CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases.

Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10-15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA's BioNeMo platform, as well as homology modeling using Innophore's CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.

Specific Cationic Antimicrobial Peptides Enhance the Recovery of Low-Load Quiescent Mycobacterium tuberculosis in Routine Diagnostics.

The culture confirmation of Mycobacterium tuberculosis (MTB) remains the gold standard for the diagnosis of Tuberculosis (TB) with culture conversion representing proof of cure. However, over 40% of TB samples fail to isolate MTB even though many patients remain infectious due to the presence of viable non-culturable forms. Previously, we have shown that two short cationic peptides, T14D and TB08L, induce a hormetic response at low concentrations, leading to a stimulation of growth in MTB and the related animal pathogen Mycobacterium bovis (bTB). Here, we examine these peptides showing they can influence the mycobacterial membrane integrity and function through membrane potential reduction. We also show this disruption is associated with an abnormal reduction in transcriptomic signalling from specific mycobacterial membrane sensors that normally monitor the immediate cellular environment and maintain the non-growing phenotype. We observe that exposing MTB or bTB to these peptides at optimal concentrations rapidly represses signalling mechanisms maintaining dormancy phenotypes, which leads to the promotion of aerobic metabolism and conversion into a replicative phenotype. We further show a practical application of these peptides as reagents able to enhance conventional routine culture methods by stimulating mycobacterial growth. We evaluated the ability of a peptide-supplemented sample preparation and culture protocol to isolate the MTB against a gold standard routine method tested in parallel on 255 samples from 155 patients with suspected TB. The peptide enhancement increased the sample positivity rate by 46% and decreased the average time to sample positivity of respiratory/faecal sampling by seven days. The most significant improvements in isolation rates were from sputum smear-negative low-load samples and faeces. The peptide enhancement increased sampling test sensitivity by 19%, recovery in samples from patients with a previously culture-confirmed TB by 20%, and those empirically treated for TB by 21%. We conclude that sample decontamination and culture enhancement with D-enantiomer peptides offer good potential for the much-needed improvement of the culture confirmation of TB.

AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak.

IMPORTANCE: The 2022 outbreak of the monkeypox virus already involves, by April 2023, 110 countries with 86,956 confirmed cases and 119 deaths. Understanding an emerging disease on a molecular level is essential to study infection processes and eventually guide drug discovery at an early stage. To support this, we provide the so far most comprehensive structural proteome of the monkeypox virus, which includes 210 structural models, each computed with three state-of-the-art structure prediction methods. Instead of building on a single-genome sequence, we generated our models from a consensus of 3,713 high-quality genome sequences sampled from patients within 1 year of the outbreak. Therefore, we present an average structural proteome of the currently isolated viruses, including mutational analyses with a special focus on drug-binding sites. Continuing dynamic mutation monitoring within the structural proteome presented here is essential to timely predict possible physiological changes in the evolving virus.

Enantioselective High-Throughput Assay Showcased for the Identification of (R)- as well as (S)-Selective Unspecific Peroxygenases for C-H Oxidation.

Identifying (bio)catalysts displaying high enantio-/stereoselectivity is a fundamental prerequisite for the advancement of asymmetric catalysis. Herein, a high-throughput, stereoselective screening assay is reported that gives information on enantioselectivity, stereopreference and activity as showcased for peroxygenase-catalyzed hydroxylation. The assay is based on spectrophotometric analysis of the simultaneous formation of NAD(P)H from the alcohol dehydrogenase catalyzed enantioselective oxidation of the sec-alcohol product formed in the peroxygenase reaction. The assay was applied to investigate a library comprising 44 unspecific peroxygenases (UPOs) containing 25 UPOs not reported yet. Thereby, previously non-described wild-type UPOs displaying (S)- as well as (R)-stereoselectivity for the hydroxylation of representative model substrates were identified, reaching up to 98 % ee for the (R)- and 94 % ee for the (S)-enantiomer. Homology models with concomitant docking studies indicated the structural reason for the observed complementary stereopreference.

Bactericidal Activity to Escherichia coli: Different Modes of Action of Two 24-Mer Peptides SAAP-148 and OP-145, Both Derived from Human Cathelicidine LL-37.

OP-145 and SAAP-148, two 24-mer antimicrobial peptides derived from human cathelicidin LL-37, exhibit killing efficacy against both Gram-positive and Gram-negative bacteria at comparable peptide concentrations. However, when it comes to the killing activity against Escherichia coli, the extent of membrane permeabilization does not align with the observed bactericidal activity. This is the case in living bacteria as well as in model membranes mimicking the E. coli cytoplasmic membrane (CM). In order to understand the killing activity of both peptides on a molecular basis, here we studied their mode of action, employing a combination of microbiological and biophysical techniques including differential scanning calorimetry (DSC), zeta potential measurements, and spectroscopic analyses. Various membrane dyes were utilized to monitor the impact of the peptides on bacterial and model membranes. Our findings unveiled distinct binding patterns of the peptides to the bacterial surface and differential permeabilization of the E. coli CM, depending on the smooth or rough/deep-rough lipopolysaccharide (LPS) phenotypes of E. coli strains. Interestingly, the antimicrobial activity and membrane depolarization were not significantly different in the different LPS phenotypes investigated, suggesting a general mechanism that is independent of LPS. Although the peptides exhibited limited permeabilization of E. coli membranes, DSC studies conducted on a mixture of synthetic phosphatidylglycerol/phosphatidylethanolamine/cardiolipin, which mimics the CM of Gram-negative bacteria, clearly demonstrated disruption of lipid chain packing. From these experiments, we conclude that depolarization of the CM and alterations in lipid packing plays a crucial role in the peptides' bactericidal activity.

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference.

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.

Recent changes in the mutational dynamics of the SARS-CoV-2 main protease substantiate the danger of emerging resistance to antiviral drugs.

Introduction: The current coronavirus pandemic is being combated worldwide by nontherapeutic measures and massive vaccination programs. Nevertheless, therapeutic options such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main-protease (Mpro) inhibitors are essential due to the ongoing evolution toward escape from natural or induced immunity. While antiviral strategies are vulnerable to the effects of viral mutation, the relatively conserved Mpro makes an attractive drug target: Nirmatrelvir, an antiviral targeting its active site, has been authorized for conditional or emergency use in several countries since December 2021, and a number of other inhibitors are under clinical evaluation. We analyzed recent SARS-CoV-2 genomic data, since early detection of potential resistances supports a timely counteraction in drug development and deployment, and discovered accelerated mutational dynamics of Mpro since early December 2021. Methods: We performed a comparative analysis of 10.5 million SARS-CoV-2 genome sequences available by June 2022 at GISAID to the NCBI reference genome sequence NC_045512.2. Amino-acid exchanges within high-quality regions in 69,878 unique Mpro sequences were identified and time- and in-depth sequence analyses including a structural representation of mutational dynamics were performed using in-house software. Results: The analysis showed a significant recent event of mutational dynamics in Mpro. We report a remarkable increase in mutational variability in an eight-residue long consecutive region (R188-G195) near the active site since December 2021. Discussion: The increased mutational variability in close proximity to an antiviral-drug binding site as described herein may suggest the onset of the development of antiviral resistance. This emerging diversity urgently needs to be further monitored and considered in ongoing drug development and lead optimization.