Reversible Unwrapping Algorithm for Constant-Pressure Molecular Dynamics Simulations

Molecular simulation technologies have afforded researchers a unique look into the nanoscale interactions driving physical processes. However, a limitation for molecular dynamics (MD) simulations is that they must be performed on finite-sized systems in order to map onto computational resources. To minimize artifacts arising from finite-sized simulation systems, it is common practice for MD simulations to be performed with periodic boundary conditions (PBCs). However, in order to calculate specific physical properties, such as mean square displacements to calculate diffusion coefficients, continuous particle trajectories where the atomic movements are continuous and do not jump between cell faces are required. In these cases, modifying atomic coordinates through unwrapping schemes is an essential post-processing tool to remove these jumps. Here, two established trajectory unwrapping schemes are applied to 1 μs wrapped trajectories for a small water box and lysozyme in water. The existing schemes can result in spurious diffusion coefficients, long bonds within unwrapped molecules, and inconsistent atomic coordinates when coordinates are rewrapped after unwrapping. We determine that prior unwrapping schemes do not account for changing periodic box dimensions and introduce an additional correction term to the existing displacement unwrapping scheme to correct for these artifacts. We also demonstrate that the resulting algorithm is a hybrid between the existing heuristic and displacement unwrapping schemes. After treatment using this new unwrapping scheme, molecular geometries are correct even after long simulations. In anticipation for longer MD trajectories, we develop implementations for this new scheme in multiple PBC handling tools

Replica-Based Protein Structure Sampling Methods: Compromising between Explicit and Implicit Solvents

The structure of a protein is often not completely accessible by experiments. In silico, replica exchange molecular dynamics (REMD) is the standard sampling method for predicting the secondary and tertiary structures from the amino acid sequence, but it is computationally very expensive. Two recent adaptations from REMD, temperature intervals with global exchange of replicas (TIGER2) and TIGER2A, have been tested here in implicit and explicit solvents. Additionally, explicit, implicit, and hybrid solvent REMD are compared. On the basis of the hybrid REMD (REMDh) method, we present a new hybrid TIGER2h algorithm for faster structural sampling, while retaining good accuracy. The implementations of REMDh, TIGER2, TIGER2A, and TIGER2h are provided for nanoscale molecular dynamics (NAMD). All the methods were tested with two model peptides of known structure, (AAQAA)3 and HP7, with helix and sheet motifs, respectively. The TIGER2 methods and REMDh were also applied to the unknown structure of the collagen type I telopeptides, which represent bigger proteins with some degree of disorder. We present simulations covering more than 180 μs and analyze the performance and convergence of the distributions of states between the particular methods by dihedral principal component and secondary structure analysis

Citizenship: Annual Program Improvement And Assessment Report, 2015-2016

University of New England (UNE) College of Arts and Sciences (CAS) core curriculum assessment document

Replica-Based Protein Structure Sampling Methods II: Advanced Hybrid Solvent TIGER2hs

In many cases, native states of proteins may be predicted with sufficient accuracy by molecular dynamics simulations (MDSs) with modern force fields. Enhanced sampling methods based on MDS are applied for exploring the phase space of a protein sequence and to overcome barriers on rough conformational energy landscapes. The minimum free energy state is obtained with sampling algorithms providing sufficient convergence and accuracy. A reliable but computationally very expensive method is replica exchange molecular dynamics, with many modifications to this approach presented in the past. Recently, we demonstrated how our temperature intervals with global exchange of replicas hybrid (TIGER2h) solvent sampling algorithm made a good compromise between efficiency and accuracy. There, all states are sampled under full explicit solvent conditions with a freely chosen number of replicas, whereas an implicit solvent is used during the swap decisions. This hybrid method yielded a much better approximation to the agreement with calculations in an explicit solvent than fully implicit solvent simulations. Here, we present an extension of TIGER2h and add a few layers of explicit water molecules around the peptide for the energy calculations, whereas the dynamics in fully explicit water is maintained. We claim that these water layers better reproduce steric effects, the polarization of the solvent, and the resulting reaction field energy than typical implicit solvent models. By investigating the protein–solvent interactions across comprehensive thermodynamic state ensembles, we found a strong conformational dependence of this reaction field energy. All simulations were performed with nanoscale molecular dynamics on two peptides, the α-helical peptide (AAQAA)3 and the β-hairpin peptide HP7. A production-ready TIGER2hs implementation is supplied, approaching the accuracy of full explicit solvent sampling at a fraction of computational resources

Replica-Based Protein Structure Sampling Methods II: Advanced Hybrid Solvent TIGER2hs

