How sensor Amt-like proteins integrate ammonium signals.

Unlike aquaporins or potassium channels, ammonium transporters (Amts) uniquely discriminate ammonium from potassium and water. This feature has certainly contributed to their repurposing as ammonium receptors during evolution. Here, we describe the ammonium receptor Sd-Amt1, where an Amt module connects to a cytoplasmic diguanylate cyclase transducer module via an HAMP domain. Structures of the protein with and without bound ammonium were determined to 1.7- and 1.9-Ångstrom resolution, depicting the ON and OFF states of the receptor and confirming the presence of a binding site for two ammonium cations that is pivotal for signal perception and receptor activation. The transducer domain was disordered in the crystals, and an AlphaFold2 prediction suggests that the helices linking both domains are flexible. While the sensor domain retains the trimeric fold formed by all Amt family members, the HAMP domains interact as pairs and serve to dimerize the transducer domain upon activation.

Towards the prediction of non-peptidic epitopes.

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

StreptomeDB 3.0: an updated compendium of streptomycetes natural products.

Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.

Engineering proton conductivity in melanin using metal doping.

Long range electrical conduction in biomaterials is an increasingly active area of research, which includes systems such as the conductive pili, proteins, biomacromolecules, biocompatible conductive polymers and their derivatives. One material of particular interest, the human skin pigment melanin, is a long range proton conductor and recently demonstrated as capable of proton-to-electron transduction in a solid-state electrochemical transistor platform. In this work, a novel "doping strategy" is proposed to enhance and control melanin's proton conductivity, potentially enhancing its utility as a transducing material. By chelating the transition metal ion Cu(ii) into the bio-macromolecular matrix, free proton concentration and hence conductivity can be modulated. We confirm these observations by demonstrating enhanced performance in a next generation electrochemical transistor. Finally, the underlying mechanism is investigated via the use of a novel in situ hydration-controlled electron paramagnetic resonance study, deducing that the enhanced proton concentration is due to controlling the internal solid-state redox chemistry of the intrinsic polyindolequinone structure. This doping strategy should be open to any transition metal ions that bind to hydroquinone systems (e.g. polydopamine). As such, the tailoring strategy could make other soft solid-state ionic systems more accessible to applications in bioelectronics, leading to the creation of higher performance ion-electron coupled devices.

SeMPI 2.0-A Web Server for PKS and NRPS Predictions Combined with Metabolite Screening in Natural Product Databases.

Microorganisms produce secondary metabolites with a remarkable range of bioactive properties. The constantly increasing amount of published genomic data provides the opportunity for efficient identification of biosynthetic gene clusters by genome mining. On the other hand, for many natural products with resolved structures, the encoding biosynthetic gene clusters have not been identified yet. Of those secondary metabolites, the scaffolds of nonribosomal peptides and polyketides (type I modular) can be predicted due to their building block-like assembly. SeMPI v2 provides a comprehensive prediction pipeline, which includes the screening of the scaffold in publicly available natural compound databases. The screening algorithm was designed to detect homologous structures even for partial, incomplete clusters. The pipeline allows linking of gene clusters to known natural products and therefore also provides a metric to estimate the novelty of the cluster if a matching scaffold cannot be found. Whereas currently available tools attempt to provide comprehensive information about a wide range of gene clusters, SeMPI v2 aims to focus on precise predictions. Therefore, the cluster detection algorithm, including building block generation and domain substrate prediction, was thoroughly refined and benchmarked, to provide high-quality scaffold predictions. In a benchmark based on 559 gene clusters, SeMPI v2 achieved comparable or better results than antiSMASH v5. Additionally, the SeMPI v2 web server provides features that can help to further investigate a submitted gene cluster, such as the incorporation of a genome browser, and the possibility to modify a predicted scaffold in a workbench before the database screening.

SeMPI: a genome-based secondary metabolite prediction and identification web server.

The secondary metabolism of bacteria, fungi and plants yields a vast number of bioactive substances. The constantly increasing amount of published genomic data provides the opportunity for an efficient identification of gene clusters by genome mining. Conversely, for many natural products with resolved structures, the encoding gene clusters have not been identified yet. Even though genome mining tools have become significantly more efficient in the identification of biosynthetic gene clusters, structural elucidation of the actual secondary metabolite is still challenging, especially due to as yet unpredictable post-modifications. Here, we introduce SeMPI, a web server providing a prediction and identification pipeline for natural products synthesized by polyketide synthases of type I modular. In order to limit the possible structures of PKS products and to include putative tailoring reactions, a structural comparison with annotated natural products was introduced. Furthermore, a benchmark was designed based on 40 gene clusters with annotated PKS products. The web server of the pipeline (SeMPI) is freely available at: http://www.pharmaceutical-bioinformatics.de/sempi.

Electron paramagnetic resonance study of ZnO varistor material.

Matsuoka-type zinc oxide (ZnO) varistor material was synthesized using a conventional solid-state reaction method. X-band electron paramagnetic resonance (EPR) data revealed that Mn ions substitute in the ZnO lattice with a 2+ paramagnetic state. Co ions with either 3+ or 2+ oxidation states are only detectable at cryogenic temperatures. A Cr(3+) EPR signal was strongly suppressed or masked by a Mn(2+) signal. Photoluminescence and electrical results indicated that the varistor sample has fewer intrinsic defects and much higher resistivity as compared to undoped and metal-ion doped ZnO.