Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently.
Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein-protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 proteinligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility.
We report the availability of grcarma, a program encoding for a fully automated set of tasks aiming to simplify the analysis of molecular dynamics trajectories of biological macromolecules. It is a cross-platform, Perl/Tk-based front-end to the program carma and is designed to facilitate the needs of the novice as well as those of the expert user, while at the same time maintaining a user-friendly and intuitive design. Particular emphasis was given to the automation of several tedious tasks, such as extraction of clusters of structures based on dihedral and Cartesian principal component analysis, secondary structure analysis, calculation and display of root-meansquare deviation (RMSD) matrices, calculation of entropy, calculation and analysis of variance–covariance matrices, calculation of the fraction of native contacts, etc. The program is free-open source software available immediately for download.
We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall’s Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-017-0049-y) contains supplementary material, which is available to authorized users.
Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily-modified pathogen proteins can be confounded by overlapping sugar signals and/or compound with known experimental constraints. ‘Universal saturation transfer analysis’ (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin lineage SARS-CoV-2 spike trimer binds sialoside sugars in an ‘end-on’ manner. uSTA-guided modelling and a high-resolution cryo-electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar-binding in SARS CoV 2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins in deeper human lung as potentially relevant to virulence and/or zoonosis.
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