Review Cite This: J. Chem. Inf. Model. 2018, 58, 1473−1482
pubs.acs.org/jcim
Steered Molecular Dynamics Simulation in Rational Drug Design Phuc-Chau Do,† Eric H. Lee,‡ and Ly Le*,† †
School of Biotechnology, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam Department of Medicine and Division of Hematology and Oncology, Loma Linda University Medical Center, Loma Linda, California 92350, United States
J. Chem. Inf. Model. 2018.58:1473-1482. Downloaded from pubs.acs.org by KAOHSIUNG MEDICAL UNIV on 11/15/18. For personal use only.
‡
ABSTRACT: Conventional de novo drug design is time consuming, laborious, and resource intensive. In recent years, emerging in silico approaches have been proven to be critical to accelerate the process of bringing drugs to market. Molecular dynamics (MD) simulations of single molecule and molecular complexes have been commonly applied to achieve accurate binding modes and binding energies of drugreceptor interactions. A derivative of MD, namely, steered molecular dynamics (SMD), has been demonstrated as a promising tool for rational drug design. In this paper, we review various studies over the last 20 years using SMD simulations, thus paving the way to determine the relationship between protein structure and function. In addition, the paper highlights the use of SMD simulation for in silico drug design. We also aim to establish an understanding on the key interactions which play a crucial role in the stabilization of peptide−ligand interfaces, the binding and unbinding mechanism of the ligand− protein complex, the mechanism of ligand translocating via membrane, and the ranking of different ligands on receptors as therapeutic candidates. KEYWORDS: Molecular dynamics simulation, Steered molecular dynamic, Drug design, Drug-receptor interactions, Ligand−protein complex, Peptide−ligand interfaces, Protein structure and function
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INTRODUCTION Proteins play a central role in all physiological processes of living organisms. Drug development is therefore dependent on the understanding of how proteins work in the pathogenesis of disease. Specifically, the development of modern targeted therapeutics to disrupt disease pathways often adopts many approaches, including specific binding of receptors with small molecules, binding of cell surface markers with monoclonal antibodies, and/or competitive inhibition of pathogenic metabolites. Molecular dynamics (MD) simulations of proteins at the atomic level are a well proven tool for characterizing both the behavior of proteins and the protein−ligand interactions involved in cell signaling for disease processes. One property of protein behavior in which molecular simulation is particularly well suited for investigation is the behavior of molecules under the various mechanical stresses that occur in a cellular environment. For example, a protein conformation change driven by the application of an external force may permit or restrict access to a binding site. As such, the mechanical properties of receptors are of crucial interest when designing drugs for targeted therapy. The simulation of steered molecular dynamics (SMD), a special type of MD simulations, applies a directional vector to a molecule or protein in order to study how it responds to external forces. The SMD method was first introduced in 1997 at the second International Symposium on Algorithms for Macromolecular Modeling by Klaus Schulten and colleagues at the University of Illinois (USA).1 During that period, mature © 2018 American Chemical Society
experimental methods such as atomic force microscopy (AFM), optical tweezers, biomembrane force probe, or surface force apparatus had been used to study the interaction of ligand−receptor or protein complexes. While the force− extension data obtained from those approaches yielded macroscopic insight into protein structure−function relationships, the interpretation of events at the atomic level was limited to speculation. In contrast, SMD simulations, which provided atomic level resolution of force-probe events, have proved to be a crucial complement to existing experimental methods.2,3 In a typical SMD simulation,3 an external vector is applied to one terminus of the molecule while the opposing terminus is fixed in space in order to investigate not just how the molecule responds to mechanical stress but also how it can used to elucidate upon the structure−function relationship of a macromolecular complex involving either protein−ligand or protein−protein interactions. In principle, forces can be applied not just to the ends of a protein but also to any atom or group of atoms within the structure. In SMD, the force vector is applied through a virtual damped harmonic spring. The measured force over time is calculated and recorded in the form of a diagram which will show the force distribution at different atomic states of reaction. This force is correlated with conformation changes of proteins or molecules in the system under investigation.4 Received: April 30, 2018 Published: July 5, 2018 1473
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resolution, one could for the first time elucidate the detail that the unfolding of the titin IgG domain I27 relied on six specific hydrogen bonds between strand A′ and G that were ruptured once a force stronger than 50 pN was applied under simulation time scales (nanoseconds). It was also discovered in simulations that the different immunoglobulin like (Ig-) domains of titin behave differently under mechanical stress. When the titin I1 domain was compared to the unfolding pathway of titin I27,9 the I27 domain was observed to unfold more readily than I1, due to the presence of a disulfide bond on I1 which enhanced its mechanical stability and served as a restrictor to the rupture of backbone hydrogen bonds. Subsequent SMD studies on fibronectin, a subunit repeat motif of titin and itself a substrate for cell anchorage, also shed light on how a chain-like network of interstrand hydrogen and disulfide bonds stabilize its globular fold against mechanical stress.4 The SMD method has been widely utilized in other investigations around the globe. Huang’s group from China employed SMD simulations to characterize heme binding to apocyt b5, a small cytochrome that can covalently or noncovalently bind heme through interstrand hydrogen bonding between β strands which stabilize folding intermediates between its two heme binding variants.11 In another study, the unfolding of a parallel human G-quadruplex from telomere DNA was found to be dependent on whether a force was applied to the sugar phosphate backbone or to the terminal nucleobases.12 This finding led to a prediction of two different intermediate folding patterns from which these structures derive mechanical stability. SMD simulations have also shed light on pathological proteins involved in neurological diseases. Prion proteins, which are responsible for the neurodegerative disorder Creutzfeldt−Jakob disease, have also been investigated using SMD, where it was found that