Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways
2014; 6 (1): 15-21
Activation pathway of Src kinase reveals intermediate states as targets for drug design
To milliseconds and beyond: challenges in the simulation of protein folding
CURRENT OPINION IN STRUCTURAL BIOLOGY
2013; 23 (1): 58-65
Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β2AR. Markov state models aggregate independent simulations into a single statistical model that is validated by previous computational and experimental results. Moreover, our models provide an atomistic description of the activation of a G-protein-coupled receptor and reveal multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design.
View details for DOI 10.1038/NCHEM.1821
View details for Web of Science ID 000328951000007
View details for PubMedID 24345941
OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
2013; 9 (1): 461-469
Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.
View details for DOI 10.1016/j.sbi.2012.11.002
View details for Web of Science ID 000315832700008
View details for PubMedID 23237705
Complex Interactions between Molecular Ions in Solution and Their Effect on Protein Stability
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
2011; 133 (46): 18713-18718
OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.
View details for DOI 10.1021/ct300857j
View details for Web of Science ID 000313378700049
View details for PubMedID 23316124
Effects of Solute-Solute Interactions on Protein Stability Studied Using Various Counterions and Dendrimers
2011; 6 (11)
Protein stability in ionic solutions depends on the delicate balance between protein-ion and ion-ion interactions. For molecular ions containing multiple charged groups, the role of ion-ion interactions is particularly important. In this study, we show how the interplay between homo- and heteroion pairing influences protein stability using polyarginine salts as a model system. For the chloride salts, protein thermostability decreases as the size of the peptide increases, indicating enhanced binding to the protein. Moreover, it indicates reduced homoion pairing between Gdm(+) and carboxylate groups that is largely responsible for aggregation suppression, rather than denaturation, in monomeric arginine solutions. However, for the sulfate salts, strong heteroion pairing between the Gdm(+) groups and the sulfate counterions compensates for the loss of homoion pairing and, in return, leads to enhanced thermostability and a dramatically reduced (up to 10-30 times) rate of protein aggregation. Molecular dynamics simulations reveal how this ion pairing enhances conformational stability and, at the same time, reduces protein association. This study provides insight into complex ion effects on protein stability and serves as an example of how these intrasolvent interactions can be leveraged to enhance protein stability.
View details for DOI 10.1021/ja205215t
View details for Web of Science ID 000297398900042
View details for PubMedID 21973239
Understanding the Synergistic Effect of Arginine and Glutamic Acid Mixtures on Protein Solubility
JOURNAL OF PHYSICAL CHEMISTRY B
2011; 115 (41): 11831-11839
Much work has been performed on understanding the effects of additives on protein thermodynamics and degradation kinetics, in particular addressing the Hofmeister series and other broad empirical phenomena. Little attention, however, has been paid to the effect of additive-additive interactions on proteins. Our group and others have recently shown that such interactions can actually govern protein events, such as aggregation. Here we use dendrimers, which have the advantage that both size and surface chemical groups can be changed and therein studied independently. Dendrimers are a relatively new and broad class of materials which have been demonstrated useful in biological and therapeutic applications, such as drug delivery, perturbing amyloid formation, etc. Guanidinium modified dendrimers pose an interesting case given that guanidinium can form multiple attractive hydrogen bonds with either a protein surface or other components in solution, such as hydrogen bond accepting counterions. Here we present a study which shows that the behavior of such macromolecule species (modified PAMAM dendrimers) is governed by intra-solvent interactions. Attractive guanidinium-anion interactions seem to cause clustering in solution, which inhibits cooperative binding to the protein surface but at the same time, significantly suppresses nonnative aggregation.
