The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code’s difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step ‘serverification’ protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.
Citation: Lyskov S, Chou F-C, Conchúir SÓ, Der BS, Drew K, et al. (2013) Serverification of Molecular Modeling Applications: The Rosetta Online Server That Includes Everyone (ROSIE). PLoS ONE 8(5): e63906. doi:10.1371/journal.pone.0063906
Editor: Vladimir N. Uversky, University of South Florida College of Medicine, United States of America
Received: January 28, 2013; Accepted: April 4, 2013; Published: May 22, 2013
Copyright: © 2013 Lyskov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors acknowledge financial support from the United States National Institutes of Health (R01-GM073151 to B.K., J.J.G. and S.L.; R01-GM078221 to J.J.G., R21-GM102716 to R.D., R00-RR024107 to J.J.H., U54CA143907-01 and PN2 EY016586-06 to R.B. and P.D.R. and K.D., T32 GM 88118-2 to K.D.), a Burroughs-Wellcome Career Award at Scientific Interface (R.D.), Governmental Scholarship for Study Abroad of Taiwan and Howard Hughes Medical and Institute International Student Research Fellowship (F.-C.C.), and the DARPA Antibody Technology Program (HR-0011-10-1-0052) for J.X. and D.K. S.Ó.C and T.K. were supported by grants from the National Science Foundation (MCB-CAREER 0744541, EF-0849400, EEC-0540879) and the UC Lab Research Program. R.B., P.D.R. and K.D. were supported by United States National Science Foundation (CHE-1151554 and NSF IOS-1126971 to R.B. and P.D.R. and K.D.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The Rosetta molecular modeling suite provides tools for a wide range of fundamental questions in structural biology, from the engineering of novel protein enzymes to the prediction of large non-coding RNA structures. The current codebase is rapidly evolving due to the efforts of more than 250 active developers, ease of integrating new functionality into a modular software architecture , cross-fertilization between teams working on different systems, and continuing improvements inspired by stringent experimental tests and blind prediction contests .
Most of the 50+ applications in the Rosetta package require familiarity with a Unix environment, access to a high-performance computing cluster, and familiarity with tools to visualize and interpret results. A growing number of tutorials, online documentation pages, scripting ,  and interactive , – interfaces, and introductory papers ,  are being written to lower barriers to Rosetta use and development. The most powerful simplification for external users, however, has been in the form of servers. Separate teams of Rosetta developers have created and added functionality to free web interfaces to nine protocols (Table 1) –. These servers are in high demand from the academic community, with wait times of at least a day in most cases. In some cases, the servers are down. These servers have relied on spare computing resources and administration provided by individual laboratories, rendering them difficult to maintain in the long term. Furthermore, the vast majority of Rosetta applications are not available on servers.
Creating and maintaining web servers – a process we denote ‘serverification’, in analogy to the term ‘gamification’ for turning tasks into games – can be complex and laborious. Besides the effort to encode and test the Rosetta protocol, much effort is required to plan database structures, create infrastructure for user interfaces, and other core server tasks. Thus, although the servers in Table 1 have similar components, they all use different application program interfaces (APIs) because they were created in five different labs by different people at different times. The duplicated effort is wasteful. In addition, support and maintenance currently requires sustained effort by each laboratory. As noted above, this post-serverification support can become especially difficult after the researcher who created the server has left the laboratory.
We hypothesized that a common server codebase, a step-by-step serverification protocol, and a virtual machine for testing would lower barriers to server development and thus rapidly accelerate the serverification process. Herein, we present ROSIE, the Rosetta Online Server that Includes Everyone, and demonstrate that it indeed accelerates the rate of serverification. ROSIE presents a common source base that has solved tedious tasks in server implementation and provides developers a simple route to create new servers. The new framework uses a common set of libraries and tools to speed and to simplify the creation of new web interfaces. Additionally, ongoing support of the servers is centralized. Thus, while previous cost-benefit decisions restricted server implementation to broad-use applications such as Robetta , RosettaDesign , and RosettaDock , ROSIE should promote serverification of not only such wide-use applications but also a diverse array of more specific-use and lower-traffic protocols.
Most papers describing new Rosetta functionalities evaluate success through experimental tests of Rosetta predictions of macromolecule structure and behavior. Instead, this paper evaluates success in the ROSIE effort by the rate of creation of novel servers, the extent to which common features are re-used across servers, and the usability of the resulting server (as assessed by number of users and jobs so far). This paper’s primary intended audience is the community of current and future Rosetta developers who wish to bring their work into wider use via serverification. Detailed descriptions of each ROSIE application – meant for potential users – are presented in the available online documentation and will also be presented in separate publications elsewhere.
The following describes the overall ROSIE infrastructure, a detailed ‘serverification’ protocol that has been used by several developers already, and the successful implementation of shared ROSIE features across nine current applications.
A Generalized Server Infrastructure
Traditionally, Rosetta servers are organized as a front-end web server, a SQL (Structured Query Language) database, a back-end job management daemon and a high-performance computing cluster, all on the same local network (Figure 1). ROSIE implements a more flexible architecture (Figure 2). The server handles multiple protocols feeding the same database, while allowing each lab to customize the appearance of each application and permit uniquely named links from their own web sites and publications.
The following services are implemented:
- A generalized database schema that stores a wide range of protocol information. The schema stores input and output files and uses the JSON format (http://www.ietf.org/rfc/rfc4627.txt) for protocol options and other structured data.
- Common user interface elements, including a job queue (Figure 3a), user self-registration (Figure 3b), password and account management tasks, and a web-based administrative interface for group, job, and priority management.
- A layer in the computational back-end to specify what to run (command-line scripts) and how to run them (parallelization scheme and data pipeline). Thus, the job specifications can be used with adapter functions to create scripts for new high-performance computing (HPC) clusters. The current strategy has been designed to consolidate spare computing cycles across multiple resources, and to prepare for the future ubiquitous nature of inexpensive cloud computing resources. At the time of writing, a 320-core cluster is active, with additional plans to make use of a second comparably sized cluster and, in the near future, national computing infrastructure and commercial resources.
(a) Global job queue page, which can be filtered by specific application (e.g., docking). (b) Self-registration (not required). (c) Coordinate file uploader using Protein Databank format, (d) Automatic visualization of uploaded coordinate file, (e) Score vs. root mean squared deviation plotting widget, (f) Automatic rendering of final models, which can be customized by developer for specific applications (in this case, RNA de novo modeling).
The first test application for ROSIE was Rosetta Docking , which was ported from a previously existing server. This test provided archetypes of all the functionalities described above (see Fig. 3). At the time of writing, 1069 jobs by 198 independent users, totaling 50,925 CPU-hours, have been successfully completed with the ROSIE Docking server.
RNA De Novo Modeling and ERRASER as ‘External’ Test Cases
The basic hypothesis underlying ROSIE was that it would permit rapid serverification of Rosetta applications, even by laboratories at different universities and with different modeling focus than the Johns Hopkins site where the ROSIE code was originally developed. RNA de novo modeling  and ERRASER , both developed by the Stanford Rosetta group, provided first test cases.
RNA de novo modeling, including high resolution refinement (Fragment Assembly of RNA with Full Atom Refinement ), was not previously available via a server, but naturally fit into the ROSIE framework. Serverification required a total development time of four weeks (accelerated for later applications; see below). The Stanford team created the application by specifying the inputs, outputs, and Rosetta command-lines for modeling, clustering and simple testing to the main ROSIE administrator (SL). Automated setup of cluster jobs, visualization of model scores vs. RMSD, PyMOL-based rendering of model images, archiving of models, and numerous other features would normally be complex to implement,but here these features could be adapted rapidly from existing components from the ROSIE Docking implementation. In addition, features such as text processing and validation of user input before job setup, model post-processing (clustering), interactive display of energy components for each model, and aesthetically pleasing application logos were developed at this time, and later were put into use in other applications. At the time of writing, the ROSIE RNA de novo server has been active for approximately one year. The server has completed 191 jobs by 45 separate users, totaling 30,494 CPU-hours. This level of use is notable given that the server was not described in any publication or advertised, aside from two links from the developer’s laboratory website at Stanford and from a forum post at the EteRNA project for massively multiplayer online RNA design (http://eterna.stanford.edu).
The third ROSIE application, ERRASER, provided a test case for more rapid and independent implementation by application developers, rather than extensive cross-correspondence between the developers and ROSIE administration. It was also the first example of a Rosetta server being created and published concomitantly with a new Rosetta application, and the first example of a ROSIE server that accepts experimental data (electron density maps). In brief, ERRASER (Enumerative Real-space Refinement ASsisted by Electron density under Rosetta) optimizes the local geometries of RNA crystallographic structures under the constraints of an electron density map and the Rosetta scoring function, and it is designed to be a practically useful tool for RNA crystallographic studies . The Stanford application developers used the previously implemented docking and RNA de novo applications as templates and developed the server nearly independently from the ROSIE administrators.
For this third application, a virtual machine image of the server was created to enable local testing of the server by the developer without requiring public deployment on the Web. After the developers were able to run the ERRASER server successfully on their local machine, the ROSIE administrator then integrated the new application to the central server. The overall development time was three weeks (two weeks for the initial deployment by the developers and one week for integrating the application to the central server). The Stanford developers also created a standard protocol for adding new applications to ROSIE (see Methods), which is constantly updated by ROSIE developers.
Rapid Creation of Additional Server Functionalities
In parallel or after the deployment of the first three applications, a diverse set of seven additional ROSETTA functionalities were serverified, briefly summarized below.
β-peptides: β-peptides are peptides with an additional backbone carbon atom, leading to an extra dihedral angle and an extended length between adjacent side chains. As polymers with non-biological backbones, structured beta peptides are often called foldamers (see also the NCBB design section below)  . Recently, high-resolution structures of multimeric β-peptide bundles have been solved by X-ray crystallography , , which opened up the possibility of performing structure-based rational redesign of the β-peptides –. A β-peptide redesign protocol was created under the Rosetta framework, and applied to redesign an octameric β-peptide bundle . Briefly, the protocol fixes the backbone of the input model and searches for the lowest-energy combination of the side chains for residues of interest (as specified by the user). For the design of the β-peptide bundle, we also included functionality for symmetric design, in which equivalent residues are forced to have the same side-chain identities and rotamers. All the features mentioned above are available in the ROSIE β-peptide design server. Users input a starting β-peptide structure and specify the residues to be redesigned. One final model with the lowest Rosetta energy is returned as output.
Adding functionality: NMR chemical shifts in RNA de novo: NMR chemical shifts have long been recognized as an important source of structural information for functional macromolecules. Backbone chemical shifts are widely used for protein analysis, including determination of protein secondary structures and backbone torsions ,  and refinement of three-dimensional models –. Recently, the integration of non-exchangeable 1H chemical shift data with Rosetta RNA de novo modeling produced high-resolution RNA structures . Rather than creating a separate ROSIE server, we chose to include the NMR chemical shift guided modeling feature into the ROSIE RNA de novo server. This chemical-shift-guided modeling mode is activated when the user uploads an NMR chemical shift data file during RNA de novo job submission. When run in this mode, the RNA structures are first generated by the standard RNA de novo method ,  and then refined and rescored using a hybrid energy function. The hybrid energy function consists of the standard Rosetta energy function plus a NMR chemical shift pseudo-energy term that is proportional to the sum of squared deviations between experimental and back-calculated chemical shifts. In this mode, the modeling results are the same as in standard RNA de novo with three additional data columns reported in the score data table: (1) rna_chem_shift, the chemical shift pseudo-energy score, (2) chem_shift_RMSD, the root-mean-square-deviation between the experimental and back-calculated chemical shifts, and (3) num_chem_shift_data, the total number of experimental chemical shift data points.
