Understanding the molecular basis of agonist/antagonist mechanism of human mu opioid receptor through gaussian accelerated molecular dynamics method

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.

Deciphering the molecular choreography of Janus kinase 2 inhibition via Gaussian accelerated molecular dynamics simulations: a dynamic odyssey

The Janus kinases (JAK) are crucial targets in drug development for several diseases. However, accounting for the impact of possible structural rearrangements on the binding of different kinase inhibitors is complicated by the extensive conformational variability of their catalytic kinase domain (KD). The dynamic KD contains mainly four prominent mobile structural motifs: the phosphate-binding loop (P-loop), the αC-helix within the N-lobe, the Asp-Phe-Gly (DFG) motif, and the activation loop (A-loop) within the C-lobe. These distinct structural orientations imply a complex signal transmission path for regulating the A-loop's flexibility and conformational preference for optimal JAK function. Nevertheless, the precise dynamical features of the JAK induced by different types of inhibitors still remain elusive. We performed comparative, microsecond-long, Gaussian accelerated molecular dynamics simulations in triplicate of three phosphorylated JAK2 systems: the KD alone, type-I ATP-competitive inhibitor (CI) bound KD in the catalytically active DFG-in conformation, and the type-II inhibitor (AI) bound KD in the catalytically inactive DFG-out conformation. Our results indicate significant conformational variations observed in the A-loop and αC helix motions upon inhibitor binding. Our studies also reveal that the DFG-out inactive conformation is characterized by the closed A-loop rearrangement, open catalytic cleft of N and C-lobe, the outward movement of the αC helix, and open P-loop states. Moreover, the outward positioning of the αC helix impacts the hallmark salt bridge formation between Lys882 and Glu898 in an inactive conformation. Finally, we compared their ligand binding poses and free energy by the MM/PBSA approach. The free energy calculations suggested that the AI's binding affinity is higher than CI against JAK2 due to an increased favorable contribution from the total non-polar interactions and the involvement of the αC helix. Overall, our study provides the structural and energetic insights crucial for developing more promising type I/II JAK2 inhibitors for treating JAK-related diseases.

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.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

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.

An Efficient Gaussian-Accelerated Molecular Dynamics (GaMD) Multilevel Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

We introduce a novel multilevel enhanced sampling strategy grounded on Gaussian-accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups, thanks to the use of fast multiple-time step integrators. To further reduce the time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD─US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2-CD58 system (168 000 atoms), allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added, enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling-US-GaMD (ASUS-GaMD) scheme. The highly parallel ASUS-GaMD setup decreases time to convergence by, respectively, 10 and 20 times, compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large-scale simulations of biological systems.

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.

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 characterise 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.

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.

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.

Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study

Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira-TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira-TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0-6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design.

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.

In Silico Infrared Characterization of Synthetic Cannabinoids by Quantum Chemistry and Chemometrics

The concept of forensic sciences as mere trace analysis has been modified by the idea of forensic intelligence, which entails applying data to make decisions within the investigative process. Many countries are engaged in combating drug trafficking and drug use because they are related to public health and safety issues. Prohibiting the consumption of traditional drugs has led new psychoactive substances (NPSs) to emerge. NPSs consist of compounds that resemble the initially banned substance and which aim to mimic the traditional drug recreational effects while circumventing drug legislation. For example, synthetic cannabinoids are sprayed on herbal products to reproduce the cannabis recreational effects. According to the United Nations Office on Drugs and Crime (UNODC), the toxic effects of synthetic cannabis types are unknown, and harm and fatalities associated with the use of these drugs have been reported. Information on the characterization related to these species is lacking. The rate at which NPSs appear poses a significant challenge because employing conventional methods to understand the characteristics of these compounds may require time and be costly. This work uses in silico practices as an alternative to understand how NPSs related to cannabis behave. We apply quantum chemistry methods to evaluate several synthetic cannabinoids recognized in forensic samples. More specifically, we generate infrared spectra that can be employed as a benchmark for NPSs. We apply a multivariate classification to evaluate the results. We conclude that in silico methods are an alternative that provide information about the spectra of undetected substances. This information can help to identify new drugs, to increase knowledge about them, and to feed information procedures.

Spontaneous Embedding of DNA Mismatches Within the RNA:DNA Hybrid of CRISPR-Cas9

CRISPR-Cas9 is the forefront technology for editing the genome. In this system, the Cas9 protein is programmed with guide RNAs to process DNA sequences that match the guide RNA forming an RNA:DNA hybrid structure. However, the binding of DNA sequences that do not fully match the guide RNA can limit the applicability of CRISPR-Cas9 for genome editing, resulting in the so-called off-target effects. Here, molecular dynamics is used to probe the effect of DNA base pair mismatches within the RNA:DNA hybrid in CRISPR-Cas9. Molecular simulations revealed that the presence of mismatched pairs in the DNA at distal sites with respect to the Protospacer Adjacent Motif (PAM) recognition sequence induces an extended opening of the RNA:DNA hybrid, leading to novel interactions established by the unwound nucleic acids and the protein counterpart. On the contrary, mismatched pairs upstream of the RNA:DNA hybrid are rapidly incorporated within the heteroduplex, with minor effect on the protein-nucleic acid interactions. As a result, mismatched pairs at PAM distal ends interfere with the activation of the catalytic HNH domain, while mismatches fully embedded in the RNA:DNA do not affect the HNH dynamics and enable its activation to cleave the DNA. These findings provide a mechanistic understanding to the intriguing experimental evidence that PAM distal mismatches hamper a proper function of HNH, explaining also why mismatches within the heteroduplex are much more tolerated. This constitutes a step forward in understanding off-target effects in CRISPR-Cas9, which encourages novel structure-based engineering efforts aimed at preventing the onset of off-target effects.

The endomorphin-1/2 and dynorphin-B peptides display biased agonism at the mu opioid receptor

BACKGROUND

Opioid agonist activation at the mu opioid receptor (MOR) can lead to a wide variety of physiological responses. Many opioid agonists share the ability to selectively and preferentially activate specific signaling pathways, a term called biased agonism. Biased opioid ligands can theoretically induce specific physiological responses and might enable the generation of drugs with improved side effect profiles.

METHODS

Dynorphins, enkephalins, and endomorphins are endogenous opioid agonist peptides that may possess distinct bias profiles; biased agonism of endogenous peptides could explain the selective roles of these ligands in vivo. Our purpose in the present study was to investigate biased signaling and potential underlying molecular mechanisms of bias using ³⁵S-GTPγS and cAMP assays, specifically focusing on the role of adenylyl cyclases (ACs) and regulators of G-protein signaling proteins (RGSs) in CHO, N2a, and SH-SY5Y cell lines, all expressing the human MOR.

RESULTS

We found that endomorphin-1/2 preferentially activated cAMP signaling, while dynorphin-B preferentially activated ³⁵S-GTPγS signaling in most cell lines. Experiments carried out in the presence of an isoform selective RGS-4 inhibitor, and siRNA knockdown of AC6 in N2a cells did not significantly affect the bias properties of endomorphins, suggesting that these proteins may not play a role in endomorphin bias.

CONCLUSION

We found that endomorphin-1/2 and dynorphin-B displayed contrasting bias profiles at the MOR, and ruled out potential AC6 and RGS4 mechanisms in this bias. This identified signaling bias could be involved in specifying endogenous peptide roles in vivo, where these peptides have low selectivity between opioid receptor family members.

Endogenous opiates and behavior: 2017

This paper is the fortieth consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2017 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

Insight into the mechanism of allosteric activation of PI3Kα by oncoprotein K-Ras4B

Ras is a key member in the superfamily of small GTPase. Transforming between GTP-bound active state and GDP-bound inactive state in response to exogenous signals, Ras serves as a binary switch in various signaling pathways. One of its downstream effectors is phosphatidylinositol-4,5-bisphosphate 3-kinase α (PI3Kα), which phosphorylates phosphatidylinositol 4,5-bisphosphate into phosphatidylinositol 3,4,5-trisphosphate in the PI3K/Akt/mTOR pathway and mediates an array of important cellular activities including cell growth, migration and survival. Hyperactivation of PI3Kα induced by the Ras isoform K-Ras4B has been unveiled as a key event during the oncogenesis of pancreatic ductal adenocarcinoma, but the underlying mechanism of how K-Ras4B allosterically activates PI3Kα still remains largely unsolved. Here, we employed accelerated molecular dynamic simulations and allosteric pathway analysis to explore into the activation process of PI3Kα by K-Ras4B and unraveled the underlying structural mechanisms. We found that K-Ras4B binding induced more conformational dynamics within PI3Kα and triggered its step-wise transition from a self-inhibited state towards an activated state. Moreover, K-Ras4B binding markedly disrupted the interactions along the p110/p85 interface, especially the ones between nSH2 in p85 and its nearby functional domains in p110 like C2, helical, and kinase domains. The altered inter-domain interactions exposed the kinase domain, which promoted the membrane association and substrate phosphorylation of PI3Kα, thereby facilitating its activation. In particular, the community networks and allosteric pathways analysis further revealed that in PI3Kα/K-Ras4B system, allosteric signaling regulating p110/p85 interaction was rewired from the helical domain to the kinase domain and several important residues and their related allosteric pathways mediating PI3Kα autoinhibition were bypassed. The obtained structural mechanisms provide an in-depth mechanistic insight into the allosteric activation of PI3Kα by K-Ras4B as well as shed light on its drug discovery.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (r_p = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (r_p = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.

Understanding the mechanistic basis of non-coding RNA through molecular dynamics simulations

Noncoding RNA (ncRNA) has a key role in regulating gene expression, mediating fundamental processes and diseases via a variety of yet unknown mechanisms. Here, we review recent applications of conventional and enhanced Molecular Dynamics (MD) simulations methods to address the mechanistic function of large biomolecular systems that are tightly involved in the ncRNA function and that are of key importance in life sciences. This compendium focuses of three biomolecular systems, namely the CRISPR-Cas9 genome editing machinery, group II intron ribozyme and the ribonucleoprotein complex of the spliceosome, which edit and process ncRNA. We show how the application of a novel accelerated MD simulations method has been key in disclosing the conformational transitions underlying RNA binding in the CRISPR-Cas9 complex, suggesting a mechanism for RNA recruitment and clarifying the conformational changes required for attaining genome editing. As well, we discuss the use of mixed quantum-classical MD simulations in deciphering the catalytic mechanism of RNA splicing as operated by group II intron ribozyme, one of the largest ncRNA structures crystallized so far. Finally, we debate the future challenges and opportunities in the field, discussing the recent application of MD simulations for unraveling the functional biophysics of the spliceosome, a multi-mega Dalton complex of proteins and small nuclear RNAs that performs RNA splicing in humans. This showcase of applications highlights the current talent of MD simulations to dissect atomic-level details of complex biomolecular systems instrumental for the design of finely engineered genome editing machines. As well, this review aims at inspiring future investigations of several other ncRNA regulatory systems, such as micro and small interfering RNAs, which achieve their function and specificity using RNA-based recognition and targeting strategies.

How Does Agonist and Antagonist Binding Lead to Different Conformational Ensemble Equilibria of the κ-Opioid Receptor: Insight from Long-Time Gaussian Accelerated Molecular Dynamics Simulation

The opioid receptors belong to the class A seven transmembrane-spanning (7TM) G protein-coupled receptors (GPCRs). The κ-opioid receptor (KOR) is a subfamily of four opioid receptors. The endogenous peptide and a variety of selective agonists and antagonists of KOR have been developed. The structurally similar ligands at the same site cause completely opposite biological functions and induce different conformational changes. To shed light on the conformation ensembles and conformational dynamics in activation and deactivation processes of KOR, we performed all-atom, long-time Gaussian accelerated molecular dynamics simulation (GaMD) on KOR binding with agonist epoxymorphinan MP1104 and antagonist JDTic, respectively. Our results revealed different conformation ensembles of KOR binding with agonist and with antagonist. Agonist binding stabilizes the active state of key motifs including DYYNM motif and CWxP motif, and biases the conformation equilibria toward the active state. Antagonist binding will not destroy inactive conformation equilibria, by keeping the stable inactive state of these crucial motifs. We found that the inactive apo form of KOR is the most stable state, while the active apo form relaxes readily to inactive state. Our results also revealed a stable intermediate (I), which is attributed to the hydrophobic interactions between Tyr246^5.58 and TM6, as well as the steric hindrance of them. Our results not only show the conformation equilibria bias of KOR by binding with agonist and antagonist, but also provide the structural information for the design and discovery of potential ligands with different functions.

Structure and Dynamics of the CRISPR-Cas9 Catalytic Complex

CRISPR-Cas9 is a bacterial immune system with exciting applications for genome editing. In spite of extensive experimental characterization, the active site chemistry of the RuvC domain-which performs DNA cleavages-has remained elusive. Its knowledge is key for structure-based engineering aimed at improving DNA cleavages. Here, we deliver an in-depth characterization by using quantum-classical (QM/MM) molecular dynamics (MD) simulations and a Gaussian accelerated MD method, coupled with bioinformatics analysis. We disclose a two-metal aided architecture in the RuvC active site, which is poised to operate DNA cleavages, in analogy with other DNA/RNA processing enzymes. The conformational dynamics of the RuvC domain further reveals that an "arginine finger" stably contacts the scissile phosphate, with the function of stabilizing the active complex. Remarkably, the formation of a catalytically competent state of the RuvC domain is only observed upon the conformational activation of the other nuclease domain of CRISPR-Cas9-i.e., the HNH domain-such allowing concerted cleavages of double stranded DNA. This structure is in agreement with the available experimental data and remarkably differs from previous models based on classical mechanics, demonstrating also that only quantum mechanical simulations can accurately describe the metal-aided active site in CRISPR-Cas9. This fully catalytic structure-in which both the HNH and RuvC domains are prone to perform DNA cleavages-constitutes a stepping-stone for understanding DNA cleavage and specificity. It calls for novel experimental verifications and offers the structural foundations for engineering efforts aimed at improving the genome editing capability of CRISPR-Cas9.

Gaussian accelerated molecular dynamics for elucidation of drug pathways

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.

Acceleration of biomolecular kinetics in Gaussian accelerated molecular dynamics

Recent studies demonstrated that Gaussian accelerated molecular dynamics (GaMD) is a robust computational technique, which provides simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. However, the exact acceleration of biomolecular dynamics or speedup of kinetic rates in GaMD simulations and, more broadly, in enhanced sampling methods, remains a challenging task to be determined. Here, the GaMD acceleration is examined using alanine dipeptide in explicit solvent as a biomolecular model system. Relative to long conventional molecular dynamics simulation, GaMD simulations exhibited ∼36-67 times speedup for sampling of the backbone dihedral transitions. The acceleration depended on level of the GaMD boost potential. Furthermore, Kramers' rate theory was applied to estimate GaMD acceleration using simulation-derived diffusion coefficients, curvatures and barriers of free energy profiles. In most cases, the calculations also showed significant speedup of dihedral transitions in GaMD, although the GaMD acceleration factors tended to be underestimated by ∼3-96 fold. Because greater boost potential can be applied in GaMD simulations of systems with increased sizes, which potentially leads to higher acceleration, it is subject to future studies on accelerating the dynamics and recovering kinetic rates of larger biomolecules such as proteins and protein-protein/nucleic acid complexes.