Reductive amination catalysed by imine reductase (IRED) and reductive aminase (RedAm) enzymes has recently been established as a powerful method for the asymmetric synthesis of chiral amines. While this biocatalytic technology has rapidly progressed from proof of concept to initial industrial applications, its scope and limitations remain to be fully explored. In this work, we report a broad and systematic profiling of reductive amination performance in the sequence space of IREDs and RedAms. This investigation employed an iterative strategy for activity and stereoselectivity screening, guided by chemo- and bioinformatic modelling as well as machine learning. By evaluating the catalytic performance of 175 IREDs against structurally diverse panels of 36 carbonyl compounds and 24 amines, we show that the majority of these enzymes is capable of asymmetric reductive amination at equimolar concentrations of the two substrates (50 mM each). The most effective enzymes identified in this study display sequence characteristics of RedAms, are active on 29–42% of the analysed substrate combinations, and combine high specific activities for the most favourable substrate pair (1.7−27.7 U/mgIRED) with excellent stereoselectivity. Beyond assembling this high-performance enzyme panel, we demonstrate extrapolation from our collected screening data to new substrate combinations by deep learning and the scale-up of selected reactions to a preparative batch size (10 mmol substrate, 200 mL reaction volume), delivering gram amounts of reductive amination products in high yield (63–89%) and optical purity (98% to >99% ee).

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.

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.

Motivated by a use case in theoretical hadron physics, we revisit an application of a pole-sum fit to dressing functions of a confined quark propagator. More precisely, we investigate approaches to determine the number and positions of the singularities closest to the origin for a function that is only known numerically on a specific finite grid of values on the positive real axis. For this problem, we compare the efficiency of standard techniques, like the Levenberg-Marquardt algorithm, to a pure artificial-neural-network approach as well as a combination of these two. This combination is more efficient than any of the two techniques separately. Such an approach is generalizable to similar situations, where the positions of poles of a function in a complex variable must be quickly and reliably estimated from real-axis information alone.

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 (IC 50 ) compared with the same variant produced in CHO cells and an almost six-fold IC 50 reduction compared with wild-type hACE2-Fc.

In this work, we present DrugSolver CavitomiX, a novel computational pipeline for drug repurposing and identifying ligands and inhibitors of target enzymes. The pipeline is based on cavity point clouds representing physico-chemical properties of the cavity induced solely by the protein. To test the pipeline’s ability to identify inhibitors, we chose enzymes essential for SARS-CoV-2 replication as a test system. The active-site cavities of the viral enzymes main protease (M pro ) and papain-like protease (Pl pro ), as well as of the human transmembrane serine protease 2 (TMPRSS2), were selected as target cavities. Using active-site point-cloud comparisons, it was possible to identify two compounds—flufenamic acid and fusidic acid—which show strong inhibition of viral replication. The complexes from which fusidic acid and flufenamic acid were derived would not have been identified using classical sequence- and structure-based methods as they show very little structural (TM-score: 0.1 and 0.09, respectively) and very low sequence (~ 5%) identity to M pro and TMPRSS2, respectively. Furthermore, a cavity-based off-target screening was performed using acetylcholinesterase (AChE) as an example. Using cavity comparisons, the human carboxylesterase was successfully identified, which is a described off-target for AChE inhibitors.

ABSTRACT The monkeypox virus (MPX) belongs to the Orthopoxvirus genus of the Poxviridae family, is endemic in parts of Africa and causes a disease in humans similar to smallpox. The most recent outbreak of MPX is already affecting 110 countries, with 86,956 confirmed cases since May 2022 and has consequently become a focus of interest. In particular, a molecular understanding of the virus is essential to study infection processes and pathogen-host interactions, predict tropism changes, or guide drug development and drug discovery as well as vaccine development or vaccine adaptation at a very early stage. Herein, we present a study of the structural proteome of the currently circulating MPX: Our consensus analysis of 3,713 genome sequences sampled within a year after the outbreak revealed 10,580 characteristic candidate open reading frames (ORFs). A search in the non-redundant protein database reduced the number of suspected ORFs to 1,079, of which 210 are representative proteins in typical MPX reference genomes. This should serve as a collection of putative proteins within the currently spreading MPX, a compound of information that could support timely drug discovery, mutational analyses, and vaccine development. We, herein, present the so far most comprehensive structural proteome by providing atomistic 3D models of 210 proteins, generated with three state-of-the-art structure prediction methods, including a mutational analysis of the proteome, with a particular focus on the drug-binding sites of tecovirimat and brincidofovir. 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.

Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.

The current COVID-19 pandemic poses a challenge to medical professionals and the general public alike. In addition to vaccination programs and nontherapeutic measures being employed worldwide to encounter SARS-CoV-2, great efforts have been made towards drug development and evaluation. In particular, the main protease (M pro ) makes an attractive drug target due to its high level characterization and relatively little similarity to host proteases. Essentially, antiviral strategies are vulnerable to the effects of viral mutation and an early detection of arising resistances supports a timely counteraction in drug development and deployment. Here we show a significant recent event of mutational dynamics in M pro . Although the protease has a priori been expected to be relatively conserved, we report a remarkable increase in mutational variability in an eight-residue long consecutive region near the active site since December 2021. The location of this event in close proximity to an antiviral-drug binding site may suggest the onset of the development of antiviral resistance. Our findings emphasize the importance of monitoring the mutational dynamics of M pro together with possible consequences arising from amino-acid exchanges emerging in regions critical with regard to the susceptibility of the virus to antivirals targeting the protease.

