NMR 1H,19F-based screening of the four stem-looped structure 5_SL1-SL4 located in the 5'-untranslated region of SARS-CoV 2 RNA.

Development of new antiviral medication against the beta-coronavirus SARS-CoV-2 (SCoV2) is actively being pursued. Both NMR spectroscopy and crystallography as structural screening technologies have been utilised to screen the viral proteome for binding to fragment libraries. Here, we report on NMR screening of elements of the viral RNA genome with two different ligand libraries using 1H-NMR-screening experiments and 1H and 19F NMR-screening experiments for fluorinated compounds. We screened against the 5'-terminal 119 nucleotides located in the 5'-untranslated region of the RNA genome of SCoV2 and further dissected the four stem-loops into its constituent RNA elements to test specificity of binding of ligands to shorter and longer viral RNA stretches. The first library (DRTL-F library) is enriched in ligands binding to RNA motifs, while the second library (DSI-poised library) represents a fragment library originally designed for protein screening. Conducting screens with two different libraries allows us to compare different NMR screening methodologies, describe NMR screening workflows, validate the two different fragment libraries, and derive initial leads for further downstream medicinal chemistry optimisation.

High-resolution structure of stem-loop 4 from the 5'-UTR of SARS-CoV-2 solved by solution state NMR.

We present the high-resolution structure of stem-loop 4 of the 5'-untranslated region (5_SL4) of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) genome solved by solution state nuclear magnetic resonance spectroscopy. 5_SL4 adopts an extended rod-like structure with a single flexible looped-out nucleotide and two mixed tandem mismatches, each composed of a G•U wobble base pair and a pyrimidine•pyrimidine mismatch, which are incorporated into the stem-loop structure. Both the tandem mismatches and the looped-out residue destabilize the stem-loop structure locally. Their distribution along the 5_SL4 stem-loop suggests a role of these non-canonical elements in retaining functionally important structural plasticity in particular with regard to the accessibility of the start codon of an upstream open reading frame located in the RNA's apical loop. The apical loop-although mostly flexible-harbors residual structural features suggesting an additional role in molecular recognition processes. 5_SL4 is highly conserved among the different variants of SARS-CoV-2 and can be targeted by small molecule ligands, which it binds with intermediate affinity in the vicinity of the non-canonical elements within the stem-loop structure.

Exploring the Druggability of Conserved RNA Regulatory Elements in the SARS-CoV-2 Genome.

SARS-CoV-2 contains a positive single-stranded RNA genome of approximately 30 000 nucleotides. Within this genome, 15 RNA elements were identified as conserved between SARS-CoV and SARS-CoV-2. By nuclear magnetic resonance (NMR) spectroscopy, we previously determined that these elements fold independently, in line with data from in vivo and ex-vivo structural probing experiments. These elements contain non-base-paired regions that potentially harbor ligand-binding pockets. Here, we performed an NMR-based screening of a poised fragment library of 768 compounds for binding to these RNAs, employing three different 1 H-based 1D NMR binding assays. The screening identified common as well as RNA-element specific hits. The results allow selection of the most promising of the 15 RNA elements as putative drug targets. Based on the identified hits, we derive key functional units and groups in ligands for effective targeting of the RNA of SARS-CoV-2.

19 F NMR-Based Fragment Screening for 14 Different Biologically Active RNAs and 10 DNA and Protein Counter-Screens.

We report here the nuclear magnetic resonance 19 F screening of 14 RNA targets with different secondary and tertiary structure to systematically assess the druggability of RNAs. Our RNA targets include representative bacterial riboswitches that naturally bind with nanomolar affinity and high specificity to cellular metabolites of low molecular weight. Based on counter-screens against five DNAs and five proteins, we can show that RNA can be specifically targeted. To demonstrate the quality of the initial fragment library that has been designed for easy follow-up chemistry, we further show how to increase binding affinity from an initial fragment hit by chemistry that links the identified fragment to the intercalator acridine. Thus, we achieve low-micromolar binding affinity without losing binding specificity between two different terminator structures.

Erratum: Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

[This corrects the article DOI: 10.1016/j.omtm.2019.09.008.].

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deamidation, using the random decision forest method. We show that our categorical model predicts antibody deamidation with nearly 5% increased accuracy and 0.2 MCC over the best currently available models. Surprisingly, our model also paces or outperforms advanced and conventional models on an independent non-antibody dataset. In addition to deamidation probability, we are able to accurately predict deamidation rate (R2 = 0.963 and Q2 = 0.822), a capability with no peer in current models. This method should enable significant improvement in protein candidate selection, especially in biopharmaceutical development, and can be applied with similar accuracy to enzymes, monoclonal antibodies, next-generation formats, vaccine component antigens, and gene therapy vectors such as adeno-associated virus.