much of the mechanical stability for the prion protein fold is due to a strong intramolecular interaction between helix 1 and helix 3 of the protein.13 In other neurological diseases such as Alzheimer’s, Parkinson’s, or type II diabetes, amyloid protein fibrils are formed by peptides or misfolded proteins that selfassemble into filamentous aggregates, but the mechanism was not clear until investigated using SMD simulations.14,15 Simulations have shown that variations in the peptide sequence for human amylin hIAPP20-19 (involved in amyloid aggregation) altered its mechanical stability to stretching forces. It was also revealed that while the wild-type, F4L, and I7 V sequence variants were able to maintain their beta-sheet content in fibrillar arrays, other variants such as the A6P, F4LA6P, and F4L-A6P-I7 V systems displayed increased disorder. As such, the latter isoforms might carry higher risk for pathologically stable amyloid fibril and plaque formation compared to wild-type variants. SMD studies have also been employed to investigate von Willebrand factor (vWF), a multidomain multimeric plasma glycoprotein important in the blood clotting pathway which is responsible for mediating the adhesion of platelets to subendothelial extracellular matrices at vascular injury sites.16,17 It was found through these studies that the stability of its two principle “A1” and “A2” domains differ due to a disulfide bond between the N-terminus and Cterminus unique to the A1 domain. When the disulfide bonds were removed in simulation, the unfolding pathways of two domains are similar in β-strand separation but different in the helical unfolding.18 Furthermore, in vitro studies have demonstrated that disruption to intrastrand disulfide bonds
While stretching or unfolding a protein, it is expected that the sensitive interactions or “Achilles’ heel” of the protein would be initially detected and broken. These key interactions, spanning from hydrogen bonds stabilizing the secondary structure or electrostatic interactions between charged side chains at the atomic level to a long-range backbone or domain−domain specific structural properties at the macroscopic level, reveal the clues of the structure−function relationship of a particular protein or molecular complex.4 For protein−ligand complexes, SMD simulations can be used to identify these interactions, thereby providing information for designing drugs which can easily access the active binding site to enhance or inhibit the activity of the targeted protein. Nevertheless, there are some limitations in this approach where SMD and AFM may have divergent results. Factors such as the choice of force field, direction of the force vector, and magnitude of force can introduce bias in simulations not present in experiments.1 Explicit solvation in molecular simulations attempt to approximate physiological conditions, but these nonetheless represent compromises as many force fields fail to account for the polarization of solvent and side chains. Furthermore, often an arbitrary force vector is defined in SMD simulations, which may not represent the same “pulling path” achieved in experiments. One approach to overcome the latter limitation would be sampling multiple vectors,5 either in a direction or pulling force, a brute force strategy that is computationally demanding. Another limitation of SMD is the simulation time scale. While AFM can be used to study the reaction during a period of microsecond to second, SMD can only investigate the mechanism for a nanosecond range while requiring a higher stretching force in order to observe progression of a reaction coordinate within the time scale constriction.6 Despite these limitations, this review highlights results demonstrating that SMD can be used as a promising approach for in silico drug design. Initially SMD was developed to address a simple question: how a single globular protein subdomain responds to mechanical stress. In one of its earliest applications, Schulten and colleagues used SMD to study the mechanical stability of titin immunoglobulin domains.7 Titin, the largest protein in nature, is a 1-μm-long modular protein composed of 240−300 immunoglobulin-like and fibronectin-like domains and is responsible for the passive elasticity of muscle.8,9 Titin functions both as an initial scaffold protein for muscle fibril development and also remains incorporated into the final fibril structure, contributing to the overall strength of a mature muscle fibril. Under normal physiological conditions, this protein is frequently subjected to stretching forces, particularly at the extreme of muscle fiber elongation during the actin− myosin cross-stroke. Six different titin structures were examined and compared by fixing one protein terminus and pulling the opposite terminus, revealing that interstrand hydrogen bonds within their beta sheets are primarily responsible for the elasticity of titin under mechanical pulling forces.7 After all interstrand hydrogen bonds are broken, the A′G and AB strands of the β-sandwiched sheets of the titin IgG are separated leading to the exposure of the hydrophobic core of the IgG domains. The study was then followed up with further simulations, which have compared the results with analogous atomic force microscopy (AFM) experiments,10 where both simulations and experiments have demonstrated force−extension profiles with remarkable agreement. Because simulations were able to capture the unfolding events at atomic 1474
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X.Yao (China)
W. Han (China)
H. Jiang (China)
K. Schulten (USA)
Group
Molecules
Inhibitors PLX4720 and TAK-632−B-RAF kinase31
Ligands (pNPC8 and Ac-Leu-pNA)−Acylaminoacyl peptidase (APH)30
Chlorpyrifos−Acylpeptide hydrolases (APH)29
Nicotine−Acetylcholine binding protein (AChBP)28
Testosterone−Cytochrome P450 2B127
E2020−Acetylcholinesterase (TcAChE)26
Huperzine A (HupA)−Acetylcholinesterase (TcAChE)25
Nonnucleoside RT inhibitors (α-APA)−HIV-1 RT24
Hormone−Retinoic Acid receptor23
Phosphate Pi−Actin filament22
Retinal−Bacteriorhodopsin (bR)21
Avidin−Biotin complex20
Table 1. Binding and Unbinding Mechanism between Ligand and Protein Using SMD Mechanism revealed by SMD
BRAF kinase is a drug target for melanoma and renal cell carcinoma. SMD simulations revealed that inhibitor PLX4720 dissociates from the B-RAF receptor along the ATP
Acylpeptide hydrolases (APH) catalyze the removal of N-acylated amino acid from blocked peptides and represent a potential drug target for Alzheimers disease. SMD simulations revealed a major unbinding pathway for its ligand chlorpyrifos, which involves key interactions with residues Arg526, Glu88, Gly86, and Asn65. Acylaminoacyl peptidase (APH) is a potential drug target for control of hypertension, amnesia, and Alzheimers disease. SMD simulations explored ligand−receptor interactions with APH, identifying three possible unbinding pathways: Arg526 which hinders the ligand from leaving in pathway 2A and cation−π interactions in Phe485-Arg526 and Lys24-Phe41, key players of pathway 1 and pathway 3.
