. All the 27 FAAs were detected in most of the samples. The dominant FAAs that are part of the NMF were found at sufficiently high concentration in the mushroom and some of the plants. This indicates that FAAs that could be included in the preparations for the management of dry skin condition can be obtained from a single natural resource and the use of these resources for the specified purpose have both economic and therapeutic advantage in addition to fulfilling customer needs.
Publikation
Böhme, B.; Moritz, B.; Wendler, J.; Hertel, T. C.; Ihling, C.; Brandt, W.; Pietzsch, M.;Enzymatic activity and thermoresistance of improved microbial transglutaminase variantsAmino Acids52313-326(2020)DOI: 10.1007/s00726-019-02764-9
Microbial transglutaminase (MTG, EC 2.3.2.13) of Streptomyces mobaraensis is widely used in industry for its ability to synthesize isopeptide bonds between the proteinogenic side chains of glutamine and lysine. The activated wild-type enzyme irreversibly denatures at 60 °C with a pseudo-first-order kinetics and a half-life time (t1/2) of 2 min. To increase the thermoresistance of MTG for higher temperature applications, we generated 31 variants based on previous results obtained by random mutagenesis, DNA shuffling and saturation mutagenesis. The best variant TG16 with a specific combination of five of seven substitutions (S2P, S23Y, S24 N, H289Y, K294L) shows a 19-fold increased half-life at 60 °C (t1/2 = 38 min). As measured by differential scanning fluorimetry, the transition point of thermal unfolding was increased by 7.9 °C. Also for the thermoresistant variants, it was shown that inactivation process follows a pseudo-first-order reaction which is accompanied by irreversible aggregation and intramolecular self-crosslinking of the enzyme. Although the mutations are mostly located on the surface of the enzyme, kinetic constants determined with the standard substrate CBZ-Gln-Gly-OH revealed a decrease in KM from 8.6 mM (± 0.1) to 3.5 mM (± 0.1) for the recombinant wild-type MTG and TG16, respectively. The improved performance of TG16 at higher temperatures is exemplary demonstrated with the crosslinking of the substrate protein β-casein at 60 °C. Using molecular dynamics simulations, it was shown that the increased thermoresistance is caused by a higher backbone rigidity as well as increased hydrophobic interactions and newly formed hydrogen bridges.
Publikation
Ruttkies, C.; Neumann, S.; Posch, S.;Improving MetFrag with statistical learning of fragment annotationsBMC Bioinformatics20376(2019)DOI: 10.1186/s12859-019-2954-7
BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.
Publikation
Nettling, M.; Treutler, H.; Cerquides, J.; Grosse, I.;Combining phylogenetic footprinting with motif models incorporating intra-motif dependenciesBMC Bioinformatics18141(2017)DOI: 10.1186/s12859-017-1495-1
BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.