Antibody: Antibody modeling is important in biological and medical applications, such as antibody design and drug development , . RosettaAntibody predicts the structure of an antibody variable region given the amino acid sequence of the heavy and light immunoglobulin chains. Originally developed under the Rosetta2 framework , RosettaAntibody was available as one of the early public Rosetta web services  (Table 1). The protocol identifies the most homologous templates for frameworks of light and heavy chains and each of the complementarity determining region loops (CDR loops). Subsequently, these templates are assembled into a crude model and then CDR-H3 is remodeled with simultaneous VL/VH domain orientation optimization and refinement of canonical CDR loops. The implementation of RosettaAntibody available through ROSIE is an expanded and improved version of the previous antibody modeling protocol built on the Rosetta3 platform. The major improvements include the ability to perform loop modeling using the Kinematic Loop Closure (KIC) algorithm , a score function restricting CDR-H3 with knowledge-based rules , and an updated structural database, which provides better templates for VL, VH, and the canonical CDR loops. The mandatory inputs are the sequences for both the light chain and heavy chain of an antibody. The output includes the coordinates of the antibody FV models, images of the models, a summary of the homologous templates and scoring information. The output models can be used for the subsequent modeling of an antibody-antigen complex using EnsembleDock  or SnugDock , both of which are being prepared for future ROSIE implementation.
Supercharge: Increasing protein net charge using surface mutations, or supercharging, has many possible uses. Increased net charge can prevent aggregation of partially unfolded states , , thereby improving protein refolding. Improved refolding of protein can increase longevity of protein-based reagents or therapeutics  and increase yields when purifying recombinantly-expressed proteins from inclusion bodies . Additionally, highly cationic proteins can undergo nonviral cell entry ,  and highly anionic proteins resist kidney filtration for longer retention time in the bloodstream . The potential users of the Rosetta supercharge protocol   are experimentalists attempting to enhance the properties of various proteins of interest.
To run the supercharge protocol, the user provides an input PDB file containing the coordinates of the protein structure to be supercharged, and, optionally, an input residue file that can specify positions to leave as wild-type. The input PDB file can be a Rosetta-relaxed crystal structure, a raw crystal structure, an NMR structure, or a homology model. Supercharge operates in fixed backbone mode by default (backbone minimization is optional) and starts by repacking all sidechains. Second, the user specifies the target net charge and either AvNAPSA-mode (Average Neighboring Atoms Per Sidechain Atom mode, implementing a protocol developed by the Liu lab ) or Rosetta-mode . As output, the user receives the PDB file of the designed protein, a residue file indicating which residues were allowed to mutate to which amino acid types (this residue file can be subsequently used in other Rosetta design protocols, such as fixed backbone design), and a log file detailing the command-line options, the net charge, the number of mutations, the list of mutated residues, a PyMOL selection text for convenient viewing of mutated residues, and an energy comparison of wild-type residues versus mutated residues for each Rosetta energy term. These Rosetta energies can be used to evaluate Rosetta-mode designs, or they can be used along with the deterministic AvNAPSA-mode as a hybrid approach. For example, AvNAPSA-mode could be used to generate fifteen mutations, and Rosetta energies might flag three of these mutations as energetically unfavorable, resulting in a final list of twelve mutations.
Sequence Tolerance: The concept of “tolerated sequence space” describes the set of sequences that are consistent with a protein’s structure and function(s). Methods to determine which sequences would be tolerated by proteins, given desired structures and functions, have many uses: designing proteins for new functions or against undesired activities , optimizing protein stability , anticipating drug resistance mutations , or predicting protein interaction specificity . Predicted tolerated sequences can also be used to construct sequence libraries to diversify existing or select for new functions. Such prediction is useful because it is often difficult to accurately identify single successful sequences, especially for functional specifications that are not explicitly modeled in current computational design methods (for example the rate of an enzymatic reaction or the emission maximum of a fluorescent protein ).
The Rosetta sequence tolerance protocol ,  predicts a set of tolerated sequences for user-defined positions (generally less than ten at a time) in a protein or protein-protein interface, using flexible backbone protein design. The protocol also allows for positions to be mutated before the sequence tolerance simulations (pre-mutation). This is useful, for example, to predict changes in interaction specificity in response to mutations. To emphasize certain functional requirements during sequence design, such as binding to another protein, the protocol allows the weighting of interfaces within and between protein chains.
Two published versions of the protocol are currently implemented in ROSIE. The earlier version  was developed originally for PDZ domains (commonly occurring interaction proteins recognizing linear peptide motifs) and has been successfully validated against a large set of phage display data on peptide interaction specificity of natural and engineered PDZ domains . A generalized version  was then developed by testing the protocol in several additional systems using a common set of parameters.
Both versions follow the same protocol. First, an ensemble of structures is generated from the starting (or pre-mutated) structure using Monte Carlo simulations involving side chain and backbone moves using the backrub method , . Next, low-energy sequences are found for each member of the ensemble using the defined interaction weights. Finally, the individual results are combined to create a predicted set of tolerated sequences.
To run the protocol on ROSIE, the user first uploads a protein structure or model in PDB format to the server or enters a PDB ID, using the common ROSIE PDB widget. This widget returns the list of chains and residues from the PDB file so that the user input controls are limited to valid options, reducing the likelihood of accidental errors. The user then selects the participating protein chains (partners) and their internal and pairwise interaction weights. Between one and ten positions can be specified for sequence tolerance prediction (‘designed’ positions) and up to ten positions can be specified for mutation before prediction. A Boltzmann factor, used to combine predictions from the ensemble, is automatically set to the optimized value determined in the published protocol : this factor may be overridden by the user. Finally, the number of structures to be created for the ensemble is specified. The computation time scales linearly with this value, but higher values allow for more sampling. Because of the limited number of sequences sampled for each structure in the ensemble in the design stage (relative to the number of total sequences possible), we recommend using no more than six designed positions. Future updates to the protocol may allow greater sampling when specifying higher numbers of designed positions.
The output page displays graphics generated using the published analysis scripts ,  so that results are presented in a similar fashion to the publications. The user is shown the ranked table of amino acids at the designed positions, individual boxplots per designed position, a sequence motif generated using the WebLogo package , and images of ten low-energy structures from the ensemble. ROSIE presents all relevant output files (PDB files, the positional weight matrix, and PNG and PDF versions of the graphics) for download.
NCBB design: The NCBB design application designs protein interaction inhibitors with noncanonical backbones (NCBB). NCBBs, also known as peptidomimetics or foldamers, are classes of molecular scaffolds that are a novel strategy to inhibit protein interactions. The NCBB design server application is capable of making inhibitor designs for three different scaffolds, oligooxopiperazines (OOP), hydrogen bond surrogates (HBS) and peptoids. OOPs are molecular scaffolds with a peptide backbone and ethylene bridges between pairs of residues that stabilize the conformation. In this stabilized conformation, OOPs mimic one face of an α-helix (i, i+4, and i+7 residues) and show promise as inhibitors for targets with helices at the interface , . The HBS scaffold is a peptide where a covalent linker is attached between the 1st and 4th residues. This modification mimics the first hydrogen bond of a helix and stabilizes the peptide into an alpha helix conformation  thus improving pharmacokinetic properties. An HBS inhibitor targeting the P300– Hif1α protein interaction (often poorly regulated in cancer cells) was successful in disrupting the angiogensis pathway in cell based assays . Peptoids are N-substituted glycine amino acids, which have known proteolytic resistance and mimic poly-proline type I and type II helices . Many research efforts have shown that peptoids are a valuable avenue for future therapeutics and have shown to be valuable protein interaction inhibitors .
In the ROSIE NCBB design application, minor rigid-body perturbations along with backbone specific moves are iterated with the design of residues on the NCBB scaffold. As input, the user submits a Rosetta-formatted PDB file with a target protein and the NCBB scaffold to be designed. Since this application does not do large docking moves, the rigid body conformation of the NCBB scaffold with respect to the target protein needs to be close to the anticipated binding mode in order to achieve a successful design. The user also can specify which residues to design on the NCBB scaffold as well as how many design cycles and perturbations per cycle the application will perform. The application selects a single final design that is chosen from a filtered set of all decoys produced (top 5% of total_score) and sorted by binding energy (REPACK_ENERGY_DIFF). Users can download the score file with additional scoring information and all decoys produced can be found in the decoys directory.
RosettaVIP: One of the first observations of early crystal structures is that the hydrophobic cores of proteins are well-packed . Cavities in the cores of proteins are associated with loss of stability and conformational specificity. Computational filters, such as RosettaHoles, can identify packing defects in computationally designed structural models . Typically, models with defects are discarded. The RosettaVIP protocol identifies mutations that are predicted to improve hydrophobic packing in the cores of proteins, and therefore to generate mutants with enhanced stability . The application of this protocol has been shown to “rescue” protein designs with packing defects. The protocol has also proven itself capable of suggesting mutations that can improve the stability of wild-type proteins.
The RosettaVIP protocol is iterative. Each iteration either suggests a mutation that is predicted to improve the packing of the protein core, or terminates the protocol if no such mutation can be identified. The output of one iteration (with the new mutation and a relaxed structural model) is the input for the next iteration. The protocol takes as initial input a structural model in PDB format. The user selects the total number of iterations (i.e., the maximum number of mutations) to try; the protocol may be configured to run iterations until no further mutations are found. Because the protocol relies on a stochastic version of the RosettaHoles algorithm to identify protein cavities, it is sometimes beneficial to retry an iteration that fails to find a mutation in the hopes that a second attempt will succeed. The user specifies the maximum number of failed attempts at each iteration before the protocol is terminated. Finally, the user may specify a list of residues that should be excluded from mutation, if some prior activity of the original protein is to be preserved. The output of the protocol is a structural model in PDB format that incorporates all suggested mutations and all structural relaxation performed during the course of the protocol. Optionally, intermediate structural models at the end of each successful iteration may be generated as output.
Server Usage to Date
From October 2012 to the time of writing (March 2013), more than 778 users have registered. Some of the ROSIE users are scientists who previously used our other servers (e.g. the RosettaDock or RosettaAntibody server). Users of well-established Rosetta protocols, such as RNA-Denovo were likely attracted by links from lab web sites. Additionally, some users approached us and volunteered to act as beta-testers. The current computational demand on ROSIE is about 25,500 CPU-h per month, or 300,000 CPU-h annually, equivalent to 40 CPUs in continuous use. Since its launch, ROSIE has completed more than 1833 jobs, reflecting more than 116,000 CPU-hours of computational modeling leveraged by the general biological and modeling communities.
We have developed a core web server infrastructure called ROSIE, the Rosetta Online Server that Includes Everyone, to lower barriers to Rosetta application deployment as servers. ROSIE was presented at the 2012 Rosetta Developers Conference (August, 2012) to illustrate its speed in implementing the RNA de novo application and to identify new protocols needing web distribution. After this conference, the first ROSIE applications RosettaDock and RNA-Denovo were joined by ERRASER, RNA de novo with chemical shifts, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP (Table 2). Additional servers in preparation are listed in Table 2. The number of these applications, created on a timescale of a few months, is similar to the number of applications previously implemented in several years of Rosetta development, supporting our hypothesis that the ROSIE framework would accelerate serverification. These ROSIE protocols are now online, free of charge for academic users, at http://rosie.rosettacommons.org.
The design philosophy of ROSIE was meant not only to accelerate serverification, but to promote maximal sharing of data and collaboration. This emphasis on sharing follows from the premise of the RosettaCommons initiative – a shared source code and a continuous open flow of scientific information, mostly funded by public research funds. Six features of the present manuscript derived from this largely open philosophy. First, this manuscript is being submitted to an open access journal that does not force privacy restrictions on described servers, unlike other journals. Second, all ROSIE input forms and documentation are open for anyone to view without registration. (Users are encouraged to register an account to track their jobs, but this is not required. Those who register receive email notifications for job submission, start, and finish, and a link to the job status page.) Third, documentation for how to use the server is a prerequisite for deployment, promoting the writing of user-friendly explanations of applications by developers. Fourth, by default, all input and output data are shared publicly on the job queue, although for those concerned about privacy there is an option to hide job output. Fifth, as a further incentive for openness, jobs that are available for open access are given priority access to the ROSIE computing resources. Sixth, the job output can be easily shared with others though email or via social networking widgets including Facebook and Google+. Continuing our open philosophy, we will make the source code for ROSIE tools available upon request. Hopefully other research projects will benefit from the plugin architecture for developing HPC web applications.
