Power Of Molecular Dynamics Simulations Research Articles

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors.

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Icephobic Durability of Molecular Brush-Structured PDMS Soft Coatings.

The accumulation of ice can pose numerous inconveniences and potential hazards, profoundly affecting both human productivity and daily life. To combat the challenges posed by icing, extensive research efforts have been dedicated to the development of low-ice adhesion surfaces. In this study, we harness the power of molecular dynamics simulations to delve into the intricate dynamics of polymer chains and their role in determining the modulus of the material. We present a novel strategy to prepare ultralow-modulus poly(dimethylsiloxane) (PDMS) elastomers with a molecular brush configuration as icephobic materials. The process involves grafting monohydride-terminated PDMS (H-PDMS) as side chains onto backbone chain PDMS with pendant vinyl functional groups to yield a molecular brush structure. The segments of this polymer structure effectively restrict interchain entanglement, thereby rendering a lower modulus compared to traditional linear structures at an equivalent cross-linking density. The developed soft coating exhibits a remarkably ultralow ice adhesion strength of 13.1 ± 1.1 kPa. Even after enduring 50 cycles of icing and deicing, the ice adhesion strength of this coating steadfastly stayed below 16 kPa, showing no notable increase. Importantly, the molecular brush coating applied to glass demonstrated an impressive light transmittance of 92.1% within the visible light spectrum, surpassing the transmittance of bare glass, which was measured at 91.3%. This icephobic coating with exceptional light transmittance offers a wide range of applications and holds significant potential as a practical icephobic material.

Molecular analysis of the type III interferon complex and its applications in protein engineering

Type III interferons (IFNλs) are cytokines with critical roles in the immune system and are attractive therapeutic candidates due to their tissue-specific activity. Despite entering several clinical trials, results have demonstrated limited efficacy and potency, partially attributed to low-affinity protein-protein interactions (PPIs) responsible for receptor complex formation. Subsequently, structural studies of the native IFNλ signaling complexes remain inaccessible. While protein engineering can overcome affinity limitations, tools to investigate low-affinity systems like these remain limited. To provide insights into previous efforts to strengthen the PPIs within this complex, we perform a molecular analysis of the extracellular ternary complexes of IFNλ3 using both computational and experimental approaches. We first use molecular simulations and modeling to quantify differences in PPIs and residue strain fluctuations, generate detailed free energy landscapes, and reveal structural differences between an engineered, high-affinity complex, and a model of the wild-type, low-affinity complex. This analysis illuminates distinct behaviors of these ligands, yielding mechanistic insights into IFNλ complex formation. We then apply these computational techniques in protein engineering and design by utilizing simulation data to identify hotspots of interaction to rationally engineer the native cytokine-receptor complex for increased stability. These simulations are then validated by experimental techniques, showing that a single mutation at a computationally predicted site of interaction between the two receptors increases PPIs and improves complex formation for all IFNλs. This study highlights the power of molecular dynamics simulations for protein engineering and design as applied to the IFNλ family but also presents a potential tool for analysis and engineering of other systems with low-affinity PPIs.

Weak nuclear spin singlet relaxation mechanisms revealed by experiment and computation.

Nuclear spin singlet states are often found to allow long-lived storage of nuclear magnetization, which can form the basis of novel applications in spectroscopy, imaging, and in studies of dynamic processes. Precisely how long such polarization remains intact, and which factors affect its lifetime is often difficult to determine and predict. We present a combined experimental/computational study to demonstrate that molecular dynamics simulations and ab initio calculations can be used to fully account for the experimentally observed proton singlet lifetimes in ethyl-d5-propyl-d7-maleate in deuterated chloroform as solvent. The correspondence between experiment and simulations is achieved without adjustable parameters. These studies highlight the importance of considering unusual and difficult-to-control mechanisms, such as dipolar couplings to low-gamma solvent nuclei, and to residual paramagnetic species, which often can represent lifetime limiting factors. These results also point to the power of molecular dynamics simulations to provide insights into little-known NMR relaxation mechanisms.

