GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors

G-protein coupled receptors (GPCRs) are key players in signal transduction and therefore a large proportion of pharmaceutical drugs target these receptors. Structural data of GPCRs are sparse yet important for elucidating the molecular basis of GPCR-related diseases and for performing structure-based drug design. To ameliorate this problem, GPCR-SSFE 2.0 (http://www.ssfa- 7tmr.de/ssfe2/), an intuitive web server dedicated to providing three- dimensional Class A GPCR homology models has been developed. The updated web server includes 27 inactive template structures and incorporates various new functionalities. Uniquely, it uses a fingerprint correlation scoring strategy for identifying the optimal templates, which we demonstrate captures structural features that sequence similarity alone is unable to do. Template selection is carried out separately for each helix, allowing both single- template models and fragment-based models to be built. Additionally, GPCR-SSFE 2.0 stores a comprehensive set of pre-calculated and downloadable homology models and also incorporates interactive loop modeling using the tool SL2, allowing knowledge-based input by the user to guide the selection process. For visual analysis, the NGL viewer is embedded into the result pages. Finally, blind-testing using two recently published structures shows that GPCR-SSFE 2.0 performs comparably or better than other state-of-the art GPCR modeling web servers

HomolWat : a web server tool to incorporate 'homologous' water molecules into GPCR structures

Internal water molecules play an essential role in the structure and function of membrane proteins including G protein-coupled receptors (GPCRs). However, technical limitations severely influence the number and certainty of observed water molecules in 3D structures. This may compromise the accuracy of further structural studies such as docking calculations or molecular dynamics simulations. Here we present HomolWat, a web application for incorporating water molecules into GPCR structures by using template-based modelling of homologous water molecules obtained from high-resolution structures. While there are various tools available to predict the positions of internal waters using energy-based methods, the approach of borrowing lacking water molecules from homologous GPCR structures makes HomolWat unique. The tool can incorporate water molecules into a protein structure in about a minute with around 85% of water recovery. The web server is freely available at

Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by promoting guanine nucleotide exchange. Here, we investigate the coupling of G proteins with GPCRs and describe the events that ultimately lead to the ejection of GDP from its binding pocket in the Gα subunit, the rate-limiting step during G-protein activation. Using molecular dynamics simulations, we investigate the temporal progression of structural rearrangements of GDP-bound Gs protein (Gs·GDP; hereafter GsGDP) upon coupling to the β2-adrenergic receptor (β2AR) in atomic detail. The binding of GsGDP to the β2AR is followed by long-range allosteric effects that significantly reduce the energy needed for GDP release: the opening of α1-αF helices, the displacement of the αG helix and the opening of the α-helical domain. Signal propagation to the Gs occurs through an extended receptor interface, including a lysine-rich motif at the intracellular end of a kinked transmembrane helix 6, which was confirmed by site-directed mutagenesis and functional assays. From this β2AR-GsGDP intermediate, Gs undergoes an in-plane rotation along the receptor axis to approach the β2AR-Gsempty state. The simulations shed light on how the structural elements at the receptor-G-protein interface may interact to transmit the signal over 30 Å to the nucleotide-binding site. Our analysis extends the current limited view of nucleotide-free snapshots to include additional states and structural features responsible for signaling and G-protein coupling specificity.This work was funded by German Research Foundation (DFG) through CRC1423, project number 421152132, subproject C01 (to P.W.H.) and subprojects A01, A05 and Z03 (to P.S.), Stiftung Charité and the Einstein Center Digital for Future to P.W.H. P.S. is further supported through CRC 1078–Project ID 221545957–SFB 1078, subproject B06; through the cluster of excellence ‘UniSysCat‘ (under Germany’s Excellence Strategy-EXC2008/1-390540038 and through the European Union’s Horizon 2020 MSCA Program under grant agreement 956314 (ALLODD). This work was also funded by National Institutes of Health grant R01NS028471 (to B.K.K.), by National Natural Science Foundation of China (Grant 32122041 to X.L.) and by Tsinghua University Initiative Scientific Research Program (to X.L.). We are grateful to A. Inoue (Tohoku University, Japan) for providing the CRISPR–Cas9-edited triple knockout barr1/barr2/β2AR HEK293A cells and to H. Schihada (Philipps-Universität Marburg, Germany) for providing the Gs-CASE sensor DNA material and advice for the BRET dissociation assay. We thank B. Bauer (Charité–Universitätsmedizin Berlin, Germany) for assistance in molecular biology and purifying reagents. B.K.K. and P.W.H. acknowledge the Einstein Foundation and the Berlin Institute of Health for their support. We are grateful to M. Heck (Charité–Universitätsmedizin Berlin, Germany) for advice on the statistical analysis of the BRET 2 assay and M. Heck and K. P. Hofmann (Charité–Universitätsmedizin Berlin, Germany) for helpful discussions. P.F.S. also holds external affiliations with the Institute of Theoretical Chemistry, University of Vienna, Austria, the Universidad Nacional de Colombia, Bogotá, Colombia, the Center for noncoding RNA in Technology and Health at the University of Copenhagen and the Santa Fe Institute, Santa Fe, New Mexico, USA. We gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project p101ae. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by DFG (440719683).Peer reviewe

