In silico studies of conformational dynamics of Mu opioid receptor performed using gaussian accelerated molecular dynamics

Interactions between Inhibitors and 5-Lipoxygenase: Insights from Gaussian Accelerated Molecular Dynamics and Markov State Models

Inflammation is a protective stress response triggered by external stimuli, with 5-lipoxygenase (5LOX) playing a pivotal role as a potent mediator of the leukotriene (Lts) inflammatory pathway. Nordihydroguaiaretic acid (NDGA) functions as a natural orthosteric inhibitor of 5LOX, while 3-acetyl-11-keto-β-boswellic acid (AKBA) acts as a natural allosteric inhibitor targeting 5LOX. However, the precise mechanisms of inhibition have remained unclear. In this study, Gaussian accelerated molecular dynamics (GaMD) simulation was employed to elucidate the inhibitory mechanisms of NDGA and AKBA on 5LOX. It was found that the orthosteric inhibitor NDGA was tightly bound in the protein's active pocket, occupying the active site and inhibiting the catalytic activity of the 5LOX enzyme through competitive inhibition. The binding of the allosteric inhibitor AKBA induced significant changes at the distal active site, leading to a conformational shift of residues 168-173 from a loop to an α-helix and significant negative correlated motions between residues 285-290 and 375-400, reducing the distance between these segments. In the simulation, the volume of the active cavity in the stable conformation of the protein was reduced, hindering the substrate's entry into the active cavity and, thereby, inhibiting protein activity through allosteric effects. Ultimately, Markov state models (MSM) were used to identify and classify the metastable states of proteins, revealing the transition times between different conformational states. In summary, this study provides theoretical insights into the inhibition mechanisms of 5LOX by AKBA and NDGA, offering new perspectives for the development of novel inhibitors specifically targeting 5LOX, with potential implications for anti-inflammatory drug development.

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.

Flexible Gaussian Accelerated Molecular Dynamics to Enhance Biological Sampling

Molecular dynamics simulations often struggle to obtain sufficient sampling to study complex molecular events due to high energy barriers separating the minima of interest. Multiple enhanced sampling techniques have been developed and improved over the years to tackle this issue. Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that works by adding a biasing potential, lifting the energy landscape up, and decreasing the height of its barriers. GaMD allows one to increase the sampling of events of interest without the need of a priori knowledge of the system or the relevant coordinates. All required acceleration parameters can be obtained from a previous search run. Upon its development, several improvements for the methodology have been proposed, among them selective GaMD in which the boosting potential is selectively applied to the region of interest. There are currently four selective GaMD methods that have shown promising results. However, all of these methods are constrained on the number, location, and scenarios in which this selective boosting potential can be applied to ligands, peptides, or protein-protein interactions. In this work, we showcase a GROMOS implementation of the GaMD methodology with a fully flexible selective GaMD approach that allows the user to define, in a straightforward way, multiple boosting potentials for as many regions as desired. We show and analyze the advantages of this flexible selective approach on two previously used test systems, the alanine dipeptide and the chignolin peptide, and extend these examples to study its applicability and potential to study conformational changes of glycans and glycosylated proteins.

Molecular dynamics study reveals key disruptors of MEIG1-PACRG interaction

Interactions between the meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) protein are critical in the formation of mature sperm cells. Targeting either MEIG1 or PACRG protein could be a contraceptive strategy. The W50A and Y68A mutations on MEIG1 are known to interrupt the MEIG1-PACRG interactions resulting in defective sperm cells. However, the details about how the mutants disrupt the protein-protein binding are not clear. In this study, we reveal insights on MEIG1 and PACRG protein dynamics by applying Gaussian-accelerated molecular dynamics (GaMD) simulations and post-GaMD analysis. Our results show that the mutations destabilize the protein-protein interfacial interaction. The effect of the Y68A mutation is more significant than W50A as Y68 forms stronger polar interactions with PACRG. Because both human and mouse models demonstrate similar dynamic properties, the findings from mouse proteins can be applied to the human system. Moreover, we report a potential ligand binding pocket on the MEIG1 and PACRG interaction surface that could be a target for future drug design to inhibit the MEIG1-PACRG interaction. PACRG shows more qualified pockets along the protein-protein interface, implying that it is a better target than MEIG1. Our work provides a fundamental understanding of MEIG1 and PACRG protein dynamics, paving the way for drug discovery in male-based contraception.

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.

