Identifying Potential Ligand Binding Sites on Glycogen Synthase Kinase 3 Using Atomistic Cosolvent Simulations.

Glycogen synthase kinase 3 ß (GSK3ß) is a serine/threonine kinase that phosphorylates several protein substrates in crucial cell signaling pathways. Owing to its therapeutic importance, there is a need to develop GSK3ß inhibitors with high specificity and potency. One approach is to find small molecules that can allosterically bind to the GSK3ß protein surface. We have employed fully atomistic mixed-solvent molecular dynamics (MixMD) simulations to identify three plausible allosteric sites on GSK3ß that can facilitate the search for allosteric inhibitors. Our MixMD simulations narrow down the allosteric sites to precise regions on the GSK3ß surface, thereby improving upon the previous predictions of the locations of these sites.

Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors.

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the µ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools.

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

The structure and characterization of human cytochrome P450 8B1 supports future drug design for nonalcoholic fatty liver disease and diabetes.

Human cytochrome P450 8B1 (CYP8B1) is involved in conversion of cholesterol to bile acids. It hydroxylates the steroid ring at C12 to ultimately produce the bile acid cholic acid. Studies implicated this enzyme as a good drug target for nonalcoholic fatty liver disease and type 2 diabetes, but there are no selective inhibitors known for this enzyme and no structures to guide inhibitor development. Herein, the human CYP8B1 protein was generated and used to identify and characterize interactions with a series of azole inhibitors, which tend to be poorly selective P450 inhibitors. Structurally related miconazole, econazole, and tioconazole bound with submicromolar dissociation constants and were effective inhibitors of the native reaction. CYP8B was cocrystallized with S-tioconazole to yield the first X-ray structure. This inhibitor bound in the active site with its azole nitrogen coordinating the heme iron, consistent with inhibitor binding and inhibition assay data. Additionally, the CYP8B1 active site was compared with similar P450 enzymes to identify features that may facilitate the design of more selective inhibitors. Selective inhibitors should promote a better understanding of the role of CYP8B1 inhibition in normal physiology and disease states and provide a possible treatment for nonalcoholic fatty liver disease and type 2 diabetes.

Cosolvent Simulations with Fragment-Bound Proteins Identify Hot Spots to Direct Lead Growth.

In drug design, chemical groups are sequentially added to improve a weak-binding fragment into a tight-binding lead molecule. Often, the direction to make these additions is unclear, and there are numerous chemical modifications to choose. Lead development can be guided by crystal structures of the fragment-bound protein, but this alone is unable to capture structural changes like closing or opening of the binding site and any side-chain movements. Accounting for adaptation of the site requires a dynamic approach. Here, we use molecular dynamics calculations of small organic solvents with protein-fragment pairs to reveal the nearest "hot spots". These close hot spots show the direction to make appropriate additions and suggest types of chemical modifications that could improve binding affinity. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that is well established for finding binding "hot spots" in active sites and allosteric sites of proteins. We simulated 20 fragment-bound and apo forms of key pharmaceutical targets to map out hot spots for potential lead space. Furthermore, we analyzed whether the presence of a fragment facilitates the probes' binding in the lead space, a type of binding cooperativity. To the best of our knowledge, this is the first use of cosolvent MD conducted with bound inhibitors in the simulation. Our work provides a general framework to extract molecular features of binding sites to choose chemical groups for growing lead molecules. Of the 20 systems, 17 systems were well mapped by MixMD. For the three not-mapped systems, two had lead growth out into solution away from the protein, and the third had very small modifications which indicated no nearby hot spots. Therefore, our lack of mapping in three systems was appropriate given the experimental data (true-negative cases). The simulations are run for very short time scales, making this method tractable for use in the pharmaceutical industry.

In silico analysis of SARS-CoV-2 proteins as targets for clinically available drugs.

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires treatments with rapid clinical translatability. Here we develop a multi-target and multi-ligand virtual screening method to identify FDA-approved drugs with potential activity against SARS-CoV-2 at traditional and understudied viral targets. 1,268 FDA-approved small molecule drugs were docked to 47 putative binding sites across 23 SARS-CoV-2 proteins. We compared drugs between binding sites and filtered out compounds that had no reported activity in an in vitro screen against SARS-CoV-2 infection of human liver (Huh-7) cells. This identified 17 "high-confidence", and 97 "medium-confidence" drug-site pairs. The "high-confidence" group was subjected to molecular dynamics simulations to yield six compounds with stable binding poses at their optimal target proteins. Three drugs-amprenavir, levomefolic acid, and calcipotriol-were predicted to bind to 3 different sites on the spike protein, domperidone to the Mac1 domain of the non-structural protein (Nsp) 3, avanafil to Nsp15, and nintedanib to the nucleocapsid protein involved in packaging the viral RNA. Our "two-way" virtual docking screen also provides a framework to prioritize drugs for testing in future emergencies requiring rapidly available clinical drugs and/or treating diseases where a moderate number of targets are known.

