Digging into Lipid Membrane Permeation for Cardiac Ion Channel Blocker d-Sotalol with All-Atom Simulations.

Interactions of drug molecules with lipid membranes play crucial role in their accessibility of cellular targets and can be an important predictor of their therapeutic and safety profiles. Very little is known about spatial localization of various drugs in the lipid bilayers, their active form (ionization state) or translocation rates and therefore potency to bind to different sites in membrane proteins. All-atom molecular simulations may help to map drug partitioning kinetics and thermodynamics, thus providing in-depth assessment of drug lipophilicity. As a proof of principle, we evaluated extensively lipid membrane partitioning of d-sotalol, well-known blocker of a cardiac potassium channel Kv11.1 encoded by the hERG gene, with reported substantial proclivity for arrhythmogenesis. We developed the positively charged (cationic) and neutral d-sotalol models, compatible with the biomolecular CHARMM force field, and subjected them to all-atom molecular dynamics (MD) simulations of drug partitioning through hydrated lipid membranes, aiming to elucidate thermodynamics and kinetics of their translocation and thus putative propensities for hydrophobic and aqueous hERG access. We found that only a neutral form of d-sotalol accumulates in the membrane interior and can move across the bilayer within millisecond time scale, and can be relevant to a lipophilic channel access. The computed water-membrane partitioning coefficient for this form is in good agreement with experiment. There is a large energetic barrier for a cationic form of the drug, dominant in water, to cross the membrane, resulting in slow membrane translocation kinetics. However, this form of the drug can be important for an aqueous access pathway through the intracellular gate of hERG. This route will likely occur after a neutral form of a drug crosses the membrane and subsequently re-protonates. Our study serves to demonstrate a first step toward a framework for multi-scale in silico safety pharmacology, and identifies some of the challenges that lie therein

Computing the gravitational potential on nested meshes using the convolution method

Aims. Our aim is to derive a fast and accurate method for computing the gravitational potential of astrophysical objects with high contrasts in density, for which nested or adaptive meshes are required. Methods. We present an extension of the convolution method for computing the gravitational potential to the nested Cartesian grids. The method makes use of the convolution theorem to compute the gravitational potential using its integral form. Results. A comparison of our method with the iterative outside-in conjugate gradient and generalized minimal residual methods for solving the Poisson equation using nonspherically symmetric density configurations has shown a comparable performance in terms of the errors relative to the analytic solutions. However, the convolution method is characterized by several advantages and outperforms the considered iterative methods by factors 10--200 in terms of the runtime, especially when graphics processor units are utilized. The convolution method also shows an overall second-order convergence, except for the errors at the grid interfaces where the convergence is linear. Conclusions. High computational speed and ease in implementation can make the convolution method a preferred choice when using a large number of nested grids. The convolution method, however, becomes more computationally costly if the dipole moments of tightly spaced gravitating objects are to be considered at coarser grids.Comment: Accepted for publication in Astronomy & Astrophysic

The role of membrane thickness in charged protein–lipid interactions

AbstractCharged amino acids are known to be important in controlling the actions of integral and peripheral membrane proteins and cell disrupting peptides. Atomistic molecular dynamics studies have shed much light on the mechanisms of membrane binding and translocation of charged protein groups, yet the impact of the full diversity of membrane physico-chemical properties and topologies has yet to be explored. Here we have performed a systematic study of an arginine (Arg) side chain analog moving across saturated phosphatidylcholine (PC) bilayers of variable hydrocarbon tail length from 10 to 18 carbons. For all bilayers we observe similar ion-induced defects, where Arg draws water molecules and lipid head groups into the bilayers to avoid large dehydration energy costs. The free energy profiles all exhibit sharp climbs with increasing penetration into the hydrocarbon core, with predictable shifts between bilayers of different thickness, leading to barrier reduction from 26kcal/mol for 18 carbons to 6kcal/mol for 10 carbons. For lipids of 10 and 12 carbons we observe narrow transmembrane pores and corresponding plateaus in the free energy profiles. Allowing for movements of the protein and side chain snorkeling, we argue that the energetic cost for burying Arg inside a thin bilayer will be small, consistent with recent experiments, also leading to a dramatic reduction in pKa shifts for Arg. We provide evidence that Arg translocation occurs via an ion-induced defect mechanism, except in thick bilayers (of at least 18 carbons) where solubility-diffusion becomes energetically favored. Our findings shed light on the mechanisms of ion movement through membranes of varying composition, with implications for a range of charged protein–lipid interactions and the actions of cell-perturbing peptides. This article is part of a Special Issue entitled: Membrane protein structure and function

Intrahippocampal pathways involved in learning/memory mechanisms are affected by intracerebral infusions of amyloid-beta25-35 peptide and hydrated fullerene C60 in rats

Primary memory impairments associated with increased level of amyloid-beta (Аβ) in the brain have been shown to be linked, partially, with early pathological changes in the entorhinal cortex (EC) which spread on the whole limbic system. While the hippocampus is known to play a key role in learning and memory mechanisms, it is as yet unclear how its structures are involved in the EC pathology. In this study, changes in memory and neuronal morphology in male Wistar rats intrahippocampally injected with Аβ25–35 were correlated on days 14 and 45 after the injection to reveal specific cognitive - structural associations. The main focus was on the dentate gyrus (DG) and hippocampal areas of CA1 and CA3 because of their involvement in afferent flows from EC to the hippocampus through tri-synaptic (EC DG CA3 CA1) and/or mono-synaptic (EC CA1) pathways. Evident memory impairments were observed at both time points after Аβ25–35 injection. However, on day 14, populations of morphological intact neurons were decreased in CA3 and, drastically, in CA1, and the DG supramedial bundle was significantly damaged. On day 45, this bundle largely and СА1 neurons partially recovered, whereas CA3 neurons remained damaged. We suggest that Аβ25–35 primarily affects the tri-synaptic pathway, destroying the granular cells in the DG supramedial area and neurons in CA3 and, through the Schaffer collaterals, in CA1. Intrahippocampal pretreatment with hydrated fullerene С60 allows the neurons and their connections to survive the amyloidosis, thus supporting the memory mechanisms

A deep learning algorithm to translate and classify cardiac electrophysiology

The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter