G‐Protein‐Coupled Receptor–Membrane Interactions Depend on the Receptor Activation State

This is the peer reviewed version of the following article: Bhattarai, A., Wang, J., & Miao, Y. (2020). G-Protein-Coupled Receptor-Membrane Interactions Depend on the Receptor Activation State. Journal of computational chemistry, 41(5), 460–471. https://doi.org/10.1002/jcc.26082, which has been published in final form at https://doi.org/10.1002/jcc.26082. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.G‐protein‐coupled receptors (GPCRs) are the largest family of human membrane proteins and serve as primary targets of approximately one‐third of currently marketed drugs. In particular, adenosine A1 receptor (A1AR) is an important therapeutic target for treating cardiac ischemia–reperfusion injuries, neuropathic pain, and renal diseases. As a prototypical GPCR, the A1AR is located within a phospholipid membrane bilayer and transmits cellular signals by changing between different conformational states. It is important to elucidate the lipid–protein interactions in order to understand the functional mechanism of GPCRs. Here, all‐atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method were performed on both the inactive (antagonist bound) and active (agonist and G‐protein bound) A1AR, which was embedded in a 1‐palmitoyl‐2‐oleoyl‐glycero‐3‐phosphocholine (POPC) lipid bilayer. In the GaMD simulations, the membrane lipids played a key role in stabilizing different conformational states of the A1AR. Our simulations further identified important regions of the receptor that interacted distinctly with the lipids in highly correlated manner. Activation of the A1AR led to differential dynamics in the upper and lower leaflets of the lipid bilayer. In summary, GaMD enhanced simulations have revealed strongly coupled dynamics of the GPCR and lipids that depend on the receptor activation state

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterize biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, Copyright © 2022 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jctc.1c01055.We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A1 receptor (A1AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A1AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW

Effects of presenilin-1 familial Alzheimer’s disease mutations on γ-secretase activation for cleavage of amyloid precursor protein

Presenilin-1 (PS1) is the catalytic subunit of γ-secretase which cleaves within the transmembrane domain of over 150 peptide substrates. Dominant missense mutations in PS1 cause early-onset familial Alzheimer’s disease (FAD); however, the exact pathogenic mechanism remains unknown. Here we combined Gaussian accelerated molecular dynamics (GaMD) simulations and biochemical experiments to determine the effects of six representative PS1 FAD mutations (P117L, I143T, L166P, G384A, L435F, and L286V) on the enzyme-substrate interactions between γ-secretase and amyloid precursor protein (APP). Biochemical experiments showed that all six PS1 FAD mutations rendered γ-secretase less active for the endoproteolytic (ε) cleavage of APP. Distinct low-energy conformational states were identified from the free energy profiles of wildtype and PS1 FAD-mutant γ-secretase. The P117L and L286V FAD mutants could still sample the “Active” state for substrate cleavage, but with noticeably reduced conformational space compared with the wildtype. The other mutants hardly visited the “Active” state. The PS1 FAD mutants were found to reduce γ-secretase proteolytic activity by hindering APP residue L49 from proper orientation in the active site and/or disrupting the distance between the catalytic aspartates. Therefore, our findings provide mechanistic insights into how PS1 FAD mutations affect structural dynamics and enzyme-substrate interactions of γ-secretase and APP

Structural Basis for Binding of Allosteric Drug Leads in the Adenosine A1 Receptor

This work is licensed under a Creative Commons Attribution 4.0 International License.Despite intense interest in designing positive allosteric modulators (PAMs) as selective drugs of the adenosine A1 receptor (A1AR), structural binding modes of the receptor PAMs remain unknown. Using the first X-ray structure of the A1AR, we have performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) technique to determine binding modes of the A1AR allosteric drug leads. Two prototypical PAMs, PD81723 and VCP171, were selected. Each PAM was initially placed at least 20 Å away from the receptor. Extensive GaMD simulations using the AMBER and NAMD simulation packages at different acceleration levels captured spontaneous binding of PAMs to the A1AR. The simulations allowed us to identify low-energy binding modes of the PAMs at an allosteric site formed by the receptor extracellular loop 2 (ECL2), which are highly consistent with mutagenesis experimental data. Furthermore, the PAMs stabilized agonist binding in the receptor. In the absence of PAMs at the ECL2 allosteric site, the agonist sampled a significantly larger conformational space and even dissociated from the A1AR alone. In summary, the GaMD simulations elucidated structural binding modes of the PAMs and provided important insights into allostery in the A1AR, which will greatly facilitate the receptor structure-based drug design.Extreme Science and Engineering Discovery Environment award TG-MCB170129National Energy Research Scientific Computing Center project M2874American Heart Association (Award 17SDG33370094)College of Liberal Arts and Sciences at the University of KansasNHMRC Senior Principal Research FellowAustralian Heart Foundation Future Leader Fello