Serverification can also benefit the Rosetta codebase itself in that it encourages common input and output file formats and command line options, and otherwise promotes protocol unification. Protocols also become more robust as they are challenged with input cases from ROSIE users that were not tested or even anticipated by the Rosetta protocol developers.
Our long-term goal is to provide free web versions of all core Rosetta protocols. In the near future, Rosetta developers are planning to use ROSIE to serverify a wide range of applications, covering the full spectrum of available functionalities: small molecule docking , multi-state design , flexible peptide docking  , enzyme design , pKa prediction , and scaffold grafting . With the server tools already implemented and detailed documentation available for new developers, the web server creation process and ROSIE maintenance has become streamlined. However, we anticipate two areas of improvement. First, although presently the ROSIE computational resources are not over-burdened, additional applications and users will eventually strain the system. Plans are being made to (1) continually expand and upgrade lab clusters, (2) include clusters from additional labs, (3) expand to national computing infrastructure resources such as the Pittsburgh Supercomputing Center or the Texas Advanced Computing Center (both funded by the National Science Foundation) or the Department of Defense Supercomputing Resource Center, and (4) allow ROSIE to use cloud resources (Amazon Elastic Compute Cloud, IBM SmartCloud, Google Cloud Platform, etc.), in which case users would pay for the needed CPU hours. It is difficult to extrapolate the needed computer power in the coming years, but exponential growth would be expected given the compounding effects of adding new users, adding new apps, and opening apps with larger computational demands (e.g. flexible backbone design or global docking).
A second area of improvement is incorporating more diverse workflows. App design necessarily requires trade-offs between ease-of-use (few control options) and a richer set of options targeted toward advanced users. Our approach has been to favor ease-of-use first with possible expansion later. For example, capturing complex workflows requiring multiple stages of calculations whose results depend on previous calculations (e.g. stepwise assembly of RNA motifs , RasRec for refining large proteins with NMR data , or homology modeling including identification of templates ), and these protocols cannot yet be implemented. In these cases, subcomponents of the workflows can be serverified to permit potential users to preview steps of the more complex workflows. Finally, we note that emerging scripting systems ,  and interactive interfaces to Rosetta , – are lowering barriers to Rosetta applications through laptops. ROSIE may offer a useful backend to these services as their users require more computational power.
The ROSIE server was implemented with PostgreSQL as a database, using the TurboGears web server framework. Dynamic web controls and widgets were implemented in jQuery, jQuery-UI, jqGrid and FlotCharts libraries. The code for the ROSIE server is under version control, and available to all Rosetta developers, through the same repository that hosts the Rosetta codebase: svn.rosettacommons.org/trac/browser/trunk/rosie.The applications are freely available on the World Wide Web to the public at http://rosie.rosettacommons.org.
Protocol for ‘Serverification’ of a New Rosetta Application
The following summarizes the steps required for a developer to turn an existing Rosetta application into a ROSIE server. It is a snapshot of the protocol at the time of writing. A continuously updated version of this protocol is being made available at http://goo.gl/Sh7oB. Importantly, the protocol has been written by new ROSIE developers and so captures the perspective required to promote faster first development cycles for other new engineers. ROSIE development tools and source code are available to registered developers through RosettaCommons.
- Install a local ROSIE test server
Download the VM (http://graylab.jhu.edu/ROSIE) and open it with VirtualBox (http://www.virtualbox.org). Before you start, you may want to do a ‘svn update’ in ‘~/rosie’ and ‘~/R/trunk/rosetta’, and rebuild the Rosetta trunk, since they may be out of date.
1 Modify the file ‘rosie/rosie.front/development.ini’. Find the line ‘host = 192.168.0.64’ and comment it out. Enable the line ‘host = 127.0.0.1’.
2 To run the server: Open two terminals. In one of them, cd into ‘rosie/rosie.back’ and execute ‘./run_rosie-daemon.sh’. In the other terminal, cd into ‘rosie/rosie.front’ and execute ‘./run-rosie-server.sh’.
3 Open ‘localhost:8080’ in your browser. Login as admin (password: managepass).
- Add a new application XXX (Tip: check other released apps to see how to format the files, see table 3 for an overview of file locations and their roles):
1 Create your application in rosie.back/protocols/XXX. You need at least two files: submit.py and analyze.py. See “rna_denovo” for example files.
2 For machine-dependent files, edit rosie.back/data.template/XXX. Edit rosie.back/rosie-daemon.ini.template, add useful shorthands and add the app into the protocol line. Copy the corresponding files to rosie.back/data/XXX and rosie.back/rosie-daemon.ini so the VM server can read the files.
3 Add the corresponding controller in rosie.front/rosie/controllers/XXX.py. See rna_denovo.py as an example.
4 Add your controller into controllers/root.py. In root.py, search for ‘rna_denovo’. Add the two corresponding lines for your application.
5 During the creation of the controller files, you may want to make some validation checks for the input format. They are in rosie.front/rosie/lib/validators. You might need to create your own validation tests.
6 Create your page in rosie.front/rosie/templates/XXX/. You need at least 3 pages: index.html, submit.html, and viewjob.html. See rna_denovo for example.
7 Link your application to the main page in template/index.html.
8 You may want an icon. Put a png file of ~ 1024*1024 into rosie/public/image/XXX_icon.png, and link it to the pages.
9 For documentation, create pages in template/documentations. Also you need to edit controllers/documentation.py to let the server know where it is. Then link your documentation to documentation/index.html and in the other pages of your application.
10 Edit rosie.front/rosie/websetup/bootstrap.py and add the name of the new app.
11 Go to rosie.front/. Run ‘source ~/prefix/TurboGears-2.2/bin/activate’ then ‘python update_protocol_schema.py’ to update the database.
12 Test the new application in the browser of the VM to make sure it runs fine.
13 Create a new file rosie/doc/XXX.txt, put a short description of protocol input, output, and command line flags. Also add an example job, with input files and a simple readme, into rosie/examples/validation_tests.
14 Commit the changes (use ‘svn commit –username XXXX’ to specify the user name of the commit). Inform the ROSIE administrators for integration into the central server.
Contributed reagents/materials/analysis tools: SL FC SÓC BD KD DK JX BW PDR PS BB JH BK TK RB JG RD. Wrote the paper: SL FC SÓC BD KD DK JX BW PDR PS BB JH BK TK RB JG RD.
- 1. Leaver-Fay A, Tyka M, Lewis SM, Lange OF, Thompson J, et al. (2011) ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods in Enzymology 487: 545–574.
- 2. Janin J (2010) Protein-protein docking tested in blind predictions: the CAPRI experiment. Mol Biosyst 6: 2351–2362. doi: 10.1039/c005060c
- 3. Chaudhury S, Lyskov S, Gray JJ (2010) PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26: 689–691. doi: 10.1093/bioinformatics/btq007
- 4. Fleishman SJ, Leaver-Fay A, Corn JE, Strauch EM, Khare SD, et al. (2011) RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PloS one 6: e20161. doi: 10.1371/journal.pone.0020161
- 5. Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-time PyMOL visualization for Rosetta and PyRosetta. PLoS ONE 6: e21931. doi: 10.1371/journal.pone.0021931
- 6. Eiben CB, Siegel JB, Bale JB, Cooper S, Khatib F, et al. (2012) Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nature biotechnology 30: 190–192. doi: 10.1038/nbt.2109
- 7. Jared Adolf-Bryfogle RD (2013) The PyRosetta Toolkit: A Graphical User Interface for the Rosetta Software Suite. PlosOne RosettaCon2012 collection.
- 8. Kaufmann KW, Lemmon GH, Deluca SL, Sheehan JH, Meiler J (2010) Practically useful: what the Rosetta protein modeling suite can do for you. Biochemistry 49: 2987–2998. doi: 10.1021/bi902153g
- 9. Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Research 32: W526–W531. doi: 10.1093/nar/gkh468
- 10. Sircar A, Kim ET, Gray JJ (2009) RosettaAntibody: Antibody variable region homology modeling server. Nucleic Acids Research 37: W474–W479. doi: 10.1093/nar/gkp387
- 11. Lyskov S, Gray JJ (2008) The RosettaDock server for local protein-protein docking. Nucleic Acids Res 36: W233–238. doi: 10.1093/nar/gkn216
- 12. London N, Schueler-Furman O (2008) Funnel hunting in a rough terrain: learning and discriminating native energy funnels. Structure 16: 269–279. doi: 10.1016/j.str.2007.11.013
- 13. London N, Schueler-Furman O (2007) Assessing the energy landscape of CAPRI targets by FunHunt. Proteins 69: 809–815. doi: 10.1002/prot.21736
- 14. London N, Raveh B, Cohen E, Fathi G, Schueler-Furman O (2011) Rosetta FlexPepDock web server–high resolution modeling of peptide–protein interactions. Nucleic Acids Research.
- 15. Raveh B, London N, Schueler-Furman O (2010) Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins: Structure, Function, and Bioinformatics 78: 2029–2040. doi: 10.1002/prot.22716
- 16. Liu Y, Kuhlman B (2006) RosettaDesign server for protein design. Nucleic Acids Research 34: W235–W238. doi: 10.1093/nar/gkl163
- 17. Chaudhury S, Berrondo M, Weitzner BD, Muthu P, Bergman H, et al. (2011) Benchmarking and analysis of protein docking performance in Rosetta v3.2. PLoS ONE 6: e22477. doi: 10.1371/journal.pone.0022477
- 18. Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Meth 7: 291–294. doi: 10.1038/nmeth.1433
- 19. Chou FC, Sripakdeevong P, Dibrov SM, Hermann T, Das R (2013) Correcting pervasive errors in RNA crystallography through enumerative structure prediction. Nat Methods 10: 74–76. doi: 10.1038/nmeth.2262
- 20. Molski MA, Goodman JL, Chou F-C, Baker D, Das R, et al. (2013) Remodeling a [small beta]-peptide bundle. Chemical Science 4: 319–324. doi: 10.1039/c2sc21117c
- 21. Bautista AD, Craig CJ, Harker EA, Schepartz A (2007) Sophistication of foldamer form and function in vitro and in vivo. Current Opinion in Chemical Biology 11: 685–692. doi: 10.1016/j.cbpa.2007.09.009
- 22. Daniels DS, Petersson EJ, Qiu JX, Schepartz A (2007) High-Resolution Structure of a β-Peptide Bundle. Journal of the American Chemical Society 129: 1532–1533. doi: 10.1021/ja068678n
- 23. Goodman JL, Petersson EJ, Daniels DS, Qiu JX, Schepartz A (2007) Biophysical and Structural Characterization of a Robust Octameric β-Peptide Bundle. Journal of the American Chemical Society 129: 14746–14751. doi: 10.1021/ja0754002
- 24. Craig CJ, Goodman JL, Schepartz A (2011) Enhancing β3-Peptide Bundle Stability by Design. ChemBioChem 12: 1035–1038. doi: 10.1002/cbic.201000753
- 25. Molski MA, Goodman JL, Craig CJ, Meng H, Kumar K, et al. (2010) β-Peptide Bundles with Fluorous Cores. Journal of the American Chemical Society 132: 3658–3659. doi: 10.1021/ja910903c
- 26. Petersson EJ, Schepartz A (2008) Toward β-Amino Acid Proteins: Design, Synthesis, and Characterization of a Fifteen Kilodalton β-Peptide Tetramer. Journal of the American Chemical Society 130: 821–823. doi: 10.1021/ja077245x
- 27. Shandler SJ, Shapovalov MV, Dunbrack, Jr., DeGrado WF (2010) Development of a Rotamer Library for Use in β-Peptide Foldamer Computational Design. Journal of the American Chemical Society 132: 7312–7320. doi: 10.1021/ja906700x
- 28. Molski MA, Goodman JL, Chou F-C, Baker D, Das R, et al. (2013) Remodeling a β-peptide bundle. Chemical Science 4: 319–324. doi: 10.1039/c2sc21117c
- 29. Szilágyi L (1995) Chemical shifts in proteins come of age. Progress in Nuclear Magnetic Resonance Spectroscopy 27: 325–442. doi: 10.1016/0079-6565(95)01011-2
- 30. Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13: 289–302. doi: 10.1007/s10858-007-9166-6
- 31. Kuszewski J, Gronenborn AM, Clore GM (1995) The impact of direct refinement against proton chemical shifts on protein structure determination by NMR. J Magn Reson B 107: 293–297. doi: 10.1006/jmrb.1995.1093
- 32. Clore GM, Gronenborn AM (1998) New methods of structure refinement for macromolecular structure determination by NMR. Proc Natl Acad Sci U S A 95: 5891–5898. doi: 10.1073/pnas.95.11.5891
- 33. Vila JA, Aramini JM, Rossi P, Kuzin A, Su M, et al. (2008) Quantum chemical 13C(alpha) chemical shift calculations for protein NMR structure determination, refinement, and validation. Proc Natl Acad Sci U S A 105: 14389–14394. doi: 10.1073/pnas.0807105105
- 34. Sripakdeevong P CM, Chang AT, Erat MC, Ziegeler M, et al. (2012) Consistent structure determination of noncanonical RNA motifs from 1H NMR chemical shift data. in preparation.
- 35. Das R, Baker D (2007) Automated de novo prediction of native-like RNA tertiary structures. Proceedings of the National Academy of Sciences 104: 14664–14669. doi: 10.1073/pnas.0703836104
- 36. Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7: 291–294. doi: 10.1038/nmeth.1433
- 37. Almagro JC, Beavers MP, Hernandez-Guzman F, Maier J, Shaulsky J, et al. (2011) Antibody modeling assessment. Proteins 79: 3050–3066. doi: 10.1002/prot.23130
- 38. Kuroda D, Shirai H, Jacobson MP, Nakamura H (2012) Computer-aided antibody design. Protein Eng Des Sel 25: 507–521. doi: 10.1093/protein/gzs024
- 39. Sivasubramanian A, Sircar A, Chaudhury S, Gray JJ (2009) Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking. Proteins 74: 497–514. doi: 10.1002/prot.22309
- 40. Sircar A, Kim ET, Gray JJ (2009) RosettaAntibody: antibody variable region homology modeling server. Nucleic Acids Res 37: W474–479. doi: 10.1093/nar/gkp387
- 41. Mandell DJ, Coutsias EA, Kortemme T (2009) Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling. Nat Methods 6: 551–552. doi: 10.1038/nmeth0809-551
- 42. Kuroda D, Shirai H, Kobori M, Nakamura H (2008) Structural classification of CDR-H3 revisited: a lesson in antibody modeling. Proteins 73: 608–620. doi: 10.1002/prot.22087
- 43. Chaudhury S, Gray JJ (2008) Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles. J Mol Biol 381: 1068–1087. doi: 10.1016/j.jmb.2008.05.042
- 44. Sircar A, Gray JJ (2010) SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models. PLoS Comput Biol 6: e1000644. doi: 10.1371/journal.pcbi.1000644
- 45. Fields GB, Alonso DOV, Stigter D, Dill KA (1992) Theory for the aggregation of proteins and copolymers. Journal Name: Journal of Physical Chemistry; Journal Volume: 96; Journal Issue: 10; Other Information: PBD: 14 May 1992: Medium: X; Size: pp 3974–3981.
- 46. Fink AL (1998) Protein aggregation: folding aggregates, inclusion bodies and amyloid. Fold Des 3: R9–23. doi: 10.1016/s1359-0278(98)00002-9
- 47. Wang W (2005) Protein aggregation and its inhibition in biopharmaceutics. International Journal of Pharmaceutics 289: 1–30. doi: 10.1016/j.ijpharm.2004.11.014
- 48. Mitraki A, King J (1989) Protein Folding Intermediates and Inclusion Body Formation. Nature Biotechnology 7: 690–697. doi: 10.1038/nbt0789-690
- 49. Heitz F, Morris MC, Divita G (2009) Twenty years of cell-penetrating peptides: from molecular mechanisms to therapeutics. Br J Pharmacol 157: 195–206. doi: 10.1111/j.1476-5381.2009.00057.x
- 50. Cronican JJ, Beier KT, Davis TN, Tseng JC, Li WD, et al. (2011) A Class of Human Proteins that Deliver Functional Proteins into Mammalian Cells In Vitro and In Vivo. Chemistry & Biology 18: 833–838. doi: 10.1016/j.chembiol.2011.07.003
- 51. Lund U, Rippe A, Venturoli D, Tenstad O, Grubb A, et al. (2003) Glomerular filtration rate dependence of sieving of albumin and some neutral proteins in rat kidneys. Am J Physiol Renal Physiol 284: F1226–1234.
- 52. Miklos AE, Kluwe C, Der BS, Pai S, Sircar A, et al. (2012) Structure-based design of supercharged, highly thermoresistant antibodies. Chem Biol 19: 449–455. doi: 10.1016/j.chembiol.2012.01.018
- 53. Der BS KC, Miklos AE, Jacak R, Lyskov S, Gray JJ, Georgiou G, Ellington AD, Kuhlman B (2012) Alternative computational protocols for supercharging protein surfaces for reversible unfolding and retention of stability. in preparation.
