The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

Time-series clustering - A decade review

Computational docking is the prediction or modeling of the three-dimensional structure of a biomolecular complex, starting from the structures of the individual molecules in their free, unbound form. HADDOCK is a popular docking program that takes a data-driven approach to docking, with support for a wide range of experimental data. Here we present the HADDOCK web server protocol, facilitating the modeling of biomolecular complexes for a wide community. The main web interface is user-friendly, requiring only the structures of the individual components and a list of interacting residues as input. Additional web interfaces allow the more advanced user to exploit the full range of experimental data supported by HADDOCK and to customize the docking process. The HADDOCK server has access to the resources of a dedicated cluster and of the e-NMR GRID infrastructure. Therefore, a typical docking run takes only a few minutes to prepare and a few hours to complete.

/pdf/the-haddock-web-server-for-data-driven-biomolecular-docking-4aytwcp1lq.pdf

The HADDOCK web server for data-driven biomolecular docking

https://www.sheffield.ac.uk/polopoly_fs/1.393312!/file/shift_mapping_2013.pdf

Using chemical shift perturbation to characterise ligand binding.

Here we present version 2.0 of HADDOCK, which incorporates considerable improvements and new features. HADDOCK is now able to model not only protein-protein complexes but also other kinds of biomolecular complexes and multi-component (N > 2) systems. In the absence of any experimental and/or predicted information to drive the docking, HADDOCK now offers two additional ab initio docking modes based on either random patch definition or center-of-mass restraints. The docking protocol has been considerably improved, supporting among other solvated docking, automatic definition of semi-flexible regions, and inclusion of a desolvation energy term in the scoring scheme. The performance of HADDOCK2.0 is evaluated on the targets of rounds 4-11, run in a semi-automated mode using the original information we used in our CAPRI submissions. This enables a direct assessment of the progress made since the previous versions. Although HADDOCK performed very well in CAPRI (65% and 71% success rates, overall and for unbound targets only, respectively), a substantial improvement was achieved with HADDOCK2.0.

/pdf/haddock-versus-haddock-new-features-and-performance-of-4mr6odu935.pdf

HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets.

HADDOCK versus HADDOCK: New features and performance of HADDOCK 2.0 on the CAPRI targets

De and M.

Human galectin-3 (Mac-2 antigen): defining molecular switches of affinity to natural glycoproteins, structural and dynamic aspects of glycan binding by flexible ligand docking and putative regulatory sequences in the proximal promoter region.

Gene duplication and sequence divergence are driving forces toward establishing protein families. To examine how sequence changes affect carbohydrate specificity, the two closely related proto-type chicken galectins CG-14 and CG-16 were selected as models. Binding properties were analyzed using a highly sensitive solid-phase assay. We tested 56 free saccharides and 34 well-defined glycoproteins. The two galectins share preference for the II (Galβ1-4GlcNAc) versus I (Galβ1-3GlcNAc) version of β-galactosides. A pronounced difference is found owing to the reactivity of CG-14 with histo-blood group ABH active oligosaccharides and A/B active glycoproteins. These experimental results prompted to determine activity-structure correlations by modeling. Computational analysis included consideration of the flexibility of binding partners and the presence of water molecules. It provided a comparative description of complete carbohydrate recognition domains, which had so far not been characterized in animal galectins. The structural models assigned II, I selectivity to a region downstream of the central Trp moiety. Docking revealed that the tetrasaccharides can be accommodated in their free-state low-energy conformations. CG-14's preference for A versus B epitopes could be attributed to a contact between His124 and the N-acetyl group of GalNAc. Regarding intergalectin comparison, the Ala53/Cys51 exchange affects the interaction potential of His54/His52. Close inspection of simulated dynamic interplay revealed reorientation of His124 at the site of the His124/Glu123 substitution, with potential impact on ligand dissociation. In summary, this study identifies activity differences and provides information on their relation to structural divergence, epitomizing the value of this combined approach beyond galectins.

/pdf/activity-structure-correlations-in-divergent-lectin-2laoy4bacl.pdf

Activity–structure correlations in divergent lectin evolution: fine specificity of chicken galectin CG-14 and computational analysis of flexible ligand docking for CG-14 and the closely related CG-16

Mickaël Krzeminski

Papers

HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets.

HADDOCK versus HADDOCK: New features and performance of HADDOCK 2.0 on the CAPRI targets

De and M.

Human galectin-3 (Mac-2 antigen): defining molecular switches of affinity to natural glycoproteins, structural and dynamic aspects of glycan binding by flexible ligand docking and putative regulatory sequences in the proximal promoter region.

Activity–structure correlations in divergent lectin evolution: fine specificity of chicken galectin CG-14 and computational analysis of flexible ligand docking for CG-14 and the closely related CG-16