G-Protein-Coupled Receptor-Membrane Interactions Depend on the Receptor Activation State

Activation of polycystin-1 signaling by binding of stalk-derived peptide agonists

Polycystin-1 (PC1) is the protein product of the PKD1 gene whose mutation causes autosomal dominant Polycystic Kidney Disease (ADPKD). PC1 is an atypical G protein-coupled receptor (GPCR) with an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal and membrane-embedded C-terminal (CTF) fragments. Recently, activation of PC1 CTF signaling was shown to be regulated by a stalk tethered agonist (TA), resembling the mechanism observed for adhesion GPCRs. Here, synthetic peptides of the first 9- (p9), 17- (p17), and 21-residues (p21) of the PC1 stalk TA were shown to re-activate signaling by a stalkless CTF mutant in human cell culture assays. Novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) simulations elucidated binding conformations of p9, p17, and p21 and revealed multiple specific binding regions to the stalkless CTF. Peptide agonists binding to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of stalk TA-mediated PC1 CTF activation. Additional sequence coevolution analyses showed the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. These insights into the structural dynamic mechanism of PC1 activation by TA peptide agonists provide an in-depth understanding that will facilitate the development of therapeutics targeting PC1 for ADPKD treatment.

Activation of Polycystin-1 Signaling by Binding of Stalk-derived Peptide Agonists

Polycystin-1 (PC1) is the membrane protein product of the PKD1 gene whose mutation is responsible for 85% of the cases of autosomal dominant polycystic kidney disease (ADPKD). ADPKD is primarily characterized by the formation of renal cysts and potential kidney failure. PC1 is an atypical G protein-coupled receptor (GPCR) consisting of 11 transmembrane helices and an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal (NTF) and membrane-embedded C-terminal (CTF) fragments. Recently, signaling activation of the PC1 CTF was shown to be regulated by a stalk tethered agonist (TA), a distinct mechanism observed in the adhesion GPCR family. A novel allosteric activation pathway was elucidated for the PC1 CTF through a combination of Gaussian accelerated molecular dynamics (GaMD), mutagenesis and cellular signaling experiments. Here, we show that synthetic, soluble peptides with 7 to 21 residues derived from the stalk TA, in particular, peptides including the first 9 residues (p9), 17 residues (p17) and 21 residues (p21) exhibited the ability to re-activate signaling by a stalkless PC1 CTF mutant in cellular assays. To reveal molecular mechanisms of stalk peptide-mediated signaling activation, we have applied a novel Peptide GaMD (Pep-GaMD) algorithm to elucidate binding conformations of selected stalk peptide agonists p9, p17 and p21 to the stalkless PC1 CTF. The simulations revealed multiple specific binding regions of the stalk peptide agonists to the PC1 protein including an "intermediate" bound yet inactive state. Our Pep-GaMD simulation findings were consistent with the cellular assay experimental data. Binding of peptide agonists to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of the PC1 CTF signaling activation mechanism. Using sequence covariation analysis of PC1 homologs, we further showed that the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. Therefore, structural dynamic insights into the mechanisms of PC1 activation by stalk-derived peptide agonists have enabled an in-depth understanding of PC1 signaling. They will form a foundation for development of PC1 as a therapeutic target for the treatment of ADPKD.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.

Mechanistic Insights into Peptide Binding and Deactivation of an Adhesion G Protein-Coupled Receptor

Adhesion G protein-coupled receptors (ADGRGs) play critical roles in the reproductive, neurological, cardiovascular, and endocrine systems. In particular, ADGRG2 plays a significant role in Ewing sarcoma cell proliferation, parathyroid cell function, and male fertility. In 2022, a cryo-EM structure was reported for the active ADGRG2 bound by an optimized peptide agonist IP15 and the Gs protein. The IP15 peptide agonist was also modified to antagonists 4PH-E and 4PH-D with mutations of the 4PH residue to Glu and Asp, respectively. However, experimental structures of inactive antagonist-bound ADGRs remain to be resolved, and the activation mechanism of ADGRs such as ADGRG2 is poorly understood. Here, we applied Gaussian accelerated molecular dynamics (GaMD) simulations to probe conformational dynamics of the agonist- and antagonist-bound ADGRG2. By performing GaMD simulations, we were able to identify important low-energy conformations of ADGRG2 in the active, intermediate, and inactive states, as well as explore the binding conformations of each peptide. Moreover, our simulations revealed critical peptide-receptor residue interactions during the deactivation of ADGRG2. In conclusion, through GaMD simulations, we uncovered mechanistic insights into peptide (agonist and antagonist) binding and deactivation of the ADGRG2. These findings will potentially facilitate rational design of new peptide modulators of ADGRG2 and other ADGRs.

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach

Dynamics of individual amino acids play key roles in the overall properties of proteins. However, the knowledge of protein structural features at the residue level is limited due to the current resolutions of experimental and computational techniques. To address this issue, we designed a novel machine-learning (ML) framework that uses Molecular Dynamics (MD) trajectories to identify the major conformational states of individual amino acids, classify amino acids switching between two distinct modes, and evaluate their degree of dynamic stability. The Random Forest model achieved 96.94% classification accuracy in identifying switch residues within proteins. Additionally, our framework distinguishes between the stable switch (SS) residues, which remain stable in one angular state and jump once to another state during protein dynamics, and unstable switch (US) residues, which constantly fluctuate between the two angular states. This study also illustrates the correlation between the dynamics of SS residues and the protein's global properties.

Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52

Within over 800 members of G-protein-coupled receptors, there are numerous orphan receptors whose endogenous ligands are largely unknown, providing many opportunities for novel drug discovery. However, the lack of an in-depth understanding of the intrinsic working mechanism for orphan receptors severely limits the related rational drug design. The G-protein-coupled receptor 52 (GPR52) is a unique orphan receptor that constitutively increases cellular 5'-cyclic adenosine monophosphate (cAMP) levels without binding any exogenous agonists and has been identified as a promising therapeutic target for central nervous system disorders. Although recent structural biology studies have provided snapshots of both active and inactive states of GPR52, the mechanism of the conformational transition between these states remains unclear. Here, an acceptable self-activation pathway for GPR52 was proposed through 6 μs Gaussian accelerated molecular dynamics (GaMD) simulations, in which the receptor spontaneously transitions from the active state to that matching the inactive crystal structure. According to the three intermediate states of the receptor obtained by constructing a reweighted potential of mean force, how the allosteric regulation occurs between the extracellular orthosteric binding pocket and the intracellular G-protein-binding site is revealed. Combined with the independent gradient model, several important microswitch residues and the allosteric communication pathway that directly links the two regions are both identified. Transfer entropy calculations not only reveal the complex allosteric signaling within GPR52 but also confirm the unique role of ECL2 in allosteric regulation, which is mutually validated with the results of GaMD simulations. Overall, this work elucidates the allosteric mechanism of GPR52 at the atomic level, providing the most detailed information to date on the self-activation of the orphan receptor.

Protons taken hostage: Dynamic H-bond networks of the pH-sensing GPR68

Proton-sensing G Protein Coupled Receptors (GPCRs) sense changes in the extracellular pH to effect cell signaling for cellular homeostasis. They tend to be overexpressed in solid tumors associated with acidic extracellular pH, and are of direct interest as drug targets. How proton-sensing GPCRs sense extracellular acidification and activate upon protonation change is important to understand, because it may guide the design of therapeutics. Lack of publicly available experimental structures make it challenging to discriminate between conflicting mechanisms proposed for proton-binding, as main roles have been assigned to either an extracellular histidine cluster or to an internal carboxylic triad. Here we present a protocol to derive and evaluate structural models of the proton-sensing GPR68. This approach integrates state-of-the-art homology modeling with microsecond-timescale atomistic simulations, and with a detailed assessment of the compatibility of the structural models with known structural features of class A GPCRs. To decipher structural elements of potential interest for protonation-coupled conformational changes of GPR68, we used the best-compatible model as a starting point for independent atomistic simulations of GPR68 with different protonation states, and graph computations to characterize the response of GPR68 to changes in protonation. We found that GPR68 hosts an extended hydrogen-bond network that inter-connects the extracellular histidine cluster to the internal carboxylic triad, and which can even reach groups at the cytoplasmic G-protein binding site. Taken together, results suggest that GPR68 relies on dynamic, hydrogen-bond networks to inter-connect extracellular and internal proton-binding sites, and to elicit conformational changes at the cytoplasmic G-protein binding site.

Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems

Gaussian accelerated molecular dynamics (GaMD) is recognized as a popular enhanced sampling method for tackling long-standing challenges in biomolecular simulations. Inspired by GaMD, Sigmoid accelerated molecular dynamics (SaMD) is proposed in this work by adding a Sigmoid boost potential to improve the balance between the highest acceleration and accurate reweighting. Compared with GaMD, SaMD extends the accessible time scale and improves the computational efficiency as tested in three tasks. In the alanine dipeptide task, SaMD can produce the free energy landscape with better accuracy and efficiency. In the chignolin folding task, the estimated Gibbs free energy difference can converge to the experimental value ∼30% faster. In the protein-ligand binding task, the bound conformations are closer to the crystal structure with a minimal ligand root-mean-square deviation of 1.7 Å. The binding of the ligand XK263 to the HIV protease is reproduced by SaMD in ∼60% less simulation time.

Elucidating the molecular determinants in the process of gastrin C-terminal pentapeptide amide end activating cholecystokinin 2 receptor by Gaussian accelerated molecular dynamics simulations

Gastrin plays important role in stimulating the initiation and development of many gastrointestinal diseases through interacting with the cholecystokinin 2 receptor (CCK2R). The smallest bioactive unit of gastrin activating CCK2R is the C-terminal tetrapeptide capped with an indispensable amide end. Understanding the mechanism of this smallest bioactive unit interacting with CCK2R on a molecular basis could provide significant insights for designing CCK2R antagonists, which can be used to treat gastrin-related diseases. To this end, we performed extensive Gaussian accelerated molecular dynamics simulations to investigate the interaction between gastrin C-terminal pentapeptide capped with/without amide end and CCK2R. The amide cap influences the binding modes of the pentapeptide with CCK2R by weakening the electrostatic attractions between the C-terminus of the pentapeptide and basic residues near the extracellular domain in CCK2R. The C-terminus with the amide cap penetrates into the transmembrane domain of CCK2R while floating at the extracellular domain without the amide cap. Different binding modes induced different conformational dynamics of CCK2R. Residue pairs in CCK2R had stronger correlated motions when binding with the amidated pentapeptide. Key residues and interactions important for CCK2R binding with the amidated pentagastrin were also identified. Our results provide molecular insights into the determinants of the bioactive unit of gastrin activating CCK2R, which would be of great help for the design of CCK2R antagonists.

Investigating the Dynamic Binding Behavior of PMX53 Cooperating with Allosteric Antagonist NDT9513727 to C5a Anaphylatoxin Chemotactic Receptor 1 through Gaussian Accelerated Molecular Dynamics and Free-Energy Perturbation Simulations

C5a anaphylatoxin chemotactic receptor 1 (C5aR1) is an important target in anti-inflammatory therapeutics. The cyclic peptide antagonist PMX53 binds to the orthosteric site located in the extracellular vestibule of C5aR1, and the non-peptide antagonist NDT9513727 binds to the allosteric site formed by the middle region of TM3 (trans-membrane helix), TM4, and TM5. We catch a sight of the variational binding mode of PMX53 during the Gaussian accelerated molecular dynamic (GaMD) simulations. In the binary complex of C5aR1 and PMX53, the PMX53 takes a dynamic binding mechanism during the simulation. Namely, the side chain of Arg⁶ of PMX53 extends to TM6-TM7 (pose 1) or swings to TM5 (pose 2), forming a salt bridge with Glu199. Meanwhile, in the ternary complex of C5aR1 with PMX53 and NDT9513727, the side chain of Arg⁶ of PMX53 swings to TM5 (pose 2) from extending to TM6-TM7 (pose 1) at the beginning of the GaMD simulation. In subsequent simulation, PMX53 stabilizes in the pose 2 binding mode by forming a stable salt bridge with Glu199. The free-energy perturbation (FEP) calculations demonstrate that pose 1 (ΔG_binding = -10.94 kcal/mol) is more stable in the binary complex and pose 2 (ΔG_binding = -7.91 kcal/mol) is unstable because of highly dynamic TM5. NDT9513727 interacts directly with TM4 and TM5 and stabilizes the hydrophobic stack between the extracellular sides of the two helices. Therefore, pose 2 (ΔG_binding = -16.27 kcal/mol) is notably stable than pose 1 (ΔG_binding = -9.78 kcal/mol) in the ternary complex. The identification of a novel binding mode of PMX53 and the detailed structural information of PMX53 interacting with a receptor obtained by GaMD simulations will be helpful in designing potent antagonists of C5aR1.

Biased Activation Mechanism Induced by GPCR Heterodimerization: Observations from μOR/δOR Dimers

GPCRs regulate multiple intracellular signaling cascades. Biasedly activating one signaling pathway over the others provides additional clinical utility to optimize GPCR-based therapies. GPCR heterodimers possess different functions from their monomeric states, including their selectivity to different transducers. However, the biased signaling mechanism induced by the heterodimerization remains unclear. Motivated by the issue, we select an important GPCR heterodimer (μOR/δOR heterodimer) as a case and use microsecond Gaussian accelerated molecular dynamics simulation coupled with potential of mean force and protein structure network (PSN) to probe mechanisms regarding the heterodimerization-induced constitutive β-arrestin activity and efficacy change of the agonist DAMGO. The results show that only the lowest energy state of the μOR/δOR heterodimer, which adopts a slightly outward shift of TM6 and an ICL2 conformation close to the receptor core, can selectively accommodate β-arrestins. PSN further reveals important roles of H8, ICL1, and ICL2 in regulating the constitutive β-arrestin-biased activity for the apo μOR/δOR heterodimer. In addition, the heterodimerization can allosterically alter the binding mode of DAMGO mainly by means of W7.35. Consequently, DAMGO transmits the structural signal mainly through TM6 and TM7 in the dimer, rather than TM3 similar to the μOR monomer, thus changing the efficacy of DAMGO from a balanced agonist to the β-arrestin-biased one. On the other side, the binding of DAMGO to the heterodimer can stabilize μOR/δOR heterodimers through a stronger interaction of TM1/TM1 and H8/H8, accordingly enhancing the interaction of μOR with δOR and the binding affinity of the dimer to the β-arrestin. The agonist DAMGO does not change main compositions of the regulation network from the dimer interface to the transducer binding pocket of the μOR protomer, but induces an increase in the structural communication of the network, which should contribute to the enhanced β-arrestin coupling. Our observations, for the first time, reveal the molecular mechanism of the biased signaling induced by the heterodimerization for GPCRs, which should be beneficial to more comprehensively understand the GPCR bias signaling.

Gaussian Accelerated Molecular Dynamics in OpenMM

Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules.

Implementing accelerated dynamics to unravel the effects of high-fidelity Cas9 mutants on target DNA and guide RNA hybrid stability

The clustered regularly interspersed short palindromic repeats (CRISPR) and its associated nuclease (Cas9) offers a unique and easily reprogrammable system for editing eukaryotic genomes. Cas9 is guided to the target by an RNA strand, and precise edits are created by introducing double-stranded breaks. However, nuclease activity of Cas9 is also triggered at other sites other than the target sit, which is a major limitation for various applications. Cas9 variants have been designed to improve the efficacy of the tool by introducing certain mutations. However, the on-target activity of such Cas9 variants is often seen as compromised. Hence, understanding the sub-molecular differences in the variants is essential to elucidate the factors that contribute to efficiency. The study reveals distortions in the PAM-distal regions of the nucleic hybrids as well as changes in the interactions between the Cas9 variants and RNA-DNA hybrid, contributing to the explanation for differences in on-target activity.Communicated by Ramaswamy H. Sarma.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT_2A/D₂ receptor antagonists without off-target activities on H₁ receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Molecular Simulations and Drug Discovery of Adenosine Receptors

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.

Mechanism of Peptide Agonist Binding in CXCR4 Chemokine Receptor

Chemokine receptors are key G-protein-coupled receptors (GPCRs) that control cell migration in immune system responses, development of cardiovascular and central nervous systems, and numerous diseases. In particular, the CXCR4 chemokine receptor promotes metastasis, tumor growth and angiogenesis in cancers. CXCR4 is also used as one of the two co-receptors for T-tropic HIV-1 entry into host cells. Therefore, CXCR4 serves as an important therapeutic target for treating cancers and HIV infection. Apart from the CXCL12 endogenous peptide agonist, previous studies suggested that the first 17 amino acids of CXCL12 are sufficient to activate CXCR4. Two 17-residue peptides with positions 1-4 mutated to RSVM and ASLW functioned as super and partial agonists of CXCR4, respectively. However, the mechanism of peptide agonist binding in CXCR4 remains unclear. Here, we have investigated this mechanism through all-atom simulations using a novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method. The Pep-GaMD simulations have allowed us to explore representative binding conformations of each peptide and identify critical low-energy states of CXCR4 activated by the super versus partial peptide agonists. Our simulations have provided important mechanistic insights into peptide agonist binding in CXCR4, which are expected to facilitate rational design of new peptide modulators of CXCR4 and other chemokine receptors.

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

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.

Most Probable Druggable Pockets in Mutant p53-Arg175His Clusters Extracted from Gaussian Accelerated Molecular Dynamics Simulations

p53, a tumor suppressor protein, is essential for preventing cancer development. Enhancing our understanding of the human p53 function and its modifications in carcinogenesis will aid in developing more highly effective strategies for cancer prevention and treatment. In this study, we have modeled five human p53 forms, namely, inactive, distal-active, proximal-active, distal-Arg175His mutant, and proximal-Arg175His mutant forms. These forms have been investigated using Gaussian accelerated molecular dynamics (GaMD) simulations in OPC water model at physiological temperature and pH. Our observations, obtained throughout [Formula: see text] of production run, are in good agreement with the relevant results in the classical molecular dynamics (MD) studies. Therefore, GaMD method is more economic and efficient method than the classical MD method for studying biomolecular systems. The featured dynamics of the five human p53-DBD forms include noticeable conformational changes of L1 and [Formula: see text]-[Formula: see text] loops as well as [Formula: see text]-[Formula: see text] and [Formula: see text]-[Formula: see text] turns. We have identified two clusters that represent two distinct conformational states in each p53-DBD form. The free-energy profiles of these clusters demonstrate the flexibility of the protein to undergo a conformational transition between the two clusters. We have predicted two out of seven possible druggability pockets on the clusters of the Arg175His forms. These two druggability pockets are near the mutation site and are expected to be actual pockets, which will be helpful for the compound clinical progression studies.

Druggable Lipid Binding Sites in Pentameric Ligand-Gated Ion Channels and Transient Receptor Potential Channels

Lipids modulate the function of many ion channels, possibly through direct lipid-protein interactions. The recent outpouring of ion channel structures by cryo-EM has revealed many lipid binding sites. Whether these sites mediate lipid modulation of ion channel function is not firmly established in most cases. However, it is intriguing that many of these lipid binding sites are also known sites for other allosteric modulators or drugs, supporting the notion that lipids act as endogenous allosteric modulators through these sites. Here, we review such lipid-drug binding sites, focusing on pentameric ligand-gated ion channels and transient receptor potential channels. Notable examples include sites for phospholipids and sterols that are shared by anesthetics and vanilloids. We discuss some implications of lipid binding at these sites including the possibility that lipids can alter drug potency or that understanding protein-lipid interactions can guide drug design. Structures are only the first step toward understanding the mechanism of lipid modulation at these sites. Looking forward, we identify knowledge gaps in the field and approaches to address them. These include defining the effects of lipids on channel function in reconstituted systems using asymmetric membranes and measuring lipid binding affinities at specific sites using native mass spectrometry, fluorescence binding assays, and computational approaches.

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.

Mechanism of RNA recognition by a Musashi RNA-binding protein

The Musashi RNA-binding proteins (RBPs) regulate translation of target mRNAs and maintenance of cell stemness and tumorigenesis. Musashi-1 (MSI1), long considered as an intestinal and neural stem cell marker, has been more recently found to be over expressed in many cancers. It has served as an important drug target for treating acute myeloid leukemia and solid tumors such as ovarian, colorectal and bladder cancer. One of the reported binding targets of MSI1 is Numb, a negative regulator of the Notch signaling. However, the dynamic mechanism of Numb RNA binding to MSI1 remains unknown, largely hindering effective drug design targeting this critical interaction. Here, we have performed extensive all-atom microsecond-timescale simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method, which successfully captured multiple times of spontaneous and highly accurate binding of the Numb RNA from bulk solvent to the MSI1 protein target site. GaMD simulations revealed that Numb RNA binding to MSI1 involved largely induced fit in both the RNA and protein. The simulations also identified important low-energy intermediate conformational states during RNA binding, in which Numb interacted mainly with the β2-β3 loop and C terminus of MSI1. The mechanistic understanding of RNA binding obtained from our GaMD simulations is expected to facilitate rational structure-based drug design targeting MSI1 and other RBPs.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Strained contacts with the cell membrane may influence ligand affinity to G protein coupled receptors: a case of free fatty acid receptor 1 agonists

A set of 1,3,4-thiadiazole-2-carboxamides bearing a substituted biphenyl in the amide portion was synthesised and tested for agonistic activity towards free fatty acid receptor 1 (FFA1). The observed activity trends were impossible to rationalised based solely on the docking energy scores of Glide SP. On the contrary, when the phospholipid cell membrane bilayer was reconstructed around FFA1, it became apparent that inactive compounds displayed significant strained contacts with the membrane while for active compounds the strain was noticeably lower. These findings justify using the improved docking protocol for modelling GPCR-ligand interactions which uses the crystal structure of the receptor and a reconstructed portion of a cell membrane.

Insights into the molecular mechanism of positive cooperativity between partial agonist MK-8666 and full allosteric agonist AP8 of hGPR40 by Gaussian accelerated molecular dynamics (GaMD) simulations

Activation of human free fatty acid receptor 1 (FFAR1, also called hGPR40) enhances insulin secretion in a glucose-dependent manner. Hence, the development of selective agonist targeting hGPR40 has been proposed as a therapeutic strategy of type 2 diabetes mellitus. Some agonists targeting hGPR40 were reported. The radioligand-binding studies and the crystal structures reveal that there are multiple sites on GPR40, and there exists positive binding cooperativity between the partial agonist MK-8666 and full allosteric agonist (AgoPAM) AP8. In this work, we carried out long-time Gaussian accelerated molecular dynamics (GaMD) simulations on hGPR40 to shed light on the mechanism of the cooperativity between the two agonists at different sites. Our results reveal that the induced-fit conformational coupling is bidirectional between the two sites. The movements and rotations of TM3, TM4, TM5 and TM6 due to their inherent flexibility are crucial in coupling the conformational changes of the two agonists binding sites. These helices adopt similar conformational states upon alternative ligand or both ligands binding. The Leu138^4.57, Leu186^5.42 and Leu190^5.46 play roles in coordinating the rearrangements of residues in the two pockets, which makes the movements of residues in the two sites like gear movements. These results provide detailed information at the atomic level about the conformational coupling between different sites of GPR40, and also provide the structural information for further design of new agonists of GPR40. In addition, these results suggest that it is necessary by considering the effect of other site bound in structure-based ligands discovery.

Pathways and Mechanism of Caffeine Binding to Human Adenosine A2A Receptor

Caffeine (CFF) is a common antagonist to the four subtypes of adenosine G-protein-coupled receptors (GPCRs), which are critical drug targets for treating heart failure, cancer, and neurological diseases. However, the pathways and mechanism of CFF binding to the target receptors remain unclear. In this study, we have performed all-atom-enhanced sampling simulations using a robust Gaussian-accelerated molecular dynamics (GaMD) method to elucidate the binding mechanism of CFF to human adenosine A_2A receptor (A_2AAR). Multiple 500–1,000 ns GaMD simulations captured both binding and dissociation of CFF in the A_2AAR. The GaMD-predicted binding poses of CFF were highly consistent with the x-ray crystal conformations with a characteristic hydrogen bond formed between CFF and residue N6.55 in the receptor. In addition, a low-energy intermediate binding conformation was revealed for CFF at the receptor extracellular mouth between ECL2 and TM1. While the ligand-binding pathways of the A_2AAR were found similar to those of other class A GPCRs identified from previous studies, the ECL2 with high sequence divergence serves as an attractive target site for designing allosteric modulators as selective drugs of the A_2AAR.

Dynamical Correlations Reveal Allosteric Sites in G Protein-Coupled Receptors

G protein-coupled Receptors (GPCRs) play a central role in many physiological processes and, consequently, constitute important drug targets. In particular, the search for allosteric drugs has recently drawn attention, since they could be more selective and lead to fewer side effects. Accordingly, computational tools have been used to estimate the druggability of allosteric sites in these receptors. In spite of many successful results, the problem is still challenging, particularly the prediction of hydrophobic sites in the interface between the protein and the membrane. In this work, we propose a complementary approach, based on dynamical correlations. Our basic hypothesis was that allosteric sites are strongly coupled to regions of the receptor that undergo important conformational changes upon activation. Therefore, using ensembles of experimental structures, normal mode analysis and molecular dynamics simulations we calculated correlations between internal fluctuations of different sites and a collective variable describing the activation state of the receptor. Then, we ranked the sites based on the strength of their coupling to the collective dynamics. In the β2 adrenergic (β2AR), glucagon (GCGR) and M2 muscarinic receptors, this procedure allowed us to correctly identify known allosteric sites, suggesting it has predictive value. Our results indicate that this dynamics-based approach can be a complementary tool to the existing toolbox to characterize allosteric sites in GPCRs.

Agonist Binding and G Protein Coupling in Histamine H2 Receptor: A Molecular Dynamics Study

The histamine H2 receptor (H₂R) plays an important role in the regulation of gastric acid secretion. Therefore, it is a main drug target for the treatment of gastroesophageal reflux or peptic ulcer disease. However, there is as of yet no 3D-structural information available hampering a mechanistic understanding of H₂R. Therefore, we created a model of the histamine-H₂R-G_s complex based on the structure of the ternary complex of the β2-adrenoceptor and investigated the conformational stability of this active GPCR conformation. Since the physiologically relevant motions with respect to ligand binding and conformational changes of GPCRs can only partly be assessed on the timescale of conventional MD (cMD) simulations, we also applied metadynamics and Gaussian accelerated molecular dynamics (GaMD) simulations. A multiple walker metadynamics simulation in combination with cMD was applied for the determination of the histamine binding mode. The preferential binding pose detected is in good agreement with previous data from site directed mutagenesis and provides a basis for rational ligand design. Inspection of the H₂R-G_s interface reveals a network of polar interactions that may contribute to H₂R coupling selectivity. The cMD and GaMD simulations demonstrate that the active conformation is retained on a μs-timescale in the ternary histamine-H₂R-G_s complex and in a truncated complex that contains only G_s helix α5 instead of the entire G protein. In contrast, histamine alone is unable to stabilize the active conformation, which is in line with previous studies of other GPCRs.

Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics

Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with <1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD_Dual simulations were also <1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

Basic concepts and physical-chemical phenomena, that have conceptual meaning for the formation of systemic clinical thinking and formalization of the knowledge of systemic structural-functional organization of the human’s organism

From the point of view of perception and generalization processes there are complex, logic and conceptual forms of thinking. Its conceptual form is the highest result of interaction between thinking and speech. While realizing it, human uses the concept, which are logically formed thoughts, that are the meaning of representation in thinking of unity of meaningful features, relations of subjects or phenomena of objective reality. Special concepts, that are used in the science and technique are called terms. They perform a function of corresponding, special, precise marking of subjects and phenomena, their features and interactions. Scientific knowledge are in that way an objective representation of material duality in our consciousness. Certain complex of terms forms a terminological system, that lies in the basis of corresponding sphere of scientific knowledge and conditions a corresponding form and way of thinking. Clinical thinking is a conceptual form, that manifests and represents by the specialized internal speech with gnostic motivation lying in its basis. Its structural elements are corresponding definitions, terms and concepts. Cardinal features of clinical systems are consistency, criticality, justification and substantiation. Principles of perception and main concepts are represented in the article along with short descriptions of physical and chemical phenomena, that have conceptual meaning for the formation of systematic clinical thinking and formalization of systemic structural-functional organization of the human’s organism

Mechanisms of γ-Secretase Activation and Substrate Processing

Amyloid β-peptide, the principal component of characteristic cerebral plaques of Alzheimer's disease (AD), is produced through intramembrane proteolysis of the amyloid precursor protein (APP) by γ-secretase. Despite the importance in the pathogenesis of AD, the mechanisms of intramembrane proteolysis and substrate processing by γ-secretase remain poorly understood. Here, complementary all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method and biochemical experiments were combined to investigate substrate processing of wildtype and mutant APP by γ-secretase. The GaMD simulations captured spontaneous activation of γ-secretase, with hydrogen bonded catalytic aspartates and water poised for proteolysis of APP at the ε cleavage site. Furthermore, GaMD simulations revealed that familial AD mutations I45F and T48P enhanced the initial ε cleavage between residues Leu49-Val50, while M51F mutation shifted the ε cleavage site to the amide bond between Thr48-Leu49. Detailed analysis of the GaMD simulations allowed us to identify distinct low-energy conformational states of γ-secretase, different secondary structures of the wildtype and mutant APP substrate, and important active-site subpockets for catalytic function of the enzyme. The simulation findings were highly consistent with experimental analyses of APP proteolytic products using mass spectrometry and Western blotting. Taken together, the GaMD simulations and biochemical experiments have enabled us to elucidate the mechanisms of γ-secretase activation and substrate processing, which should facilitate rational computer-aided drug design targeting this functionally important enzyme.

Pathway and mechanism of drug binding to chemokine receptors revealed by accelerated molecular simulations

Background: Chemokine GPCRs play key roles in biology and medicine. Particularly, CXCR4 promotes cancer metastasis and facilitate HIV entry into host cells. Plerixafor (PLX) is a CXCR4 drug, but the pathway and binding site of PLX in CXCR4 remain unknown. Results & methodology: We have performed molecular docking and all-atom simulations using Gaussian accelerated molecular dynamics (GaMD), which are consistent with previous mutation experiments, suggesting that PLX binds to the orthosteric site of CXCR4 as an antagonist. The GaMD simulations further revealed an intermediate allosteric binding site at the extracellular mouth of CXCR4. Conclusion: The newly identified allosteric site can be targeted for novel drug design targeting CXCR4 and other chemokine receptors.

In Silico Drug Design for Purinergic GPCRs: Overview on Molecular Dynamics Applied to Adenosine and P2Y Receptors

Molecular modeling has contributed to drug discovery for purinergic GPCRs, including adenosine receptors (ARs) and P2Y receptors (P2YRs). Experimental structures and homology modeling have proven to be useful in understanding and predicting structure activity relationships (SAR) of agonists and antagonists. This review provides an excursus on molecular dynamics (MD) simulations applied to ARs and P2YRs. The binding modes of newly synthesized A1AR- and A3AR-selective nucleoside derivatives, potentially of use against depression and inflammation, respectively, have been predicted to recapitulate their SAR and the species dependence of A3AR affinity. P2Y12R and P2Y1R crystallographic structures, respectively, have provided a detailed understanding of the recognition of anti-inflammatory P2Y14R antagonists and a large group of allosteric and orthosteric antagonists of P2Y1R, an antithrombotic and neuroprotective target. MD of A2AAR (an anticancer and neuroprotective target), A3AR, and P2Y1R has identified microswitches that are putatively involved in receptor activation. The approach pathways of different ligands toward A2AAR and P2Y1R binding sites have also been explored. A1AR, A2AAR, and A3AR were utilizes to study allosteric phenomena, but locating the binding site of structurally diverse allosteric modulators, such as an A3AR enhancer LUF6000, is challenging. Ligand residence time, a predictor of in vivo efficacy, and the structural role of water were investigated through A2AAR MD simulations. Thus, new MD and other modeling algorithms have contributed to purinergic GPCR drug discovery.

Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor

BACKGROUND

Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A₁ receptor (A₁AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A₁AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A₁AR.

METHODS

Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A₁AR.

RESULTS

Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased.

CONCLUSIONS

Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A₁AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking.

GENERAL SIGNIFICANCE

Ensemble docking is a promising approach for drug discovery targeting flexible receptors.