Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic.

Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein-protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein-protein and protein-nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

Design and evaluation of Lumefantrine - Oleic acid self nanoemulsifying ionic complex for enhanced dissolution.

BACKGROUND: Lumefantrine, an antimalarial molecule has very low and variable bioavailability owing to its extremely poor solubility in water. It is recommended to be taken with milk to enhance its solubility and bioavailability. The aim of present study was to develop a Self Nanoemulsifying Delivery system (SNEDs) of lumefantrine (LF) to achieve rapid and complete dissolution independent of food-fat and surfactant in dissolution media. METHODS: Solubility of LF in oil, co-solvent/co-surfactant and surfactant solution and emulsification efficiency of surfactant were analyzed to optimize the LF loaded self nanoemulsifying preconcentrate. Effect of LF-oleic acid complexation on emulsification, droplet size, zeta potential and dissolution were investigated. Effect of milk concentration and fat content on saturation solubility and dissolution of LF was investigated. Dissolution of marketed formulation and LF-SNEDs was carried out in pH 1.2 and pH 6.8 phosphate buffer. RESULTS: LF exhibited very high solubility in oleic acid owing to complexation between tertiary amine of LF and carboxyl group of oleic acid (OA). Cremophore EL and medium chain monoglyceride were selected surfactant and co-surfactant, respectively. Significantly smaller droplet size (37 nm), shift in zeta potential from negative to positive value, very high drug loading in lipid based system (> 10%), no precipitation after dissolution are the major distinguish characteristics contributed by LF-OA complex in the SNED system. Saturation solubility and dissolution study in milk containing media pointed the significant increment in solubility of LF in the presence of milk-food fat. LF-SNEDs showed > 90% LF release within 30 min in pH 1.2 while marketed tablet showed almost 0% drug release. CONCLUSION: Self nanoemulsification promoting ionic complexation between basic drug and oleic acid hold great promise in enhancing solubility of hydrophobic drugs.

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network.

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key post-translational modification involved in physiology and disease. The ability to robustly and rapidly predict protease substrate specificity would also enable targeted proteolytic cleavage - editing - of a target protein by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Age-Dependent Changes in the Plasma and Brain Pharmacokinetics of Amyloid-ß Peptides and Insulin.

BACKGROUND: Age is the most common risk factor for Alzheimer's disease (AD), a neurodegenerative disorder characterized by the hallmarks of toxic amyloid-ß (Aß) plaques and hyperphosphorylated tau tangles. Moreover, sub-physiological brain insulin levels have emerged as a pathological manifestation of AD. OBJECTIVE: Identify age-related changes in the plasma disposition and blood-brain barrier (BBB) trafficking of Aß peptides and insulin in mice. METHODS: Upon systemic injection of 125I-Aß40, 125I-Aß42, or 125I-insulin, the plasma pharmacokinetics and brain influx were assessed in wild-type (WT) or AD transgenic (APP/PS1) mice at various ages. Additionally, publicly available single-cell RNA-Seq data [GSE129788] was employed to investigate pathways regulating BBB transport in WT mice at different ages. RESULTS: The brain influx of 125I-Aß40, estimated as the permeability-surface area product, decreased with age, accompanied by an increase in plasma AUC. In contrast, the brain influx of 125I-Aß42 increased with age, accompanied by a decrease in plasma AUC. The age-dependent changes observed in WT mice were accelerated in APP/PS1 mice. As seen with 125I-Aß40, the brain influx of 125I-insulin decreased with age in WT mice, accompanied by an increase in plasma AUC. This finding was further supported by dynamic single-photon emission computed tomography (SPECT/CT) imaging studies. RAGE and PI3K/AKT signaling pathways at the BBB, which are implicated in Aß and insulin transcytosis, respectively, were upregulated with age in WT mice, indicating BBB insulin resistance. CONCLUSION: Aging differentially affects the plasma pharmacokinetics and brain influx of Aß isoforms and insulin in a manner that could potentially augment AD risk.

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic.

Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

Constrained Combinatorial Libraries of Gp2 Proteins Enhance Discovery of PD-L1 Binders.