ngl v0.7

Added Store and Proxy classes for memory efficiency MMTF, DXBIN, DCD files format parsers 'unitcell' representation stage.makeImage (returns Promise) take NCS operations into account when creating unitcell & supercell assemblies added multi sample antialias rendering added support for spinning around an axis use bitsets for storing selections of atoms Assembly and AssemblyPart classes stage.toggleFullscreen method read occupancy data when available (mmCIF, pdb, mmtf) occupancy color scheme alternate location support in selections, e.g. %B read insertion codes when available (mmCIF, pdb, mmtf) insertion code support in selections, e.g. ^A numeric residue name support in selections, e.g. [032] Queue class to handle async tasks Changed fixed transformation matrix in mrc/ccp4 parser optimized near clipping Fiber class remanamed to Polymer more consistent fog use workers more sparsely due to the large overhead of creating them create font SDF on demand, remove asset dependency integrated three.js lighting into custom shaders MIGRATION: chainname read from auth_asym_id instead of from label_asym_id field DOC: clarified apache configuration for deployment FIX: cif parser, ignore non-displayable bonds between symmetry mates FIX: cif parser, struct_conn bonds not added for multiple altloc atoms LIB: updated signals.js LIB: updated promise.js LIB: updated three.js LIB: updated pako.js to pako_inflate.js (no deflation support needed) CODE: support loading of Blob objects in addition to File objects CODE: tweaked DistanceRepresentation visibility params Removed zip, lzma, bzip2 decompression removed async.js mdsrv related code and documentation stage.exportImage (makes image and triggers download), use stage.makeImag

A fragment based method for modeling of protein segments into cryo-EM density maps

Abstract Background Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded. Results Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map. Conclusion Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps

SL2: an interactive webtool for modeling of missing segments in proteins

SuperLooper2 (SL2) (http://proteinformatics.charite.de/sl2) is the updated version of our previous web-server SuperLooper, a fragment based tool for the prediction and interactive placement of loop structures into globular and helical membrane proteins. In comparison to our previous version, SL2 benefits from both a considerably enlarged database of fragments derived from high-resolution 3D protein structures of globular and helical membrane proteins, and the integration of a new protein viewer. The database, now with double the content, significantly improved the coverage of fragment conformations and prediction quality. The employment of the NGL viewer for visualization of the protein under investigation and interactive selection of appropriate loops makes SL2 independent of third-party plug-ins and additional installations

MDsrv:visual sharing and analysis of molecular dynamics simulations

Molecular dynamics simulation is a proven technique for computing and visualizing the time-resolved motion of macromolecules at atomic resolution. The MDsrv is a tool that streams MD trajectories and displays them interactively in web browsers without requiring advanced skills, facilitating interactive exploration and collaborative visual analysis. We have now enhanced the MDsrv to further simplify the upload and sharing of MD trajectories and improve their online viewing and analysis. With the new instance, the MDsrv simplifies the creation of sessions, which allows the exchange of MD trajectories with preset representations and perspectives. An important innovation is that the MDsrv can now access and visualize trajectories from remote datasets, which greatly expands its applicability and use, as the data no longer needs to be accessible on a local server. In addition, initial analyses such as sequence or structure alignments, distance measurements, or RMSD calculations have been implemented, which optionally support visual analysis. Finally, the MDsrv now offers a faster and more efficient visualization of even large trajectories.Comment: 9 pages, 3 figure

Sharing Data from Molecular Simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations