Sialic acid identity modulates host tropism of sialoglycan-binding viridans group streptococci
Not provided.
Biochemistry & Molecular Biology↗
Engineering topics
Agarwal, Rupesh
Publications and source records attributed to Agarwal, Rupesh.
Osteocalcin binds to a GPRC6A Venus fly trap allosteric site to positively modulate GPRC6A signaling
GPRC6A, a member of the Family C G-protein coupled receptors, regulates energy metabolism and sex hormone production and is activated by diverse ligands, including cations, L-amino acids, the osteocalcin (Ocn) peptide and the steroid hormone testosterone. We sought a structural framework for the ability of multiple distinct classes of ligands to active GPRC6A. We created a structural model of GPRC6A using Alphafold2. Using this model we explored a putative orthosteric ligand binding site in the bilobed Venus fly trap (VFT) domain of GPRC6A and two positive allosteric modulator (PAM) sites, one in the VFT and the other in the 7 transmembrane (7TM) domain. We provide evidence that Ocn peptides act as a PAM for GPRC6A by binding to a site in the VFT that is distinct from the orthosteric site for calcium and L-amino acids. In agreement with this prediction, alternatively spliced GPRC6A isoforms 2 and 3, which lack regions of the VFT, and mutations in the computationally predicted Ocn binding site, K352E and H355P, prevent Ocn activation of GPRC6A. These observations explain how dissimilar ligands activate GPRC6A and set the stage to develop novel molecules to activate and inhibit this previously poorly understood receptor.
59 BASIC BIOLOGICAL SCIENCES↗
Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors
Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.
60 APPLIED LIFE SCIENCES↗
Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking
Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein–ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA–small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand–target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ~27%. Further, Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand–protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein–target docking illustrates a need for further methods refinement.
59 BASIC BIOLOGICAL SCIENCES↗
Characterization and molecular simulation of lignin in Cyrene pretreatment of switchgrass
Biomass-derived solvents have been proposed as a novel pathway in biorefining for the realization of biofuels and bioproducts derived from lignocellulosic biomass. Cyrene derived from cellulose has recently been shown to have a high potential as a green organic solvent for pretreating poplar biomass. However, due to its high dynamic viscosity nature, high Cyrene concentration could cause negative effects on the sugar release of the pretreated biomass as well as driving up the operational cost of the lignin recovery. In this study, we combine experimental and computational approaches to examine the impact of Cyrene pretreatment with reduced Cyrene concentration under mild conditions on switchgrass lignin. Our experimental studies indicated correlation between pretreatment condition and recovery and structure modification of lignin. Switchgrass lignin extracted by Cyrene pretreatment possessed high preservation of β-O-4 ether inter-unit linkage, which could provide versatility in the integration of downstream lignin valorization into the modern biorefinery industries. Molecular modeling examining the solvation of switchgrass lignin polymer and the disaggregation of low-molecular weight lignin aggregates under pretreatment conditions indicated that a preferential interaction exists between Cyrene and lignin, which likely drives lignin release, and that the disruption of inter-lignin contacts can be modulated as a non-monotonic function of Cyrene : water ratio. Further, while Cyrene–lignin interactions permit the solubilization of lignin, simulations with proxy reactive-species reveal that changes to the diffusion of these reactive proxies and their localization near linkage sites under Cyrene conditions may inhibit chemical processes. In conclusion, the results indicated that loss of pretreatment efficacy caused by low Cyrene concentration could be compensated by prolonged pretreatment time and high catalyst dosage.
09 BIOMASS FUELS↗
Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina
Abstract Structure‐based virtual high‐throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the ‘docking power’. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein‐ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.
Agarwal, Rupesh↗
Origins of glycan selectivity in streptococcal Siglec-like adhesins suggest mechanisms of receptor adaptation
Bacterial binding to host receptors underlies both commensalism and pathogenesis. Many streptococci adhere to protein-attached carbohydrates expressed on cell surfaces using Siglec-like binding regions (SLBRs). The precise glycan repertoire recognized may dictate whether the organism is a strict commensal versus a pathogen. However, it is currently not clear what drives receptor selectivity. Here, we use five representative SLBRs and identify regions of the receptor binding site that are hypervariable in sequence and structure. We show that these regions control the identity of the preferred carbohydrate ligand using chimeragenesis and single amino acid substitutions. We further evaluate how the identity of the preferred ligand affects the interaction with glycoprotein receptors in human saliva and plasma samples. As point mutations can change the preferred human receptor, these studies suggest how streptococci may adapt to changes in the environmental glycan repertoire.
59 BASIC BIOLOGICAL SCIENCES↗
Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor
Inhibition of the SARS-CoV-2 main protease (M pro ) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of M pro . Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC 50 values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for M pro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.
60 APPLIED LIFE SCIENCES↗
A Model for the Signal Initiation Complex Between Arrestin-3 and the Src Family Kinase Fgr
Arrestins regulate a wide range of signaling events, most notably when bound to active G protein-coupled receptors (GPCRs). Among the known effectors recruited by GPCR-bound arrestins are Src family kinases, which regulate cellular growth and proliferation. Here, we focus on arrestin-3 interactions with Fgr kinase, a member of the Src family. Previous reports demonstrated that Fgr exhibits high constitutive activity, but can be further activated by both arrestin-dependent and arrestin-independent pathways. We report that arrestin-3 modulates Fgr activity with a hallmark bell-shaped concentration-dependence, consistent with a role as a signaling scaffold. Here, we further demonstrate using NMR spectroscopy that a polyproline motif within arrestin-3 interacts directly with the SH3 domain of Fgr. To provide a framework for this interaction, we determined the crystal structure of the Fgr SH3 domain at 1.9 Å resolution and developed a model for the GPCR-arrestin-3-Fgr complex that is supported by mutagenesis. This model suggests that Fgr interacts with arrestin-3 at multiple sites and is consistent with the locations of disease-associated Fgr mutations. Collectively, these studies provide a structural framework for arrestin-dependent activation of Fgr.
59 BASIC BIOLOGICAL SCIENCES↗
SARS-CoV2 Docking Dataset
Description: Small-molecule conformations and docking scores for 1.4 billion molecules docked against 6 protein targets from SARS-CoV2: MPro 5R84, MPro 6WQF, NSP15 6WLC, PLPro 7JIR, Spike 6M0J, and a hand-optimized model of the RNA-dependent RNA polymerase. Docking was carried out using the Autodock-GPU program performing 20 independent structure minimizations per dock - saving 3 results per molecule. Scores reported include the Autodock free energy estimate as well as RF3 and VS-DUD-E v2 machine-learned rescoring models. Protein structure files and maps in the format input to Autodock-GPU are included. Literature Ref: Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19, J. Chem. Inf. Model. 2020, 60(12): 5832–5852.
36 MATERIALS SCIENCE↗