In many cases, native states of proteins may be predicted with sufficient accuracy by molecular dynamics simulations (MDSs) with modern force fields. Enhanced sampling methods based on MDS are applied for exploring the phase space of a protein sequence and to overcome barriers on rough conformational energy landscapes. The minimum free energy state is obtained with sampling algorithms providing sufficient convergence and accuracy. A reliable but computationally very expensive method is replica exchange molecular dynamics, with many modifications to this approach presented in the past. Recently, we demonstrated how our temperature intervals with global exchange of replicas hybrid (TIGER2h) solvent sampling algorithm made a good compromise between efficiency and accuracy. There, all states are sampled under full explicit solvent conditions with a freely chosen number of replicas, whereas an implicit solvent is used during the swap decisions. This hybrid method yielded a much better approximation to the agreement with calculations in an explicit solvent than fully implicit solvent simulations. Here, we present an extension of TIGER2h and add a few layers of explicit water molecules around the peptide for the energy calculations, whereas the dynamics in fully explicit water is maintained. We claim that these water layers better reproduce steric effects, the polarization of the solvent, and the resulting reaction field energy than typical implicit solvent models. By investigating the protein–solvent interactions across comprehensive thermodynamic state ensembles, we found a strong conformational dependence of this reaction field energy. All simulations were performed with nanoscale molecular dynamics on two peptides, the α-helical peptide (AAQAA)3 and the β-hairpin peptide HP7. A production-ready TIGER2hs implementation is supplied, approaching the accuracy of full explicit solvent sampling at a fraction of computational resources

Schematic illustration of the proteoliposomes and structure of integrin αIIbβ3.

A) Schematic illustration of the proteoliposomes as obtained after the reconstitution procedure before adsorption on SiO2 surface. αIIbβ3 (αIIb-subunit in blue and β3-subunit in orange) is reconstituted into liposomes and treated with Triton X-100 as well as biobeads and activated by manganese ions (Mn2+) or drugs. B) Structure of αIIbβ3 in bent (left) and open/active (right) conformation in a DMPG:DMPC (1:20) lipid membrane (cyan). The integrin model combines the αIIbβ3 transmembrane domain (PDB-code 2k9j) and ectodomain (PDB-code 3fcs), missing residues were added as random coils. The VMD 1.9. and PyMOL 2.1. softwarepackages were used to create this figure.</p

Validation of αIIbβ3 reconstitution into liposomes.

A) TEM images of proteoliposomes. The inset shows a close-up view of αIIbβ3 (indicated by arrow) incorporated in a membrane environment. B) DLS data showing the hydrodynamic diameter of liposomes (black) and proteoliposomes (red) of three independent experiments measured in liposome buffer at 37°C. C) Statistical analysis of FACS-plots with liposomes and proteoliposomes from three independent measurements. Percentages of the mean ± standard error of the mean (SEM) of anti-CD41 (red) and anti-CD61 binding (blue) on PE CF- liposomes were plotted. D) Reductive SDS-PAGE of liposomes (-) and proteoliposomes (+), protein molecular weight standard (M) is shown on the left. The bands corresponding to the αIIb- (red) and β3-subunit (blue) are indicated by arrows.</p

Activation of αIIbβ3.

A) Activation assay using PAC-1 antibody. Each value is the mean of three replicate measurements ± SEM. The binding of PE CF-liposomes/proteoliposomes to 5 μg/mL PAC-1 coated on a microtiter plate was detected after incubation with buffer (green), 1 mM Mn2+ (red) and 5 mM EDTA (blue). Values of bare liposome samples were subtracted from proteoliposome values. The y-axis shows relative fluorescence units (RFU). B) Integrin activation investigated by flow cytometry with PE CF-liposomes (black)/proteoliposomes (red) by adding PAC-1-Alexa 647-coupled antibody. Percentages of the mean ± SEM of Alexa-647 signal of PE positive events incubated with buffer, Mn2+ and EDTA are shown. C) Representative QCM-D data showing the changes in frequency f (top) and dissipation D (bottom) of the seventh overtone for the binding of the conformation-specific antibody PAC-1 at 37°C. Buffer was injected over the SiO2 sensors (phase I) and after reaching a baseline liposomes or proteoliposomes were injected and the formation of a bilayer was observed (phase II). After a washing step with either liposome buffer, liposome buffer with 1 mM Mn2+, or 5 mM EDTA (phase III), PAC-1 antibody was injected (phase IV) and binding was observed (indicated by arrows). Rinsing with the respective buffer followed (phase V).</p

αIIbβ3 activation by drugs.

A) Changes in frequency (Δf) and dissipation (ΔD) after PAC-1 injection event (indicated by the respective arrows) in representative QCM-D experiments with proteoliposomes after treatment with buffer (green), 1 mM Mn2+ (red), 250 μg/mL fondaparinux (purple), 50 μg/mL quinine (black), 250 μg/mL UFH (blue) and 5 mM EDTA (light blue) for at least 15 min at 37°C. B) Drug activation assay using PAC-1 antibody. Each value is the mean of three replicate measurements ± SEM. The binding of PE CF-liposomes/proteoliposomes to 5 μg/mL PAC-1 coated to a microtiter plate was detected after the incubation with buffer (green), 1 mM Mn2+ (red), 250 μg/mL fondaparinux (purple), 250 μg/mL UFH (blue) and 50 μg/mL quinine (black). Liposome sample results were subtracted from proteoliposomes values. The y-axis shows relative fluorescence units (RFU). C) Normalized single wavelength plot for corresponding MRDE values at 210 nm from far-UV region CD spectra of αIIbβ3 incorporated into liposomes/proteoliposomes treated with increasing concentrations of UFH (blue), fondaparinux (purple) and quinine (black), respectively. Normalized averages from three independent measurements ± SEM are shown as dots that were recorded with proteoliposomes with a protein concentration of approximately 0.4 μM or liposomes in 5 mm path length cuvettes at 37°C. Liposomes spectra were subtracted from the respective proteoliposome spectra. Respective trendlines were applied to guide the reader.</p