Nonnucleoside reverse transcriptase (RT) inhibitors (NNTRIs) are a backbone for HIV treatment. The unbinding pathway of the NNRTI, α-APA, is via the pocket entrance with a three-phased process based on the position of α-APA. Val179 and Leu100 form a bottleneck entrance, and two special water molecules form water bridges to prevent the inhibitor’s dissociation. SMD simulations reveal that NNRTIs inhibit RT by enlarging the DNAbinding cleft and restricting the mobility of the p66 subdomain essential during DNA translocation and polymerization. Huperzine A (HupA) is an inhibitor of acetylcholinesterase (AChE), a modulator of the neurotransmitter acetylcholine (ACh). SMD simulations showed that a specific residue, Asp72, plays a crucial role for recruiting HupA into the active site of AChE, with water molecules acting as a lubricant within the narrow gorge of the binding channel. AChE ligand binding investigated via SMD revealed a strong direct hydrogen bond and water bridge which form by association of aromatic residues (Tyr121, Phe330) and a benzene ring, essentially functioning as an “atomic conveyer belt” for the binding and unbinding process. Cytochrome P450s are a class of enzymes responsible for drug metabolism and clearance. SMD simulations probed the testosterone:P450 complex to elucidate a ligand exit pathway, revealing two channels of the egress: channel 1 is due to the rotation of the aromatic ring of Phe297 and the B′−C loop bending; channel 2 is due to the expansion of the B′−C loop/B′ helix and backbone displacement. SMD simulations revealed that while nicotine enters and leaves the binding site along a similar path, a global rotation of the protein−ligand complex is involved in the unbinding process. During simulation, the binding between nicotine and AChBP follows the quasiparabolic track, while the unbinding process follows a tangential pulling pathway.
Avidin is a protein which binds strongly to biotin, a cofactor that plays a role in many eukaryotic biological processes. SMD studies revealed that a key amino acid, TRP110, plays a role in the strong protein−ligand interaction between these two molecules. Furthermore, a structure called the 3−4 loop of avidin appears to stabilize the complex against mechanical forces that may dissociate the two molecules. bR is a light-driven proton pump found in Halobacterium. SMD studies revealed that a Schiff base bond to Lys216 plays a critical role for stabilizing the retinal ligand in the binding pocket. Actin-based cell motility requires phosphate release after ATP hydrolysis. SMD simulations showed that Pi exits actin via the putative exit near His73. His73+ makes a salt bridge with HPO42− for stabilizing the electrostatic barrier. Retinoic acid receptor is a transcription factor that regulates cell growth, differentiation, and development. SMD simulations revealed that retinoic acid binding follows a path through two helices H11 and H12. However, the unbinding was found to take a different pathway through interaction with Lys236.
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Ring D aromatic steroidal antiestrogen Salpichrolides−Human estrogen receptor41
3,6-bis(1-Methyl-4-vinylpyridinium) carbazole diiodide (BMVC)−Human telomeric G-quadruplex42
G. Burton (Argentina)
S.-Y. Sheu (Taiwan)
Selectin−Ligand complexes43
4-Bromophenol (4-BP)−Dehaloperoxidase-hemoglobin (DHP)40
S. Franzen (USA)
M. Long (China)
Sorafenib/Sunitinib−Vascular endothelial growth factor receptor 2 (VEGFR2)39
Glucose−Human Pulmonary surfactant protein D37
H.-x. Zhang (China)
A.M. Capelli (Italy)
Imatinib−Kinases c-Kit and AbI36
S.-Y. Yang (China)
Small ligands (O2, CO, NO)−Heme protein Mt-trHbN38
Peptides−Antibody fragment scFv fragment H635
K. Blank and H. Gaub (Germany)
M.A. Marti (Argentina)
3,5,3′-Triiodo-L-thyronine (T3)−Thyroid Hormone receptor TRα133,34
Inhibitor GS-461230−Hepatitis C virus nonstructural protein 5B32
Molecules
I. Polikarpov (Brazil)
Group
Table 1. continued Mechanism revealed by SMD
Nuclear receptor (NR) transcription factors include receptors for thyroid hormone, retinoids, steroids, vitamin D, and cholesterols. SMD simulations of the T3 thyroid hormone with its receptor revealed three competing dissociation pathways by which ligand escape can occur, suggesting that there may be multiple ways to target nuclear receptors. SMD simulations were used in conjunction with atomic force microscopy to generate multidimensional energy landscape profiles for receptor−ligand unbinding. The main barrier for the unbinding action is the backbone hydrogen bond between GlyH-H40 of the antibody fragment and the GluOε-6peptide of the peptide. Imatinib is a kinase inhibitor used to treat chronic myelogenous leukemia and gastrointestinal stromal tumors. SMD simulations were used to elucidate the dissociation pathway between imatinib and its target c-Kit and Abl, favoring an ATP channel rather than the allosteric pocket channel. Pulmonary surfactant is a protein that assists in mechanical ventilation as well as serving as a regulator of immunity within lung tissue. SMD studies revealed that residues Glu321, Asp325, Glu329, and Arg343 play a key role in pulmonary surfactant D−protein ligand binding. SMD simulations were used to study small ligand binding and migration from their binding sites to heme, compared to implicit ligand sampling calculations, to generate a free energy landscape for small molecule migration within an active site. Sorafenib and sunitinib are VEGF inhibitors used to treat renal cell and hepatocellular carcinoma. Sunitinib is a known fast dissociating binding, while sorafenib has longer duration at the VEGF enzyme, leading to its longer half-life. SMD studies identified the two different mechanisms through which these drugs exit the binding site; under tension, sunitinib exits the ATP binding site without a rupture point, whereas sorafenib moved opposite to the ATP binding entrance, leading to a change in the orientation of the VEGF αC-helix. The binding free energy of 4-bromophenol (4-BP), an inhibitor that binds to dehaloperoxidase-hemoglobin (DHP) was calculated using SMD. The escape barrier is constructed by one hydrogen bond between 4-BP and the heme propionate, and the steric blocking effect by three residues His55, Lys51, or Tyr38. Salpichrolides is a potential antiestrogen agent for the treatment of hormone sensitive breast cancer. The unbinding pathway elucidated via SMD is through a cavity formed by residues in H3, H7, and H11 and a minor change in the receptor conformation. Telomeres are genomic regions responsible for controlling cell senescence and can form tandom guanine repeats called G-quadrupex structures which can serve as cancer drug targets. Free energy landscapes derived from SMD simulations involving G-quadrupex and its BMVC ligand revealed multiple binding pathways through which the development of drugs can target. Selectin−ligand interactions regulate inflammatory cascade and tumor metastasis. SMD simulations demonstrated that the unbinding pathway from selectin occurs via two steps: intramolecular dissociation by rupture of two antiparallel β-sheets and breaking of hydrogen bonding cluster and the intermolecular dissociation by separation of fucose from a covalently bound Ca2+ ion.
channel, while inhibitor TAK-632 is unbound along either the ATP channel or allosteric channel. SMD simulations studied the interaction between the hepatitis C drug sofosbuvir and its binding site, revealing a three-step unbinding process with the participation of two hydrogen bonds from 3′-hydroxyl of ribose, π−π stacking interaction, adoption of opposite conformation to initial binding conformation, and involvement of residues S282 and I160, shedding light on strategies which may be used to develop new antiviral medications.
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pounds pose a viable chemical terrorism threat, as they are potent nerve agents, acting as inhibitors of acetylcholinerase (AChE) to cause cardiovascular complications and respiratory arrest, having been deployed to devastating effect in incidents such as the terror attack on the Japanese subway in 1995, the Iran−Iraq war in 1988, and the Syrian civil conflict in 2013. Current therapeutic treatment focuses on reactivation of inhibited AChE using anticholinergic oximes in combination with anticonvulsants. SMD has played the key role in understanding how a tabun (nerve agent) bound wild-type and mutant AChE complex binds with Ortho-7, an oxime reactivator.46 The simulations revealed how Ortho-7 had higher binding affinity toward single mutant AChE at Y337A than toward wild-type and double mutant Y337A/F338A AChE. The simulation demonstrated that the peripheral pyridinium ring of Ortho-7 is sandwiched by the aromatic residues of Tyr72 and Trp286 through cation−π interactions in the wild-type and double mutant enzymes. This interaction is missing in the single mutant (Y337A) enzyme. It was therefore suggested that the weaker hydrophobic interactions of Ortho-7 with the peripheral anionic site for the single mutant compared to the wild-type and double mutant can enhance the reactivation ability of the former case. The mechanism by which Ortho-7 binds, therefore, establishes a drug development strategy to optimize binding of antidotes to inactivated toxin−AChE complexes, which can play a crucial role in patient rescue during a chemoterrorism scenario. Peptide−Ligand Interaction. Protein−ligand interactions govern the majority of drug−receptor interactions and play a critical role not only in drug specificity but also in regulating local conformation changes that control the communication with other proteins in a signaling cascade or pathway. SMD simulations to probe the force-structure function of a receptor−ligand complex can therefore uncover not just the key stabilizing elements that govern drug stability within a binding pocket but also whether the mechanical flexibility of the receptor plays a role in drug specificity. Furthermore, mutations or substitutions to specific residues or sets of residues can be evaluated in a series of simulations in order to elucidate how non wild-type variants may confer resistance to existing targeted drugs. SMD simulations study peptide−ligand interactions by applying a specified force to a protein complex of interest. The required force can be applied linearly to uncouple the two bound entities or it can be provided directly through an axis of rotation in order to apply a twisting force between coupled domains. Kirpichnikov and colleagues employed SMD simulations to study the interaction of cobra cytotoxin with a zwitterionic POPC lipid bilayer.47 They revealed that the cytotoxin settled into the membrane surface of POPC but did not penetrate it, suggesting that its biological effect likely engages a yet unknown secondary mechanism to drive the protein through the membrane and into the cell. Selectin is a protein that plays a key role in modulating both the inflammatory cascade and has also been found to be involved in tumor metastases. One variant, P-selectin, has been targeted to prevent vaso-occlusive pain crises in sickle cell disease. SMD simulations by Lu and colleagues demonstrated that the sheer forces from blood flow drive selectin-ligand association which in turn modulates leukocyte rolling in the bloodstream. The study41 showed that the unbinding pathway of ligand from selectin occurs via two steps: intramolecular dissociation by rupture of two antiparallel β-sheets and breaking of a hydrogen bonding cluster, followed by the
between the D3 domains may play a role in the pathogenesis of von Willebrand disease variants.19 Furthermore, SMD simulations have proven not just to be a tool for hypothesis generation but also for prediction before the same mechanism is suggested using experimental techniques such as atomic force microscopy and X-ray crystallography or NMR.6 In brief, SMD has shown that it is a proven method which offers atomic-level insights into the mechanical stability as well as the structure−function relationship of proteins. Unbinding Mechanism of Ligand from Protein. The central dogma of molecular biology is the transcription and translation of DNA to RNA to protein, where the protein ultimately acts in concert with other biomolecules to regulate the processes necessary for life. While therapies exist that target malignant alterations in DNA or RNA, or directly disrupt the DNA replication cycle or the proteins, the final product of biological synthesis is considered as the most attractive therapeutic target. This is because drugs can be adapted to protein structure and biochemical properties. One avenue of drug design involves