The second paper is Rodríguez-Espigares, I., Torrens-Fontanals, M., Tiemann, J.K.S. et al. GPCRmd uncovers the dynamics of the 3D-GPCRome. Nat Methods (2020). [Link] 10.1038/s41592-020-0884-y here, your computations have been used to build a database of configurations for most types and subtypes of GPCR-class proteins. GPCRs are "do-all" receptors - there are many many subtypes, each with activators and inhibitors. The [Link] GPCRmd database provides for the first time a *systematic* coverage of their dynamics. G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a com...

By gpugrid at 2020-07-24

Two recent papers which came out with results from GPUGRID. First, PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations Gerard Martinez-Rosell, Silvia Lovera, Zara A. Sands, and Gianni De Fabritiis Journal of Chemical Information and Modeling 2020 60 (4), 2314-2324 DOI: 10.1021/acs.jcim.9b01209 In this paper (and the corresponding live system) GPUGRID's computations are used to power a search for elusive (transient) pockets which may become drug targets. Thanks for the contributions! Cryptic pockets are protein cavities that remain hidden in resolved apo structures and generally require the presence of a co-crystallized ligand to become visible. Finding new cryptic pockets is crucial for structure-based drug discovery to identify new ways of modulating protein activity and thus expand the druggable space. We present here a new method and associated web application leveraging mixed-solvent molecular dynamics (MD) simulations using benzene as a hydrophobic probe to detect cryptic pockets. Our all-atom MD-based workflow was systematically tested on 18 different systems and 5 additional kinases and represents the largest validation study of this kind. CrypticScout identifies benzene probe binding hotspots on a protein surface by mapping probe occupancy, residence time, and the benzene occupancy reweighed by the residence time. The method is presented to the scientific community in a web application available via www.playmolecule.org using a distributed computing infrastructure to perform the simulations. List of mo...

By gpugrid at 2020-07-24

I'm filling up the task queue again- - these are called MDAD and suffix. Happy crunching! T

By gpugrid at 2020-05-21