CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 µM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 µM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Combining structural and coevolution information to unveil allosteric sites.

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Discovery by Virtual Screening of an Inhibitor of CDK5-Mediated PPARγ Phosphorylation.

Thiazolidinedione PPARγ agonists such as rosiglitazone and pioglitazone are effective antidiabetic drugs, but side effects have limited their use. It has been posited that their positive antidiabetic effects are mainly mediated by the inhibition of the CDK5-mediated Ser273 phosphorylation of PPARγ, whereas the side effects are linked to classical PPARγ agonism. Thus compounds that inhibit PPARγ Ser273 phosphorylation but lack classical PPARγ agonism have been sought as safer antidiabetic therapies. Herein we report the discovery by virtual screening of 10, which is a potent PPARγ binder and in vitro inhibitor of the CDK5-mediated phosphorylation of PPARγ Ser273 and displays negligible PPARγ agonism in a reporter gene assay. The pharmacokinetic properties of 10 are compatible with oral dosing, enabling preclinical in vivo testing, and a 7 day treatment demonstrated an improvement in insulin sensitivity in the ob/ob diabetic mouse model.

Unraveling the Allosteric Cross-Talk between the Coactivator Peptide and the Ligand-Binding Site in the Glucocorticoid Receptor.

The glucocorticoid receptor (GR) is a nuclear receptor that controls critical biological processes by regulating the transcription of specific genes. There is a known allosteric cross-talk between the ligand and coregulator binding sites within the GR ligand-binding domain that is crucial for the control of the functional response. However, the molecular mechanisms underlying such an allosteric control remain elusive. Here, molecular dynamics (MD) simulations, bioinformatic analysis, and biophysical measurements are integrated to capture the structural and dynamic features of the allosteric cross-talk within the GR. We identified a network of evolutionarily conserved residues that enables the allosteric signal transduction, in agreement with experimental data. MD simulations clarify how such a network is dynamically interconnected and offer a mechanistic explanation of how different peptides affect the intensity of the allosteric signal. This study provides useful insights to elucidate the GR allosteric regulation, ultimately providing a foundation for designing novel drugs.

Membrane Permeability in Cyclic Peptides is Modulated by Core Conformations.

Cyclic peptides have the potential to bind to challenging targets, which are undruggable with small molecules, but their application is limited by low membrane permeability. Here, using a series of cyclic pentapeptides, we showed that established physicochemical criteria of permeable peptides are heavily violated. We revealed that a dominant core conformation, stabilized by amides' shielding pattern, could guide the design of novel compounds. As a result, counter-intuitive strategies, such as incorporation of polar residues, can be beneficial for permeability. We further find that core globularity is a promising descriptor, which can extend the capability of standard predictive models.

Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models.

We present a multistep protocol, combining Monte Carlo and molecular dynamics simulations, for the estimation of absolute binding free energies, one of the most significant challenges in computer-aided drug design. The protocol is based on an initial short enhanced Monte Carlo simulation, followed by clustering of the ligand positions, which serve to identify the most relevant states of the unbinding process. From these states, extensive molecular dynamics simulations are run to estimate an equilibrium probability distribution obtained with Markov State Models, which is subsequently used to estimate the binding free energy. We tested the procedure on two different protein systems, the Plasminogen kringle domain 1 and Urokinase, each with multiple ligands, for an aggregated molecular dynamics length of 760 µs. Our results indicate that the initial sampling of the unbinding events largely facilitates the convergence of the subsequent molecular dynamics exploration. Moreover, the protocol is capable to properly rank the set of ligands examined, albeit with a significant computational cost for the, more realistic, Urokinase complexes. Overall, this work demonstrates the usefulness of combining enhanced sampling methods with regular simulation techniques as a way to obtain more reliable binding affinity estimates.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Identification of Mineralocorticoid Receptor Modulators with Low Impact on Electrolyte Homeostasis but Maintained Organ Protection.

The mechanism-based risk for hyperkalemia has limited the use of mineralocorticoid receptor antagonists (MRAs) like eplerenone in cardio-renal diseases. Here, we describe the structure and property-driven lead generation and optimization, which resulted in identification of MR modulators ( S)-1 and ( S)-33. Both compounds were partial MRAs but still demonstrated equally efficacious organ protection as eplerenone after 4 weeks of treatment in uni-nephrectomized rats on high-salt diet and aldosterone infusion. Importantly, and in sharp contrast to eplerenone, this was achieved without substantial changes to the urine Na+/K+ ratio after acute treatment in rat, which predicts a reduced risk for hyperkalemia. This work led to selection of ( S)-1 (AZD9977) as the clinical candidate for treating MR-mediated cardio-renal diseases, including chronic kidney disease and heart failure. On the basis of our findings, we propose an empirical model for prediction of compounds with low risk of affecting the urinary Na+/K+ ratio in vivo.