Engineered protein ligands are used for molecular therapy, diagnostics, and industrial biotechnology. The Gp2 domain is a 45-amino acid scaffold that has been evolved for specific, high-affinity binding to multiple targets by diversification of two solvent-exposed loops. Inspired by sitewise enrichment of select amino acids, including cysteine pairs, in earlier Gp2 discovery campaigns, we hypothesized that the breadth and efficiency of de novo Gp2 discovery will be aided by sitewise amino acid constraint within combinatorial library design. We systematically constructed eight libraries and comparatively evaluated their efficacy for binder discovery via yeast display against a panel of targets. Conservation of a cysteine pair at the termini of the first diversified paratope loop increased binder discovery 16-fold ( p < 0.001). Yet two other libraries with conserved cysteine pairs, within the second loop or an interloop pair, did not aid discovery thereby indicating site-specific impact. Via a yeast display protease resistance assay, Gp2 variants from the loop one cysteine pair library were 3.3 ± 2.1-fold ( p = 0.005) more stable than nonconstrained variants. Sitewise constraint of noncysteine residues-guided by previously evolved binders, natural Gp2 homology, computed stability, and structural analysis-did not aid discovery. A panel of binders to programmed death ligand 1 (PD-L1), a key target in cancer immunotherapy, were discovered from the loop 1 cysteine constraint library. Affinity maturation via loop walking resulted in strong, specific cellular PD-L1 affinity ( Kd = 6-9 nM).

Insulin differentially affects the distribution kinetics of amyloid beta 40 and 42 in plasma and brain.

Impaired brain clearance of amyloid-beta peptides (Aß) 40 and 42 across the blood-brain barrier (BBB) is believed to be one of the pathways responsible for Alzheimer's disease (AD) pathogenesis. Hyperinsulinemia prevalent in type II diabetes was shown to damage cerebral vasculature and increase Aß accumulation in AD brain. However, there is no clarity on how aberrations in peripheral insulin levels affect Aß accumulation in the brain. This study describes, for the first time, an intricate relation between plasma insulin and Aß transport at the BBB. Upon peripheral insulin administration in wild-type mice: the plasma clearance of Aß40 increased, but Aß42 clearance reduced; the plasma-to-brain influx of Aß40 increased, and that of Aß42 reduced; and the clearance of intracerebrally injected Aß40 decreased, whereas Aß42 clearance increased. In hCMEC/D3 monolayers (in vitro BBB model) exposed to insulin, the luminal uptake and luminal-to-abluminal permeability of Aß40 increased and that of Aß42 reduced; the abluminal-to-luminal permeability of Aß40 decreased, whereas Aß42 permeability increased. Moreover, Aß cellular trafficking machinery was altered. In summary, Aß40 and Aß42 demonstrated distinct distribution kinetics in plasma and brain compartments, and insulin differentially modulated their distribution. Cerebrovascular disease and metabolic disorders may disrupt this intricate homeostasis and aggravate AD pathology.

The Relationship Between Dopamine Neurotransmitter Dynamics and the Blood-Oxygen-Level-Dependent (BOLD) Signal: A Review of Pharmacological Functional Magnetic Resonance Imaging.

Functional magnetic resonance imaging (fMRI) is widely used in investigations of normal cognition and brain disease and in various clinical applications. Pharmacological fMRI (pharma-fMRI) is a relatively new application, which is being used to elucidate the effects and mechanisms of pharmacological modulation of brain activity. Characterizing the effects of neuropharmacological agents on regional brain activity using fMRI is challenging because drugs modulate neuronal function in a wide variety of ways, including through receptor agonist, antagonist, and neurotransmitter reuptake blocker events. Here we review current knowledge on neurotransmitter-mediated blood-oxygen-level dependent (BOLD) fMRI mechanisms as well as recently updated methodologies aimed at more fully describing the effects of neuropharmacologic agents on the BOLD signal. We limit our discussion to dopaminergic signaling as a useful lens through which to analyze and interpret neurochemical-mediated changes in the hemodynamic BOLD response. We also discuss the need for future studies that use multi-modal approaches to expand the understanding and application of pharma-fMRI.