De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish brain reward deficits in rodents during withdrawal, as well as to reduce the escalation of opioid use in rats with extended access to opioids. Although KOR antagonists represent promising candidates for the treatment of opioid addiction, very few potent selective KOR antagonists are known to date and most of them exhibit significant safety concerns. Here, we used a generative deep-learning framework for the de novo design of chemotypes with putative KOR antagonistic activity. Molecules generated by models trained with this framework were prioritized for chemical synthesis based on their predicted optimal interactions with the receptor. Our models and proposed training protocol were experimentally validated by binding and functional assays.

A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rates poses significant challenges, both computationally and experimentally. This is particularly palpable for complex systems like G Protein-Coupled Receptors (GPCRs) whose ligand unbinding typically requires very long timescales oftentimes inaccessible by standard molecular dynamics simulations. Enhanced sampling methods offer a useful alternative, and their efficiency can be further improved by using machine learning tools to identify optimal reaction coordinates. Here, we test the combination of two machine learning techniques, automatic mutual information noise omission and reweighted autoencoded variational Bayes for enhanced sampling, with infrequent metadynamics to efficiently study the unbinding kinetics of two classical drugs with different RTs in a prototypic GPCR, the µ-opioid receptor. Dissociation rates derived from these computations are within one order of magnitude from experimental values. We also use the simulation data to uncover the dissociation mechanisms of these drugs, shedding light on the structures of rate-limiting transition states, which, alongside metastable poses, are difficult to obtain experimentally but important to visualize when designing drugs with a desired kinetic profile.

Machine learning approaches for analyzing and enhancing molecular dynamics simulations.

Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges.

Allostery in G protein-coupled receptors investigated by molecular dynamics simulations.

G-protein-coupled receptors (GPCRs) are allosteric signaling machines that trigger distinct functional responses depending on the particular conformational state they adopt upon binding. This so-called GPCR functional selectivity is prompted by ligands of different efficacy binding at orthosteric or allosteric sites on the receptor, as well as by interactions with intracellular protein partners or other receptor types. Molecular dynamics (MD) simulations can provide important mechanistic, thermodynamic, and kinetic insights into these interactions at a level of molecular detail that is necessary to rightly inform modern drug discovery. Here, we review the most recent MD contributions to understanding GPCR allostery, with an emphasis on their strengths and limitations.

Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE.

In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149, 072301) for investigating ligand-protein unbinding mechanisms and calculating absolute binding free energies, Δ Gb, when plagued with difficult to sample rare events. In order to do so, we introduce a simple but powerful extension to RAVE that allows learning a reaction coordinate expressed as a piecewise function that is linear over all intervals. Such an approach allows us to retain the physical interpretation of a RAVE-derived reaction coordinate while making the method more applicable to a wider range of complex biophysical problems. As we will demonstrate, using as our test-case the slow dissociation of benzene from the L99A variant of lysozyme, the RAVE extension led to observing an unbinding event in 100% of the independent all-atom MD simulations, all within 3-50 ns for a process that takes on an average close to few hundred milliseconds, which reflects a 7 orders of magnitude acceleration relative to straightforward MD. Furthermore, we will show that without the use of time-dependent biasing, clear back-and-forth movement between metastable intermediates was achieved during the various simulations, demonstrating the caliber of the RAVE-derived piecewise reaction coordinate and bias potential, which together drive efficient and accurate sampling of the ligand-protein dissociation event. Last, we report the results for Δ Gb, which via very short MD simulations, can form a strict lower-bound that is ∼2-3 kcal/mol off from experiments. We believe that RAVE, together with its multidimensional extension that we introduce here, will be a useful tool for simulating the slow unbinding process of practical ligand-protein complexes in an automated manner with minimal use of human intuition.