Cavity-Based Discovery of New Fatty Acid Photodecarboxylases.

Light-dependent fatty acid photodecarboxylases (FAPs) hold significant potential for biotechnology, due to their capability to produce alka(e)nes directly from the corresponding (un)saturated natural fatty acids requiring light as the only reagent. This study expands the family of FAPs through cavity-based enzyme discovery methods. Thirty enzyme candidates with potential photodecarboxylation activity were identified by matching the cavities of four related template structures against the Protein Data Bank's flavoproteins, a library of proteins identified via the Foldseek Search Server, and homology models of sequences resulting from BLAST.

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.

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.

Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design.

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.

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

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.

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.

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 (M) 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 M 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.

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.

Structural basis for inhibition of the AAA-ATPase Drg1 by diazaborine.

The hexameric AAA-ATPase Drg1 is a key factor in eukaryotic ribosome biogenesis and initiates cytoplasmic maturation of the large ribosomal subunit by releasing the shuttling maturation factor Rlp24. Drg1 monomers contain two AAA-domains (D1 and D2) that act in a concerted manner. Rlp24 release is inhibited by the drug diazaborine which blocks ATP hydrolysis in D2.

Serine 477 plays a crucial role in the interaction of the SARS-CoV-2 spike protein with the human receptor ACE2.

Since the worldwide outbreak of the infectious disease COVID-19, several studies have been published to understand the structural mechanism of the novel coronavirus SARS-CoV-2. During the infection process, the SARS-CoV-2 spike (S) protein plays a crucial role in the receptor recognition and cell membrane fusion process by interacting with the human angiotensin-converting enzyme 2 (hACE2) receptor. However, new variants of these spike proteins emerge as the virus passes through the disease reservoir.

Crystal structure of the Saccharomyces cerevisiae monoglyceride lipase Yju3p.

Monoglyceride lipases (MGLs) are a group of α/β-hydrolases that catalyze the hydrolysis of monoglycerides (MGs) into free fatty acids and glycerol. This reaction serves different physiological functions, namely in the last step of phospholipid and triglyceride degradation, in mammalian endocannabinoid and arachidonic acid metabolism, and in detoxification processes in microbes. Previous crystal structures of MGLs from humans and bacteria revealed conformational plasticity in the cap region of this protein and gave insight into substrate binding.

Identification of promiscuous ene-reductase activity by mining structural databases using active site constellations.

The exploitation of catalytic promiscuity and the application of de novo design have recently opened the access to novel, non-natural enzymatic activities. Here we describe a structural bioinformatic method for predicting catalytic activities of enzymes based on three-dimensional constellations of functional groups in active sites ('catalophores'). As a proof-of-concept we identify two enzymes with predicted promiscuous ene-reductase activity (reduction of activated C-C double bonds) and compare them with known ene-reductases, that is, members of the Old Yellow Enzyme family.

Fusion of binding domains to Thermobifida cellulosilytica cutinase to tune sorption characteristics and enhancing PET hydrolysis.

A cutinase from Thermomyces cellullosylitica (Thc_Cut1), hydrolyzing the synthetic polymer polyethylene terephthalate (PET), was fused with two different binding modules to improve sorption and thereby hydrolysis. The binding modules were from cellobiohydrolase I from Hypocrea jecorina (CBM) and from a polyhydroxyalkanoate depolymerase from Alcaligenes faecalis (PBM). Although both binding modules have a hydrophobic nature, it was possible to express the proteins in E.

The 2.5 Å structure of the enterococcus conjugation protein TraM resembles VirB8 type IV secretion proteins.

Conjugative plasmid transfer is the most important means of spreading antibiotic resistance and virulence genes among bacteria and therefore presents a serious threat to human health. The process requires direct cell-cell contact made possible by a multiprotein complex that spans cellular membranes and serves as a channel for macromolecular secretion. Thus far, well studied conjugative type IV secretion systems (T4SS) are of Gram-negative (G-) origin.

The structure of monoacylglycerol lipase from Bacillus sp. H257 reveals unexpected conservation of the cap architecture between bacterial and human enzymes.

Monoacylglycerol lipases (MGLs) catalyse the hydrolysis of monoacylglycerol into free fatty acid and glycerol. MGLs have been identified throughout all genera of life and have adopted different substrate specificities depending on their physiological role. In humans, MGL plays an integral part in lipid metabolism affecting energy homeostasis, signalling processes and cancer cell progression.

Molecular modeling of lipase binding to a substrate-water interface.

Interactions of lipases with hydrophobic substrate-water interfaces are of great interest to design improved lipase variants and engineer reaction conditions. This chapter describes the necessary steps to carry out molecular dynamics simulations of Candida antarctica lipase B at tributyrin-water interface using the GROMACS simulation software. Special attention is drawn to the preparation of the protein and the substrate-water interface and to the analysis of the obtained trajectory.

Systematic benchmarking of large molecular dynamics simulations employing GROMACS on massive multiprocessing facilities.

The influence of the total number of cores, the number of cores dedicated to Particle mesh Ewald (PME) calculation and the choice of single vs. double precision on the performance of molecular dynamic (MD) simulations in the size of 70,000 to 1.7 million of atoms was analyzed on three different high-performance computing facilities employing GROMACS 4 by running about 6000 benchmark simulations.

Orchestration of concurrent oxidation and reduction cycles for stereoinversion and deracemisation of sec-alcohols.

Black and white are opposites as are oxidation and reduction. Performing an oxidation, for example, of a sec-alcohol and a reduction of the corresponding ketone in the same vessel without separation of the reagents seems to be an impossible task. Here we show that oxidative cofactor recycling of NADP (+) and reductive regeneration of NADH can be performed simultaneously in the same compartment without significant interference.

Emulation of racemase activity by employing a pair of stereocomplementary biocatalysts.

Racemization is the key step to turn a kinetic resolution process into dynamic resolution. A general strategy for racemization under mild reaction conditions by employing stereoselective biocatalysts is presented, in which racemization is achieved by employing a pair of stereocomplementary biocatalysts that reversibly interconvert an sp3 to a sp2 center. The formal interconversion of the enantiomers proceeds via a prochiral sp2 intermediate the formation of which is catalyzed either by two stereocomplementary enzymes or by a single enzyme with low stereoselectivity.

An algorithm for the deconvolution of mass spectroscopic patterns in isotope labeling studies. Evaluation for the hydrogen-deuterium exchange reaction in ketones.

An easy to use computerized algorithm for the determination of the amount of each labeled species differing in the number of incorporated isotope labels based on mass spectroscopic data is described and evaluated. Employing this algorithm, the microwave-assisted synthesis of various alpha-labeled deuterium ketones via hydrogen-deuterium exchange with deuterium oxide was optimized with respect to time, temperature, and degree of labeling. For thermally stable ketones the exchange of alpha-protons was achieved at 180 degrees C within 40-200 min.

Biocatalytic deuterium- and hydrogen-transfer using over-expressed ADH-'A': enhanced stereoselectivity and 2H-labeled chiral alcohols.

Employing the over-expressed highly organic solvent tolerant alcohol dehydrogenase ADH-'A' from Rhodococcus ruber DSM 44541, versatile building blocks, which were not accessible by the wild type catalyst, were obtained in > 99% e.e.; furthermore, employing d8-2-propanol as deuterium source, stereoselective biocatalytic deuterium transfer was made feasible to furnish enantiopure deuterium labeled sec-alcohols on a preparative scale employing a single enzyme.