View details for DOI 10.1371/journal.pone.0027665
View details for Web of Science ID 000297789200020
View details for PubMedID 22125620
Molecular level insight into intra-solvent interaction effects on protein stability and aggregation
ADVANCED DRUG DELIVERY REVIEWS
2011; 63 (13): 1074-1085
Understanding protein solubility is a key part of physical chemistry. In particular, solution conditions can have a major effect, and the effect of multiple cosolutes is little understood. It has been shown that the simultaneous addition of L-arginine hydrochloride and L-glutamic acid enhances the maximum achievable solubility of several poorly soluble proteins up to 4-8 times (Golovanov et. al, J. Am. Chem. Soc., 2004, 126, 8933-8939) and reduces the intermolecular interactions between proteins. The observed solubility enhancement is negligible for arginine and glutamic acid solutions as compared to the equimolar mixtures. In this study, we have established the molecular mechanism behind this observed synergistic effect of arginine and glutamic acid mixtures using preferential interaction theory and molecular dynamics simulations of Drosophilia Su(dx) protein (ww34). It was found that the protein solubility enhancement is related to the relative increase in the number of arginine and glutamic acid molecules around the protein in the equimolar mixtures due to additional hydrogen bonding interactions between the excipients on the surface of the protein when both excipients are present. The presence of these additional molecules around the protein leads to enhanced crowding, which suppresses the protein association. These results highlight the role of additive-additive interaction in tuning the protein-protein interactions. Furthermore, this study reports a unique behavior of additive solutions, where the presence of one additive in solution affects the concentration of another on the protein surface.
View details for DOI 10.1021/jp204462t
View details for Web of Science ID 000295700700009
View details for PubMedID 21894928
Effects of PAMAM Dendrimer Salt Solutions on Protein Stability
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
2011; 2 (14): 1782-1788
Arginine and the Hofmeister Series: The Role of Ion-Ion Interactions in Protein Aggregation Suppression
JOURNAL OF PHYSICAL CHEMISTRY B
2011; 115 (22): 7447-7458
Protein based therapeutics hold great promise in the treatment of human diseases and disorders and subsequently, they have become the fastest growing sector of new drugs being developed. Proteins are, however, inherently unstable and the degraded form can be quite harmful if administered to a patient. Of the various degradation pathways, aggregation is one of the most common and a cause for great concern. Aggregation suppressing additives have long been used to stabilize proteins, and they still remain the most viable option for combating this problem. Much work has been devoted toward investigating the behavior of commonly used additives and the resulting models give valuable insight toward explaining aggregation suppression. In a few cases, an explanation for unique behavior is lacking or new insight provides an alternate explanation. Additive selection and the development of better performing additives may benefit from a more refined understanding of how commonly used additives inhibit or enhance aggregation. In this review, we focus on recent molecular-level studies into how a select group of commonly used additives interact with proteins and subsequently influence aggregation. The intent of the review is not meant to be comprehensive for each additive but rather to provide new insights into additive-additive interactions, which may be contributing to protein-additive interactions. This is something that is often overlooked but yet essential to understanding the effect of additives on aggregation. The importance of understanding such interactions is clear when one considers that most formulations contain a mixture of cosolutes and that ideal stability might be better achieved through tuning intra-solvent interactions. We give an example of this when we describe how novel aggregation suppressing additives were developed from the knowledge gained from the reviewed studies.
