All PLOS journals have an open data policy that, amongst other things, states that all data and related metadata underlying the findings reported in a submitted manuscript should be deposited in an appropriate public repository, or for smaller datasets, as supporting information.This should obviously apply to computational methods as well, but unfortunately this is not always applied in practice, although it is of greatest importance for the scientific quality of simulations [1] and other modeling projects [2].Molecular dynamics [3] and other type of simulations [2,4] have become a fundamental part of life sciences.The simulations are dependent on a number of parameters such as force fields, initial configurations, simulation protocols, and software.Researchers have different opinions about the types of software they prefer, and in general, we believe authors should be free to choose the tools that best fit their needs.However, as scientists, we also have a common obligation to critically test each other's statements to find mistakes (including errors in the algorithms and bugs in the code), which can be exemplified by a heated debate over simulations of supercooled water that ended up being due to a subtle algorithmic issue [5], and we believe PLOS has a particularly strong responsibility to lead this development even if it might cause some short-term grief [6].In particular, all published results should, in principle, be possible to reproduce independently by scientists in other labs using different tools.To ensure this, we propose a set of standards that any publication in PLOS Computational Biology, and hopefully, publications in other journals as well, should follow.We do believe that the sooner such policies are widely adapted, the more open and collaborative science will flourish [7].These 10 simple rules should not be limited to molecular dynamics but also include Monte Carlo simulations, quantum mechanics calculations, molecular docking, and any other computational methods involving computations on biological molecules. Rule 1: The simulation protocol should be providedThe complete set of input files that are used in the simulations should be provided, either as supplementary material or preferably through a publicly available repository.

The flipping-out of a DNA base from the double helical structure is a key step of many cellular processes, such as DNA replication, modification and repair. Base pair opening is the first step of base flipping and the exact mechanism is still not well understood. We investigate sequence effects on base pair opening using extensive classical molecular dynamics simulations targeting the opening of 11 different canonical base pairs in two DNA sequences. Two popular biomolecular force fields are applied. To enhance sampling and calculate free energies, we bias the simulation along a simple distance coordinate using a newly developed adaptive sampling algorithm. The simulation is guided back and forth along the coordinate, allowing for multiple opening pathways. We compare the calculated free energies with those from an NMR study and check assumptions of the model used for interpreting the NMR data. Our results further show that the neighboring sequence is an important factor for the opening free energy, but also indicates that other sequence effects may play a role. All base pairs are observed to have a propensity for opening toward the major groove. The preferred opening base is cytosine for GC base pairs, while for AT there is sequence dependent competition between the two bases. For AT opening, we identify two non-canonical base pair interactions contributing to a local minimum in the free energy profile. For both AT and CG we observe long-lived interactions with water and with sodium ions at specific sites on the open base pair.

MOTIVATION: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. RESULTS: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. AVAILABILITY: GROMACS is an open source and free software available from http://www.gromacs.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biomolecular simulation is a core application on supercomputers, but it is exceptionally difficult to achieve the strong scaling necessary to reach biologically relevant timescales. Here, we present a new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus. This framework combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring. Copernicus enables execution as single parallel jobs with automatic resource allocation. Even for a small protein such as villin (9,864 atoms), Copernicus exhibits near-linear strong scaling from 1 to 5,376 AMD cores. Starting from extended chains we observe structures 0.6 Å from the native state within 30h, and achieve sufficient sampling to predict the native state without a priori knowledge after 80--90h. To match Copernicus' efficiency, a classical simulation would have to exceed 50 microseconds per day, currently infeasible even with custom hardware designed for simulations.