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• W2016039503 abstract "The complexity and scale of computational biology protocols have tracked the exponential growth of measurements in systems-biology and high-throughput structure determination. Reproduction of the computational protocols underlying important works can be prohibitively difficult outside the constructed computational environment of the original authors. This impedes transmission, pedagogy, and validation. Typical macromolecular modeling protocols involve multiple levels of prediction and design: for example the design of a protein complex that binds a specific DNA sequence will typically iterate amongst algorithms specialized for sequence design, docking, and structure prediction. Many algorithms are stochastic so the results of many Monte-Carlo simulations must be analyzed for ensemble properties. Searches of large sequence databases for homologous proteins [15] may need to be pre-cached or results from multiple secondary structure prediction methods may need to be merged [16]. External codes each require their own installation and databases. Distributing a monolithic code to perform such multi-layered tasks is rarely feasible and might not even allow reproduction if required files or external tools are missing, or complex post-processing steps are required to determine the best designs or models.In this collection we focus on computational structural biology protocols that use Rosetta [1], [2], [3], [4], [5], [6], [7], [8], [9], [10],[26]. Rosetta was originally developed for de novo fold prediction [1], [2], [3], [4], [5] but has been expanded to include methods for design, docking, experimental determination of structure from partial datasets, protein-protein interaction design and prediction, enzyme design, RNA structure prediction and protein-DNA interaction prediction and design [6], [7], [8], [9], [10]. The code is developed by the RosettaCommons. This working collaborative is composed of over 15 academic groups and thus the code is being applied to a very wide diversity of problems. Recent examples of Rosetta's application to challenging problems include: enzyme design, design of novel nucleases, design of new protein topologies, proteome wide de novo structure prediction, prediction and engineering of protein-protein and protein-DNA and protein-surface interfaces, and others. Since these works describe new cutting-edge research, and are not focused solely on the algorithms or workflows employed, the published results typically contain method descriptions with inconsistently stated protocols and dependencies.RosettaCon 2010 featured three main types of contributions: 1) new features that enabled new applications (vaccine design, enzyme design [11]), 2) new code developments [12], [13] that improve accessibility to the code and support the large development team including a refactoring of the code (Rosetta 3.0) and bindings to popular scripting languages (PyRosetta), and 3) new core scientific developments (multi-state protein design, modeling and design of symmetric protein complexes). This collection aims to make several of these latest Rosetta macromolecular modeling protocols accessible to all. Articles describing 16 of the most important contributions to the conference ranging from new applications, core science, and even reflections on Rosetta's current weaknesses are included in this collection. Frequently an overall process in Rosetta is built from multiple invocations of the Rosetta software with differing command line arguments or inputs, as well as auxiliary tools, specific data-bases, large input data files, and complex post processing steps. Our goal in this special issue is to attempt the capture of all these protocols in a sufficiently complete and formal way that enables outside groups to carry out the complete workflow described in the paper.It should be noted that this special issue is itself a social experiment (encouraged by the far-looking editors at PLOS) in which we grapple with how best to capture a dynamically evolving set of processes in a way that does not overly burden the authors, works across a distributed community without a central authority for methods capture, is timely, and is sufficiently self-consistent that readers will invest their time in the results. Simply put, if we make the process of capturing protocols too formal and brittle authors and readers will not participate. Not every protocol can be captured as a method from first principles. Not every data set can be encapsulated or kept current automatically. Thus we try to divide processes into incremental sets with defined inputs and outputs. We think that this may advance the baseline in publishing protocols in a robustly reproducible manner for our community and, as we learn from this experience, mature over subsequent efforts by our community.Each of the articles in this collection are accompanied by an archive containing links to the exact version of the code used in the paper, all input data, links to external tools, and an example script to illustrate the use of the code to carry out the protocol described in the paper. Each paper has this archive (called a protocol capture) as well as a detailed procedural section in the methods section of each paper to describe the proper use of the code with reference to the archived code, scripts, tools and data. Our efforts in this collection are a start on the road to reproducible publication of complex computational analysis. Below we briefly review the history and working structure of the RosettaCommons (the developers body that makes all of this possible), the content of the special issue, and the structure of the Protocol Capture archives that will accompany each article." @default.
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• W2016039503 date "2011-08-31" @default.
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• W2016039503 title "The 2010 Rosetta Developers Meeting: Macromolecular Prediction and Design Meets Reproducible Publishing" @default.
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• W2016039503 doi "https://doi.org/10.1371/journal.pone.0022431" @default.
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