Mapping child growth failure across low- and middle-income countries

Child growth failure (CGF), manifested as stunting, wasting, and underweight, is associated with high 5 mortality and increased risks of cognitive, physical, and metabolic impairments. Children in low- and middle-income countries (LMICs) face the highest levels of CGF globally. Here we illustrate national and subnational variation of under-5 CGF indicators across LMICs, providing 2000–2017 annual estimates mapped at a high spatial resolution and aggregated to policy-relevant administrative units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the World Health 10 Organization’s ambitious Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and rates of progress exist across regions, countries, and within countries; our maps identify areas where high prevalence persists even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where subnational disparities exist and the highest-need populations reside, these geospatial estimates can support policy-makers in planning locally 15 tailored interventions and efficient directing of resources to accelerate progress in reducing CGF and its health implications

Gaussian accelerated molecular dynamics for elucidation of drug pathways

This is an Accepted Manuscript of an article published by Taylor & Francis in Expert Opinion on Drug Discovery on 29 Oct 2018, available online: http://www.tandfonline.com/10.1080/17460441.2018.1538207.Introduction: Understanding pathways and mechanisms of drug binding to receptors is important for rational drug design. Remarkable advances in supercomputing and methodological developments have opened a new era for application of computer simulations in predicting drug-receptor interactions at an atomistic level. Gaussian accelerated molecular dynamics (GaMD) is a computational enhanced sampling technique that works by adding a harmonic boost potential to reduce energy barriers. GaMD enables free energy calculations without the requirement of predefined collective variables. GaMD has proven useful in biomolecular simulations, in particular, the prediction of drug-receptor interactions. Areas covered: Herein, the authors review recent GaMD simulation studies that elucidated pathways of drug binding to proteins including the G-protein-coupled receptors and HIV protease. Expert opinion: GaMD is advantageous for enhanced simulations of, amongst many biological processes, drug binding to target receptors. Compared with conventional molecular dynamics, GaMD speeds up biomolecular simulations by orders of magnitude. GaMD enables routine drug binding simulations using personal computers with GPUs or common computing clusters. GaMD and, more broadly, enhanced sampling simulations are expected to dramatically increase our capabilities to determine the mechanisms of drug binding to a wide range of receptors in the near future. This will greatly facilitate computer-aided drug design

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design

Structural Basis for Binding of Allosteric Drug Leads in the Adenosine A₁ Receptor

Abstract Despite intense interest in designing positive allosteric modulators (PAMs) as selective drugs of the adenosine A1 receptor (A1AR), structural binding modes of the receptor PAMs remain unknown. Using the first X-ray structure of the A1AR, we have performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) technique to determine binding modes of the A1AR allosteric drug leads. Two prototypical PAMs, PD81723 and VCP171, were selected. Each PAM was initially placed at least 20 Å away from the receptor. Extensive GaMD simulations using the AMBER and NAMD simulation packages at different acceleration levels captured spontaneous binding of PAMs to the A1AR. The simulations allowed us to identify low-energy binding modes of the PAMs at an allosteric site formed by the receptor extracellular loop 2 (ECL2), which are highly consistent with mutagenesis experimental data. Furthermore, the PAMs stabilized agonist binding in the receptor. In the absence of PAMs at the ECL2 allosteric site, the agonist sampled a significantly larger conformational space and even dissociated from the A1AR alone. In summary, the GaMD simulations elucidated structural binding modes of the PAMs and provided important insights into allostery in the A1AR, which will greatly facilitate the receptor structure-based drug design