Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software

In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of 1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations

Chemical Space Exploration with Active Learning and Alchemical Free Energies

Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives

Alchemical absolute protein-ligand binding free energies for drug design

The recent advances in relative protein–ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets. We use a novel rigorous method to generate protein–ligand ensembles for the ligand in its decoupled state. Not only do the calculations deliver accurate protein– ligand binding affinity estimates, but they also provide detailed physical insight into the structural determinants of binding. We identify subtle rotamer rearrangements between apo and holo states of a protein that are crucial for binding. When compared to relative binding free energy calculations, obtaining absolute binding free energies is considerably more challenging in large part due to the need to explicitly account for the protein in its apo state. In this work we present several approaches to obtain apo state ensembles for accurate absolute DG calculations, thus outlining protocols for prospective application of the methods for drug discovery

Application of the ESMACS Binding Free Energy Protocol to a Multi‐Binding Site Lactate Dehydogenase A Ligand Dataset

Over the past two decades, the use of fragment‐based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble‐based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other—although the latter is computationally far cheaper—but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking

Large scale relative protein ligand binding affinities using non-equilibrium alchemy.

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrodinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 +/- 0.14 kJ mol(-1), equivalent to Schrodinger's FEP+ AUE of 3.66 +/- 0.14 kJ mol(-1). For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software

Impact of allosteric modulation: exploring the binding kinetics of glutamate and other orthosteric ligands of the metabotropic glutamate receptor 2

While many orthosteric ligands have been developed for the mGlu2 receptor, little is known about their target binding kinetics and how these relate to those of the endogenous agonist glutamate. Here, the kinetic rate constants, i.e. kon and koff, of glutamate were determined for the first time followed by those of the synthetic agonist LY354740 and antagonist LY341495. To increase the understanding of the binding mechanism and impact of allosteric modulation thereon, kinetic experiments were repeated in the presence of allosteric modulators. Functional assays were performed to further study the interplay between the orthosteric and allosteric binding sites, including an impedance-based morphology assay. We found that dissociation rate constants of orthosteric mGlu2 ligands were all within a small 6-fold range, whereas association rate constants were ranging over more than three orders of magnitude and correlated to both affinity and potency. The latter showed that target engagement of orthosteric mGlu2 ligands is kon-driven in vitro. Moreover, only the off-rates of the two agonists were decreased by a positive allosteric modulator (PAM), thereby increasing their affinity. Interestingly, a PAM increased the duration of a glutamate-induced cellular response. A negative allosteric modulator (NAM) increased both on- and off-rate of glutamate without changing its affinity, while it did not affect these parameters for LY354740, indicating probe-dependency. In conclusion, we found that affinity- or potency-based orthosteric ligand optimization primarily results in ligands with high kon values. Moreover, positive allosteric modulators alter the binding kinetics of orthosteric agonists mainly by decreasing koff, which we were able to correlate to a lengthened cellular response. Together, this study shows the importance of studying binding kinetics in early drug discovery, as this may provide important insights towards improved efficacy in vivo.Medicinal Chemistr

Application of ESMACS binding free energy protocols to diverse datasets: Bromodomain-containing protein 4

As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis. Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell

Identification of Allosteric Modulators of Metabotropic Glutamate 7 Receptor Using Proteochemometric Modeling

Proteochemometric modeling (PCM) is a computational approach that can be considered an extension of quantitative structure–activity relationship (QSAR) modeling, where a single model incorporates information for a family of targets and all the associated ligands instead of modeling activity versus one target. This is especially useful for situations where bioactivity data exists for similar proteins but is scarce for the protein of interest. Here we demonstrate the application of PCM to identify allosteric modulators of metabotropic glutamate (mGlu) receptors. Given our long-running interest in modulating mGlu receptor function we compiled a matrix of compound-target bioactivity data. Some members of the mGlu family are well explored both internally and in the public domain, while there are much fewer examples of ligands for other targets such as the mGlu7 receptor. Using a PCM approach mGlu7 receptor hits were found. In comparison to conventional single target modeling the identified hits were more diverse, had a better confirmation rate, and provide starting points for further exploration. We conclude that the robust structure–activity relationship from well explored target family members translated to better quality hits for PCM compared to virtual screening (VS) based on a single target.FWN – Publicaties zonder aanstelling Universiteit Leide

Constitutive activity of the metabotropic glutamate receptor 2 explored with a whole-cell label-free biosensor

 Label-free cellular assays using a biosensor provide new opportunities for studying G protein-coupled receptor (GPCR) signaling. As opposed to conventional in vitro assays, integrated receptor-mediated cellular responses are determined in real-time rather than a single downstream signaling pathway. In this study, we examined the potential of a label-free whole cell impedance-based biosensor system (i.e. xCELLigence) to study the pharmacology of one GPCR in particular, the mGlu2 receptor. This receptor is a target for the treatment of several psychiatric diseases such as schizophrenia and depression. After optimization of assay conditions to prevent interference of endogenous glutamate in the culture medium, detailed pharmacological assessments were performed. Concentration-response curves showed a concentration-dependent increase in impedance for agonists and positive allosteric modulators, whereas receptor inhibition by an antagonist or negative allosteric modulator resulted in a concentration-dependent decrease in cellular impedance. Interestingly, constitutive receptor activity was observed that was decreased by LY341495, which therefore behaved as an inverse agonist here, a property that was heretofore unappreciated. This was confirmed by concentration-dependent modulation of LY341495 potency and efficacy by a allosteric modulators. In summary, the use of the xCELLigence system to study mGlu2 receptor pharmacology was validated. This is the first class C GPCR to be characterized extensively by such method, opening new avenues to study receptor pharmacology including inverse agonism and demonstrating its value for future drug discovery efforts of mGlu receptors as well as other GPCRs.Medicinal Chemistr