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…
Vasco, A. V.; Ceballos, L. G.; Wessjohann, L. A.; Rivera, D. G.;Multicomponent functionalization of the octreotide peptide macrocyclic scaffoldEur. J. Org. Chem.2022e202200687(2022)DOI: 10.1002/ejoc.202200687
The replacement of the disulfide bridge by other types of side chain linkages has been a continuous endeavor in the development of cyclic peptide drugs with improved metabolic stability. Octreotide is a potent and selective somatostatin analog that has been used as an anticancer agent, in radiolabeled conjugates for the localization of tumors and as targeting moiety in peptide-drug conjugates. Here, we describe an onresin methodology based on a multicomponent macrocyclization that enables the substitution of the disulfide bond by a tertiary lactam bridge functionalized with a variety of exocyclic moieties, including lipids, fluorophores, and charged groups. Conformational analysis in comparison with octreotide provides key information on the type of functionalization permitting the conformational mimicry of the bioactive peptide.
Publikation
Ditfe, T.; Bette, E.; N. Sultani, H.; Otto, A.; Wessjohann, L. A.; Arnold, N.; Westermann, B.;Synthesis and biological evaluation of highly potent fungicidal deoxy‐hygrophoronesEur. J. Org. Chem.20213827-3836(2021)DOI: 10.1002/ejoc.202100729
Although stripped from hydroxyl-groups, deoxygenated
hygrophorones remain highly active against severe phytopathogens. The
synthesis to these natural product congeners is achieved in
rearrangement sequences, with an optimized deprotection strategy
avoiding retro-aldol reactions. The activities are comparable to
fungicides used in agriculture.
Based on naturally occurring hygrophorones, racemic di-
and mono-hydroxylated cyclopentenones bearing an aliphatic side chain
have been produced in short synthetic sequences starting from furfuryl
aldehyde. For the series of dihydroxylated trans-configured derivatives, an Achmatowicz-rearrangement and a Caddick-ring contraction were employed, and for the series of trans-configured
mono-hydroxylated derivatives a Piancatelli-rearrangement. All final
products showed good to excellent fungicidal activities against the
plant pathogens B. cinerea, S. tritici and P. infestans.
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.