In contrast to the myriad of methods available to produce α‐helices and antiparallel β‐sheets in synthetic peptides, just a few are known for the construction of stable, non‐cyclic parallel β‐sheets. Herein, we report an efficient on‐resin approach for the assembly of parallel β‐sheet peptides in which the N‐alkylated turn moiety enhances the stability and gives access to a variety of functionalizations without modifying the parallel strands. The key synthetic step of this strategy is the multicomponent construction of an N‐alkylated turn using the Ugi reaction on varied isocyano‐resins. This four‐component process assembles the orthogonally protected turn fragment and incorporates handles serving for labeling/conjugation purposes or for reducing peptide aggregation. NMR and circular dichroism analyses confirm the better‐structured and more stable parallel β‐sheets in the N‐alkylated peptides compared to the non‐functionalized variants.

Die Funktionalisierung von C‐H‐Bindungen mit Nichtedelmetallkatalysatoren ist ein wichtiges Forschungsgebiet für die Entwicklung effizienter und nachhaltiger Synthesemethoden. In diesem Artikel beschreiben wir die Entwicklung Eisenporphyrin‐katalysierter Reaktionen von Diazoacetonitril mit N‐Heterocyclen um so einen Zugang zu wertvollen Vorläufern zu Tryptaminen zu erhalten. Darüberhinaus berichten wir über experimentelle mechanistische Studien sowie über konzeptionelle Studien zu einer enzymatischen Synthese mit dem Enzym YfeX. Mit dem leicht zugänglichen FeTPPCl‐Katalysator konnten wir hoch effiziente C‐H‐Funktionalisierungsreaktionen von Indol und Indazol‐Heterocyclen zeigen. Diese Reaktionen können unter milden Reaktionsbedingungen, mit exzellenten Ausbeuten und großer Toleranz funktioneller Gruppen inklusive Anwendungen im Grammmaßstab durchgeführt werden und eröffnen so einen einzigartigen, effizienten Zugang zu Tryptaminen.

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.

For the first time, the Petasis (borono‐Mannich) reaction is employed for the multicomponent labeling and stapling of peptides. The report includes the solid‐phase derivatization of peptides at the N‐terminus, Lys, and Nϵ‐MeLys side‐chains by an on‐resin Petasis reaction with variation of the carbonyl and boronic acid components. Peptides were simultaneously functionalized with aryl/vinyl substituents bearing fluorescent/affinity tags and oxo components such as dihydroxyacetone, glyceraldehyde, glyoxylic acid, and aldoses, thus encompassing a powerful complexity‐generating approach without changing the charge of the peptides. The multicomponent stapling was conducted in solution by linking Nϵ‐MeLys or Orn side‐chains, positioned at i, i+7 and i, i+4, with aryl tethers, while hydroxy carbonyl moieties were introduced as exocyclic fragments. The good efficiency and diversity oriented character of these methods show prospects for peptide drug discovery and chemical biology.

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.

An important development in the field of macrocyclization strategies towards molecular cages is described. The approach comprises the utilization of a double Ugi four‐component macrocyclization for the assembly of macromulticycles with up to four different tethers, that is, hybrid cages. The innovation of this method rests on setting up the macromulticycle connectivities not through the tethers but through the bridgeheads, which in this case involve N‐substituted amino acids. Both dilution and metal‐template‐driven macrocyclization conditions were implemented with success, enabling the one‐pot formation of cryptands and cages including steroidal, polyether, heterocyclic, peptidic, and aryl tethers. This method demonstrates substantial complexity‐generating character and is suitable for applications in molecular recognition and catalysis.

An important development in the field of macrocyclization strategies towards molecular cages is described. The approach comprises the utilization of a double Ugi four‐component macrocyclization for the assembly of macromulticycles with up to four different tethers, that is, hybrid cages. The innovation of this method rests on setting up the macromulticycle connectivities not through the tethers but through the bridgeheads, which in this case involve N‐substituted amino acids. Both dilution and metal‐template‐driven macrocyclization conditions were implemented with success, enabling the one‐pot formation of cryptands and cages including steroidal, polyether, heterocyclic, peptidic, and aryl tethers. This method demonstrates substantial complexity‐generating character and is suitable for applications in molecular recognition and catalysis.

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.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

BackgroundUntargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.ResultsWe implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.IPO optimizes XCMS peak picking parameters by using natural, stable 13C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third.ConclusionsIPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO.