Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Mechanism of antagonist ligand binding to REV-ERBα

REV-ERBα, a therapeutically promising nuclear hormone receptor, plays a crucial role in regulating various physiological processes such as the circadian clock, inflammation, and metabolism. However, the availability of chemical probes to investigate the pharmacology of this receptor is limited, with SR8278 being the only identified synthetic antagonist. Moreover, no X-ray crystal structures are currently available that demonstrate the binding of REV-ERBα to antagonist ligands. This lack of structural information impedes the development of targeted therapeutics. To address this issue, we employed Gaussian accelerated molecular dynamics (GaMD) simulations to investigate the binding pathway of SR8278 to REV-ERBα. For comparison, we also used GaMD to observe the ligand binding process of STL1267, for which an X-ray structure is available. GaMD simulations successfully captured the binding of both ligands to the receptor's orthosteric site and predicted the ligand binding pathway and important amino acid residues involved in the antagonist SR8278 binding. This study highlights the effectiveness of GaMD in investigating protein-ligand interactions, particularly in the context of drug recognition for nuclear hormone receptors.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

Analysing the effect caused by increasing the molecular volume in M1-AChR receptor agonists and antagonists: a structural and computational study

M1 muscarinic acetylcholine receptor (M1-AChR), a member of the G protein-coupled receptors (GPCR) family, plays a crucial role in learning and memory, making it an important drug target for Alzheimer's disease (AD) and schizophrenia. M1-AChR activation and deactivation have shown modifying effects in AD and PD preclinical models, respectively. However, understanding the pharmacology associated with M1-AChR activation or deactivation is complex, because of the low selectivity among muscarinic subtypes, hampering their therapeutic applications. In this regard, we constructed two quantitative structure-activity relationship (QSAR) models, one for M1-AChR agonists (total and partial), and the other for the antagonists. The binding mode of 59 structurally different compounds, including agonists and antagonists with experimental binding affinity values (pKi), were analyzed employing computational molecular docking over different structures of M1-AChR. Furthermore, we considered the interaction energy (Einter), the number of rotatable bonds (NRB), and lipophilicity (ilogP) for the construction of the QSAR model for agonists (R2 = 89.64, QLMO2 = 78, and Qext2 = 79.1). For the QSAR model of antagonists (R2 = 88.44, QLMO2 = 82, and Qext2 = 78.1) we considered the Einter, the fraction of sp3 carbons fCsp3, and lipophilicity (MlogP). Our results suggest that the ligand volume is a determinant to establish its biological activity (agonist or antagonist), causing changes in binding energy, and determining the affinity for M1-AChR.

Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.

In Silico Study in MPO and Molecular Docking of the Synthetic Drynaran Analogues Against the Chronic Tinnitus: Modulation of the M1 Muscarinic Acetylcholine Receptor

Tinnitus is a syndrome that affects the human auditory system and is characterized by a perception of sounds in the absence of acoustic stimuli, or in total silence. Research indicates that muscarinic acetylcholine receptors (mAChRs), especially the M1 type, have a fundamental role in the alterations of auditory perceptions of tinnitus. Here, a series of computer-aided tools were used, from molecular surface analysis software to services available on the web for estimating pharmacokinetics and pharmacodynamics. The results infer that the low lipophilicity ligands, that is, the 1a-d alkyl furans, present the best pharmacokinetic profile, as compounds with an optimal alignment between permeability and clearance. However, only ligands 1a and 1b have properties that are safe for the central nervous system, the site of cholinergic modulation. These ligands showed similarity with compounds deposited in the European Molecular Biology Laboratory chemical (ChEMBL) database acting on the mAChRs M1 type, the target selected for the molecular docking test. The simulations suggest that the 1 g ligand can form the ligand-receptor complex with the best affinity energy order and that, together with the 1b ligand, they are competitive agonists in relation to the antagonist Tiotropium, in addition to acting in synergism with the drug Bromazepam in the treatment of chronic tinnitus.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Xanomeline displays concomitant orthosteric and allosteric binding modes at the M4 mAChR

The M4 muscarinic acetylcholine receptor (M4 mAChR) has emerged as a drug target of high therapeutic interest due to its expression in regions of the brain involved in the regulation of psychosis, cognition, and addiction. The mAChR agonist, xanomeline, has provided significant improvement in the Positive and Negative Symptom Scale (PANSS) scores in a Phase II clinical trial for the treatment of patients suffering from schizophrenia. Here we report the active state cryo-EM structure of xanomeline bound to the human M4 mAChR in complex with the heterotrimeric Gi1 transducer protein. Unexpectedly, two molecules of xanomeline were found to concomitantly bind to the monomeric M4 mAChR, with one molecule bound in the orthosteric (acetylcholine-binding) site and a second molecule in an extracellular vestibular allosteric site. Molecular dynamic simulations supports the structural findings, and pharmacological validation confirmed that xanomeline acts as a dual orthosteric and allosteric ligand at the human M4 mAChR. These findings provide a basis for further understanding xanomeline's complex pharmacology and highlight the myriad of ways through which clinically relevant ligands can bind to and regulate GPCRs.

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the t_META-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.

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.

Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket

Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.

Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.

Ligand-induced shifts in conformational ensembles that describe transcriptional activation

Nuclear receptors function as ligand-regulated transcription factors whose ability to regulate diverse physiological processes is closely linked with conformational changes induced upon ligand binding. Understanding how conformational populations of nuclear receptors are shifted by various ligands could illuminate strategies for the design of synthetic modulators to regulate specific transcriptional programs. Here, we investigate ligand-induced conformational changes using a reconstructed, ancestral nuclear receptor. By making substitutions at a key position, we engineer receptor variants with altered ligand specificities. We combine cellular and biophysical experiments to characterize transcriptional activity, as well as elucidate mechanisms underlying altered transcription in receptor variants. We then use atomistic molecular dynamics (MD) simulations with enhanced sampling to generate ensembles of wildtype and engineered receptors in combination with multiple ligands, followed by conformational analysis and correlation of MD-based predictions with functional ligand profiles. We determine that conformational ensembles accurately describe ligand responses based on observed population shifts. These studies provide a platform which will allow structural characterization of physiologically-relevant conformational ensembles, as well as provide the ability to design and predict transcriptional responses in novel ligands.

Recognition between CD147 and cyclophilin A deciphered by accelerated molecular dynamics simulations

CD147 functions as the receptor of extracellular cyclophilin A (CypA) in various diseases, and CD147-CypA binding ulteriorly underlies the pathological process of various viral infections including HIV-1, SARS, and SARS-CoV-2. Although CyPA has been identified as a key intermediate pro-inflammatory factor, the mechanism by which CD147 cooperates with CypA in the development of the cytokine storm remains largely unknown, and the binding profile of CD147 with CypA remains to be elucidated as well. Here, we prepared three binding models of the CD147-CypA complex, including the active site of CypA severally binding to the groove bound by the Ig1 and Ig2 domains (model-0), P180-G181 (model-1), and P211 (model-2) of CD147, as well as introducing mutations P180A-G181A and P211A individually in each model. All systems were studied using accelerated molecular dynamics simulations and the molecular mechanics generalized Born surface area (MM/GBSA) method. For model-0, CypA bound to the ectodomain of CD147 with the highest binding affinity. Moreover, mutations P180A-G181A of CD147 in model-0 decreased the binding affinity and weakened the dynamic correlation between CD147 and CypA, which resulted in CypA shifting from the initial binding location. Other residue mutations of CD147 did not significantly affect the CD147-CypA binding, as reflected by the energy and structural analyses. Compared with surface plasmon resonance results and nuclear magnetic resonance shift signals, CypA should tend to reciprocally bind to the groove of CD147, and the binding process might be modulated by P180-G181 rather than P211. Besides, residue R201 of CD147 is critical for CD147-CypA binding and needs further experimental verification. These findings further our understanding of the recruitment between CD147 and CypA and its potential role in the development of inflammation and viral infection.

Computational and experimental approaches to probe GPCR activation and signaling

G protein-coupled receptors (GPCRs) regulate different physiological functions, e.g., sensation, growth, digestion, reproductivity, nervous and immune systems response, and many others. In eukaryotes, they are also responsible for intercellular communication in response to pathogens. The major primary messengers binding to these cell-surface receptors constitute small-molecule or peptide hormones and neurotransmitters, nucleotides, lipids as well as small proteins. The simplicity of the way how GPCR signaling can be regulated by their endogenous agonists prompted the usage of GPCRs as major drug targets in modern pharmacology. Drugs targeting GPCRs inhibit pathological processes at the very beginning. This enables to significantly reduce the occurrence of morphological changes caused by diseases. Until recently, X-ray crystallography was the method of the first choice to obtain high-resolution structural information about GPCRs. Following X-ray crystallography, cryo-EM gained attention in GPCR studies as a quick and low-cost alternative. FRET microscopy is also widely used for GPCRs in the analysis of protein-protein interactions (PPIs) in intact cells as well as for screening purposes. Regarding computational methods, molecular dynamics (MD) for many years has proven its usefulness in studying the GPCR activation. MODELLER and Rosetta were widely used to generate preliminary homology models of GPCRs for MD simulation systems. Apart from the conventional all-atom approach with explicitly defined solvent, also other techniques have been applied to GPCRs, e.g., MARTINI or hybrid methods involving the coarse-grained representation, less demanding regarding computational resources, and thus offering much larger simulation timescales.

TAB1 binding induced p38α conformation change: an accelerated molecular dynamics simulation study

p38α mitogen-activated protein kinase (MAPK) undergoes autophosphorylation induced by the binding of TGFβ-activated kinase 1 binding protein 1 (TAB1) in myocardial ischemia. Investigation of the conformational transformations in p38α triggered by TAB1 binding is motivated by the need to find selective p38α activation inhibitors to treat myocardial ischemia. Herein, the conformational transformations of p38α were studied via all-atom accelerated molecular dynamics simulations and principal component analysis. With the binding of TAB1, the conformational changes of p38α auto-activation were characterized by the movement of the activation loop (A-loop) away from the αG helix toward the αF, αE helixes and L16-loop. In addition, a diverse intermediate state with an extensional and phosphorylated A-loop different from the transition intermediate state was explored. The conformational changes, including the A-loop alpha-structure breaking and the stronger hydrogen bond network formation, are accompanied by the extension of the A-loop and more intramolecular interactions in p38α. TAB1 correlates with other regions of p38α that are distal from the TAB1-binding site, including the A-loop, αC helix, and L16-loop, which regulates the intramolecular correlation of p38α. And, the phosphorylation further enhances the correlations between the A-loop and the other regions of p38α. The correlation results imply the regulation process of p38α conformational transformations. These findings will improve our understanding of the autophosphorylation of kinase and facilitate the development of selective inhibitors for the treatment of ischemic injury.

In Silico Modeling and Scoring of PROTAC-Mediated Ternary Complex Poses

Proteolysis targeting chimeras (PROTACs) are molecules that induce protein degradation via formation of ternary complexes between an E3 ubiquitin ligase and a target protein. The rational design of PROTACs requires accurate knowledge of the native configuration of the PROTAC-induced ternary complex. This study demonstrates that native and non-native ternary complex poses can be distinguished based on the pose occupancy time in MD, where native poses exhibit longer occupancy times at both room and higher temperatures. Candidate poses are generated by MD sampling and pre-ranked by classic MM/GBSA. A specific heating scheme is then applied to accelerate ternary pose departure, with the pose occupancy time and fraction being measured. This scoring identifies the native pose in all systems tested. Its success is partially attributed to the dynamic nature of pose departure analyses, which accounts for entropic effects typically neglected in the faster static scoring methods, while entropy plays a greater role in protein-protein than in protein-ligand systems.

Molecular insights into the allosteric coupling mechanism between an agonist and two different transducers for μ-opioid receptors

G protein-coupled receptors (GPCRs) as the most important class of pharmacological targets regulate G-protein and β-arrestin-mediated signaling through allosteric interplay, which are responsible for different biochemical and physiological actions like therapeutic efficacy and side effects. However, the allosteric mechanism underlying preferentially recruiting one transducer versus the other has been poorly understood, limiting drug design. Motivated by this issue, we utilize accelerated molecular dynamics simulation coupled with potential of mean force (PMF), molecular mechanics Poisson Boltzmann surface area (MM/PBSA) and protein structure network (PSN) to study two ternary complex systems of a representative class A GPCR (μ-opioid receptor (μOR)) bound by an agonist and one specific transducer (G-protein and β-arrestin). The results show that no significant difference exists in the whole structure of μOR between two transducer couplings, but displays transducer-dependent changes in the intracellular binding region of μOR, where the β-arrestin coupling results in a narrower crevice with TM7 inward movement compared with the G-protein. In addition, both the G-protein and β-arrestin coupling can increase the binding affinity of the agonist to the receptor. However, the interactions between the agonist and μOR also exhibit transducer-specific changes, in particular for the interaction with ECL2 that plays an important role in recruiting β-arrestin. The allosteric network analysis further indicates that Y148^3.33, F152^3.37, F156^3.41, N191^4.49, T160^3.45, Y106^2.42, W293^6.48, F289^6.44, I248^5.54 and Y252^5.58 play important roles in equally activating G-protein and β-arrestin. In contrast, M161^3.46 and R165^3.50 devote important contributions to preferentially recruit G-protein while D164^3.49 and R179^ICL2 are revealed to be important for selectively activating β-arrestin. The observations provide useful information for understanding the biased activation mechanism.

Gaussian Accelerated Molecular Dynamics Simulations Investigation on the Mechanism of Angiotensin-Converting Enzyme (ACE) C-Domain Inhibition by Dipeptides

Angiotensin-converting enzyme (ACE)-inhibitory peptides extracted from food proteins can lower blood pressure by inhibiting ACE activity. A recent study showed that the inhibitory activity of IY (Ile-Tyr, a dipeptide derived from soybean protein) against ACE was much higher than that of LL (Leu-Leu), although they had similar hydrophobic and predicted activity values. It was difficult to reveal the deep molecular mechanism underlying this phenomenon by traditional experimental methods. The Apo and two complex systems (i.e., ACE–LL and ACE–IY) were therefore subjected to 1 μs long Gaussian accelerated molecular dynamics (GaMD) simulations. The results showed that the binding of IY can cause obvious contraction of the active site of ACE, mainly manifested by a significant lateral shift of α13, α14, and α15. In addition, hinge 2 and hinge 3 were more stable in the ACE–IY system, while these phenomena were not present in the ACE–LL system. Moreover, the α10 of the IY-bound ACE kept an inward state during the simulation progress, which facilitated the ACE to remain closed. However, for the LL-bound ACE, the α10 switched between two outward states. To sum up, our study provides detailed insights into inhibitor-induced conformational changes in ACE that may help in the design of specific inhibitors targeting ACE for the treatment of hypertension.

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.

Tetracycline Derivatives Inhibit Plasmodial Cysteine Protease Falcipain-2 through Binding to a Distal Allosteric Site

Allosteric inhibitors regulate enzyme activity from remote and usually specific pockets. As they promise an avenue for less toxic and safer drugs, the identification and characterization of allosteric inhibitors has gained great academic and biomedical interest in recent years. Research on falcipain-2 (FP-2), the major papain-like cysteine hemoglobinase of Plasmodium falciparum, might benefit from this strategy to overcome the low selectivity against human cathepsins shown by active site-directed inhibitors. Encouraged by our previous finding that methacycline inhibits FP-2 noncompetitively, here we assessed other five tetracycline derivatives against this target and characterized their inhibition mechanism. As previously shown for methacycline, tetracycline derivatives inhibited FP-2 in a noncompetitive fashion, with K_i values ranging from 121 to 190 μM. A possible binding to the S' side of the FP-2 active site, similar to that described by X-ray crystallography (PDB: 6SSZ) for the noncompetitive inhibitor E-chalcone 48 (EC48), was experimentally discarded by kinetic analysis using a large peptidyl substrate spanning the whole active site. By combining lengthy molecular dynamics (MD) simulations that allowed methacycline to diffuse from solution to different FP-2 surface regions and free energy calculations, we predicted the most likely binding mode of the ligand. Of note, the proposed binding pose explains the low differences in K_i values observed for the tested tetracycline derivatives and the calculated binding free energies match the experimental values. Overall, this study has implications for the design of novel allosteric inhibitors against FP-2 and sets the basis for further optimization of the tetracycline scaffold to produce more potent and selective inhibitors.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

Probing Allosteric Regulation Mechanism of W7.35 on Agonist-Induced Activity for μOR by Mutation Simulation

The residue located at 15 positions before the most conserved residue in TM7 (7.35 of Ballesteros-Weinstein number) plays important roles in ligand binding and the receptor activity for class A GPCRs. Nevertheless, its regulation mechanism has not been clearly clarified in experiments, and some controversies also exist for its impact on μ-opioid receptors (μOR) bound by agonists. Thus, we chose the μ-opioid receptor (μOR) of class A GPCRs as a representative and utilized a microsecond accelerated molecular dynamics simulation (aMD) coupled with a protein structure network (PSN) to explore the effect of W318^7.35 on its functional activity induced by the agonist endomorphin2 mainly by a comparison of the wild system and its W7.35A mutant. When endomorphin2 binds to the wild-type μOR, TM6 in μOR moves outward to form an open intracellular conformation that is beneficial to accommodating the β-arrestin transducer, rather than the G-protein transducer due to the clash with the α5 helix of G-protein, thus acting as a β-arrestin biased agonist. However, the W318A mutation induces the intracellular part of μOR to form a closed state, which disfavors coupling with either G-protein or β-arrestin. The allosteric pathway analysis further reveals that the binding of endomorphin2 to the wild-type μOR transmits more activation signals to the β-arrestin binding site while the W318A mutation induces more structural signals to transmit to common binding residues of the G protein and β-arrestin. More interestingly, the residue at the 7.35 position regulates the shortest allosteric pathway in indirect ways by influencing the interactions between other ligand-binding residues and endomorphin2. W293^6.48 and F289^6.44 are important for regulating the different activities of μOR induced either by the agonist or by the mutation. Y336^7.53, F343^8.50, and D340^8.47 play crucial roles in activating the β-arrestin biased signal induced by the agonist endomorphin2, while L158^3.43 and V286^6.41 devote important contributions to the change in the activity of endomorphin2 from the β-arrestin biased agonist to the antagonist upon the W318A mutation.

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.

Conformational dynamics of androgen receptors bound to agonists and antagonists

The androgen receptor (AR) is critical in the progression of prostate cancer (PCa). Small molecule antagonists that bind to the ligand binding domain (LBD) of the AR have been successful in treating PCa. However, the structural basis by which the AR antagonists manifest their therapeutic efficacy remains unclear, due to the lack of detailed structural information of the AR bound to the antagonists. We have performed accelerated molecular dynamics (aMD) simulations of LBDs bound to a set of ligands including a natural substrate (dihydrotestosterone), an agonist (RU59063) and three antagonists (bicalutamide, enzalutamide and apalutamide) as well as in the absence of ligand (apo). We show that the binding of AR antagonists at the substrate binding pocket alter the dynamic fluctuations of H12, thereby disrupting the structural integrity of the agonistic conformation of AR. Two antagonists, enzalutamide and apalutamide, induce considerable structural changes to the agonist conformation of LBD, when bound close to H12 of AR LBD. When the antagonists bind to the pocket with different orientations having close contact with H11, no significant conformational changes were observed, suggesting the AR remains in the functionally activated (agonistic) state. The simulations on a drug resistance mutant F876L bound to enzalutamide demonstrated that the mutation stabilizes the agonistic conformation of AR LBD, which compromises the efficacy of the antagonists. Principal component analysis (PCA) of the structural fluctuations shows that the binding of enzalutamide and apalutamide induce conformational fluctuations in the AR, which are markedly different from those caused by the agonist as well as another antagonist, bicalutamide. These fluctuations could only be observed with the use of aMD.

Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments

Pepsin-like aspartic proteases (PAPs) are a class of aspartic proteases which shares tremendous structural similarity with human pepsin. One of the key structural features of PAPs is the presence of a β-hairpin motif otherwise known as flap. The biological function of the PAPs is highly dependent on the conformational dynamics of the flap region. In apo PAPs, the conformational dynamics of the flap is dominated by the rotational degrees of freedom associated with χ1 and χ2 angles of conserved Tyr (or Phe in some cases). However it is plausible that dihedral order parameters associated with several other residues might play crucial roles in the conformational dynamics of apo PAPs. Due to their size, complexities associated with conformational dynamics and clinical significance (drug targets for malaria, Alzheimer's disease etc.), PAPs provide a challenging testing ground for computational and experimental methods focusing on understanding conformational dynamics and molecular recognition in biomolecules. The opening of the flap region is necessary to accommodate substrate/ligand in the active site of the PAPs. The BIG challenge is to gain atomistic details into how reversible ligand binding/unbinding (molecular recognition) affects the conformational dynamics. Recent reports of kinetics (K i, K d) and thermodynamic parameters (ΔH, TΔS, and ΔG) associated with macro-cyclic ligands bound to BACE1 (belongs to PAP family) provide a perfect challenge (how to deal with big ligands with multiple torsional angles and select optimum order parameters to study reversible ligand binding/unbinding) for computational methods to predict binding free energies and kinetics beyond typical test systems e.g. benzamide-trypsin. In this work, i reviewed several order parameters which were proposed to capture the conformational dynamics and molecular recognition in PAPs. I further highlighted how machine learning methods can be used as order parameters in the context of PAPs. I then proposed some open ideas and challenges in the context of molecular simulation and put forward my case on how biophysical experiments e.g. NMR, time-resolved FRET etc. can be used in conjunction with biomolecular simulation to gain complete atomistic insights into the conformational dynamics of PAPs.

Conformational Changes of Glutamine 5'-Phosphoribosylpyrophosphate Amidotransferase for Two Substrates Analogue Binding: Insight from Conventional Molecular Dynamics and Accelerated Molecular Dynamics Simulations

Glutamine 5′-phosphoribosylpyrophosphate amidotransferase (GPATase) catalyzes the synthesis of phosphoribosylamine, pyrophosphate, and glutamate from phosphoribosylpyrophosphate, as well as glutamine at two sites (i.e., glutaminase and phosphoribosylpyrophosphate sites), through a 20 Å NH₃ channel. In this study, conventional molecular dynamics (cMD) simulations and enhanced sampling accelerated molecular dynamics (aMD) simulations were integrated to characterize the mechanism for coordination catalysis at two separate active sites in the enzyme. Results of cMD simulations illustrated the mechanism by which two substrate analogues, namely, DON and cPRPP, affect the structural stability of GPATase from the perspective of dynamic behavior. aMD simulations obtained several key findings. First, a comparison of protein conformational changes in the complexes of GPATase–DON and GPATase–DON–cPRPP showed that binding cPRPP to the PRTase flexible loop (K326 to L350) substantially effected the formation of the R73-DON salt bridge. Moreover, only the PRTase flexible loop in the GPATase–DON–cPRPP complex could remain closed and had sufficient space for cPRPP binding, indicating that binding of DON to the glutamine loop had an impact on the PRTase flexible loop. Finally, both DON and cPRPP tightly bonded to the two domains, thereby inducing the glutamine loop and the PRTase flexible loop to move close to each other. This movement facilitated the transfer of NH3 via the NH3 channel. These theoretical results are useful to the ongoing research on efficient inhibitors related to GPATase.

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.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Increasing the Sampling Efficiency of Protein Conformational Change by Combining a Modified Replica Exchange Molecular Dynamics and Normal Mode Analysis

Understanding conformational change at an atomic level is significant when determining a protein functional mechanism. Replica exchange molecular dynamics (REMD) is a widely used enhanced sampling method to explore protein conformational space. However, REMD with an explicit solvent model requires huge computational resources, immensely limiting its application. In this study, a variation of parallel tempering metadynamics (PTMetaD) with the omission of solvent-solvent interactions in exchange attempts and the use of low-frequency modes calculated by normal-mode analysis (NMA) as collective variables (CVs), namely ossPTMetaD, is proposed with the aim to accelerate MD simulations simultaneously in temperature and geometrical spaces. For testing the performance of ossPTMetaD, five protein systems with diverse biological functions and motion patterns were selected, including large-scale domain motion (AdK), flap movement (HIV-1 protease and BACE1), and DFG-motif flip in kinases (p38α and c-Abl). The simulation results showed that ossPTMetaD requires much fewer numbers of replicas than temperature REMD (T-REMD) with a reduction of ∼70% to achieve a similar exchange ratio. Although it does not obey the detailed balance condition, ossPTMetaD provides consistent results with T-REMD and experimental data. The high accessibility of the large conformational change of protein systems by ossPTMetaD, especially in simulating the very challenging DFG-motif flip of protein kinases, demonstrated its high efficiency and robustness in the characterization of the large-scale protein conformational change pathway and associated free energy profile.

Simulating Multiple Substrate-Binding Events by γ-Glutamyltransferase Using Accelerated Molecular Dynamics

γ-Glutamyltransferase (GGT) is an enzyme that uses γ-glutamyl compounds as substrates and catalyzes their transfer to a water molecule or an acceptor substrate with varied physiological function in bacteria, plants, and animals. Crystal structures of GGT are known for different species and in different states of the chemical reaction; however, the structural dynamics of the substrate binding to the catalytic site of GGT are unknown. Here, we modeled Escherichia coli GGT's glutamine binding by using a swarm of accelerated molecular dynamics (aMD) simulations. Characterization of multiple binding events identified three structural binding motifs composed of polar residues in the binding pocket that govern glutamine binding into the active site. Simulated open and closed conformations of a lid-loop protecting the binding cavity suggest its role as a gating element by allowing or blocking substrates entry into the binding pocket. Partially open states of the lid-loop are accessible within thermal fluctuations, while the estimated free energy cost of a complete open state is 2.4 kcal/mol. Our results suggest that both specific electrostatic interactions and GGT conformational dynamics dictate the molecular recognition of substrate-GGT complexes.

Molecular dynamics of the histamine H3 membrane receptor reveals different mechanisms of GPCR signal transduction

In this work, we studied the mechanisms of classical activation and inactivation of signal transduction by the histamine H3 receptor, a 7-helix transmembrane bundle G-Protein Coupled Receptor through long-time-scale atomistic molecular dynamics simulations of the receptor embedded in a hydrated double layer of dipalmitoyl phosphatidyl choline, a zwitterionic polysaturated ordered lipid. Three systems were prepared: the apo receptor, representing the constitutively active receptor; and two holo-receptors-the receptor coupled to the antagonist/inverse agonist ciproxifan, representing the inactive state of the receptor, and the receptor coupled to the endogenous agonist histamine and representing the active state of the receptor. An extensive analysis of the simulation showed that the three states of H3R present significant structural and dynamical differences as well as a complex behavior given that the measured properties interact in multiple and interdependent ways. In addition, the simulations described an unexpected escape of histamine from the orthosteric binding site, in agreement with the experimental modest affinities and rapid off-rates of agonists.

Nucleotide Loading Modes of Human RNA Polymerase II as Deciphered by Molecular Simulations

Mapping the route of nucleoside triphosphate (NTP) entry into the sequestered active site of RNA polymerase (RNAP) has major implications for elucidating the complete nucleotide addition cycle. Constituting a dichotomy that remains to be resolved, two alternatives, direct NTP delivery via the secondary channel (CH2) or selection to downstream sites in the main channel (CH1) prior to catalysis, have been proposed. In this study, accelerated molecular dynamics simulations of freely diffusing NTPs about RNAPII were applied to refine the CH2 model and uncover atomic details on the CH1 model that previously lacked a persuasive structural framework to illustrate its mechanism of action. Diffusion and binding of NTPs to downstream DNA, and the transfer of a preselected NTP to the active site, are simulated for the first time. All-atom simulations further support that CH1 loading is transcription factor IIF (TFIIF) dependent and impacts catalytic isomerization. Altogether, the alternative nucleotide loading systems may allow distinct transcriptional landscapes to be expressed.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

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.

