Daniel R. Roe

PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data

We describe PTRAJ and its successor CPPTRAJ, two complementary, portable, and freely available computer programs for the analysis and processing of time series of three-dimensional atomic positions (i.e., coordinate trajectories) and the data therein derived. Common tools include the ability to manipulate the data to convert among trajectory formats, process groups of trajectories generated with ensemble methods (e.g., replica exchange molecular dynamics), image with periodic boundary conditions, create average structures, strip subsets of the system, and perform calculations such as RMS fitting, measuring distances, B-factors, radii of gyration, radial distribution functions, and time correlations, among other actions and analyses. Both the PTRAJ and CPPTRAJ programs and source code are freely available under the GNU General Public License version 3 and are currently distributed within the AmberTools 12 suite of support programs that make up part of the Amber package of computer programs (see http://ambermd.org ). This overview describes the general design, features, and history of these two programs, as well as algorithmic improvements and new features available in CPPTRAJ.

Multidimensional Replica Exchange Molecular Dynamics Yields a Converged Ensemble of an RNA Tetranucleotide.

A necessary step to properly assess and validate the performance of force fields for biomolecules is to exhaustively sample the accessible conformational space, which is challenging for large RNA structures. Given questions regarding the reliability of modeling RNA structure and dynamics with current methods, we have begun to use RNA tetranucleotides to evaluate force fields. These systems, though small, display considerable conformational variability and complete sampling with standard simulation methods remains challenging. Here we compare and discuss the performance of known variations of replica exchange molecular dynamics (REMD) methods, specifically temperature REMD (T-REMD), Hamiltonian REMD (H-REMD), and multidimensional REMD (M-REMD) methods, which have been implemented in Amber’s accelerated GPU code. Using two independent simulations, we show that M-REMD not only makes very efficient use of emerging large-scale GPU clusters, like Blue Waters at the University of Illinois, but also is critically important in generating the converged ensemble more efficiently than either T-REMD or H-REMD. With 57.6 μs aggregate sampling of a conformational ensemble with M-REMD methods, the populations can be compared to NMR data to evaluate force field reliability and further understand how putative changes to the force field may alter populations to be in more consistent agreement with experiment.

Evaluation of enhanced sampling provided by accelerated molecular dynamics with Hamiltonian replica exchange methods.

Many problems studied via molecular dynamics require accurate estimates of various thermodynamic properties, such as the free energies of different states of a system, which in turn requires well-converged sampling of the ensemble of possible structures. Enhanced sampling techniques are often applied to provide faster convergence than is possible with traditional molecular dynamics simulations. Hamiltonian replica exchange molecular dynamics (H-REMD) is a particularly attractive method, as it allows the incorporation of a variety of enhanced sampling techniques through modifications to the various Hamiltonians. In this work, we study the enhanced sampling of the RNA tetranucleotide r(GACC) provided by H-REMD combined with accelerated molecular dynamics (aMD), where a boosting potential is applied to torsions, and compare this to the enhanced sampling provided by H-REMD in which torsion potential barrier heights are scaled down to lower force constants. We show that H-REMD and multidimensional REMD (M-REMD) combined with aMD does indeed enhance sampling for r(GACC), and that the addition of the temperature dimension in the M-REMD simulations is necessary to efficiently sample rare conformations. Interestingly, we find that the rate of convergence can be improved in a single H-REMD dimension by simply increasing the number of replicas from 8 to 24 without increasing the maximum level of bias. The results also indicate that factors beyond replica spacing, such as round trip times and time spent at each replica, must be considered in order to achieve optimal sampling efficiency.

Improving Convergence of Replica-Exchange Simulations through Coupling to a High-Temperature Structure Reservoir.

Parallel tempering or replica-exchange molecular dynamics (REMD) significantly increases the efficiency of conformational sampling for complex molecular systems. However, obtaining converged data with REMD remains challenging, especially for large systems with complex topologies. We propose a new variant to REMD where the replicas are also permitted to exchange with an ensemble of structures that have been generated in advance using high-temperature MD simulations, similar in spirit to J-walking methods. We tested this approach on two non-trivial model systems, a β-hairpin and a 3-stranded β-sheet and compared the results to those obtained from very long (>100 ns) standard REMD simulations. The resulting ensembles were indistinguishable, including relative populations of different conformations on the unfolded state. The use of the reservoir is shown to significantly reduce the time required for convergence.

On the absence of intrahelical DNA dynamics on the μs to ms timescale

DNA helices display a rich tapestry of motion on both short (< 100 ns) and long (> 1 ms) timescales. However, with the exception of mismatched or damaged DNA, experimental measures indicate that motions in the 1 µs to 1 ms range are effectively absent, which is often attributed to difficulties in measuring motions in this time range. We hypothesized that these motions have not been measured because there is effectively no motion on this timescale, as this provides a means to distinguish faithful Watson-Crick base paired DNA from damaged DNA. The absence of motion on this timescale would present a “static” DNA sequence-specific structure that matches the encounter timescales of proteins, thereby facilitating recognition. Here we report long timescale (~10-44 µs) molecular dynamics simulations of a B-DNA duplex structure that addresses this hypothesis using both an “Anton” machine and large ensembles of AMBER GPU simulations.

Reliable oligonucleotide conformational ensemble generation in explicit solvent for force field assessment using reservoir replica exchange molecular dynamics simulations.

