Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2.

Coarse graining molecular dynamics with graph neural networks

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features to machine learn the force field. In the present contribution, we build upon the advance of Wang et al. and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.

The Aminotriazole Antagonist Cmpd-1 Stabilises a Distinct Inactive State of the Adenosine 2A Receptor

The widely expressed G-protein coupled receptors (GPCRs) are versatile signal transducer proteins that are attractive drug targets but structurally challenging to study. GPCRs undergo a number of conformational rearrangements when transitioning from the inactive to the active state but have so far been believed to adopt a fairly conserved inactive conformation. Using ¹⁹ F NMR spectroscopy and advanced molecular dynamics simulations we describe a novel inactive state of the adenosine 2A receptor which is stabilised by the aminotriazole antagonist Cmpd-1. We demonstrate that the ligand stabilises a unique conformation of helix V and present data on the putative binding mode of the compound involving contacts to the transmembrane bundle as well as the extracellular loop 2.

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.

Dynamic and Kinetic Elements of µ-Opioid Receptor Functional Selectivity

While the therapeutic effect of opioids analgesics is mainly attributed to µ-opioid receptor (MOR) activation leading to G protein signaling, their side effects have mostly been linked to β-arrestin signaling. To shed light on the dynamic and kinetic elements underlying MOR functional selectivity, we carried out close to half millisecond high-throughput molecular dynamics simulations of MOR bound to a classical opioid drug (morphine) or a potent G protein-biased agonist (TRV-130). Statistical analyses of Markov state models built using this large simulation dataset combined with information theory enabled, for the first time: a) Identification of four distinct metastable regions along the activation pathway, b) Kinetic evidence of a different dynamic behavior of the receptor bound to a classical or G protein-biased opioid agonist, c) Identification of kinetically distinct conformational states to be used for the rational design of functionally selective ligands that may eventually be developed into improved drugs; d) Characterization of multiple activation/deactivation pathways of MOR, and e) Suggestion from calculated transition timescales that MOR conformational changes are not the rate-limiting step in receptor activation.

Amyloid-dependent triosephosphate isomerase nitrotyrosination induces glycation and tau fibrillation

Alzheimer's disease neuropathology is characterized by neuronal death, amyloid beta-peptide deposits and neurofibrillary tangles composed of paired helical filaments of tau protein. Although crucial for our understanding of the pathogenesis of Alzheimer's disease, the molecular mechanisms linking amyloid beta-peptide and paired helical filaments remain unknown. Here, we show that amyloid beta-peptide-induced nitro-oxidative damage promotes the nitrotyrosination of the glycolytic enzyme triosephosphate isomerase in human neuroblastoma cells. Consequently, nitro-triosephosphate isomerase was found to be present in brain slides from double transgenic mice overexpressing human amyloid precursor protein and presenilin 1, and in Alzheimer's disease patients. Higher levels of nitro-triosephosphate isomerase (P < 0.05) were detected, by Western blot, in immunoprecipitates from hippocampus (9 individuals) and frontal cortex (13 individuals) of Alzheimer's disease patients, compared with healthy subjects (4 and 9 individuals, respectively). Triosephosphate isomerase nitrotyrosination decreases the glycolytic flow. Moreover, during its isomerase activity, it triggers the production of the highly neurotoxic methylglyoxal (n = 4; P < 0.05). The bioinformatics simulation of the nitration of tyrosines 164 and 208, close to the catalytic centre, fits with a reduced isomerase activity. Human embryonic kidney (HEK) cells overexpressing double mutant triosephosphate isomerase (Tyr164 and 208 by Phe164 and 208) showed high methylglyoxal production. This finding correlates with the widespread glycation immunostaining in Alzheimer's disease cortex and hippocampus from double transgenic mice overexpressing amyloid precursor protein and presenilin 1. Furthermore, nitro-triosephosphate isomerase formed large beta-sheet aggregates in vitro and in vivo, as demonstrated by turbidometric analysis and electron microscopy. Transmission electron microscopy (TEM) and atomic force microscopy studies have demonstrated that nitro-triosephosphate isomerase binds tau monomers and induces tau aggregation to form paired helical filaments, the characteristic intracellular hallmark of Alzheimer's disease brains. Our results link oxidative stress, the main etiopathogenic mechanism in sporadic Alzheimer's disease, via the production of peroxynitrite and nitrotyrosination of triosephosphate isomerase, to amyloid beta-peptide-induced toxicity and tau pathology.