DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites

Over the past decade, protein-protein interactions have emerged as attractive but challenging targets for therapeutic intervention using small molecules. Due to the relatively flat surfaces that typify protein interaction sites, modern virtual screening tools developed for optimal performance against “traditional” protein targets perform less well when applied instead at protein interaction sites. Previously, we described a docking method specifically catered to the shallow binding modes characteristic of small-molecule inhibitors of protein interaction sites. This method, called DARC (Docking Approach using Ray Casting), operates by comparing the topography of the protein surface when “viewed” from a vantage point inside the protein against the topography of a bound ligand when “viewed” from the same vantage point. Here, we present five key enhancements to DARC. First, we use multiple vantage points to more accurately determine protein-ligand surface complementarity. Second, we describe a new scheme for rapidly determining optimal weights in the DARC scoring function. Third, we incorporate sampling of ligand conformers “on-the-fly” during docking. Fourth, we move beyond simple shape complementarity and introduce a term in the scoring function to capture electrostatic complementarity. Finally, we adjust the control flow in our GPU implementation of DARC to achieve greater speedup of these calculations. At each step of this study, we evaluate the performance of DARC in a “pose recapitulation” experiment: predicting the binding mode of 25 inhibitors each solved in complex with its distinct target protein (a protein interaction site). Whereas the previous version of DARC docked only one of these inhibitors to within 2 Å RMSD of its position in the crystal structure, the newer version achieves this level of accuracy for 12 of the 25 complexes, corresponding to a statistically significant performance improvement (p < 0.001). Collectively then, we find that the five enhancements described here – which together make up DARC 2.0 – lead to dramatically improved speed and performance relative to the original DARC method

Alternative Computational Protocols for Supercharging Protein Surfaces for Reversible Unfolding and Retention of Stability

Bryan S. Der, Ron Jacak, Brian Kuhlman, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of AmericaChristien Kluwe, Aleksandr E. Miklos, Andrew D. Ellington , Center for Systems and Synthetic Biology, University of Texas at Austin, Austin, Texas, United States of AmericaChristien Kluwe, Aleksandr E. Miklos, George Georgiou, Andrew D. Ellington, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, United States of AmericaAleksandr E. Miklos, Andrew D. Ellington , Applied Research Laboratories, University of Texas at Austin, Austin, Texas, United States of AmericaSergey Lyskov, Jeffrey J. Gray, Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of AmericaBrian Kuhlman, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of AmericaReengineering protein surfaces to exhibit high net charge, referred to as “supercharging”, can improve reversibility of unfolding by preventing aggregation of partially unfolded states. Incorporation of charged side chains should be optimized while considering structural and energetic consequences, as numerous mutations and accumulation of like-charges can also destabilize the native state. A previously demonstrated approach deterministically mutates flexible polar residues (amino acids DERKNQ) with the fewest average neighboring atoms per side chain atom (AvNAPSA). Our approach uses Rosetta-based energy calculations to choose the surface mutations. Both protocols are available for use through the ROSIE web server. The automated Rosetta and AvNAPSA approaches for supercharging choose dissimilar mutations, raising an interesting division in surface charging strategy. Rosetta-supercharged variants of GFP (RscG) ranging from −11 to −61 and +7 to +58 were experimentally tested, and for comparison, we re-tested the previously developed AvNAPSA-supercharged variants of GFP (AscG) with +36 and −30 net charge. Mid-charge variants demonstrated ~3-fold improvement in refolding with retention of stability. However, as we pushed to higher net charges, expression and soluble yield decreased, indicating that net charge or mutational load may be limiting factors. Interestingly, the two different approaches resulted in GFP variants with similar refolding properties. Our results show that there are multiple sets of residues that can be mutated to successfully supercharge a protein, and combining alternative supercharge protocols with experimental testing can be an effective approach for charge-based improvement to refolding.This work was supported by the Defense Advanced Research Projects Agency (HR-0011-10-1-0052 to A.E.) and the Welch Foundation (F-1654 to A.E.), the National Institutes of Health grants GM073960 (B.K.) and R01-GM073151 (J.G. and S.L.), the Rosetta Commons (S.L.), the National Science Foundation graduate research fellowship (2009070950 to B.D.), the UNC Royster Society Pogue fellowship (B.D.), and National Institutes of Health grant T32GM008570 for the UNC Program in Molecular and Cellular Biophysics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Center for Systems and Synthetic BiologyCellular and Molecular BiologyApplied Research LaboratoriesEmail: [email protected]

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

DARC weights obtained via different approaches.

<p>The first four parameters (c1/c2/c3/c4) refer to those used by DARC to evaluate shape complementarity (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131612#pone.0131612.e001" target="_blank">Eq 1</a>); the last parameter (c5) is used to scale the electrostatic term, when this term is used (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131612#pone.0131612.e004" target="_blank">Eq 4</a>). The original DARC weights were obtained by minimizing the collective RMSD for docking a series of seven ligands to their cognate receptors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131612#pone.0131612.ref022" target="_blank">22</a>]. We also report the weights that arise from our new weight fitting approach (<i>Enhancement #2</i>), trained on all 25 proteins in our latest test set. Because this new approach is much faster, we were able to apply leave-one-out cross-validation to this set; here we also report the standard deviation observed among the weights trained on the 24-protein subsets. The magnitudes of the weights are <i>not</i> indicative of the relative importance of each term in the scoring function, since the magnitudes of the unscaled contributions vary broadly (e.g. the unscaled electrostatic term is typically much larger than the other terms, so a very small weight is needed to balance its contributions to the total score).</p

Predicting Ion Mobility Collision Cross Sections using Projection Approximation with ROSIE-PARCS Webserver

Ion mobility (IM) coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). While there are several computational methods for predicting CCSIM based on protein structures, including our previously developed PARCS, the process usually requires prior experience with the command-line interface (CLI). To overcome this challenge, here we present a web application on the ROSIE webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly, and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu)

Updated GPU control flow.

<p><b>(A)</b> Schematic illustration of CPU-GPU control flow in DARC 2.0. Previously, the ligand conformation was generated on the CPU and passed to the GPU; now, the conformer index / displacement / rotation (relative to a “reference” position) is instead passed, and the GPU is responsible for applying this transformation to the ligand’s atomic coordinates. The new electrostatic complementarity term is also computed entirely on the GPU. <b>(B)</b> For each protein-ligand complex in our set (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131612#pone.0131612.s006" target="_blank">S1 Table</a>), we timed DARC when docking the ligand back into its cognate receptor, using either GPU+CPU or CPU alone. We find an average speedup of 90-fold when using the GPU (red line), an improvement over the 27-fold speedup we achieved in our original GPU implementation of DARC [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131612#pone.0131612.ref026" target="_blank">26</a>]. <b>(C)</b> The GPU led to an even greater speedup over the analogous calculation on the CPU when electrostatic complementarity was included in both calculations (190-fold speedup).</p