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.

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.

Abstract The monkeypox virus (MPX) belongs to the orthopoxvirus genus of the Poxviridae family, is endemic in parts of Africa, and causes a disease in humans similar to smallpox. The most recent outbreak of MPX in 2022 is already affecting 19 countries on different continents and has consequently become a focus of interest. In particular, a molecular understanding of the virus is essential to study infection processes and pathogen-host interactions, predict tropism changes, or guide drug development and discovery as well as vaccine development or adaptation at a very early stage. Herein we present a study of the structural genome of the currently emerging MPX virus: our analysis revealed 10,043 characteristic candidate open reading frames (ORFs), and a subsequent BLAST search of the non-redundant protein database and PDB reduced the number of suspected ORFs to 925 and 123 protein sequences, respectively. Finally, we provide the 3D structures of these 123 protein sequences, which were predicted by homology modeling and are available for download.

We discuss how to extract observables from an inhomogeneous vertex Bethe-Salpeter equation without resorting to the corresponding homogeneous equation. As an example we pres nt a prediction for the e+e− decay width of theρ(1450) or ρ ′ meson. We also attempt to identify the momentum range contri buting to a vector meson’s decay constant.

Recently, we completed a comprehensive investigation of a huge part of the entire meson spectrum by considering both quarkonia and open-flavour mesons by means of a single common framework which unites the homogeneous Bethe-Salpeter equation that describes mesons as quark-antiquark bound states and the Dyson-Schwinger equation that governs the full quark propagator: Adopting two (as a matter of fact, not extremely diverse) models that attempt to grasp all principal aspects of the effective strong interactions entering identically in both these equations, we derived within this unique setup, for all mesons analysed, their masses and leptonic decay constants as well as, for the pseudoscalar ones among these mesons, their in-hadron condensates. Here, as a kind of promotion or teaser, we give but a few examples of the resulting collections of data, laying the main emphasis on the dependence of our insights on the effective-interaction model underlying all such outcomes.

Exploiting an interplay of the Bethe-Salpeter equation enabling us to regard mesons as bound states of quark and antiquark and the Dyson-Schwinger equation controlling the dressed quark propagator, we amend existing studies of quarkonia by a comprehensive description of open-flavour mesons composed of all conceivable combinations of quark flavour. Employing throughout a fixed set of model parameters, we predict some basic characteristics of these mesons, i.e., their masses, leptonic decay constants and corresponding in-hadron condensates entering in a generalized formulation of the Gell-Mann-Oakes-Renner relation.

We revisit earlier calculations of leptonic decay constants of vector charmonia and present and illustrate our decomposition of the corresponding covariant Bethe-Salpeter amplitudes in terms of orbital angular momentum as interpreted in the meson's rest frame. Our results confirm our previous conclusions drawn from the magnitudes of vector-meson leptonic decay constants, identifying the psi(3770) and psi(4160) as D-wave states in our setup.

We investigate the light-quarkonium spectrum using a covariant Dyson–Schwinger–Bethe–Salpeter-equation approach to QCD. We discuss splittings among as well as orbital angular momentum properties of various states in detail and analyze common features of mass splittings with regard to properties of the effective interaction. In particular, we predict the mass of \(\bar{s}s\) exotic \(1^{-+}\) states, and identify orbital angular momentum content in the excitations of the \(\rho \) meson. Comparing our covariant model results, the \(\rho \) and its second excitation being predominantly S-wave, the first excitation being predominantly D-wave, to corresponding conflicting lattice-QCD studies, we investigate the pion-mass dependence of the orbital-angular-momentum assignment and find a crossing at a scale of \(m_\pi \sim 1.4\) GeV. If this crossing turns out to be a feature of the spectrum generated by lattice-QCD studies as well, it may reconcile the different results, since they have been obtained at different values of \(m_\pi \).

Open-flavour meson studies are the necessary completion to any comprehensive investigation of quarkonia. We extend recent studies of quarkonia in the Dyson-Schwinger-Bethe-Salpeter equation approach to explore their results for all possible flavour combinations. Within the inherent limitations of the setup, we present the most comprehensive results for meson masses and leptonic decay constants currently available and put them in perspective with respect to experiment and other approaches.

We discuss charmed mesons in the covariant Dyson-Schwinger-Bethe-Salpeter-equation approach. In particular we computed masses, leptonic decay constants, and an orbital-angular-momentum decomposition for a basic set of states. We also report an efficient way to treat the two coupled quark propagator dressing functions via a single function.

Meson states with exotic quantum numbers arise naturally in a covariant bound-state framework in QCD. We investigate the consequences of shifting quark masses such that the states are no longer restricted to certain C-parities, but only by J(P).Then, a priori, one can no longer distinguish exotic or conventional states. In order to identify signatures of the different states to look for experimentally, we provide the behavior of masses, leptonic decay constants, and orbital-angular-momentum decomposition of such mesons, as well as the constellations in which they could be found. Most prominently, we consider the case of charged quasi-exotic excitations of the pion.