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.

Biocatalytic Heteroaromatic Amide Formation in Water Enabled by a Catalytic Tetrad and Two Access Tunnels.

The amide moiety belongs to the most common motives in pharmaceutical chemistry, present in many prescribed small-molecule pharmaceuticals. Methods for its manufacture are still in high demand, especially using water/buffer as a solvent and avoiding stoichiometric amounts of activation reagents. Herein, we identified from a library of lipases/esterases/acyltransferases and variants thereof a lipase originating from Sphingomonas sp. HXN-200 (SpL) able to form amides in aqueous solution starting from a broad scope of sterically demanding heteroaromatic ethyl esters as well as aliphatic amines, reaching isolated yields up to 99% on preparative scale and space time yields of up to 864 g L-1 d-1; thus, in selected cases, the amide was formed within minutes. The enzyme features an aspartate next to the canonical serine of the catalytic triad, which was essential for amide formation. Furthermore, the enzyme structure revealed two tunnels to the active site, presumably one for the ester and one for the amine, which permit the bringing together of the sterically demanding heteroaromatic esters and the amine in the active site. This work shows that biocatalytic amide formation starting from various five- and six-membered heteroaromatic ethyl esters in the buffer can serve as a platform for preparative amide synthesis.

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.

Asymmetric Synthesis of Chiral 2-Cyclohexenones with Quaternary Stereocenters via Ene-Reductase Catalyzed Desymmetrization of 2,5-Cyclohexadienones.

Stereoselective synthesis of quaternary stereocenters represents a significant challenge in organic chemistry. Herein, we describe the use of ene-reductases OPR3 and YqjM for the efficient asymmetric synthesis of chiral 4,4-disubstituted 2-cyclohexenones via desymmetrizing hydrogenation of prochiral 4,4-disubstituted 2,5-cyclohexadienones. This transformation breaks the symmetry of the cyclohexadienone substrates, generating valuable quaternary stereocenters with high enantioselectivities (ee, up to >99%). The mechanistic causes for the observed high enantioselectivities were investigated both experimentally (stopped-flow kinetics) as well as theoretically (quantum mechanics/molecular mechanics calculations). The synthetic potential of the resulting chiral enones was demonstrated in several diversification reactions in which the stereochemical integrity of the quaternary stereocenter could be preserved.

Structural investigation of Trypanosoma cruzi Akt-like kinase as drug target against Chagas disease.

According to the World Health Organization, Chagas disease (CD) is the most prevalent poverty-promoting neglected tropical disease. Alarmingly, climate change is accelerating the geographical spreading of CD causative parasite, Trypanosoma cruzi, which additionally increases infection rates. Still, CD treatment remains challenging due to a lack of safe and efficient drugs. In this work, we analyze the viability of T. cruzi Akt-like kinase (TcAkt) as drug target against CD including primary structural and functional information about a parasitic Akt protein. Nuclear Magnetic Resonance derived information in combination with Molecular Dynamics simulations offer detailed insights into structural properties of the pleckstrin homology (PH) domain of TcAkt and its binding to phosphatidylinositol phosphate ligands (PIP). Experimental data combined with Alpha Fold proposes a model for the mechanism of action of TcAkt involving a PIP-induced disruption of the intramolecular interface between the kinase and the PH domain resulting in an open conformation enabling TcAkt kinase activity. Further docking experiments reveal that TcAkt is recognized by human inhibitors PIT-1 and capivasertib, and TcAkt inhibition by UBMC-4 and UBMC-6 is achieved via binding to TcAkt kinase domain. Our in-depth structural analysis of TcAkt reveals potential sites for drug development against CD, located at activity essential regions.

Stability Increase of Phenolic Acid Decarboxylase by a Combination of Protein and Solvent Engineering Unlocks Applications at Elevated Temperatures.

Enzymatic decarboxylation of biobased hydroxycinnamic acids gives access to phenolic styrenes for adhesive production. Phenolic acid decarboxylases are proficient enzymes that have been applied in aqueous systems, organic solvents, biphasic systems, and deep eutectic solvents, which makes stability a key feature. Stabilization of the enzyme would increase the total turnover number and thus reduce the energy consumption and waste accumulation associated with biocatalyst production. In this study, we used ancestral sequence reconstruction to generate thermostable decarboxylases. Investigation of a set of 16 ancestors resulted in the identification of a variant with an unfolding temperature of 78.1 °C and a half-life time of 45 h at 60 °C. Crystal structures were determined for three selected ancestors. Structural attributes were calculated to fit different regression models for predicting the thermal stability of variants that have not yet been experimentally explored. The models rely on hydrophobic clusters, salt bridges, hydrogen bonds, and surface properties and can identify more stable proteins out of a pool of candidates. Further stabilization was achieved by the application of mixtures of natural deep eutectic solvents and buffers. Our approach is a straightforward option for enhancing the industrial application of the decarboxylation process.

Flattening the curve-How to get better results with small deep-mutational-scanning datasets.

Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches use some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pretraining, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.

Deciphering the Unconventional Reduction of C=N Bonds by Old Yellow Enzymes Using QM/MM.