designing a small molecule which can competitively bind a receptor versus the native ligand. Understanding which molecular interactions will drive native ligand binding and unbinding from the active site is of paramount significance when designing a drug to outcompete the native ligand. Furthermore, some diseases progress due to acquired resistance to a particular drug or due to an alteration of drug binding affinity. Toward this end, SMD simulations can be used to investigate the relative binding affinity for a receptor-protein complex in order to elucidate the specific molecular mechanisms responsible for favoring drug binding or favoring drug uncoupling (resistance). From this knowledge, new small molecule ligands can therefore be designed to overcome obstacles for drug binding and to maintain action on targeted proteins. One of the first studies employing SMD to study receptor−ligand binding was conducted by Schulten and colleagues on the avidin−biotin complex.20 This later led to a widespread use of SMD to simulate the binding and unbinding pathways in different protein−ligand complexes (Table 1). SMD simulations are particularly well designed for explaining how substrates enter and products exit the buried active site of enzyme and can serve not only as a hypothesis generation too, but also as a method for predicting novel mechanisms for drug−ligand interactions. One example is cytochrome P450cam,44,45 a heme protein monooxygenase which plays a crucial role in oxidative transformation of many small molecules or drugs from their metabolically active form to inactive forms for excretion or clearance. Therefore, the P450s have an important role in therapeutic bioavailability. A force-probe study on a P450−ligand complex had shown three possible pathways of ligands leaving the active binding site. While one pathway was consistent with the prediction from previous experiments, the simulations have also shown two previously unknown alternate pathways which required less force to unbind the ligand.44 One of these alternative pathways was then hypothesized to possess a lower free energy barrier for ligand egress compared to the other two pathways based on predicted accessibility to the active site, short length of the channel, and presence of highly mobile aromatic side chains along the binding channel. In this example, SMD proved to be a powerful tool for not only being able to visualize and validate an experimental prediction but also as a method to shed light on molecular landscapes not accessible with conventional experimentation. In another example, organophosphate com1477
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of gene proximity, or colocalization, on chromosome 19, demonstrating that constrained conformation of chromosome 19 are actually organized into spatial macrodomains through which a physical proximity can be correlated with coregulated genes, where nearly 80% of gene pairs could be brought simultaneously into contact.55 Membrane Translocation. Once a new substance has been identified as a potential drug, it must have a way to travel from vessels and localize into the cell interior in order to exert its effect on its target. A drug with excellent specificity without good penetration (ability to reach its target inside the cell) lacks cellular potency, and its action on treating disease would therefore be weak. The ability of a drug to cross the cell membrane, then, is of vital significance. Small molecules can either diffuse through a cell membrane, a relatively inefficient process, or they may take a “cellular shortcut” and pass through specialized membrane channels created by transmembrane proteins which often have a hydrophilic extramembrane component and a hydrophobic transmembrane component. In this context, SMD simulation is widely chosen to pull a ligand through a membrane channel. The route is examined to collect the significant factors which can interfere with the translocation process in order to develop therapeutic compounds which pass easily into the cell. Schulten and colleagues were among the first to study the phospholipid membrane using SMD,56 in which the lipid headgroup cleavage by human synovial phospholipase A2 was simulated by pulling a lipid molecule from a monolayer of dilauroylphosphatidyl-ethanolamin lipids. They also later observed, in atomistic detail using SMD, the membrane tension-induced gating action of mechanosensitive channels and the transportation of sugar across lactose permease, both in Escherichia coli (E. coli).57,58 In the mechanosensitive channel of E. coli (MscL), the gate is opened when there is a force applied predominantly on the cytoplasmic side, and the transmembrane helices are tilted for an iris-like expansion of the pores.57 In the case of E. coli lactose permease (LacY), the periplasmic half-channel is narrow with hydrophobic residues, and the cytoplasmic half-channel is wide with ionic residues. In simulation, the channel was observed to open due to changes in protonation states of key protein side groups.58 Specifically, protonation of Glu-325 arrested the protein in the “inward open” conformation, while deprotonation of Glu-325 and simultaneous protonation of Glu-269 led to partial closure of the cytoplasmic half-channel due to loss of a salt bridge between Glu-269 and Arg-144 and subsequent displacement of its guanidinium group. Subsequent work by other groups extended transmembrane channel SMD studies to voltagegated ion channels.59 The KvAP voltage-gated potassium channel is a key component underlying electrical signaling in the nervous system, and SMD simulations have been used to understand how the protein is able to generate electrical signals in response to changes in the membrane potential. Notably, Monticelli and colleagues found that the arginine residues (R117, 120, 123, 126, and 133) on the S4 segment of KvAP interact with sodium and chloride ions (particularly R117 and 120) to control the channel opening.59 Other similar studies have been carried out on ionotropic glutamate receptors (iGluRs) that function as ligand-gated cation channels triggering excitatory signaling in the central nervous system. With gramacidin A, simulations were able to construct an energy landscape detailing the “ion hopping” of a sodium ion
intermolecular dissociation by separation of fucose from a bound Ca2+ ion. Cyclodextrins, by virtue of their hydrophilic side chains, are often used to increase the solubility of poor water-soluble drugs. Their ability to form complexes with drugs are one type of receptor−ligand interaction studied using simulation.48 SMD simulations of a cyclodextrin−progesterone complex generated an energy landscape of binding that not only agreed with that previously predicted from experiment but also demonstrated a preferred binding orientation for progesterone. This finding helps future drug design efforts toward more stable bound complexes with increased photostability, suppression of bitterness, and reduction of side effects. Zhang and colleagues used SMD to investigate how viral proteins attach to specific host molecules for Reovirus, suggesting that a multireceptor binding mechanism for the association of Reovirus σ1 to junctional adhesion molecule A (JAM-A)49 can guide future viral infection and vector targeting studies. In another trial, monobodies were under investigation. Monobodies are antibody alternatives derived from fibronectin and can be produced in bacterial systems. They have been engineered to bind a wide variety of target proteins with high affinity and specificity. Owing to their thermodynamical stability and small size, they have therapeutic potential as targeted agents. Cheung and colleagues have employed SMD simulations to identify two critical scaffold residues involved in the binding of monobody YS1 and maltose-binding protein,50 providing guidance for the improved engineering of future monobodies. SMD simulations focused on protein−ligand interactions which have increasingly been applied to study disease-specific problems. Angiotensin-converting enzyme (ACE) inhibitors play a key role in the management of hypertension and heart failure. However, these drugs have side effects such as bradykinin-induced cough and angioedema. Guan and colleagues from China showed that the interaction of Angiotensin II with the C-domain of human somatic ACE is more stable than that of Angiotensin II with the N-domain of ACE.51 The study provided an insightful view and theoretical clues for designing the next generation of ACE inhibitors, which can focus on the C-domain (blood pressure control) specificity while sparing the N-domain in order to preserve bradykinin degration. Furthermore, a recent study on wild-type and mutant cardiac myosin binding protein-C (cMyBP-C), which are associated with development of hypertrophic cardiomyopathy, suggested that the mutation causes significant modification to the regulatory C1−motif−C2 region and to the native affinity required for the assembly of the domains in cMyBP-C.52 In another SMD study, ATP-binding cassette (ABC) transporters, which are integral membrane protein complexes consisting of transmembrane domains (TMDs) and nucleotide-binding domains (NBDs) that regulate protein translocation across the cell membrane, revealed that rotation of the C2 dimer of ABC transporter MetNI played a key role in substrate releasing from the ABC transporter complex.53 In addition to studying protein−ligand interaction, SMD simulations have also been used in probing the interaction between DNA and protein. Ettig and colleagues from Germany demonstrated that three different factors inherent to the DNA nucleosome−histone complex involving DNA interaction with histone H2A, H3, and H4 can prevent or slow the unwrapping DNA process of DNA−histone complexes.54 SMD simulations have also been ambitiously employed to answer the question of whether gene coregulation is a function 1478
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is the molecular mechanics Poisson−Boltzmann solvent accessible surface area (MM/PBSA). This method was first introduced by Kollman’s group, with its first application used to estimate the solvent stability of DNA, RNA, and DNA structures by Srinivasan and colleagues.73,74 It has also been demonstrated that there is an excellent correlation between the MM/PBSA and SMD in estimating binding affinity.75 This aspect had been especially reviewed in influenza studies by Li and Mai,76 on the CDK9-cyclin T1 complex by Randjelović et al. and Charuvaka et al., and on bromodomain BRD4.77,78 One key example of computational-based drug screening involves studies conducted on the influenza virus. By using SMD simulation of multiple ligands on wild-type and mutated A/H5N1 neuraminidase, Mai and co-workers have identified four ligands from a larger screened database (emsemble-based screening by McCammon and colleagues) which had higher binding affinity, and therefore possibility higher therapeutic capacity, to neuraminidase than commercial tamiflu (oseltamivir).79 Because molecular simulations yield atomic detail of drug−receptor interactions, this study has also identified the action of different mutated residues in the binding pocket which might interfere with the binding and unbinding of the ligands. A simultaneously published study employed the SMD method to study the binding of tamiflu to a different influenza N1 neuraminidase.80 It was found that mutations His274Tyr and Asn294Ser in the neuraminidase disrupted the binding of oseltamivir to the receptors causing drug resistance. This computational experiment explained the affect of drug-binding kinetics in ligand−receptor interactions. The effect was not caused by the end-point interaction between ligand and receptor but rather that these mutations led to less favorable interactions along the binding channel before the drug has migrated to its active site. On the basis of SMD-derived rupture forces and binding affinities, Mai and Li predicted that compound R-125489 may have the same binding affinity for N294S and H274Y mutants of A/H5N1 as oseltamavir has for the wild-type virus.81 The utility of employing SMD toward drug design was once again shown by Nguyen and Le in a study of M2 protein channel of influenza A virus H5N1,82 which also correlated simulation results and MM-PBSA data. In another publication using SMD for screening, Singh and colleagues investigated the use of existing drugs for chronic myeloid leukemia (CML) and their combined in silico and in vitro investigation83 for identifying inhibitors of allosteric BcrAbl fusion protein with deregulated Abl tyrosine kinase, the gene fusion product of CML as a result of Philadelphia chromosome 9:22 translocation. Their study identified that gefitinib, an EGFR inhibitor used primarily for lung cancer, and subsequent in vitro experiments, revealed that the drug had action on the K562 CML cell line for antiproliferative activity. Rungrotmongkol and colleagues in Thailand also used SMD simulation to screen inhibitors for HVC NS5B polymerase,84 a target for hepatitis C treatment. The group identified compound 49888724 which had a greater maximum rupture force, high potency of inhibitory, and reflected a good binding strength to the polymerase. Gloriam and colleagues in Denmark used SMD to study 5-hydroxytryptamine (serotonin, a neurotransmitter) bound to G protein-coupled receptors.85 A group led by Chelli and colleagues used SMD to find the antagonist drugs for the focal adhesion kinase (FAK), a potential cancer drug target due to its overexpression in many types of tumor tissues.86 Histone deacetylases (HDAC) are an emerging class of targets for anticancer targeted therapy, as
being carried on a relay by coordinating oxygen atoms within the ion channel.60,61 Drugs may also permeate the cell membrane to reach intracellular targets without the need for passage through membrane channels. Therefore, membrane permeability has been a key subject of investigation when developing therapeutic small molecules. In 1992 and 2002, semiempirical and mechanistic models were introduced to study the permeability by Potts, Guy, and Mitragotri.62,63 Recently, Rocco and colleagues have used SMD as a tool to study membrane permeation,64 aiming to improve the semiempirical methods for correlating the permeability coefficient to experimental physicochemical models. In its early stage, while SMD applications have provided understanding on the mechanical properties of monoproteins, protein polymers, protein−ligand interactions, and even drug diffusion or active transport through channels, the method has subsequently found its way into drug design field. Numerous studies65−68 have shown both correlation with prior experimentation in subsequent computation studies and also the utility of simulation to reveal novel drug binding pathways. Vpu, a type I integral membrane protein from human immunodeficiency virus type-1 (HIV-1), has been shown by SMD to act as a weak cation-selective channel, confirming prediction from experiment.65 Another SMD study on human glucose transporter GLUT1 has also provided a model consistent with biochemical, mutagenesis, and functional experiments.66 In the case of cytochrome P450s, hemecontaining monooxygenases involved in the biometabolism of endogenous and exogenous compounds, the SMD technique has been applied to investigate the channel selectivity of molecules67 for coumarin (a chemical precursor to the common blood thinning agent Coumadin), identifying independently from experiments a preferred binding channel for coumarin ingress and egress. The recent discovery of cargocarrying cell-penetrating peptides, which bypasses the transmembrane channels, has offered a new way for targeting intracellular enzymes. Alaybeyoglu and colleagues from Turkey have proposed, using simulation, a mechanism for the cellpenetrating pVEC peptide to translocate through the cell membrane lipid bilayer,68 revealing strategies for designing future therapeutics that can circumvent the need for channeldirected transmembrane transport. The SMD method has been further extended to include an applied electrostatic potential, defined on a grid, called “grid-steered molecular dynamics”, or “G-SMD”, in order to investigate at simulation time scales (nanoseconds) the permeation of DNA strands, DNA hairpins, and α-helical peptides through α-hemolysin, a bacterial toxin which can assemble holes in a lipid membrane to form a pore.69 It has been proposed that G-SMD can be extended toward the goal of extracting the potential of mean force for membrane crossing events. Drug Binding Affinity for Drug Screening. The binding affinity of a ligand can be predicted with reasonable accuracy based on the unbinding force calculated from receptor−ligand dissociation simulations.70,71 SMD simulations have been used in comparing binding affinity of drugs based on their Fmax, or rupture force (maximum force of pulling state).72 Employing simulations has been particularly beneficial to the pharmaceutical industry, where there is a priority for high accuracy, low cost, and reduction in time and labor in drug development. One of the most widely used computational methods to calculate the binding affinity ΔGbind between substrate−protein 1479
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Journal of Chemical Information and Modeling small molecules inhibiting HDAC have been shown in vitro to cause growth arrest and apoptosis of cancerous cells.87,88 Kalyaanamoorthy and Chen in Australia employed a hybrid pharmacophore-based and structure-based virtual highthroughput screening that incorporated SMD simulations as a filter for identifying drug candidates.89 These studies have demonstrated that SMD is a powerful tool for not only predicting the mechanism for efficient drug bindings but also for a potent hypothesis-generating tool for screening of new drug candidates. Follow-up studies employing in vivo and in vitro studies, therefore, are the next logical steps in the drug development pathway. Whalen and colleagues used a combination of SMD simulations, ensemble docking, and solvation free energy calculation to form a new method called Flexible Enzyme Receptor Method by Steered Molecular Dynamics (FERMSMD).90 They validated the capacity of the FERM-SMD method by using 17 ligands against glutamate racemase. They have then compared the result with the actual binding constant measured by a coupled-enzyme assay, demonstrating a Spearman correlation coefficient of 0.79 and RMS error of 0.7 kcal/mol. FERM-SMD is less computationally costly than the conventional method in studying molecular dynamics applied for flexible targets, such as enzymes, which is challenging with conventional SMD in systems that have large, poorly defined, active sites. This hybrid method therefore has potential for further extending SMD as a hypothesis generation tool independent of experiments.
edged. Ly Le and Eric Lee especially thank the late Professor Klaus Schulten for his guidance and inspiration.
