Serverification of Molecular Modeling Applications: the Rosetta Online Server that Includes Everyone (ROSIE)

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

Real-Time PyMOL Visualization for Rosetta and PyRosetta

Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schrödinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design

Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction

Next-generation antibody modeling

Antibodies are important immunological molecules that can bind a diverse array of foreign molecules. The genetic mechanism that gives rise to antibodies and many antibody sequences is known, but only by studying three-dimensional structures of antibodies and antibody–antigen complexes can we reveal immunological mechanisms and provide a starting point for developing rationally designed antibodies. With the advent of high-throughput sequencing technologies, the gap between the number of sequences and structures is widening, demanding accurate antibody modeling methods. Our previously developed method, RosettaAntibody, served as a starting point for antibody structure prediction. In this dissertation, I detail my work assessing the predictive power of RosettaAntibody, and the development and testing of new methods to address its weaknesses. First, I describe an effort to assess the accuracy of RosettaAntibody on a set of unpublished crystal structures. This challenge enabled us to combine manual and automated methods for selecting models and compare RosettaAntibody to other antibody modeling methods. The most challenging aspect of structure prediction in this assessment proved to be modeling the third complementarity determining region loop on the heavy chain (CDR H3). Next I detail my work in studying CDR H3 loops to uncover why a vast majority of them contain a kink at the loop's C-terminus. Part of this work involved searching the Protein Data Bank (PDB) for structures with a similar geometry of the amino acid residues at the base of the loop, leading to a set of CDR H3-like loops from non-antibody proteins. With a clearer understanding of CDR H3 loop structures and the most detailed description of the kink to date, I developed a new loop modeling routine that utilizes this information to restrict the geometry of the loop to be kinked, resulting in an improvement in the weakest aspect of antibody structure prediction. In summary, the structure prediction methods I have developed and structural analyses I have performed provide a means to begin to address the widening sequence–structure gap. Additionally, these methods can be used to perform structural analysis in the development of rationally designed antibodies

A Framework to Simplify Combined Sampling Strategies in Rosetta.

A core task in computational structural biology is the search of conformational space for low energy configurations of a biological macromolecule. Because conformational space has a very high dimensionality, the most successful search methods integrate some form of prior knowledge into a general sampling algorithm to reduce the effective dimensionality. However, integrating multiple types of constraints can be challenging. To streamline the incorporation of diverse constraints, we developed the Broker: an extension of the Rosetta macromolecular modeling suite that can express a wide range of protocols using constraints by combining small, independent modules, each of which implements a different set of constraints. We demonstrate expressiveness of the Broker through several code vignettes. The framework enables rapid protocol development in both biomolecular design and structural modeling tasks and thus is an important step towards exposing the rich functionality of Rosetta's core libraries to a growing community of users addressing a diverse set of tasks in computational biology

A computational method for design of connected catalytic networks in proteins

Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side‐chain functional groups which maximize transition‐state stabilization, and then searching for sites in protein scaffolds where the specified side‐chain–transition‐state interactions can be realized. A limitation of this approach is that the interactions between the side chains themselves are not constrained. An extensive connected hydrogen bond network involving the catalytic residues was observed in a designed retroaldolase following directed evolution. Such connected networks could increase catalytic activity by preorganizing active site residues in catalytically competent orientations, and enabling concerted interactions between side chains during catalysis, for example, proton shuffling. We developed a method for designing active sites in which the catalytic side chains, in addition to making interactions with the transition state, are also involved in extensive hydrogen bond networks. Because of the added constraint of hydrogen‐bond connectivity between the catalytic side chains, to find solutions, a wider range of interactions between these side chains and the transition state must be considered. Our new method starts from a ChemDraw‐like two‐dimensional representation of the transition state with hydrogen‐bond donors, acceptors, and covalent interaction sites indicated, and all placements of side‐chain functional groups that make the indicated interactions with the transition state, and are fully connected in a single hydrogen‐bond network are systematically enumerated. The RosettaMatch method can then be used to identify realizations of these fully‐connected active sites in protein scaffolds. The method generates many fully‐connected active site solutions for a set of model reactions that are promising starting points for the design of fully‐preorganized enzyme catalysts.ISSN:0961-8368ISSN:1469-896

The design of the broking mechanism.

<p>a) right: the central resources of a Rosetta protocol (blue) are acted upon by many independent Movers (green) in an uncontrolled fashion. Movers differ in the actions they perform on these resources, including configuration (green arrows) and sampling (black arrows). Left: A Broker layer (purple) receives requests from numerous clients (green) using a standard interface, and configures the core resources appropriately. Access is restricted to these resources using an access control framework, but requests invisibly “pass through” this layer to avoid interface differences. b) The Broker communicates with client Movers by receiving claims and responding with a passport. c) Client Movers (light green) convert user-specified configurations (brown) into convert developer-friendly claims (light purple, left) through the claiming interface. The Broker (dark purple) converts claims into specific, machine-readable needs, which are processed and returned to the client Mover as a DoF passport (light purple, right). d) The DoF access assignment behavior of the Broker when two clients request access to the same DoF. If one Mover claims exclusive and another claims must control or exclusive, broking fails, because it is not possible to satisfy both. If one claims exclusive and the other claims can control, only the Mover claiming exclusive receives access. If a Mover claims “does not control,” it never receives access. In all other cases, both Movers receive access. e) The procedure by which the conformation validates a modification to a DoF. The client Mover creates an unlock, which is shared by the conformation and the Mover. Then, whenever to the conformation change the DoF, the conformation checks latest active unlock to ensure the active Mover has access to the changing degrees of freedom.</p

Multi-resolution constraints in a simple folding protocol.

<p>a) The crystal structure of ubiquitin is color- coded and annotated by the sampling procedures applied to each region. Green strands indicate regions of β-strand pair sampling, the blue region has fixed internal coordinates drawn from the native crystal structure, and grey and green are subject to fragment insertion using fragments from chemical shifts. b) One possible fold tree generated by the Broker to satisfy the constraints given in (a). Black pointing arrows represent jumps between β strands, which are represented by green block arrows. Breaks in the underlying black line indicate possible chain break locations. The fixed region is indicated by the blue rounded rectangle. c) The main part of a RosettaScripts XML script that implements this protocol. The full script is available in the supplement and the script along with all required files is available in the protocol capture.</p

A SnugDock-inspired antibody modeling protocol configuration.

<p>a) An antibody heavy (red) and light (blue) chain in complex with an antigen (green), which interacts with the antibody’s CDR loops (cyan). Call-outs identify the sampling procedures that are active on this structure using this protocol, and colors indicate the regions that each prodcedure targets: the gradient-based minimizer (cyan), loop closure (magenta), and fixed backbone docking (red, green, and blue) of antibody chains and antigen. Additionally, the explicitly-monitored centers of mass of each of the three polypeptide chains are indicated (blue, green, and red circles) and each is docked to a central reference point (grey circle). b) The fold tree that underlies the situation in (a). Each chain is docked via its center of mass virtual residue (red, blue, and green circles) to a central virtual residue (grey circle). The antigen and antibody regions outside of CDR loops are fixed, whereas the CDR loops, each of which is interrupted by a cut, are flexibly modeled by minimization and subjected to loop closure. The color of the line indicates where Mover active: red, green, and blue are docking Movers, magenta and cyan are loop closure and minimization, respectively, and grey is unmoved. c) The definition of the ResidueSelectors used in the body of the script XML script. Note that many residue selectors are created using Boolean logic operators depending on other ResidueSelectors, making alterations straightforward.</p