Recent advances in computational studies of GPCR-G protein interactions

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~ 1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches

Mechanistic Insights into Specific G Protein Interactions with Adenosine Receptors

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry B, copyright © 2019 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.jpcb.9b04867.Coupling between G-protein-coupled receptors (GPCRs) and the G proteins is a key step in cellular signaling. Despite extensive experimental and computational studies, the mechanism of specific GPCR–G protein coupling remains poorly understood. This has greatly hindered effective drug design of GPCRs that are primary targets of ∼1/3 of currently marketed drugs. Here, we have employed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method to decipher the mechanism of the GPCR–G protein interactions. Adenosine receptors (ARs) were used as model systems based on very recently determined cryo-EM structures of the A1AR and A2AAR coupled with the Gi and Gs proteins, respectively. Changing the Gi protein to the Gs led to increased fluctuations in the A1AR and agonist adenosine (ADO), while agonist 5′-N-ethylcarboxamidoadenosine (NECA) binding in the A2AAR could be still stabilized upon changing the Gs protein to the Gi. Free energy calculations identified one stable low-energy conformation for each of the A1AR-Gi and A2AAR-Gs complexes as in the cryo-EM structures, similarly for the A2AAR-Gi complex. In contrast, the ADO agonist and Gs protein sampled multiple conformations in the A1AR-Gs system. GaMD simulations thus indicated that the A1AR preferred to couple with the Gi protein to the Gs, while the A2AAR could couple with both the Gs and Gi proteins, being highly consistent with experimental findings of the ARs. More importantly, detailed analysis of the atomic simulations showed that the specific AR-G protein coupling resulted from remarkably complementary residue interactions at the protein interface, involving mainly the receptor transmembrane 6 helix and the Gα α5 helix and α4-β6 loop. In summary, the GaMD simulations have provided unprecedented insights into the dynamic mechanism of specific GPCR–G protein interactions at an atomistic level

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

Deepfake Detection: A Comprehensive Study from the Reliability Perspective

The mushroomed Deepfake synthetic materials circulated on the internet have raised serious social impact to politicians, celebrities, and every human being on earth. In this paper, we provide a thorough review of the existing models following the development history of the Deepfake detection studies and define the research challenges of Deepfake detection in three aspects, namely, transferability, interpretability, and reliability. While the transferability and interpretability challenges have both been frequently discussed and attempted to solve with quantitative evaluations, the reliability issue has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake related cases in court. We therefore conduct a model reliability study scheme using statistical random sampling knowledge and the publicly available benchmark datasets to qualitatively validate the detection performance of the existing models on arbitrary Deepfake candidate suspects. A barely remarked systematic data pre-processing procedure is demonstrated along with the fair training and testing experiments on the existing detection models. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of reliably qualified detection models. The model reliability study provides a workflow for the detection models to act as or assist evidence for Deepfake forensic investigation in court once approved by authentication experts or institutions.Comment: 20 pages for peer revie

Assessment of Hydrodynamic and Water Quality Impacts for Channel Deepening in the Thimble Shoals, Norfolk Harbor, and Elizabeth River Channels : Final report on the “hydrodynamic modeling”

For over twenty years, the U. S. Army Corps of Engineers (USACE) and the Virginia Port Authority (VPA), representing the Commonwealth Secretary of Transportation, have collaborated on projects key to port development that also preserve the environmental integrity of both Hampton Roads and the Elizabeth River. The USACE and the VPA are working to investigate channel deepening in this region to provide access to a new generation of cargo ships (e.g., Panamax-class). The main goal of this project is to investigate the feasibility for Norfolk Harbor channel deepening in the lower James and Elizabeth Rivers and assess the environmental impact of the shipping channels dredging in Atlantic Ocean Channel, Thimble Shoal Channel, Elizabeth River channel, and the Southern Branch. Specifically, we support the request of “Planning and Engineering Services for Norfolk Harbor” in three areas: (1) using high-resolution hydrodynamic modeling to evaluate the change of hydrodynamics resulting from Channel Deepening (2) assessment of water quality modeling using the Hydrodynamic Eutrophication Model (HEM3D) (3) conducting the statistical measure of impacts resulting from Channel Deepening. Virginia Institute of Marine Science (VIMS) team has applied a3D unstructured-grid hydrodynamic model (SCHISM, Zhang et al., 2016) in the study of impact of channel dredging on hydrodynamics in the project area. The model was adopted due to its flexible gridding systems used: hybrid triangular-quadrangular unstructured grids in the horizontal and flexible vertical coordinate system in the vertical (Zhang et al. 2015). High resolution (up to 15m) is used to faithfully resolve the channels and other important features such as tunnel islands, etc

Robust Identity Perceptual Watermark Against Deepfake Face Swapping

Notwithstanding offering convenience and entertainment to society, Deepfake face swapping has caused critical privacy issues with the rapid development of deep generative models. Due to imperceptible artifacts in high-quality synthetic images, passive detection models against face swapping in recent years usually suffer performance damping regarding the generalizability issue. Therefore, several studies have been attempted to proactively protect the original images against malicious manipulations by inserting invisible signals in advance. However, the existing proactive defense approaches demonstrate unsatisfactory results with respect to visual quality, detection accuracy, and source tracing ability. In this study, we propose the first robust identity perceptual watermarking framework that concurrently performs detection and source tracing against Deepfake face swapping proactively. We assign identity semantics regarding the image contents to the watermarks and devise an unpredictable and unreversible chaotic encryption system to ensure watermark confidentiality. The watermarks are encoded and recovered by jointly training an encoder-decoder framework along with adversarial image manipulations. Extensive experiments demonstrate state-of-the-art performance against Deepfake face swapping under both cross-dataset and cross-manipulation settings.Comment: Submitted for revie