Thermostabilizing mechanisms of canonical single amino acid substitutions at a GH1 β-glucosidase probed by multiple MD and computational approaches

β-glucosidases play a pivotal role in second-generation biofuel (2G-biofuel) production. For this application, thermostable enzymes are essential due to the denaturing conditions on the bioreactors. Random amino acid substitutions have originated new thermostable β-glucosidases, but without a clear understanding of their molecular mechanisms. Here, we probe by different molecular dynamics simulation approaches with distinct force fields and submitting the results to various computational analyses, the molecular bases of the thermostabilization of the Paenibacillus polymyxa GH1 β-glucosidase by two-point mutations E96K (TR1) and M416I (TR2). Equilibrium molecular dynamic simulations (eMD) at different temperatures, principal component analysis (PCA), virtual docking, metadynamics (MetaDy), accelerated molecular dynamics (aMD), Poisson-Boltzmann surface analysis, grid inhomogeneous solvation theory and colony method estimation of conformational entropy allow to converge to the idea that the stabilization carried by both substitutions depend on different contributions of three classic mechanisms: (i) electrostatic surface stabilization; (ii) efficient isolation of the hydrophobic core from the solvent, with energetic advantages at the solvation cap; (iii) higher distribution of the protein dynamics at the mobile active site loops than at the protein core, with functional and entropic advantages. Mechanisms i and ii predominate for TR1, while in TR2, mechanism iii is dominant. Loop A integrity and loops A, C, D, and E dynamics play critical roles in such mechanisms. Comparison of the dynamic and topological changes observed between the thermostable mutants and the wildtype protein with amino acid co-evolutive networks and thermostabilizing hotspots from the literature allow inferring that the mechanisms here recovered can be related to the thermostability obtained by different substitutions along the whole family GH1. We hope the results and insights discussed here can be helpful for future rational approaches to the engineering of optimized β-glucosidases for 2G-biofuel production for industry, biotechnology, and science.

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.

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.

Exploring the activation pathway and Gi-coupling specificity of the μ-opioid receptor

Understanding the activation mechanism of the μ-opioid receptor (μ-OR) and its selective coupling to the inhibitory G protein (Gi) is vital for pharmaceutical research aimed at finding treatments for the opioid overdose crisis. Many attempts have been made to understand the mechanism of the μ-OR activation, following the elucidation of new crystal structures such as the antagonist- and agonist-bound μ-OR. However, the focus has not been placed on the underlying energetics and specificity of the activation process. An energy-based picture would not only help to explain this coupling but also help to explore why other possible options are not common. For example, one would like to understand why μ-OR is more selective to Gi than a stimulatory G protein (Gs). Our study used homology modeling and a coarse-grained model to generate all of the possible "end states" of the thermodynamic cycle of the activation of μ-OR. The end points were further used to generate reasonable intermediate structures of the receptor and the Gi to calculate two-dimensional free energy landscapes. The results of the landscape calculations helped to propose a plausible sequence of conformational changes in the μ-OR and Gi system and for exploring the path that leads to its activation. Furthermore, in silico alanine scanning calculations of the last 21 residues of the C terminals of Gi and Gs were performed to shed light on the selective binding of Gi to μ-OR. Overall, the present work appears to demonstrate the potential of multiscale modeling in exploring the action of G protein-coupled receptors.

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.

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.

Conformational flexibility correlates with glucose tolerance for point mutations in β-glucosidases - a computational study

β-glucosidases (EC 3.2.1.21) have been described as essential to second-generation biofuel production. They act in the last step of the lignocellulosic saccharification, cleaving the β - 1,4 glycosidic bonds in cellobiose to produce two molecules of glucose. However, β-glucosidases have been described as strongly inhibited by glucose, causing an increment of cellobiose concentration. Also, cellobiose is an inhibitor of other enzymes used in this process, such as exoglucanases and endoglucanases. Hence, the engineering of thermostable and glucose-tolerant β-glucosidases has been targeted by many studies. In this study, we performed high sampling accelerated molecular dynamics for a wild glucose-tolerant GH1 β-glucosidase (Bgl1A), a wild non-tolerant (Bgl1B), and a set of glucose-tolerant Bgl1B's mutants: V302F, N301Q/V302F, F172I, V227M, G246S, T299S, and H228T. Our results suggest that point mutations promissory to induce glucose tolerance trend to enhance the mobility of the flexible loops around the active site. Mutations affected B and C loops regions, and an αβ-hairpin motif between them. Conformational clusters and free energy landscape profiles suggest that the mobility acquired by mutants allows a higher closure of the substrate channel. This closure is compatible with a higher impedance for glucose entrance and stimulus of its withdrawal. Based on mutants' structural analyses, we inferred that both the direct stereochemical effect on the glucose path and the changes in the mobility affect glucose tolerance. We hope these results be useful for the rational design of glucose-tolerant and industrially promising enzymes.Communicated by Ramaswamy H. Sarma.

Revealing the Positive Binding Cooperativity Mechanism between the Orthosteric and the Allosteric Antagonists of CCR2 by Metadynamics and Gaussian Accelerated Molecular Dynamics Simulations

CC chemokine receptor 2 (CCR2) and its endogenous CC chemokine ligands are associated with numerous inflammatory, neurodegenerative diseases, and cancer. CCR2 is becoming an attractive target in the treatment of autoimmune disease and neurodegenerative diseases. The orthosteric antagonist BMS-681 and allosteric antagonist CCR2-RA-[R] of CCR2 show positive binding cooperativity. We performed well-tempered metadynamics simulations and Gaussian accelerated MD simulations to reveal the influence of the orthosteric antagonist on the unbinding of allosteric antagonist of CCR2. We revealed different unbinding pathways of CCR2-RA-[R] in binary complex CCR2-VT5 and ternary complex CCR2-73R-VT5. The different unbinding pathways of CCR2-RA-[R] are due to the conformational dynamics of TM6. We obtained the significant conformational differences of the intracellular side of TM6 upon CCR2 binding to different ligands by GaMD simulation. The conformational dynamics of TM6 are consistent with the unbinding pathway analysis. GaMD simulations indicate that BMS-681 binding restricts the bend of intracellular side of TM6 by stabilizing the extracellular sides of TM6 and TM7. The charged residues Arg206^5.43 of TM5 and Glu291^7.39 of TM7 play key roles in stabling TM7 and TM6. TM6 and TM7 are crucial components in the orthosteric and allosteric binding sites. Our results illustrate the conformational details about the effect of the orthosteric antagonist on the allosteric antagonist of CCR2. The conformational dynamics of CCR2 upon binding to different ligands can provide a rational basis for development of allosteric ligands of CCR2.

The mechanism of high affinity pentasaccharide binding to antithrombin, insights from Gaussian accelerated molecular dynamics simulations

The activity of antithrombin (AT), a serpin protease inhibitor, is enhanced by heparin and heparin analogs against its target proteases, mainly thrombin, factors Xa and IXa. Considerable amount of information is available on the multistep mechanism of the heparin pentasaccharide binding and conformational activation. However, much of the details were inferred from 'static' structures obtained by X-ray diffraction. Moreover, limited information is available for the early steps of binding mechanism other than kinetic studies with various ligands. To gain insights into these processes, we performed enhanced sampling molecular dynamics (MD) simulations using the Gaussian Accelerated Molecular Dynamics (GAMD) method, applied previously in drug binding studies. We were able to observe the binding of the pentasaccharide idraparinux to a 'non-activated' AT conformation in two separate trajectories with low root mean square deviation (RMSD) values compared to X-ray structures of the bound state. These trajectories along with further simulations of the AT-pentasaccharide complex provided insights into the mechanisms of multiple conformational transitions, including the expulsion of the hinge region, the extension of helix D and the conformational behavior of the reactive center loop (RCL). We could also confirm the high stability of helix P in non-activated AT conformations, such states might play an important role in heparin binding. 'Generalized correlation' matrices revealed possible paths of allosteric signal propagation to the binding sites for the target proteases, factors Xa and IXa. Enhanced MD simulations of ligand binding to AT may assist the design of new anticoagulant drugs.Communicated by Ramaswamy H. Sarma.

Computational investigation on effect of mutations in PCNA resulting in structural perturbations and inhibition of mismatch repair pathway

From bacteria to mammals, DNA mismatch repair (MMR) pathway plays an essential role in eliminating mismatched nucleotides and insertion-deletion mismatches during the process of DNA replication. Among many of the proteins which participate in the mismatch repair process, proliferating cell nuclear antigen (PCNA) remains the principal conductor at the replication fork. The pol30-201 and pol30-204 are the two mutated alleles which encode for C22Y and C81R mutant forms of PCNA proteins. We performed long term molecular dynamics (MD) simulations analysis (0.8 μs) to understand the dynamic behavior and alterations in the structure of wild type and mutated forms of PCNA at the atomic level. We observed changes in the structural characteristics like length, radius, rise per residue of alpha helices in both the mutated forms of PCNA. Apart from it, disfigurement of the charge distribution which effects binding with the dsDNA due to mutant C22Y and other structural perturbations were also seen in regions significant for the formation of a biologically active trimeric form of PCNA due to mutant C81R. Our analysis of native and mutated forms of PCNA provides an insight into the essential structural and functional features required for proper and well-coordinated DNA mismatch repair process and consequences of the mutation leading to an impaired process of MMR. These structural characteristics are fundamental for the MMR process and hence our analysis likely contributes to or presents the novel mechanism involved in the process of MMR.Communicated by Ramaswamy H. Sarma.

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.