Randomized Trial of a Palliative Care Intervention to Improve End-of-Life Care Discussions in Patients With Metastatic Breast Cancer.

BACKGROUND: Studies show that early, integrated palliative care (PC) improves quality of life (QoL) and end-of-life (EoL) care for patients with poor-prognosis cancers. However, the optimal strategy for delivering PC for those with advanced cancers who have longer disease trajectories, such as metastatic breast cancer (MBC), remains unknown. We tested the effect of a PC intervention on the documentation of EoL care discussions, patient-reported outcomes, and hospice utilization in this population. PATIENTS AND METHODS: Patients with MBC and clinical indicators of poor prognosis (n=120) were randomly assigned to receive an outpatient PC intervention (n=61) or usual care (n=59) between May 2, 2016, and December 26, 2018, at an academic cancer center. The intervention entailed 5 structured PC visits focusing on symptom management, coping, prognostic awareness, decision-making, and EoL planning. The primary outcome was documentation of EoL care discussions in the electronic health record (EHR). Secondary outcomes included patient-report of discussions with clinicians about EoL care, QoL, and mood symptoms at 6, 12, 18, and 24 weeks after baseline and hospice utilization. RESULTS: The rate of EoL care discussions documented in the EHR was higher among intervention patients versus those receiving usual care (67.2% vs 40.7%; P=.006), including a higher completion rate of a Medical Orders for Life-Sustaining Treatment form (39.3% vs 13.6%; P=.002). Intervention patients were also more likely to report discussing their EoL care wishes with their doctor (odds ratio [OR], 3.10; 95% CI, 1.21-7.94; P=.019) and to receive hospice services (OR, 4.03; 95% CI, 1.10-14.73; P=.035) compared with usual care patients. Study groups did not differ in patient-reported QoL or mood symptoms. CONCLUSIONS: This PC intervention significantly improved rates of discussion and documentation regarding EoL care and delivery of hospice services among patients with MBC, demonstrating that PC can be tailored to address the supportive care needs of patients with longer disease trajectories. ClinicalTrials.gov identifier: NCT02730858.

Computational Identification of Possible Allosteric Sites and Modulators of the SARS-CoV-2 Main Protease.

In this study, we target the main protease (Mpro) of the SARS-CoV-2 virus as it is a crucial enzyme for viral replication. Herein, we report three plausible allosteric sites on Mpro that can expand structure-based drug discovery efforts for new Mpro inhibitors. To find these sites, we used mixed-solvent molecular dynamics (MixMD) simulations, an efficient computational protocol that finds binding hotspots through mapping the surface of unbound proteins with 5% cosolvents in water. We have used normal mode analysis to support our claim of allosteric control for these sites. Further, we have performed virtual screening against the sites with 361 hits from Mpro screenings available through the National Center for Advancing Translational Sciences (NCATS). We have identified the NCATS inhibitors that bind to the remote sites better than the active site of Mpro, and we propose these molecules may be allosteric regulators of the system. After identifying our sites, new X-ray crystal structures were released that show fragment molecules in the sites we found, supporting the notion that these sites are accurate and druggable.

Mixed-solvent molecular dynamics simulation-based discovery of a putative allosteric site on regulator of G protein signaling 4.

Regulator of G protein signaling 4 (RGS4) is an intracellular protein that binds to the Gα subunit ofheterotrimeric G proteins and aids in terminating G protein coupled receptor signaling. RGS4 has been implicated in pain, schizophrenia, and the control of cardiac contractility. Inhibitors of RGS4 have been developed but bind covalently to cysteine residues on the protein. Therefore, we sought to identify alternative druggable sites on RGS4 using mixed-solvent molecular dynamics simulations, which employ low concentrations of organic probes to identify druggable hotspots on the protein. Pseudo-ligands were placed in consensus hotspots, and perturbation with normal mode analysis led to the identification and characterization of a putative allosteric site, which would be invaluable for structure-based drug design of non-covalent, small molecule inhibitors. Future studies on the mechanism of this allostery will aid in the development of novel therapeutics targeting RGS4.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Chemical validation of a druggable site on Hsp27/HSPB1 using in silico solvent mapping and biophysical methods.

