Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data

We present Farseer-NMR (https://git.io/vAueU), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems’ responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension

Strategies Towards Protease Inhibitors for Emerging Flaviviruses

Infections with flaviviruses are a continuing public health threat. In addition to vaccine development and vector control, the search for antiviral agents that alleviate symptoms in patients are of considerable interest. Among others, the flaviviral protease NS2B-NS3 is a promising drug target to inhibit viral replication. Flaviviral proteases share a high degree of structural similarity and substrate-recognition profile, which may facilitate a strategy towards development of pan-flaviviral protease inhibitors. However, the success of various drug discovery attempts during the last decade has been limited by the nature of the viral enzyme as well as a lack of robust structural templates. Small-molecular, structurally diverse protease inhibitors have been reported to reach affinities in the lower micromolar range. Peptide-based, substrate-derived compounds are often nanomolar inhibitors, however, with highly compromised drug-likeness. With some exceptions, the antiviral cellular activity of most of the reported compounds have been patchy and insufficient for further development. Recent progress has been made in the elucidation of inhibitor binding using different structural methods. This will hopefully lead to more rational attempts for the identification of various lead compounds that may be successful in cellular assays, animal models and ultimately translated to patients.Funding by the Alexander von Humboldt Foundation is gratefully acknowledged