Journal of Theoretical and Computational Chemistry

Integrins are cellular adhesion proteins located on cell surface. They are known to have open and closed conformations that correspond to high and low binding affinity to ligands, respectively. Integrin $\alpha$2 binds to the ligands via the ligand binding domain, $\alpha$2-I domain, which also has open and closed conformations. Experimentally, the closed to open conformation change is shown to be triggered by pulling the C-terminal away from the ligand binding site, but how the signal propagates from the distant C-terminal to the binding site is unknown. To explain the mechanisms of the conformation change, we built models of the $\alpha$2-I domain open and closed conformations in ligand free and ligand bound states, respectively. We found that the signaling pathway consists of F313-I280-V252 residues that connect the C-terminal and the ligand binding site. The pathway is highly conserved as revealed by a protein sequence analysis among 55 species. Furthermore, MM/PBSA energy calculations on the stabilities and ligand binding affinities of the closed and open conformations are consistent with experimental measurements. The open conformation is more favorable for ligand binding, and the closed conformation is more stable in unbound state. Energy analysis also revealed the “hot spots” for ligand binding, and most residues that contribute strongly to ligand binding free energy are highly conserved in evolution. In addition, the electrostatic analysis showed that the closed conformation has stronger long-range electrostatic attraction to the ligand compared with the open conformation. The difference is caused by the rearrangement of several charged residues during the binding. These observations make us suggest that the integrin $\alpha$2-I domain binding process involves the two-step “dock-lock” mechanism. The closed conformation first attracts the ligand from a long distance and afterwards, the open conformation locks the ligand at the binding site with high binding affinity.

We describe a strategy for performing canonical and isothermal-isobaric ensemble hybrid Monte Carlo (HMC) simulations with the widely-used Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics (MD) software package. The overall workflow for the HMC simulations is handled using an external Python driver script, which invokes LAMMPS’ library interface to perform numerically intensive tasks such as MD integration. We document several rigorous consistency checks that have been used to validate our HMC implementation. We also demonstrate that our approach can be readily extended to implement biased HMC sampling schemes for computing free energies. Codes and input files from the documented examples are available on the web.

Understanding the thermodynamic driving forces underlying any chemical process requires a description of the underlying free energy surface. However, computation of free energies is difficult, often requiring advanced sampling techniques. Moreover, these computations can be further complicated by the evaluation of any long-ranged interactions in the system of interest, such as Coulomb interactions in charged and polar media. Local molecular field theory is a promising approach to avoid many of the conceptual and computational difficulties associated with long-ranged interactions. We present frameworks for performing alchemical free energy calculations and non-Boltzmann sampling with local molecular field theory. We demonstrate that local molecular field theory can be used to perform these free energy calculations with accuracy comparable to traditional methodologies while eliminating the need for explicit treatment of long-ranged interactions in simulations.

Large-scale molecular dynamics simulations consisting of more than 88,000–106,000 atoms for approximately 250 ns (including equilibration and production) were conducted to assess the effect of polar, nonpolar and amphiphilic molecular solvents on the nanoscale structuring of 1-$n$-dodecyl-3-methylimidazolium [C${}_{12}$mim] octylsulfate [C₈SO₄] ionic liquid (IL). Water [H₂O], $n$-octane [C₈H${}_{18}$] and 1-octanol [C₈H${}_{17}$OH] are employed as examples of polar, nonpolar, and amphiphilic molecules, respectively. The results indicate that each of these molecular solvents modify the nanosegregation behavior of the ionic liquid in a unique way. Water induces a high order of structuring of the ionic liquid as indicated by extremely high nematic order parameter for the system. In addition, the morphology of the neat ionic liquid is transformed from layer-like to that of bilayer-like in which the polar and nonpolar domains alternate. The presence of water also causes the stretching of the nonpolar domain, thus, increasing its size. At the concentration examined in this work, $n$-octane is found to be only partially miscible with the ionic liquid. The polar network is maintained; however, the continuous cationic nonpolar domain is split into multiple domains. $n$-octane is accommodated in the ionic liquid nonpolar domain. Similarly, the amphiphilicity of 1-octanol leads to an increase in the number of cationic as well as anionic domains. The overall nonpolar domain length, however, remains nearly identical to that found for the pure ionic liquid. Additional characterization of structural features of the three systems is discussed in terms of one-dimensional number densities, nematic order parameters for the overall systems and their components and structure factors.

Mesoscale simulation techniques have helped to bridge the length scales and time scales needed to predict the microstructures of cured epoxies, but gaps in computational cost and experimental relevance have limited their impact. In this work, we develop an open-source plugin epoxpy for HOOMD-Blue that enables epoxy crosslinking simulations of millions of particles to be routinely performed on a single modern graphics card. We demonstrate the first implementation of custom temperature-time curing profiles with dissipative particle dynamics and show that reaction kinetics depend sensitively on the stochastic bonding rates. We provide guidelines for modeling first-order reaction dynamics in a classic epoxy/hardener/toughener system and show structural sensitivity to the temperature-time profile during cure. We conclude with a discussion of how these efficient large-scale simulations can be used to evaluate ensembles of epoxy processing protocols to quantify the sensitivity of microstructure on processing.

Biological environments are often “crowded” due to high concentrations (300–400g/L) of macromolecules. Computational modeling approaches like Molecular Dynamics (MD), rigid-body Brownian Dynamics and Monte Carlo simulations have recently emerged, which allow to study the effects macromolecular crowding at a microscopic level and to provide complementary information to experiments. Here, we use a recently introduced multiple-conformation Monte Carlo (mcMC) approach in order to study the influence of intermolecular interactions on the structural equilibrium of flexible polyethylene glycol (PEG) polymers under self-crowding conditions. The large conformational space accessible to PEG polymers allows us to evaluate the general applicability of the mcMC approach, which describes the intramolecular degrees of freedom by a finite-size ensemble of discrete conformations. Despite the simplicity of the approach, we show that influences of intermolecular interactions on the intramolecular free energy surface can be described qualitatively using mcMC. By varying the magnitude of distinct terms in the intermolecular potential, we can further study the compensating effects of repulsive and nonspecific attractive intermolecular interactions, which favor compact and extended polymer states, respectively. We use our simulation results to derive an analytical model that describes the effects of intermolecular interactions on the stability of PEG polymer conformations as a function of the radius of gyration and the corresponding solvent accessible surface. We use this model to confirm the role of molecular surfaces for attractive interactions that can counteract excluded volume effects. Extrapolation of the model further allows for the analysis of scenarios that are not easily accessible to direct simulations as described here.

Experiment-directed simulation (EDS) is a technique to minimally bias molecular dynamics simulations to match experimentally observed results. The method improves accuracy but does not address the sampling problem of molecular dynamics simulations of large systems. This work combines EDS with both the parallel-tempering or parallel-tempering well-tempered ensemble replica-exchange methods to enhance sampling. These methods are demonstrated on the GYG tripeptide in explicit water. The collective variables biased by EDS are chemical shifts, where the set-points are determined by NMR experiments. The results show that it is possible to enhance sampling with either parallel-tempering and parallel-tempering well-tempered ensemble in the EDS method. This combination of methods provides a novel approach for both accurately and exhaustively simulating biological systems.