The reduction of C=X (X = N, O) bonds is a cornerstone in both synthetic organic chemistry and biocatalysis. Conventional reduction mechanisms usually involve a hydride ion targeting the less electronegative carbon atom. In a departure from this paradigm, our investigation into Old Yellow Enzymes (OYEs) reveals a mechanism involving transfer of hydride to the formally more electronegative nitrogen atom within a C=N bond. Beyond their known ability to reduce electronically activated C=C double bonds, e.g., in α, ß-unsaturated ketones, these enzymes have recently been shown to reduce α-oximo-ß-ketoesters to the corresponding amines. It has been proposed that this transformation involves two successive reduction steps and proceeds via imine intermediates formed by the reductive dehydration of the oxime moieties. We employ advanced quantum mechanics/molecular mechanics (QM/MM) simulations, enriched by a two-tiered approach incorporating QM/MM (UB3LYP-6-31G*/OPLS2005) geometry optimization, QM/MM (B3LYP-6-31G*/amberff19sb) steered molecular dynamics simulations, and detailed natural-bond-orbital analyses to decipher the unconventional hydride transfer to nitrogen in both reduction steps and to delineate the role of active site residues as well as of substituents present in the substrates. Our computational results confirm the proposed mechanism and agree well with experimental mutagenesis and enzyme kinetics data. According to our model, the catalysis of OYE involves hydride transfer from the flavin cofactor to the nitrogen atom in oximoketoesters as well as iminoketoesters followed by protonation at the adjacent oxygen or carbon atoms by conserved tyrosine residues and active site water molecules. Two histidine residues play a key role in the polarization and activation of the C=N bond, and conformational changes of the substrate observed along the reaction coordinate underline the crucial importance of dynamic electron delocalization for efficient catalysis.

Unravelling the respiratory health path across the lifespan for survivors of preterm birth.

Many survivors of preterm birth will have abnormal lung development, reduced peak lung function and, potentially, an increased rate of physiological lung function decline, each of which places them at increased risk of chronic obstructive pulmonary disease across the lifespan. Current rates of preterm birth indicate that by the year 2040, around 50 years since the introduction of surfactant therapy, more than 700 million individuals will have been born prematurely-a number that will continue to increase by about 15 million annually. In this Personal View, we describe current understanding of the impact of preterm birth on lung function through the life course, with the aim of putting this emerging health crisis on the radar for the respiratory community. We detail the potential underlying mechanisms of prematurity-associated lung disease and review current approaches to prevention and management. Furthermore, we propose a novel way of considering lung disease after preterm birth, using a multidimensional model to determine individual phenotypes of lung disease-a first step towards optimising management approaches for prematurity-associated lung disease.

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.

Ligand-Directed Chemistry on Glycoside Hydrolases - A Proof of Concept Study.

Selective covalent labelling of enzymes using small molecule probes has advanced the scopes of protein profiling. The covalent bond formation to a specific target is the key step of activity-based protein profiling (ABPP), a method which has become an indispensable tool for measuring enzyme activity in complex matrices. With respect to carbohydrate processing enzymes, strategies for ABPP so far involve labelling the active site of the enzyme, which results in permanent loss of activity. Here, we report in a proof of concept study the use of ligand-directed chemistry (LDC) for labelling glycoside hydrolases near - but not in - the active site. During the labelling process, the competitive inhibitor is cleaved from the probe, departs the active site and the enzyme maintains its catalytic activity. To this end, we designed a building block synthetic concept for small molecule probes containing iminosugar-based reversible inhibitors for labelling of two model ß-glucosidases. The results indicate that the LDC approach can be adaptable for covalent proximity labelling of glycoside hydrolases.

A Cold-Active Flavin-Dependent Monooxygenase from Janthinobacterium svalbardensis Unlocks Applications of Baeyer-Villiger Monooxygenases at Low Temperature.

Cold-active enzymes maintain a large part of their optimal activity at low temperatures. Therefore, they can be used to avoid side reactions and preserve heat-sensitive compounds. Baeyer-Villiger monooxygenases (BVMO) utilize molecular oxygen as a co-substrate to catalyze reactions widely employed for steroid, agrochemical, antibiotic, and pheromone production. Oxygen has been described as the rate-limiting factor for some BVMO applications, thereby hindering their efficient utilization. Considering that oxygen solubility in water increases by 40% when the temperature is decreased from 30 to 10 °C, we set out to identify and characterize a cold-active BVMO. Using genome mining in the Antarctic organism Janthinobacterium svalbardensis, a cold-active type II flavin-dependent monooxygenase (FMO) was discovered. The enzyme shows promiscuity toward NADH and NADPH and high activity between 5 and 25 °C. The enzyme catalyzes the monooxygenation and sulfoxidation of a wide range of ketones and thioesters. The high enantioselectivity in the oxidation of norcamphor (eeS = 56%, eeP > 99%, E > 200) demonstrates that the generally higher flexibility observed in the active sites of cold-active enzymes, which compensates for the lower motion at cold temperatures, does not necessarily reduce the selectivity of these enzymes. To gain a better understanding of the unique mechanistic features of type II FMOs, we determined the structure of the dimeric enzyme at 2.5 Å resolution. While the unusual N-terminal domain has been related to the catalytic properties of type II FMOs, the structure shows a SnoaL-like N-terminal domain that is not interacting directly with the active site. The active site of the enzyme is accessible only through a tunnel, with Tyr-458, Asp-217, and His-216 as catalytic residues, a combination not observed before in FMOs and BVMOs.