View details for DOI 10.1016/j.addr.2011.06.014
View details for Web of Science ID 000296996800003
View details for PubMedID 21762737
Understanding the Role of Arginine as an Eluent in Affinity Chromatography via Molecular Computations
JOURNAL OF PHYSICAL CHEMISTRY B
2011; 115 (11): 2645-2654
L-Arginine hydrochloride is a very important aggregation suppressor for which there has been much attention given regarding elucidating its mechanism of action. Little consideration, however, has been given toward other salt forms besides chloride, even though the counterion likely imparts a large influence per the Hofmeister Series. Here, we report an in depth analysis of the role the counterion plays in the aggregation suppression behavior of arginine. Consistent with the empirical Hofmeister series, we found that the aggregation suppression ability of other arginine salt forms on a model protein (?-chymotrypsinogen) follows the order: H(2)PO(4)(-) > SO(4)(2-) > citrate(2-) > acetate(-) ? F(-) ? Cl(-) > Br(-) > I(-) ? SCN(-). Mechanistically, preferential interaction and osmotic virial coefficient measurements, in addition to molecular dynamics simulations, indicate that attractive ion-ion interactions, particularly attractive interactions between arginine molecules, play a dominate role in the observed behavior. Furthermore, it appears that dihydrogen phosphate, sulfate, and citrate have strong attractive interactions with the guanidinium group of arginine, which seems to contribute to the superior aggregation suppression ability of those salt forms by bridging together multiple arginine molecules into clusters. These results not only further our understanding of how arginine influences protein stability, they also help to elucidate the mechanism behind the Hofmeister Series. This should help to improve biopharmaceutical stabilization through the use of other arginine salts and possibly, the development of novel excipients.
View details for DOI 10.1021/jp111920y
View details for Web of Science ID 000291080000031
View details for PubMedID 21568311
Preferential Interaction Coefficients of Proteins in Aqueous Arginine Solutions and Their Molecular Origins
JOURNAL OF PHYSICAL CHEMISTRY B
2011; 115 (5): 1243-1253
Substantial loss in yield can occur during the purification of antibodies, up to nearly half of the product. The first and the most critical step in the purification process is affinity chromatography, in which a ligand (protein A) is used to bind the antibody to a column, and eluents are then used to elute the bound antibodies. Arginine and citrate salt are two commonly used eluents for elution of antibodies. The role of eluents in protein A affinity chromatography in general, and the role of arginine and citrate in particular, are not well understood. Arginine and citrate both work well at low pH, but at high pH, arginine improves the recovery of antibodies much better than citrate, which gives negligible recovery. Milder elution conditions are desired because, at low pH, much product is lost due to aggregation. Via molecular computations, we gained insight into the mechanism by which arginine promotes the elution of antibodies. We show that arginine facilitates the dissociation of the antibody-protein A complex and inhibits the aggregation of eluted antibodies, whereas citrate works in an opposite manner. These observations explain the low recovery of antibodies in the presence of citrate and improved performance in the presence of arginine. These results also shed light on the nature of molecular interactions between cosolutes and protein-protein binding sites that weaken or strengthen the binding.
View details for DOI 10.1021/jp111156z
View details for Web of Science ID 000288401100019
View details for PubMedID 21355601
Interaction of Arginine with Proteins and the Mechanism by Which It Inhibits Aggregation
JOURNAL OF PHYSICAL CHEMISTRY B
2010; 114 (42): 13426-13438
Preferential interaction coefficients provide a thermodynamic measure to quantify the interactions between cosolutes and a protein. Preferential interactions of cosolutes can be measured experimentally using dialysis/densimetry and vapor pressure osmometry (VPO) techniques. The cosolute arginine is a widely used aggregation suppressor with a seemingly unique behavior. Its role in protein aggregation has been studied extensively, although a complete mechanistic understanding of its behavior is lacking. Moreover, due to experimental limitations, experimental preferential interaction data for arginine has only been reported at low concentrations. Schneider and Trout ( J. Phys. Chem. B 2009 , 113 , 7 ) have reported experimental preferential interaction data for argHCl (up to 0.7 m), and their study raised several interesting questions about the preferential interaction of arginine with proteins. Arginine is attracted to proteins at low concentrations but it was highly excluded at high concentrations. Furthermore, the preferential interaction coefficient values were found to vary as a square of the concentration, which is different from commonly observed linear relationship for other cosolutes like urea, glycerol, guanidinium hydrochloride, etc. In this study, preferential interaction coefficients of argHCl have been estimated computationally for two proteins (lysozyme and ?-chymotripsinogen A) for a large concentration range (up to 2.8 m). On the basis of these results, the molecular level interactions responsible for the nonlinear exclusion of arginine from the protein surface are identified.