Exploring Binding Mechanisms in Nuclear Hormone Receptors by Monte Carlo and X-ray-derived Motions.

In this study, we performed an extensive exploration of the ligand entry mechanism for members of the steroid nuclear hormone receptor family (androgen receptor, estrogen receptor α, glucocorticoid receptor, mineralocorticoid receptor, and progesterone receptor) and their endogenous ligands. The exploration revealed a shared entry path through the helix 3, 7, and 11 regions. Examination of the x-ray structures of the receptor-ligand complexes further showed two distinct folds of the helix 6-7 region, classified as "open" and "closed", which could potentially affect ligand binding. To improve sampling of the helix 6-7 loop, we incorporated motion modes based on principal component analysis of existing crystal structures of the receptors and applied them to the protein-ligand sampling. A detailed comparison with the anisotropic network model (an elastic network model) highlights the importance of flexibility in the entrance region. While the binding (interaction) energy of individual simulations can be used to score different ligands, extensive sampling further allows us to predict absolute binding free energies and analyze reaction kinetics using Markov state models and Perron-cluster cluster analysis, respectively. The predicted relative binding free energies for three ligands binding to the progesterone receptor are in very good agreement with experimental results and the Perron-cluster cluster analysis highlighted the importance of a peripheral binding site. Our analysis revealed that the flexibility of the helix 3, 7, and 11 regions represents the most important factor for ligand binding. Furthermore, the hydrophobicity of the ligand influences the transition between the peripheral and the active binding site.

Structure-Based Drug Design of Mineralocorticoid Receptor Antagonists to Explore Oxosteroid Receptor Selectivity.

The mineralocorticoid receptor (MR) is a nuclear hormone receptor involved in the regulation of body fluid and electrolyte homeostasis. In this study we explore selectivity triggers for a series of nonsteroidal MR antagonists to improve selectivity over other members of the oxosteroid receptor family. A biaryl sulfonamide compound was identified in a high-throughput screening (HTS) campaign. The compound bound to MR with pKi =6.6, but displayed poor selectivity over the glucocorticoid receptor (GR) and the progesterone receptor (PR). Following X-ray crystallography of MR in complex with the HTS hit, a compound library was designed that explored an induced-fit hypothesis that required movement of the Met852 side chain. An improvement in MR selectivity of 11- to 79-fold over PR and 23- to 234-fold over GR was obtained. Given the U-shaped binding conformation, macrocyclizations were explored, yielding a macrocycle that bound to MR with pKi =7.3. Two protein-ligand X-ray structures were determined, confirming the hypothesized binding mode for the designed compounds.

3D-Lab: a collaborative web-based platform for molecular modeling.

AIM: The use of 3D information has shown impact in numerous applications in drug design. However, it is often under-utilized and traditionally limited to specialists. We want to change that, and present an approach making 3D information and molecular modeling accessible and easy-to-use 'for the people'. METHODOLOGY/RESULTS: A user-friendly and collaborative web-based platform (3D-Lab) for 3D modeling, including a blazingly fast virtual screening capability, was developed. 3D-Lab provides an interface to automatic molecular modeling, like conformer generation, ligand alignments, molecular dockings and simple quantum chemistry protocols. 3D-Lab is designed to be modular, and to facilitate sharing of 3D-information to promote interactions between drug designers. Recent enhancements to our open-source virtual reality tool Molecular Rift are described. CONCLUSION: The integrated drug-design platform allows drug designers to instantaneously access 3D information and readily apply advanced and automated 3D molecular modeling tasks, with the aim to improve decision-making in drug design projects.

Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD < 1.0 Å) can be obtained by the PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.

Development of an ELISA for High-Throughput Screening of Inhibitors of Cdk5-Mediated PPARγ Phosphorylation.

The peroxisome proliferator-activated receptor gamma (PPARγ) is the target for the thiazolidinedione class of potent insulin-sensitizing drugs, which includes rosiglitazone and pioglitazone. However, their usage has been restricted due to severe side effects. Recent data have shown that specifically inhibiting the cyclin-dependent kinase 5 (Cdk5)-mediated phosphorylation of PPARγ at Ser273 may lead to novel insulin sensitizers with fewer side effects. Here we describe a novel enzyme-linked immunosorbent assay (ELISA) in the 384-well format, which enables screening for PPARγ ligands that inhibit phosphorylation at Ser273 by Cdk5. The assay is robust with a Z-factor > 0.6, demonstrating its suitability for high-throughput screening. We demonstrate the suitability of this assay for profiling of published PPARγ ligands and identification of novel compounds that prevent the Cdk5-mediated phosphorylation of PPARγ at Ser273 in a 622 compound pilot study. Our assay enables the discovery and development of novel therapeutic agents for use in type-2 diabetes. Furthermore, our results in combination with structural analysis of reported PPARγ ligand binding domain X-ray structures give a molecular rationale for the Cdk5-mediated phosphorylation of PPARγ at Ser273.

