Exploration of Methanomethylophilus alvus pyrrolysyl-tRNA synthetase activity in yeast

Archaeal pyrrolysyl-tRNA synthetases (PylRSs) have been used to genetically encode over 200 distinct noncanonical amino acids (ncAAs) in proteins in E. coli and mammalian cells. This vastly expands the range of chemical functionality accessible within proteins produced in these organisms. Despite these clear successes, explorations of PylRS function in yeast remains limited. In this work, we demonstrate that the Methanomethylophilus alvus PylRS (MaPylRS) and its cognate tRNACUA support the incorporation of ncAAs into proteins produced in S. cerevisiae using stop codon suppression methodologies. Additionally, we prepared three MaPylRS mutants originally engineered in E. coli and determined that all three were translationally active with one or more ncAAs, although with low efficiencies of ncAA incorporation in comparison to the parent MaPylRS. Alongside MaPylRS variants, we evaluated the translational activity of previously reported Methanosarcina mazei, Methanosarcina barkeri, and chimeric M. mazei and M. barkeri PylRSs. Using the yeast strain RJY100, and pairing these aaRSs with the M. barkeri tRNACUA, we did not observe any detectable stop codon suppression activity under the same conditions that produced moderately efficient ncAA incorporation with MaPylRS. The addition of MaPylRS to the orthogonal translation machinery toolkit in yeast potentially opens the door to hundreds of ncAAs that have not previously been genetically encodable using other aminoacyl-tRNA synthetase/tRNA pairs. Extending the scope of ncAA incorporation in yeast could powerfully advance chemical and biological research for applications ranging from basic biological discovery to enzyme engineering and therapeutic protein lead discovery.

PERSPECTIVE INSIGHT AND APPLICATION OF IN-SILICO TOOL AS VIRTUAL SCREENING METHOD FOR LEAD DESIGNING AND DEVELOPMENT

Humans are now in a bioinformatics and chemo informatics century, where we can foresee data across domains like as healthcare, the environmental, technology, and public health. The use of information sharing in silico methodologies has impacted sickness administration by predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) patterns of synthetic compounds and efficient and environmentally succeeding pharmaceuticals upfront. The purpose of lead discovery and design is to create the appearance of novel drug candidates that can attach to a specific illness cause. The lead investigative process starts with the recognition of the lead structure, which is followed by the synthesis of its analogs and their estimation in order to produce a candidate for lead improvement. The finding of the proper lead exact is the fundamental and primary worked in the traditional lead discovery progression, and the use of computer (in silico) approaches is widely used in lead innovation. A medicinal chemist's passion for building lead structure is piqued by biomolecules, which are often made up of DNA, RNA, and proteins (such as enzymes, receptors, transporters, and ion channels). The underlying principle of such nuts and bolts is noteworthy to be acquainted with their pharmacological implication to the disease under examination. The motive of this review piece of writing is to emphasize several of the in silico methods that are used in lead discovery and to express the applications of these computational methods.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB−) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and 10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB− compounds.

A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry

Effective small molecule therapies to combat the SARS-CoV-2 infection are still lacking as the COVID-19 pandemic continues globally. High throughput screening assays are needed for lead discovery and optimization of small molecule SARS-CoV-2 inhibitors. In this work, we have applied viral pseudotyping to establish a cell-based SARS-CoV-2 entry assay. Here, the pseudotyped particles (PP) contain SARS-CoV-2 spike in a membrane enveloping both the murine leukemia virus (MLV) gag-pol polyprotein and luciferase reporter RNA. Upon addition of PP to HEK293-ACE2 cells, the SARS-CoV-2 spike protein binds to the ACE2 receptor on the cell surface, resulting in priming by host proteases to trigger endocytosis of these particles, and membrane fusion between the particle envelope and the cell membrane. The internalized luciferase reporter gene is then expressed in cells, resulting in a luminescent readout as a surrogate for spike-mediated entry into cells. This SARS-CoV-2 PP entry assay can be executed in a biosafety level 2 containment lab for high throughput screening. From a collection of 5,158 approved drugs and drug candidates, our screening efforts identified 7 active compounds that inhibited the SARS-CoV-2-S PP entry. Of these seven, six compounds were active against live replicating SARS-CoV-2 virus in a cytopathic effect assay. Our results demonstrated the utility of this assay in the discovery and development of SARS-CoV-2 entry inhibitors as well as the mechanistic study of anti-SARS-CoV-2 compounds. Additionally, particles pseudotyped with spike proteins from SARS-CoV-2 B.1.1.7 and B.1.351 variants were prepared and used to evaluate the therapeutic effects of viral entry inhibitors.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug–drug and drug–metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

Autophagy Regulation by Lipid Factors with Implications for Parkinson's Disease

Parkinson’s disease is the second most common neurodegenerative disorder. It is characterized by the death of dopaminergic neurons in the substantia nigra and a series of debilitating motor symptoms. Macroautophagy (hereafter referred to as autophagy) is a cellular process by which cells degrade proteins, lipids, organelles or dysfunctional components. Autophagy is thought to play an important role in Parkinson’s disease, because it is the only cellular process known to remove large protein aggregates, such as those seen in Parkinson’s disease pathology. Historically, a large body of work has focused on reporting on protein effectors of autophagy, and regulation of autophagy but lipophilic molecules has garnered less attention. This dissertation focuses on the regulatory contributions of lipid molecules to autophagy in addition to describing the identification and lead discovery of autophagy-regulating lipid factors using an endogenous lipid chaperone protein, known as Fatty Acid Binding Protein 5, as a ‘bait’ molecule.

Application of Cryo-Electron Microscopy on Drug Discovery

The key aspect for development of novel drug molecules is to perform structural determination of target molecule associated with its ligand. One such tool that provides insights towards structure of molecule is Cryo-electron microscopy which covers biological targets that are intractable. Examination of proteins can be carried out in native state, as the samples are frozen at -175 degree Celsius i.e. cryogenic temperatures. In addition to this, there were no limits for molecular and functional structures of proteins that can be imagined in 3-dimensional form. This includes ligands which unravel mechanisms that are biologically relevant. This will enable to better understand the mechanisms that are used for development of new therapeutics. Application of Cryo-electron microscopy is not limited to protein complexes and is considered as non-specific. Intervention of Cryo-EM would allow to analyse the structures and also able to dissect the interaction with therapeutic molecules. The study determines the usage of cryo-EM for providing resolutions that are acceptable for lead discovery. It also provides support for lead optimization and also for discovery of vaccines and therapeutics.