Rational design of a (S)-selective-transaminase for asymmetric synthesis of (1S)-1-(1,1′-biphenyl-2-yl)ethanamine

Amine transaminases offer an environmentally sustainable synthesis route for the production of pure chiral amines. However, their catalytic efficiency toward bulky ketone substrates is greatly limited by steric hindrance and therefore presents a great challenge for industrial synthetic applications. We hereby report an example of rational transaminase enzyme design to help alleviate these challenges. Starting from the Vibrio fluvialis amine transaminase that has no detectable catalytic activity toward the bulky aromatic ketone 2-acetylbiphenyl, we employed a rational design strategy combining in silico and in vitro studies to engineer the transaminase enzyme with a minimal number of mutations, achieving an high catalytic activity and high enantioselectivity. We found that, by introducing two mutations W57G/R415A, detectable enzyme activity was achieved. The rationally designed variant, W57F/R88H/V153S/K163F/I259M/R415A/V422A, showed an improvement in reaction rate by more than 1716-fold toward the bulky ketone under study, producing the corresponding enantiomeric pure (S)-amine (enantiomeric excess (ee) value of >99%)

Adaptive simulations, towards interactive protein-ligand modeling

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.We thank Drs Anders Hogner and Christoph Grebner, from AstraZeneca, and Jorge Estrada, from BSC, for fruitful discussions and feedback on the manuscript. We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by the CTQ2016-79138-R grant from the Spanish Government. D.L. acknowledges the support of SEV-2011-00067, awarded by the Spanish Government.Peer ReviewedPostprint (published version

Myricetin, the main flavonoid in Syzygium cumini leaf, is a novel inhibitor of platelet thiol isomerases PDI and ERp5

Background: Flavonoids have been characterized as a prominent class of compounds to treat thrombotic diseases through the inhibition of thiol isomerases. Syzygium cumini is a flavonoid-rich medicinal plant that contains myricetin and gallic acid. Little is known about the potential anti-platelet properties of S. cumini and its constituent flavonoids. Objective To evaluate the anti-platelet effects and mechanism of action of a polyphenol-rich extract (PESc) from S. cumini leaf and its most prevalent polyphenols, myricetin and gallic acid. Methods PESc, myricetin and gallic acid were incubated with platelet-rich plasma and washed platelets to assess platelet aggregation and activation. In vitro platelet adhesion and thrombus formation as well as in vivo bleeding time were performed. Finally, myricetin was incubated with recombinant thiol isomerases to assess its potential to bind and inhibit these, whilst molecular docking studies predicted possible binding sites. Results: PESc decreased platelet activation and aggregation induced by different agonists. Myricetin exerted potent anti-platelet effects, whereas gallic acid did not. Myricetin reduced the ability of platelets to spread on collagen, form thrombi in vitro without affecting haemostasis in vivo. Fluorescence quenching studies suggested myricetin binds to different thiol isomerases with similar affinity, despite inhibiting only protein disulphide isomerase (PDI) and ERp5 reductase activities (IC50~3.5 μM). Finally, molecular docking studies suggested myricetin formed non-covalent bonds with PDI and ERp5. Conclusions: PESc and its most abundant flavonoid myricetin strongly inhibit platelet function. Additionally, myricetin is a novel inhibitor of ERp5 and PDI, unveiling a new therapeutic perspective for the treatment of thrombotic disorders

Structural and Functional Analysis of a β2-Adrenergic Receptor Complex with GRK5.

The phosphorylation of agonist-occupied G-protein-coupled receptors (GPCRs) by GPCR kinases (GRKs) functions to turn off G-protein signaling and turn on arrestin-mediated signaling. While a structural understanding of GPCR/G-protein and GPCR/arrestin complexes has emerged in recent years, the molecular architecture of a GPCR/GRK complex remains poorly defined. We used a comprehensive integrated approach of cross-linking, hydrogen-deuterium exchange mass spectrometry (MS), electron microscopy, mutagenesis, molecular dynamics simulations, and computational docking to analyze GRK5 interaction with the β2-adrenergic receptor (β2AR). These studies revealed a dynamic mechanism of complex formation that involves large conformational changes in the GRK5 RH/catalytic domain interface upon receptor binding. These changes facilitate contacts between intracellular loops 2 and 3 and the C terminus of the β2AR with the GRK5 RH bundle subdomain, membrane-binding surface, and kinase catalytic cleft, respectively. These studies significantly contribute to our understanding of the mechanism by which GRKs regulate the function of activated GPCRs. PAPERCLIP

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification

Novel Naphthalene-Based Inhibitors of Trypanosoma brucei RNA Editing Ligase 1

African sleeping sickness is a devastating disease that plagues sub-Saharan Africa. Neglected tropical diseases like African sleeping sickness cause significant death and suffering in the world's poorest countries. Current treatments for African sleeping sickness either have high costs, terrible side effects, or limited effectiveness. Consequently, new medicines are urgently needed. RNA editing ligase 1 is an important protein critical for the survival of Trypanosoma brucei, the unicellular parasite that causes African sleeping sickness. In this paper, we describe our recent efforts to use advanced computer techniques to identify chemicals predicted to prevent RNA editing ligase 1 from functioning properly. We subsequently tested our predicted chemicals and confirmed that a number of them inhibited the protein's function. Additionally, one of the chemicals was effective at stopping the growth of the parasite in culture. Although substantial work remains to be done in order to optimize these chemicals so they are effective and safe to use in human patients, the identification of these parasite-killing compounds is nevertheless a valuable step towards finding a better cure for this devastating disease

Challenges of docking in large, flexible and promiscuous binding sites

After decades of work, the correct determination of the binding mode of a small molecule into a target protein is still a challenging problem, whose difficulty depends on: (i) the sizes of the binding site and the ligand; (ii) the flexibility of both interacting partners, and (iii) the differential solvation of bound and unbound partners. We have evaluated the performance of standard rigid(receptor)/flexible(ligand) docking approaches with respect to last-generation fully flexible docking methods to obtain reasonable poses in a very challenging case: soluble Epoxide Hydrolase (sEH), a flexible protein showing different binding sites. We found that full description of the flexibility of both protein and ligand and accurate description of solvation leads to significant improvement in the ability of docking to reproduce well known binding modes, and at the same time capture the intrinsic binding promiscuity of the protein.Peer ReviewedPostprint (author's final draft