Molecular Dynamics Prediction Verified by Experimental Evaluation of the Solubility of Different Drugs in Poly(decalactone) for the Fabrication of Polymeric Nanoemulsions

Nanoemulsions are a rapidly growing drug delivery technology capable of increasing a drug's aqueous solubility and stability. A novel oil‐in‐water nanoemulsion using a polymer, poly(decalactone) (PDL), instead of a conventional oil was recently reported. The amount of drug loading in a polymer‐based formulation is mainly governed by the drug's solubility in the polymer. Thus, herein the power of molecular dynamics simulations (MDS) for the calculation of the Hildebrand solubility parameter to predict PDL–drug miscibility is utilized. The MDS results are subsequently verified by formulating a PDL nanoemulsion with a dispersed droplet size of less than 200 nm by using a block copolymer of PDL (mPEG‐b‐PDL) as a surfactant, with seven different hydrophobic drug molecules. The MDS results are consistent with the experimental findings in terms of increment in the aqueous solubilities of the drugs in PDL nanoemulsion, where celecoxib demonstrated the highest while methotrexate exhibited the lowest solubility increment. Consequently, the reported MDS method can be utilized to predict a drug's solubility/miscibility in PDL to estimate the level of drug loading. The MDS facilitated screening of drugs could consequently emerge as an efficient approach in designing PDL nanoemulsions stabilized by mPEG‐b‐PDL or other similar systems.

Enhancement of the predictive power of molecular dynamics simulations for the determination of self-diffusion coefficient and viscosity demonstrated for propane

For the development and optimization of classical effective force fields (FFs) commonly used in molecular dynamics (MD) simulations, often experimental data are required. An alternative fully-predictive approach is the use of simplified pair-specific, ab initio-based FFs (AI-FFs) derived from quantum calculations in the limit of zero density. This approach, which could successfully be employed in our recent work for methane (CH4) and carbon dioxide (CO2), has not been investigated for relatively large systems including propane (C3H8). In the present study, C3H8 was selected as model system to evaluate the predictive power of MD simulations with respect to the determination of the self-diffusion coefficient and viscosity. For this aim, a semi-rigid version of our new simplified all-atom AI-FF was applied over a broad density range from the superheated vapor to the gas state and supercritical region up to the compressed liquid state. To elucidate the effects of the AI-FF on the calculated dynamical properties of C3H8, corresponding MD simulations representing the first investigations for the studied thermodynamic states were also performed using commonly employed, effective literature FFs in the form of TraPPE and OPLS. With the exception of the compressed liquid state, it was found that our ab initio-based approach improves the prediction results for the self-diffusion coefficient and viscosity at the studied thermodynamic states corresponding to pressures between (0.1 and 5) MPa and temperatures between (293.15 and 403.15) K compared to the use of effective FFs. This could be demonstrated by comparison with the ab initio-based calculations at the limit of zero density and the scarce experimental data. It could be figured out that the combination of an all-atom and a rigid or semi-rigid representation in our ab initio-based FFs for C3H8 as well as CH4 and CO2 suits well for a reliable simulation of dynamical properties over a broad density range.

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082–1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230–233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeller through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO_MD.

Probing the Structure and Dynamics of Proteins by Combining Molecular Dynamics Simulations and Experimental NMR Data.

NMR experiments provide detailed structural information about biological macromolecules in solution. However, the amount of information obtained is usually much less than the number of degrees of freedom of the macromolecule. Moreover, the relationships between experimental observables and structural information, such as interatomic distances or dihedral angle values, may be multiple-valued and may rely on empirical parameters and approximations. The extraction of structural information from experimental data is further complicated by the time- and ensemble-averaged nature of NMR observables. Combining NMR data with molecular dynamics simulations can elucidate and alleviate some of these problems, as well as allow inconsistencies in the NMR data to be identified. Here, we use a number of examples from our work to highlight the power of molecular dynamics simulations in providing a structural interpretation of solution NMR data.