Capturing the multiscale dynamics of membrane protein complexes with all-atom, mixed-resolution, and coarse-grained models

Molecular Basis of PAC1R Allosteric Modulation with Lipids in Membranes

The pituitary adenylate cyclase-activating polypeptide receptor I (PAC1R) represents a highly sought-after therapeutic target for chronic pain, migraine, and post-traumatic stress. As a class B G protein-coupled receptor (GPCR), the PAC1R is highly expressed throughout the neuronal and central nervous system membranes, with the receptor subject to hormone activation and subsequent signal transduction. Despite its desirable indication, small molecule agonists for PAC1R have been notoriously difficult to develop due to competition with PACAP. For this reason, allosteric activation of the receptor has emerged as a promising pathway. To probe potential allosteric sites, herein, we present a study of the receptor in biomimetic concentrations of lipid membranes. Our results reveal that cholesterol recognizes two canonical and two non-canonical binding sites at PAC1R, which may influence critical residues in the transmembrane domain and PAC1R activation. Also, our simulations suggest the glycolipid GM3 interacts with PAC1R in both the extracellular and transmembrane domains. These lipid binding hotspots may hold high potential for advancing our understanding of class B GPCR signaling and the discovery of new molecules targeting PAC1R.

Evaluation of Essential Dynamics and Fixed-Length Coarse Graining for Multidomain Proteins

For multiscale modeling of biomolecules, reliable coarse-grained (CG) models can offer great potential to simulate larger temporal and spatial scales than traditional all-atom (AA) models. In this study, we explore the essential dynamics coarse graining (EDCG) and fixed-length coarse graining (FLCG) approaches for constructing highly coarse-grained models for multidomain proteins (MDPs), with 1 to 10 amino acid residues per CG site. In the studies of 13 MDPs, our data indicate that both EDCG and FLCG can preserve the protein dynamics of MDPs. FLCG, which restricts an equal number of residues in each CG site, represents an excellent approximation to EDCG and a straightforward approach for coarse-graining MDPs. Furthermore, FLCG is tested with a class B G-protein-coupled receptor protein, and the agreement with prior experiments suggests its general application to various MDPs in different environments or conditions. Finally, we demonstrate another application of FLCG through progressive backmapping, showcasing the ability to recover from lower-resolution CG models (6 residues/CG site) to higher-resolution ones (1 residue/CG site). These promising outcomes underscore the broad applicability of FLCG to construct highly or ultra-coarse-grained models of complex biomolecules for multiscale simulations.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

A double bilayer to study the nonequilibrium environmental response of GIRK2 in complex states

G protein-gated inwardly rectifying potassium (GIRK) channels play essential roles in electrical signaling in neurons and muscle cells. Nonequilibrium environments provide crucial driving forces behind many cellular events. Here, we apply the antiparallel alignment double bilayer model to study GIRK2 in response to the time-dependent membrane potential. Using molecular dynamics and umbrella sampling, we examined the time-dependent environmental impact on the ion conduction, energy basis, and primary motions of GIRK2 in different complex states with phosphatidylinositol-4,5-bisphosphate (PIP2) and G-protein βγ subunits (Gβγ). The antiparallel alignment double bilayer model enables us to study the transport performance in inward and outward K+ and mixed K+ and Na+. We obtained the recoverable discharge process of GIRK2 complexed with both PIP2 and Gβγ, compared with occasional conduction under PIP2-only regulation. Calculations of potential of mean force suggest different regulation by the helix bundle crossing (HBC) gate and G-loop gate regarding different complex states and under a membrane potential. In a nonequilibrium environment, distinct functional rocking motions of GIRK2 were identified under strengthened correlations between the transmembrane helices and downstream cytoplasmic domains with binding of PIP2, cations, and Gβγ. The findings suggest the potential domain motions and dynamics associated with a nonequilibrium environment and highlight the application of the antiparallel alignment double bilayer model to investigate factors in an asymmetric environment.

Selective USP7 inhibition elicits cancer cell killing through a p53-dependent mechanism

Ubiquitin specific peptidase 7 (USP7) is a deubiquitinating enzyme (DUB) that removes ubiquitin tags from specific protein substrates in order to alter their degradation rate and sub-cellular localization. USP7 has been proposed as a therapeutic target in several cancers because it has many reported substrates with a role in cancer progression, including FOXO4, MDM2, N-Myc, and PTEN. The multi-substrate nature of USP7, combined with the modest potency and selectivity of early generation USP7 inhibitors, has presented a challenge in defining predictors of response to USP7 and potential patient populations that would benefit most from USP7-targeted drugs. Here, we describe the structure-guided development of XL177A, which irreversibly inhibits USP7 with sub-nM potency and selectivity across the human proteome. Evaluation of the cellular effects of XL177A reveals that selective USP7 inhibition suppresses cancer cell growth predominantly through a p53-dependent mechanism: XL177A specifically upregulates p53 transcriptional targets transcriptome-wide, hotspot mutations in TP53 but not any other genes predict response to XL177A across a panel of ~500 cancer cell lines, and TP53 knockout rescues XL177A-mediated growth suppression of TP53 wild-type (WT) cells. Together, these findings suggest TP53 mutational status as a biomarker for response to USP7 inhibition. We find that Ewing sarcoma and malignant rhabdoid tumor (MRT), two pediatric cancers that are sensitive to other p53-dependent cytotoxic drugs, also display increased sensitivity to XL177A.

Targeting the PAC1 Receptor for Neurological and Metabolic Disorders

The pituitary adenylate cyclase-activating polypeptide (PACAP)-selective PAC1 receptor (PAC1R, ADCYAP1R1) is a member of the vasoactive intestinal peptide (VIP)/secretin/glucagon family of G protein-coupled receptors (GPCRs). PAC1R has been shown to play crucial roles in the central and peripheral nervous systems. The activation of PAC1R initiates diverse downstream signal transduction pathways, including adenylyl cyclase, phospholipase C, MEK/ERK, and Akt pathways that regulate a number of physiological systems to maintain functional homeostasis. Accordingly, at times of tissue injury or insult, PACAP/PAC1R activation of these pathways can be trophic to blunt or delay apoptotic events and enhance cell survival. Enhancing PAC1R signaling under these conditions has the potential to mitigate cellular damages associated with cerebrovascular trauma (including stroke), neurodegeneration (such as Parkinson's and Alzheimer's disease), or peripheral organ insults. Conversely, maladaptive PACAP/PAC1R signaling has been implicated in a number of disorders, including stressrelated psychopathologies (i.e., depression, posttraumatic stress disorder, and related abnormalities), chronic pain and migraine, and metabolic diseases; abrogating PAC1R signaling under these pathological conditions represent opportunities for therapeutic intervention. Given the diverse PAC1R-mediated biological activities, the receptor has emerged as a relevant pharmaceutical target. In this review, we first describe the current knowledge regarding the molecular structure, dynamics, and function of PAC1R. Then, we discuss the roles of PACAP and PAC1R in the activation of a variety of signaling cascades related to the physiology and diseases of the nervous system. Lastly, we examine current drug design and development of peptides and small molecules targeting PAC1R based on a number of structure- activity relationship studies and key pharmacophore elements. At present, the rational design of PAC1R-selective peptide or small-molecule therapeutics is largely hindered by the lack of structural information regarding PAC1R activation mechanisms, the PACAP-PAC1R interface, and the core segments involved in receptor activation. Understanding the molecular basis governing the PACAP interactions with its different cognate receptors will undoubtedly provide a basis for the development and/or refinement of receptor-selective therapeutics.