- 54. Lawrence MS, Phillips KJ, Liu DR (2007) Supercharging proteins can impart unusual resilience. J Am Chem Soc 129: 10110–10112. doi: 10.1021/ja071641y
- 55. Mandell DJ, Kortemme T (2009) Computer-aided design of functional protein interactions. Nat Chem Biol 5: 797–807. doi: 10.1038/nchembio.251
- 56. Pokala N, Handel TM (2001) Review: protein design–where we were, where we are, where we’re going. J Struct Biol 134: 269–281. doi: 10.1006/jsbi.2001.4349
- 57. Humphris-Narayanan E, Akiva E, Varela R, S OC, Kortemme T (2012) Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design. PLoS Comput Biol 8: e1002639. doi: 10.1371/journal.pcbi.1002639
- 58. Smith CA, Kortemme T (2010) Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains. Journal of Molecular Biology 402: 460–474. doi: 10.1016/j.jmb.2010.07.032
- 59. Treynor TP, Vizcarra CL, Nedelcu D, Mayo SL (2007) Computationally designed libraries of fluorescent proteins evaluated by preservation and diversity of function. Proc Natl Acad Sci U S A 104: 48–53. doi: 10.1073/pnas.0609647103
- 60. Smith CA, Kortemme T (2011) Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design. PLoS ONE 6: e20451. doi: 10.1371/journal.pone.0020451
- 61. Tonikian R, Zhang Y, Sazinsky SL, Currell B, Yeh JH, et al. (2008) A specificity map for the PDZ domain family. PLoS Biol 6: e239. doi: 10.1371/journal.pbio.0060239
- 62. Davis IW, Arendall WB, 3rd, Richardson DC, Richardson JS (2006) The backrub motion: how protein backbone shrugs when a sidechain dances. Structure 14: 265–274. doi: 10.1016/j.str.2005.10.007
- 63. Smith CA, Kortemme T (2008) Backrub-Like Backbone Simulation Recapitulates Natural Protein Conformational Variability and Improves Mutant Side-Chain Prediction. Journal of Molecular Biology 380: 742–756. doi: 10.1016/j.jmb.2008.05.023
- 64. Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14: 1188–1190. doi: 10.1101/gr.849004
- 65. Tošovská P, Arora PS (2010) Oligooxopiperazines as Nonpeptidic α-Helix Mimetics. Organic Letters 12: 1588–1591. doi: 10.1021/ol1003143
- 66. Bullock BN, Jochim AL, Arora PS (2011) Assessing helical protein interfaces for inhibitor design. J Am Chem Soc 133: 14220–14223. doi: 10.1021/ja206074j
- 67. Patgiri A, Jochim AL, Arora PS (2008) A hydrogen bond surrogate approach for stabilization of short peptide sequences in alpha-helical conformation. Acc Chem Res 41: 1289–1300. doi: 10.1021/ar700264k
- 68. Henchey LK, Kushal S, Dubey R, Chapman RN, Olenyuk BZ, et al. (2010) Inhibition of hypoxia inducible factor 1-transcription coactivator interaction by a hydrogen bond surrogate alpha-helix. J Am Chem Soc 132: 941–943. doi: 10.1021/ja9082864
- 69. Butterfoss GL, Renfrew PD, Kuhlman B, Kirshenbaum K, Bonneau R (2009) A preliminary survey of the peptoid folding landscape. J Am Chem Soc 131: 16798–16807. doi: 10.1021/ja905267k
- 70. Zuckermann RN, Kodadek T (2009) Peptoids as potential therapeutics. Curr Opin Mol Ther 11: 299–307.
- 71. Lee B, Richards FM (1971) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55: 379–400. doi: 10.1016/0022-2836(71)90324-x
- 72. Sheffler W, Baker D (2009) RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation. Protein Sci 18: 229–239. doi: 10.1002/pro.8
- 73. Borgo B, Havranek JJ (2012) Automated selection of stabilizing mutations in designed and natural proteins. Proc Natl Acad Sci U S A 109: 1494–1499. doi: 10.1073/pnas.1115172109
- 74. Kilambi KP, Gray JJ (2012) Rapid calculation of protein pKa values using Rosetta. Biophys J 103: 587–595. doi: 10.1016/j.bpj.2012.06.044
- 75. Sripakdeevong P, Kladwang W, Das R (2011) An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling. Proc Natl Acad Sci U S A 108: 20573–20578. doi: 10.1073/pnas.1106516108
- 76. Lange OF, Baker D (2012) Resolution-adapted recombination of structural features significantly improves sampling in restraint-guided structure calculation. Proteins 80: 884–895. doi: 10.1002/prot.23245
- 77. Friedland GD, Lakomek N-A, Griesinger C, Meiler J, Kortemme T (2009) A Correspondence Between Solution-State Dynamics of an Individual Protein and the Sequence and Conformational Diversity of its Family. PLoS Comput Biol 5: e1000393. doi: 10.1371/journal.pcbi.1000393
- 78. Humphris EL, Kortemme T (2008) Prediction of Protein-Protein Interface Sequence Diversity Using Flexible Backbone Computational Protein Design. Structure 16: 1777–1788. doi: 10.1016/j.str.2008.09.012
- 79. Das R, Baker D (2007) Automated de novo prediction of native-like RNA tertiary structures. Proc Natl Acad Sci U S A 104: 14664–14669. doi: 10.1073/pnas.0703836104
- 80. Der BS, Kluwe C, Miklos AE, Jacak R, Lyskov S, et al.. (2013) Alternative computational protocols for supercharging protein surfaces for reversible unfolding and retention of stability. Submitted to PLoS One Rosetta Special Collection.
- 81. Drew K, Renfrew PD, Craven T, Butterfoss GL, Chou F-C, et al.. (2013) Adding Diverse Noncanonical Backbones to Rosetta: Enabeling Peptidomimetic and Foldamer Design. Submitted to PLoS One Rosetta Special Collection.
- 82. Kilambi Krishna P, Gray Jeffrey J (2012) Rapid Calculation of Protein pKa Values Using Rosetta. Biophysical journal 103: 587–595. doi: 10.1016/j.bpj.2012.06.044
- 83. Chaudhury S, Gray JJ (2008) Conformer Selection and Induced Fit in Flexible Backbone Protein–Protein Docking Using Computational and NMR Ensembles. Journal of Molecular Biology 381: 1068–1087. doi: 10.1016/j.jmb.2008.05.042
- 84. Lemmon G, Meiler J (2012) RosettaLigand Docking with Flexible XML Protocols. Methods in Molecular Biology 819: 143–155. doi: 10.1007/978-1-61779-465-0_10