Discovery of (3-(4-(2-Oxa-6-azaspiro[3.3]heptan-6-ylmethyl)phenoxy)azetidin-1-yl)(5-(4-methoxyphenyl)-1,3,4-oxadiazol-2-yl)methanone (AZD1979), a Melanin Concentrating Hormone Receptor 1 (MCHr1) Antagonist with Favorable Physicochemical Properties.

A novel series of melanin concentrating hormone receptor 1 (MCHr1) antagonists were the starting point for a drug discovery program that culminated in the discovery of 103 (AZD1979). The lead optimization program was conducted with a focus on reducing lipophilicity and understanding the physicochemical properties governing CNS exposure and undesired off-target pharmacology such as hERG interactions. An integrated approach was taken where the key assay was ex vivo receptor occupancy in mice. The candidate compound 103 displayed appropriate lipophilicity for a CNS indication and showed excellent permeability with no efflux. Preclinical GLP toxicology and safety pharmacology studies were without major findings and 103 was taken into clinical trials.

Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods.

In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R(2) = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R(2) = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

Ligand Binding Mechanism in Steroid Receptors: From Conserved Plasticity to Differential Evolutionary Constraints.

Steroid receptor drugs have been available for more than half a century, but details of the ligand binding mechanism have remained elusive. We solved X-ray structures of the glucocorticoid and mineralocorticoid receptors to identify a conserved plasticity at the helix 6-7 region that extends the ligand binding pocket toward the receptor surface. Since none of the endogenous ligands exploit this region, we hypothesized that it constitutes an integral part of the binding event. Extensive all-atom unbiased ligand exit and entrance simulations corroborate a ligand binding pathway that gives the observed structural plasticity a key functional role. Kinetic measurements reveal that the receptor residence time correlates with structural rearrangements observed in both structures and simulations. Ultimately, our findings reveal why nature has conserved the capacity to open up this region, and highlight how differences in the details of the ligand entry process result in differential evolutionary constraints across the steroid receptors.

Oxadiazoles in medicinal chemistry.

Oxadiazoles are five-membered heteroaromatic rings containing two carbons, two nitrogens, and one oxygen atom, and they exist in different regioisomeric forms. Oxadiazoles are frequently occurring motifs in druglike molecules, and they are often used with the intention of being bioisosteric replacements for ester and amide functionalities. The current study presents a systematic comparison of 1,2,4- and 1,3,4-oxadiazole matched pairs in the AstraZeneca compound collection. In virtually all cases, the 1,3,4-oxadiazole isomer shows an order of magnitude lower lipophilicity (log D), as compared to its isomeric partner. Significant differences are also observed with respect to metabolic stability, hERG inhibition, and aqueous solubility, favoring the 1,3,4-oxadiazole isomers. The difference in profile between the 1,2,4 and 1,3,4 regioisomers can be rationalized by their intrinsically different charge distributions (e.g., dipole moments). To facilitate the use of these heteroaromatic rings, novel synthetic routes for ready access of a broad spectrum of 1,3,4-oxadiazoles, under mild conditions, are described.

Do structurally similar ligands bind in a similar fashion?

The scope of the current work is to investigate whether structurally similar ligands bind in a similar fashion by exhaustively analyzing experimental data from the protein database (PDB). The complete PDB was searched for pairs of structurally similar ligands binding to the same biological target. The binding sites of the pairs of proteins complexing structurally similar ligands were found to differ in 83% of the cases. The most recurrent structural change among the pairs involves different water molecule architecture. Side-chain movements are observed in half of the pairs, whereas backbone movements rarely occurred. However, two structurally similar ligands generally confirm a high degree of structural conservation. That is, a majority of the ligand pairs occupy the same region in the binding sites, providing support for the use of shape matching in the drug design process. We allow ourselves to draw general conclusions because our data set consists of ligands with drug-like physicochemical properties complexed to a broad spectrum of different protein classes.

Three-dimensional structure of the ligand-binding core of GluR2 in complex with the agonist (S)-ATPA: implications for receptor subunit selectivity.

Two X-ray structures of the GluR2 ligand-binding core in complex with (S)-2-amino-3-(5-tert-butyl-3-hydroxy-4-isoxazolyl)propionic acid ((S)-ATPA) have been determined with and without Zn(2+) ions. (S)-ATPA induces a domain closure of ca. 21 degrees compared to the apo form. The tert-butyl moiety of (S)-ATPA is buried in a partially hydrophobic pocket and forces the ligand into the glutamate-like binding mode. The structures provide new insight into the molecular basis of agonist selectivity between AMPA and kainate receptors.