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(1) Izrailev, S.; Stepaniants, S.; Isralewitz, B.; Kosztin, D.; Lu, H.; Molnar, F.; Wriggers, W.; Schulten, K. Computational Molecular Dynamics: Challenges, Methods. Lect. Notes Comput. Sci. Eng. 1999, 4, 39−65. (2) Isralewitz, B.; Gao, M.; Schulten, K. Steered molecular dynamics and mechanical functions of proteins. Curr. Opin. Struct. Biol. 2001, 11, 224−230. (3) Florin, E.-L.; Moy, V. T.; Gaub, H. E. Adhesion force between individual ligand-receptor pairs. Science 1994, 264, 415−417. (4) Lu, H.; Schulten, K. Steered molecular dynamics simulations of force-induced protein domain unfolding. Proteins: Struct., Funct., Genet. 1999, 35, 453−463. (5) Virnau, P.; Muller, M. Calculation of free energy through successive umbrella sampling. J. Chem. Phys. 2004, 120, 10925− 10930. (6) Lee, E. H.; Hsin, J.; Sotomayor, M.; Comellas, G.; Schulten, K. Discovery through the computational microscope. Structure 2009, 17, 1295−1306. (7) Lu, H.; Isralewitz, B.; Krammer, A.; Vogel, V.; Schulten, K. Unfolding of Titin immunoglobin domains by Steered molecular dynamics simulation. Biophys. J. 1998, 75, 662−671. (8) Marszalek, P. E.; Lu, H.; Li, H.; Carrion-Vazquez, M.; Oberhauser, A. F.; Schulten, K.; Fernandez, J. M. Mechanical unfolding intermediates in Titin modules. Nature 1999, 402, 100− 103. (9) Gao, M.; Wilmanns, M.; Schulten, K. Steered molecular dynamics studies of Titin I1 domain unfolding. Biophys. J. 2002, 83, 3435−3445. (10) Lu, H.; Schulten, K. Steered molecular dynamics simulation of conformational changes of immunoglobulin domain I27 interprete Atomic force microscopy observation. Chem. Phys. 1999, 247, 141− 153. (11) Lin, Y.-W.; Wang, Z.-H.; Ni, F.-Y.; Huang, Z.-X. Forced unfolding of Apocytochrome b5 by Steered molecular dynamics simulation. Protein J. 2008, 27, 197−203. (12) Li, H.; Cao, E.-h.; Gisler, T. Force-induced unfolding of human telomeric G-quadruplex: A Steered molecular dynamics simulation study. Biochem. Biophys. Res. Commun. 2009, 379, 70−75. (13) Pappalardo, M.; Milardi, D.; Grasso, D.; La Rosa, C. L. Steered molecular dynamics studies reveal different unfolding pathways of prions from mammalian and non-mammalian species. New J. Chem. 2007, 31, 901−905. (14) Chiti, F.; Dobson, C. M. Protein misfolding, functional amyloid, and human disease. Annu. Rev. Biochem. 2006, 75, 333−366. (15) Aguzzi, A.; O’Connor, T. Protein aggregation diseases: Pathogenicity and therapeutic perspectives. Nat. Rev. Drug Discovery 2010, 9, 237−248. (16) Franchini, M.; Lippi, G. Von Willebrand factor and thrombosis. Ann. Hematol. 2006, 85, 415−423. (17) Sadler, J. E. Von Willebrand factor: Two sides of a coin. J. Thromb. Haemostasis 2005, 3, 1702−1709. (18) Chen, W.; Lou, J.; Zhu, C. Molecular dynamics simulated unfolding of von Willebrand factor A domain by force. Cell. Mol. Bioeng. 2009, 2, 75−86. (19) Wang, J.-W.; Groeneveld, D. J.; Cosemans, G.; Dirven, R. J.; Valentijn, K. M.; Voorberg, J.; Reitsma, P. H.; Eikenboom, J. Biogenesis of Weibel -Palade bodies in von Willebrand’s disease variants with impaired von Willebrand factor intrachain or interchain disulfide bond formation. Haematologica. 2012, 97, 859−866. (20) Izrailev, S.; Stepaniants, S.; Balsera, M.; Oono, Y.; Schulten, K. Molecular dynamics study of unbinding of the Avidin-Biotin complex. Biophys. J. 1997, 72, 1568−1581. (21) Isralewitz, B.; Izrailev, S.; Schulten, K. Binding pathway of Retinal to Bacterio-Opsin: A prediction by Molecular dynamic simulations. Biophys. J. 1997, 73, 2972−2979.
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CONCLUSION AND FUTURE PROSPECT The majority of drug targets are proteins whose conformation, stability, and interactions with surrounding macromolecules (membrane, DNA, RNA, or other proteins) can be characterized in part by SMD simulations. Furthermore, SMD is a reliable and beneficial tool to gain insight into binding mechanisms and relative binding energy between potential compounds and targeted proteins by taking into account the mechanical components such as the flexibility of ligands and their targets. While most of the prior computational efforts have been focused on accurate binding energy calculations using MD simulations, SMD has the potential to offer more effective ranking procedures while reducing computational expense, in combination with existing methods for end-point free energy calculations such as molecular mechanics/Poisson−Boltzmann surface area, free energy perturbation, and thermodynamic integration. SMD therefore may play a crucial role for in silico-driven approaches in the rational design of new drug candidates.
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REFERENCES
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Ly Le: 0000-0002-3182-0007 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The work was supported by the NAFOSTED (The National Foundation for Science and Technology) under Grant No. 108.06-2017.332. The computational resources provided by the Computational Biology Center are gratefully acknowl1480
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