Molecular dynamics force field development and assessment requires a reliable means for obtaining a well-converged conformational ensemble of a molecule in both a time-efficient and cost-effective manner. This remains a challenge for RNA because its rugged energy landscape results in slow conformational sampling and accurate results typically require explicit solvent which increases computational cost. To address this, we performed both traditional and modified replica exchange molecular dynamics simulations on a test system (alanine dipeptide) and an RNA tetramer known to populate A-form-like conformations in solution (single-stranded rGACC). A key focus is on providing the means to demonstrate that convergence is obtained, for example, by investigating replica RMSD profiles and/or detailed ensemble analysis through clustering. We found that traditional replica exchange simulations still require prohibitive time and resource expenditures, even when using GPU accelerated hardware, and our results are not well converged even at 2 μs of simulation time per replica. In contrast, a modified version of replica exchange, reservoir replica exchange in explicit solvent, showed much better convergence and proved to be both a cost-effective and reliable alternative to the traditional approach. We expect this method will be attractive for future research that requires quantitative conformational analysis from explicitly solvated simulations.

Self-Tensioning Aquatic Caddisfly Silk: Ca2+-Dependent Structure, Strength, and Load Cycle Hysteresis

Caddisflies are aquatic relatives of silk-spinning terrestrial moths and butterflies. Casemaker larvae spin adhesive silk fibers for underwater construction of protective composite cases. The central region of Hesperophylax sp. H-fibroin contains a repeating pattern of three conserved subrepeats, all of which contain one or more (SX)n motifs with extensively phosphorylated serines. Native silk fibers were highly extensible and displayed a distinct yield point, force plateau, and load cycle hysteresis. FTIR spectroscopy of native silk showed a conformational mix of random coil, β-sheet, and turns. Exchanging multivalent ions with Na(+) EDTA disrupted fiber mechanics, shifted the secondary structure ratios from antiparallel β-sheet toward random coil and turns, and caused the fibers to shorten, swell in diameter, and disrupted fiber birefringence. The EDTA effects were reversed by restoring Ca(2+). Molecular dynamic simulations provided theoretical support for a hypothetical structure in which the (pSX)n motifs may assemble into two- and three-stranded, Ca(2+)-stabilized β-sheets.

Molecular basis for the broad substrate selectivity of a peptide prenyltransferase

Significance The cyanobactin prenyltransferases serve as a tool kit for regioselective and chemoselective peptide and protein modifications, in which each enzyme can catalyze the same chemistry on an enormous number of different substrates. Installation of a minimal motif is sufficient to direct modifications on any peptide substrate, which can alter their properties to be more drug-like. The cyanobactin prenyltransferases catalyze a series of known or unprecedented reactions on millions of different substrates, with no easily observable recognition motif and exquisite regioselectivity. Here we define the basis of broad substrate tolerance for the otherwise uncharacterized TruF family. We determined the structures of the Tyr-prenylating enzyme PagF, in complex with an isoprenoid donor analog and a panel of linear and macrocyclic peptide substrates. Unexpectedly, the structures reveal a truncated barrel fold, wherein binding of large peptide substrates is necessary to complete a solvent-exposed hydrophobic pocket to form the catalytically competent active site. Kinetic, mutational, chemical, and computational analyses revealed the structural basis of selectivity, showing a small motif within peptide substrates that is sufficient for recognition by the enzyme. Attaching this 2-residue motif to two random peptides results in their isoprenylation by PagF, demonstrating utility as a general biocatalytic platform for modifications on any peptide substrate.

Data model, dictionaries, and desiderata for biomolecular simulation data indexing and sharing

BackgroundFew environments have been developed or deployed to widely share biomolecular simulation data or to enable collaborative networks to facilitate data exploration and reuse. As the amount and complexity of data generated by these simulations is dramatically increasing and the methods are being more widely applied, the need for new tools to manage and share this data has become obvious. In this paper we present the results of a process aimed at assessing the needs of the community for data representation standards to guide the implementation of future repositories for biomolecular simulations.ResultsWe introduce a list of common data elements, inspired by previous work, and updated according to feedback from the community collected through a survey and personal interviews. These data elements integrate the concepts for multiple types of computational methods, including quantum chemistry and molecular dynamics. The identified core data elements were organized into a logical model to guide the design of new databases and application programming interfaces. Finally a set of dictionaries was implemented to be used via SQL queries or locally via a Java API built upon the Apache Lucene text-search engine.ConclusionsThe model and its associated dictionaries provide a simple yet rich representation of the concepts related to biomolecular simulations, which should guide future developments of repositories and more complex terminologies and ontologies. The model still remains extensible through the decomposition of virtual experiments into tasks and parameter sets, and via the use of extended attributes. The benefits of a common logical model for biomolecular simulations was illustrated through various use cases, including data storage, indexing, and presentation. All the models and dictionaries introduced in this paper are available for download at http://ibiomes.chpc.utah.edu/mediawiki/index.php/Downloads.

Computational Assessment of Potassium and Magnesium Ion Binding to a Buried Pocket in GTPase-Associating Center RNA

An experimentally well-studied model of RNA tertiary structures is a 58mer rRNA fragment, known as GTPase-associating center (GAC) RNA, in which a highly negative pocket walled by phosphate oxygen atoms is stabilized by a chelated cation. Although such deep pockets with more than one direct phosphate to ion chelation site normally include magnesium, as shown in one GAC crystal structure, another GAC crystal structure and solution experiments suggest potassium at this site. Both crystal structures also depict two magnesium ions directly bound to the phosphate groups comprising this controversial pocket. Here, we used classical molecular dynamics simulations as well as umbrella sampling to investigate the possibility of binding of potassium versus magnesium inside the pocket and to better characterize the chelation of one of the binding magnesium ions outside the pocket. The results support the preference of the pocket to accommodate potassium rather than magnesium and suggest that one of the closely binding magnesium ions can only bind at high magnesium concentrations, such as might be present during crystallization. This work illustrates the complementary utility of molecular modeling approaches with atomic-level detail in resolving discrepancies between conflicting experimental results.