Top-down Multiscale Approach To Simulate Peptide Self-Assembly from Monomers

Modeling peptide assembly from monomers on large time and length scales is often intractable at the atomistic resolution. To address this challenge, we present a new approach which integrates coarse-grained (CG), mixed-resolution, and all-atom (AA) modeling in a single simulation. We simulate the initial encounter stage with the CG model, while the further assembly and reorganization stages are simulated with the mixed-resolution and AA models. We have implemented this top-down approach with new tools to automate model transformations and to monitor oligomer formations. Further, a theory was developed to estimate the optimal simulation length for each stage using a model peptide, melittin. The assembly level, the oligomer distribution, and the secondary structures of melittin simulated by the optimal protocol show good agreement with prior experiments and AA simulations. Finally, our approach and theory have been successfully validated with three amyloid peptides (β-amyloid 16-22, GNNQQNY fragment from the yeast prion protein SUP35, and α-synuclein fibril 35-55), which highlight the synergy from modeling at multiple resolutions. This work not only serves as proof of concept for multiresolution simulation studies but also presents practical guidelines for further self-assembly simulations at more physically and chemically relevant scales.

Assessment of Conformational State Transitions of Class B GPCRs Using Molecular Dynamics

Class B G protein-coupled receptors (GPCRs) comprise a family of 15 peptide-binding members, which are crucial targets for endocrine, metabolic, and stress-related disorders. While their protein structures and dynamics remain largely unclear, computer modeling and simulations represent a promising means to help solve such puzzles. Herein, we present a basic introduction to the methodology of molecular dynamics (MD) simulations and two analytical methods to assess the conformational ensembles and transitions of Class B GPCRs, using our recent studies of the human pituitary adenylate cyclase activating polypeptide (PAC₁) receptor as an example. From long MD simulations, conformational ensembles with different roles in ligand binding and receptor activation are sampled to establish four states identified as either "open" or "closed" for the PAC₁ receptor. Next, the dynamical network can be applied to analyze the simulations and identify key features within each conformational ensemble, which help distinguish the ligand-bound states of the PAC₁ receptor from the ligand-free one. Further, the Markov State Model has emerged as a key approach to construct the transition network and connect the GPCR ensembles, providing detailed information for the transition pathways and kinetics. For the ligand-free PAC₁ receptor, the transitions within the closed states are near 10-30 times faster than the open-closed transitions, which is likely related to the activation mechanism of the receptor. Overall, long MD simulations and analyses are useful to assess conformational transitions for the Class B GPCRs and to gain mechanistic insight, which is difficult to obtain using other methods.

Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations

Membrane receptors constitute major targets for pharmaceutical intervention. Drug design efforts rely on the identification of ligand binding poses. However, the limited experimental structural information available may make this extremely challenging, especially when only low-resolution homology models are accessible. In these cases, the predictions may be improved by molecular dynamics simulation approaches. Here we review recent developments of multiscale, hybrid molecular mechanics/coarse-grained (MM/CG) methods applied to membrane proteins. In particular, we focus on our in-house MM/CG approach. It is especially tailored for G-protein coupled receptors, the largest membrane receptor family in humans. We show that our MM/CG approach is able to capture the atomistic details of the receptor/ligand binding interactions, while keeping the computational cost low by representing the protein frame and the membrane environment in a highly simplified manner. We close this review by discussing ongoing improvements and challenges of the current implementation of our MM/CG code.

Conformational Transitions of the Pituitary Adenylate Cyclase-Activating Polypeptide Receptor, a Human Class B GPCR

The G protein-coupled pituitary adenylate cyclase-activating polypeptide receptor (PAC1R) is a potential therapeutic target for endocrine, metabolic and stress-related disorders. However, many questions regarding the protein structure and dynamics of PAC1R remain largely unanswered. Using microsecond-long simulations, we examined the open and closed PAC1R conformations interconnected within an ensemble of transitional states. The open-to-closed transition can be initiated by “unzipping” the extracellular domain and the transmembrane domain, mediated by a unique segment within the β3-β4 loop. Transitions between different conformational states range between microseconds to milliseconds, which clearly implicate allosteric effects propagating from the extracellular face of the receptor to the intracellular G protein-binding site. Such allosteric dynamics provides structural and mechanistic insights for the activation and modulation of PAC1R and related class B receptors.