Andrean Goede

Metabolomic microbiome research has become an important topic for understanding agricultural, ecological as well as health correlations. Only the determination of both the non-volatile and the volatile organic compound (mVOC) production by microorganisms allows a holistic view for understanding the complete potential of metabolomes and metabolic capabilities of bacteria. In the recent past, more and more bacterial headspaces and culture media were analyzed, leading to an accumulation of about 3500 mVOCs in the updated mVOC 4.0 database, including compounds synthesized by the newly discovered non-canonical terpene pathway. Approximately 10% of all mVOCs can be assigned with a biological function, some mVOCs have the potential to impact agriculture in the future (e.g. eco-friendly pesticides) or animal and human health care. mVOC 4.0 offers various options for exploring extensively annotated mVOC data from different perspectives, including improved mass spectrometry matching. The mVOC 4.0 database includes literature searches with additional relevant keywords, making it the most up-to-date and comprehensive publicly available mVOC platform at: http://bioinformatics.charite.de/mvoc.

One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.

Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers’ increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.

Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has been extensively filtered and is no longer only based on confirmed binders but also includes non-binding substances to reduce false positives. Using these methods, accuracy for the ATC prediction could be increased by almost 5% to 80.5% compared to the previous version, and additionally the scoring function now offers values which are easily assessable at first glance. SuperPred 3.0 is publicly available without the need for registration at: https://prediction.charite.de/index.php.

Background: The COVID-19 pandemic is being battled via the largest vaccination campaign in history, with more than eight billion doses administered thus far. Therefore, discussions about potentially adverse reactions, and broader safety concerns, are critical. The U.S. Vaccination Adverse Event Reporting System (VAERS) has recorded vaccination side effects for over 30 years. About 580,000 events have been filed for COVID-19 thus far, primarily for the Johnson & Johnson (New Jersey, USA), Pfizer/BioNTech (Mainz, Germany), and Moderna (Cambridge, USA) vaccines. Methods: Using available databases, we evaluated these three vaccines in terms of the occurrence of four generally-noticed adverse reactions—namely, cerebral venous sinus thrombosis, Guillain–Barré syndrome (a severe paralytic neuropathy), myocarditis, and pericarditis. Our statistical analysis also included a calculation of odds ratios (ORs) based on total vaccination numbers, accounting for incidence rates in the general population. Results: ORs for a number of adverse events and patient groups were (largely) increased, most notably for the occurrence of cerebral venous sinus thrombosis after vaccination with the Johnson & Johnson vaccine. The overall population OR of 10 increases to 12.5 when limited to women, and further yet (to 14.4) among women below age 50 yrs. In addition, elevated risks were found (i) for Guillain–Barré syndrome (OR of 11.6) and (ii) for myocarditis/pericarditis (ORs of 5.3/4.1, respectively) among young men (<25 yrs) vaccinated with the Pfizer/BioNTech vaccine. Conclusions: Any conclusions from such a retrospective, real-world data analysis must be drawn cautiously, and should be confirmed by prospective double-blinded clinical trials. In addition, we emphasize that the adverse events reported here are not specific side effects of COVID vaccines, and the significant, well-established benefits of COVID-19 vaccination outweigh the potential complications surveyed here.

We present an updated version of the Voronoia service that enables fully automated analysis of the atomic packing density of macromolecules. Voronoia combines previous efforts to analyse 3D protein and RNA structures into a single service, combined with state-of-the-art online visualization. Voronoia uses the Voronoi cell method to calculate the free space between neighbouring atoms to estimate van der Waals interactions. Compared to other methods that derive van der Waals interactions by calculating solvent-free surfaces, it explicitly considers volume or packing defects. Large internal voids refer either to water molecules or ions unresolved by X-ray crystallography or cryo-EM, cryptic ligand binding pockets, or parts of a structural model that require further refinement. Voronoia is, therefore mainly used for functional analyses of 3D structures and quality assessments of structural models. Voronoia 4-ever updates the database of precomputed packing densities of PDB entries, allows uploading multiple structures, adds new filter options and facilitates direct access to the results through intuitive display with the NGL viewer. Voronoia is available at: htttp://proteinformatics.org/voronoia.

Proteasome-catalyzed peptide splicing (PCPS) of cancer-driving antigens could generate attractive neoepitopes to be targeted by T cell receptor (TCR)-based adoptive T cell therapy. Based on a spliced peptide prediction algorithm, TCRs were generated against putative KRAS^G12V- and RAC2^P29L-derived neo-splicetopes with high HLA-A*02:01 binding affinity. TCRs generated in mice with a diverse human TCR repertoire specifically recognized the respective target peptides with high efficacy. However, we failed to detect any neo-splicetope-specific T cell response when testing the in vivo neo-splicetope generation and obtained no experimental evidence that the putative KRAS^G12V- and RAC2^P29L-derived neo-splicetopes were naturally processed and presented. Furthermore, only the putative RAC2^P29L-derived neo-splicetopes was generated by in vitro PCPS. The experiments pose severe questions on the notion that available algorithms or the in vitro PCPS reaction reliably simulate in vivo splicing and argue against the general applicability of an algorithm-driven ‘reverse immunology’ pipeline for the identification of cancer-specific neo-splicetopes.

The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients’ lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

Regular monitoring of drug regulatory agency web sites and similar resources for information on new drug approvals and changes to legal status of marketed drugs is impractical. It requires navigation through several resources to find complete information about a drug as none of the publicly accessible drug databases provide all features essential to complement in silico drug discovery. Here, we propose SuperDRUG2 (http://cheminfo.charite.de/superdrug2) as a comprehensive knowledge-base of approved and marketed drugs. We provide the largest collection of drugs (containing 4587 active pharmaceutical ingredients) which include small molecules, biological products and other drugs. The database is intended to serve as a one-stop resource providing data on: chemical structures, regulatory details, indications, drug targets, side-effects, physicochemical properties, pharmacokinetics and drug–drug interactions. We provide a 3D-superposition feature that facilitates estimation of the fit of a drug in the active site of a target with a known ligand bound to it. Apart from multiple other search options, we introduced pharmacokinetics simulation as a unique feature that allows users to visualise the ‘plasma concentration versus time’ profile for a given dose of drug with few other adjustable parameters to simulate the kinetics in a healthy individual and poor or extensive metabolisers.

SuperLooper2 (SL2) (http://proteinformatics.charite.de/sl2) is the updated version of our previous web-server SuperLooper, a fragment based tool for the prediction and interactive placement of loop structures into globular and helical membrane proteins. In comparison to our previous version, SL2 benefits from both a considerably enlarged database of fragments derived from high-resolution 3D protein structures of globular and helical membrane proteins, and the integration of a new protein viewer. The database, now with double the content, significantly improved the coverage of fragment conformations and prediction quality. The employment of the NGL viewer for visualization of the protein under investigation and interactive selection of appropriate loops makes SL2 independent of third-party plug-ins and additional installations.