View details for DOI 10.1021/jp108586b
View details for Web of Science ID 000286797700055
View details for PubMedID 21186800
Molecular Computations of Preferential Interaction Coefficients of Proteins
JOURNAL OF PHYSICAL CHEMISTRY B
2009; 113 (37): 12546-12554
Aqueous arginine solutions are used extensively for inhibiting protein aggregation. There are several theories proposed to explain the effect of arginine on protein stability, but the exact mechanism is still not clear. To understand the mechanism of protein cosolvent interaction, the intraprotein, protein-solvent, and intrasolvent interactions have to be understood. Molecular dynamics simulations of aqueous arginine solutions were carried out for experimentally accessible concentrations and temperature ranges to study the structure of the solution and its energetic properties and obtain insight into the mechanism by which arginine inhibits protein aggregation. Simulations of proteins (?-chymotrypsinogen A and melittin) were performed. Structurally, the most striking feature of the aqueous arginine solutions is the self-association of arginine molecules. Arginine shows a marked tendency to form clusters with head to tail hydrogen bonding. Due to the presence of the three charged groups, there are several possible configurations in which arginine molecules interact. At relatively high concentrations, these arginine clusters associate with other clusters and monomeric arginine molecules to form large clusters. The hydrogen bonds between arginine molecules were found to be stronger than those between arginine and water, which makes the process of self-association enthalpically favorable. From the simulation of the proteins in aqueous arginine solution, arginine is found to interact with the aromatic and charged side chains of surface residues. A probable mechanism of the effect of arginine on protein stability consistent with our findings is proposed. In particular, arginine interacts with aromatic and charged residues due to cation-? interaction and salt-bridge formation, respectively, to stabilize the partially unfolded intermediates. The self-interaction of arginine leads to the formation of clusters which, due to their size, crowd out the protein-protein interaction. The mechanisms proposed in the literature are analyzed on the basis of the simulation results reported in this paper and recent experimental data.
View details for DOI 10.1021/jp108399g
View details for Web of Science ID 000283110500022
View details for PubMedID 20925358
Modeling of Formation of Nanoparticles in Reverse Micellar Systems: Ostwald Ripening of Silver Halide Particles
2009; 25 (6): 3786-3793
Estimation of the thermodynamic properties of proteins in mixed solvents is crucial for understanding the effect of cosolvents on rates and equilibrium constants of reactions involving proteins. In this paper, a predictive, molecular level approach for the study of preferential interactions of proteins with either water or cosolvents based on all-atom, statistical mechanical models is used to calculate the preferential interaction coefficient of proteins. Model systems comprised of the cosolvents urea, glycerol, arginine hydrochloride, guanidinium hydrochloride, and glucose with the proteins RNase T1, Hen egg white lysozyme, and alpha-chymotrypsinogen A(alpha-Cgn A) are studied. Trajectories in the range 10-20 ns are analyzed in order to validate this method. From the computational perspective, several key aspects of these simulations are investigated in detail. Protein dynamics and cosolvent dynamics play an important role in the estimation of preferential interaction coefficients, and in determining the length of simulation required to get a reliable estimate of the coefficient. Further, simulation results are found to be sensitive to changes in the cosolvent force field parameters. A comparison of simulated and experimental data is performed for two different force field parameters for glycerol and urea in order to assess the sensitivity of the preferential interaction coefficient to changes in force field parameters. This work highlights the effect of length of simulation, cosolvent force field parameters, and protein structure fluctuations on estimation of the preferential interaction coefficient of proteins in mixed solvents.