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.

Probing the Molecular Mechanism of Rifampin Resistance Caused by the Point Mutations S456L and D441V on Mycobacterium tuberculosis RNA Polymerase through Gaussian Accelerated Molecular Dynamics Simulation

Rifampin is the first-line antituberculosis drug, with Mycobacterium tuberculosis RNA polymerase as the molecular target. Unfortunately, M. tuberculosis strains that are resistant to rifampin have been identified in clinical settings, which limits its therapeutic effects. In clinical isolates, S531L and D516V (in Escherichia coli) are two common mutated codons in the gene rpoB, corresponding to S456L and D441V in M. tuberculosis However, the resistance mechanism at the molecular level is still elusive. In this work, Gaussian accelerated molecular dynamics simulations were performed to uncover the resistance mechanism of rifampin due to S456L and D441V mutations at the atomic level. The binding free energy analysis revealed that the reduction in the ability of two mutants to bind rifampin is mainly due to a decrease in electrostatic interaction, specifically, a decrease in the energy contribution of the R454 residue. R454 acts as an anchor and forms stable hydrogen bond interaction with rifampin, allowing rifampin to be stably incorporated in the center of the binding pocket. However, the disappearance of the hydrogen bond between R454 and the mutated residues increases the flexibility of the side chain of R454. The conformation of R454 changes, and the hydrogen bond interaction between it and rifampin is disrupted. As result, the rifampin molecule moves to the outside of the pocket, and the binding affinity decreases. Overall, these findings can provide useful information for understanding the drug resistance mechanism of rifampin and also can give theoretical guidance for further design of novel inhibitors to overcome the drug resistance.

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.

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.

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

G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and serve as primary targets of approximately one-third of currently marketed drugs. In particular, adenosine A1 receptor (A1 AR) is an important therapeutic target for treating cardiac ischemia-reperfusion injuries, neuropathic pain, and renal diseases. As a prototypical GPCR, the A1 AR is located within a phospholipid membrane bilayer and transmits cellular signals by changing between different conformational states. It is important to elucidate the lipid-protein interactions in order to understand the functional mechanism of GPCRs. Here, all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method were performed on both the inactive (antagonist bound) and active (agonist and G-protein bound) A1 AR, which was embedded in a 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) lipid bilayer. In the GaMD simulations, the membrane lipids played a key role in stabilizing different conformational states of the A1 AR. Our simulations further identified important regions of the receptor that interacted distinctly with the lipids in highly correlated manner. Activation of the A1 AR led to differential dynamics in the upper and lower leaflets of the lipid bilayer. In summary, GaMD enhanced simulations have revealed strongly coupled dynamics of the GPCR and lipids that depend on the receptor activation state. © 2019 Wiley Periodicals, Inc.

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.

Allosteric modulation of the sarcoplasmic reticulum Ca2+ ATPase by thapsigargin via decoupling of functional motions

The sarcoplasmic reticulum Ca2+-ATPase (SERCA) is a widely studied member of the large family of phosphorylation(P)-type ATPase membrane transporters. Ligands and nucleotide binding naturally modulate the conformational space of P-type ATPases through allosteric inter-domain communications. Whereas many inhibitory ATPase ligands act by directly blocking substrate uptake or release, SERCA is a target for thapsigargin (TG), a plant-derived natural product that allosterically inhibits the transport cycle. While thapsigargin's inhibitory effects on SERCA have been widely studied experimentally, the molecular mechanisms underlying these remain incompletely understood. Here, we apply modelling and molecular simulations to probe the effects of TG binding to the major functional states along SERCA's reaction cycle. Our results provide insight into the atomic-level details of the conformational changes induced by TG binding to SERCA, and suggest mechanisms for its effect. Since other P-type ATPases share closely related reaction cycles, our data suggests that similar modulators might exist for these.

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.

Function2Form Bridge-Toward synthetic protein holistic performance prediction

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Computational chemistry methods to investigate the effects caused by DNA variants linked with disease

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.