Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding

Using Dynamic Interaction Fingerprints to Derive Baseline Machine Learning Model for the Prediction of Protein-Ligand Dissociation Rate Constant

Model building for the prediction of protein-ligand unbinding kinetics gaining popularity with the increasing availability of experimental structural data for the protein-ligand complexes and their relevant kinetic parameters. Limited but major effort has been already put forward in choosing appropriate machine learning (ML) methods among the popular ones like least squares (LS), support vector machine (SVM), random forest (RF), and a few more. The RF and Bayesian neural network (BNN) algorithms have been reported to be promising when combined with advanced descriptors representing ligand properties and protein-ligand interactions. However, the selection of descriptors that would correlate well with the unbinding kinetic properties is still a challenge. In this work, we derived a baseline RF model using descriptors representing the protein-ligand interaction fingerprints (IFPs) along the ligand unbinding pathway otherwise can be called dynamic IFPs. We found that the dynamic IFPs in addition to the static or binding pocket IFPs significantly improved the quality of our model for the prediction of ligand dissociation rate constant (koff). To the best of our knowledge, this work is the first attempt towards using the dynamic IFPs in deriving a quantitative structure-kinetics relationship (QSKR) model for the prediction of koff

The role of Tat peptide self-aggregation in membrane pore stabilization: insights from a computational study

It is widely accepted that endocytosis mediates the uptake of cationic cell penetrating peptides (CPPs) at relatively low concentrations (i.e. nano- to micromolar), while direct transduction across the plasma membrane comes into play at higher concentrations (i.e. micro- to millimolar). This latter process appears to depend on peptide-driven cellular processes, which in turn may induce local perturbations of plasma-membrane composition and/or integrity, and to be favored by peptide aggregation, especially into dimers. Besides, in most studies CPPs are tethered to fluorescent dyes in order to track peptide transduction events under the microscope, although often overlooking the possible role played by the dyes in assisting translocation. In an effort to provide some insights into the transduction process, here we report on a molecular dynamics (MD) simulation study of a prototype of the CPP family, namely the Tat11arginine-rich motif. To be specific, the translocation of Tat11across a purposely-created membrane pore, either or not covalently-linked to the tetramethylrhodamine-5-maleimide (TAMRA) dye and in both its monomeric and dimeric form, is analyzed in some detail. Results from several unconstrained and steered MD simulations, as well as energy decomposition analysis, nicely support the latest experimental evidence and help to shed light on key factors enabling peptide transduction. In particular, our study highlights the much slower translocation kinetics of Tat11dimer in comparison to the single peptide, and therefore its enhanced capability to stabilize membrane pores. Notably, it also shows how TAMRA has overall negligible kinetic and energetic effects on peptide transduction, yet it promotes this process indirectly by favoring peptide aggregation

Interplay between lipid lateral diffusion, dye concentration and membrane permeability unveiled by a combined spectroscopic and computational study of a model lipid bilayer

Lipid lateral diffusion in membrane bilayers is a fundamental process exploited by cells to enable complex protein structural and dynamic reorganizations. For its importance, lipid mobility in both cellular and model bilayers has been extensively investigated in recent years, especially through the application of time-resolved, fluorescence-based, optical microscopy techniques. However, one caveat of fluorescence techniques is the need to use dye-labeled variants of the lipid of interest, thus potentially perturbing the structural and dynamic properties of the native species. Generally, the effect of the dye/tracer molecule is implicitly assumed to be negligible. Nevertheless, in view of the widespread use of optically modified lipids for studying lipid bilayer dynamics, it is highly desirable to well assess this point. Here, fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulations have been combined together to uncover subtle structural and dynamic effects in DOPC planar membranes enriched with a standard Rhodamine-labeled lipid. Our findings support a non-neutral role of the dye-labeled lipids in diffusion experiments, quantitatively estimating a decrease in lipid mobility of up to 20% with respect to the unlabeled species. Moreover, results highlight the existing interplay between dye concentration, lipid lateral diffusion and membrane permeability, thus suggesting possible implications for future optical microscopy studies of biophysical processes occurring at the membrane level