View details for DOI 10.1021/jp810949t
View details for Web of Science ID 000269655700014
View details for PubMedID 19697945
CaCO3 nanoparticle synthesis by carbonation of lime solution in microemulsion systems
2007; 18 (3)
There are many possible size enhancement processes that affect the formation of nanoparticles in reverse micelles, such as coagulation and Ostwald ripening, and different physical systems are likely to follow one or more of these mechanisms depending upon the properties of the system. It has been suggested that silver halide particles, prepared from a reverse micellar system of AgNO3 and KCl in NP-6/cyclohexane solution, increase in size due to Ostwald ripening (Kimijima, K.; Sugimoto, T. J. Phys. Chem. B 2004, 108, 3735), which occurs due to the dependence of the solubility of the particles on the particle size so that the larger particles grow at the expense of smaller particles. This study provides a modeling framework to quantitatively analyze the ripening process of nanoparticles produced in reverse micellar systems.
View details for DOI 10.1021/la803684y
View details for Web of Science ID 000264145000067
View details for PubMedID 19708254
Modeling shell formation in core-shell nanocrystals in reverse micelle systems
2006; 22 (23): 9500-9506
Various aspects of nanoparticle precipitation in gas-reverse micellar systems have been studied. The experimental system chosen for investigation deals with the precipitation of CaCO(3) nanoparticles. The effect of operating variables, such as water-to-surfactant molar ratio, different continuous phases, surfactant concentration and stirring speed, have been investigated experimentally. The results indicate that using low concentrations of Ca(OH)(2) particles outside the micelles, low surfactant concentrations, low stirring speeds and water-to-surfactant molar ratios lead to the formation of smaller nanoparticles in gas-reverse micellar systems.
View details for DOI 10.1088/0957-4484/18/3/035607
View details for Web of Science ID 000243840700021
View details for PubMedID 19636130
A Monte Carlo model for the formation of core-shell nanocrystals in reverse micellar systems
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
2006; 45 (7): 2249-2254
A model for particle coagulation in reverse micelles with a size dependent coagulation rate
2006; 17 (1): 261-267
Coagulation of nanoparticles in reverse micellar systems: A Monte Carlo model
2005; 21 (24): 11528-11533
The mechanisms responsible for the formation of the shell in core-shell nanocrystals are ion-displacement and heterogeneous nucleation. In the ion-displacement mechanism, the shell is formed by the displacement reaction at the surface of the core nanoparticle whereas in heterogeneous nucleation the core particle induces the nucleation (or direct deposition) of shell material on its surface. The formation of core-shell nanocrystals via the post-core route has been examined in the current investigation. A purely probabilistic Monte Carlo scheme for the formation of the shell has been developed to predict the experimental results of Hota et al. (Hota, G.; Jain, S.; Khilar, K. C. Colloids Surf., A 2004, 232, 119) for the precipitation of Ag2S-coated CdS (Ag2S@CdS) nanoparticles. The simulation procedure involves two stages. In the first stage, shell formation takes place as a result of the consumption of supersaturation, ion displacement, and reaction between Ag+ and excess sulfide ions. The growth in the second stage is driven by the coagulation of nanoparticles. The results indicate that the fraction of shell deposited by the ion-displacement mechanism increases with increasing ion ratio and decreases with increasing water-to-surfactant molar ratio.
View details for DOI 10.1021/la061499z
View details for Web of Science ID 000241669800009
View details for PubMedID 17073471
The process of formation of nanoparticles obtained by mixing two micellized, aqueous solutions has been simulated using the Monte Carlo technique. The model includes the phenomena of finite nucleation, growth via intermicellar exchange, and coagulation of nanoparticles after their formation. Using the model, an exploratory study has been conducted to analyze whether the coagulation of nanoparticles is the reason for the formation of nanoparticles whose sizes are comparable to the size of the reverse micelles. The model explains the possible mechanism of coagulation of semiconductor nanoparticles formed within reverse micelles and its effect on the evolution of their size with time. The model is predictive in nature, and the simulation results compare well with those observed experimentally.
View details for DOI 10.1021/la0523208
View details for Web of Science ID 000233371200088
View details for PubMedID 16285836