Destabilizing mutations in small heat shock proteins (sHsps) are linked to multiple diseases; however, sHsps are conformationally dynamic, lack enzymatic function and have no endogenous chemical ligands. These factors render sHsps as classically "undruggable" targets and make it particularly challenging to identify molecules that might bind and stabilize them. To explore potential solutions, we designed a multi-pronged screening workflow involving a combination of computational and biophysical ligand-discovery platforms. Using the core domain of the sHsp family member Hsp27/HSPB1 (Hsp27c) as a target, we applied mixed solvent molecular dynamics (MixMD) to predict three possible binding sites, which we confirmed using NMR-based solvent mapping. Using this knowledge, we then used NMR spectroscopy to carry out a fragment-based drug discovery (FBDD) screen, ultimately identifying two fragments that bind to one of these sites. A medicinal chemistry effort improved the affinity of one fragment by ~50-fold (16 µM), while maintaining good ligand efficiency (~0.32 kcal/mol/non-hydrogen atom). Finally, we found that binding to this site partially restored the stability of disease-associated Hsp27 variants, in a redox-dependent manner. Together, these experiments suggest a new and unexpected binding site on Hsp27, which might be exploited to build chemical probes.

Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics.

Protein dynamics play an important role in small molecule binding and can pose a significant challenge in the identification of potential binding sites. Cryptic binding sites have been defined as sites which require significant rearrangement of the protein structure to become physically accessible to a ligand. Mixed-solvent MD (MixMD) is a computational protocol which maps the surface of the protein using molecular dynamics (MD) of the unbound protein solvated in a 5% box of probe molecules with explicit water. This method has successfully identified known active and allosteric sites which did not require reorganization. In this study, we apply the MixMD protocol to identify known cryptic sites of 12 proteins characterized by a wide range of conformational changes. Of these 12 proteins, three require reorganization of side chains, five require loop movements, and four require movement of more significant structures such as whole helices. In five cases, we find that standard MixMD simulations are able to map the cryptic binding sites with at least one probe type. In two cases (guanylate kinase and TIE-2), accelerated MD, which increases sampling of torsional angles, was necessary to achieve mapping of portions of the cryptic binding site missed by standard MixMD. For more complex systems where movement of a helix or domain is necessary, MixMD was unable to map the binding site even with accelerated dynamics, possibly due to the limited timescale (100 ns for individual simulations). In general, similar conformational dynamics are observed in water-only simulations and those with probe molecules. This could imply that the probes are not driving opening events but rather take advantage of mapping sites that spontaneously open as part of their inherent conformational behavior. Finally, we show that docking to an ensemble of conformations from the standard MixMD simulations performs better than docking the apo crystal structure in nine cases and even better than half of the bound crystal structures. Poorer performance was seen in docking to ensembles of conformations from the accelerated MixMD simulations.

Predicting binding sites from unbound versus bound protein structures.

We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITEcsc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew's correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing.

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (Ka, Kd, Ki, IC50) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations.

In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD) (Ghanakota, P.; Carlson, H. A. Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J. Phys. Chem. B 2016, 120, 8685-8695), we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets: Abl Kinase, Androgen Receptor, Pdk1 Kinase, Farnesyl Pyrophosphate Synthase, Chk1 Kinase, Glucokinase, and Protein Tyrosine Phosphatase 1B.

Inherent versus induced protein flexibility: Comparisons within and between apo and holo structures.

Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 Å. Analysis of χ1 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the χ1 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, χ1 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.

MixMD Probeview: Robust Binding Site Prediction from Cosolvent Simulations.

Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique for identifying binding hotspots and specific favorable interactions on a protein's surface. MixMD studies have the ability to identify these biologically relevant sites by examining the occupancy of the cosolvent over the course of the simulation. However, previous MixMD analysis required a great deal of manual inspection to identify relevant sites. To address this limitation, we have developed MixMD Probeview as a plugin for the freely available, open-source version of the molecular visualization program PyMOL. MixMD Probeview incorporates two analysis procedures: (1) identifying and ranking whole binding sites and (2) identifying and ranking local maxima for each probe type. These functionalities were validated using four common benchmark proteins, including two with both active and allosteric sites. In addition, three different cosolvent procedures were compared to examine the impact of including more than one cosolvent in the simulations. For all systems tested, MixMD Probeview successfully identified known active and allosteric sites based on the total occupancy of neutral probe molecules. As an easy-to-use PyMOL plugin, we expect that MixMD Probeview will facilitate identification and analysis of binding sites from cosolvent simulations performed on a wide range of systems.

Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics.

Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.

Are there physicochemical differences between allosteric and competitive ligands?

Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.