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.

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Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.

The collective dynamics of the nascent polypeptide exit tunnel are investigated with the computationally efficient elastic network model using normal mode analysis. The calculated normal modes are considered individually and in linear combinations with different coefficients mimicking the phase angles between modes, in order to follow the mechanistic motions of tunnel wall residues. The low frequency fluctuations indicate three distinct regions along the tunnel -the entrance, the neck and the exit -each having distinctly different domain motions. Generally the lining of the entrance region moves in the exit direction, with the exit region having significantly larger motions, but in a perpendicular direction, whereas the confined neck region generally has rotational motions. Especially the universally conserved extensions of ribosomal proteins L4 and L22 located at the narrowest and mechanistically strategic region of tunnel undergo generally antior non-correlated motions, which may have an important role in nascent polypeptide gating mechanism. These motions appear to be sufficiently robust so as to be unaffected by the presence of a peptide chain in the tunnel.

Our information‐driven docking approach HADDOCK is a consistent top predictor and scorer since the start of its participation in the CAPRI community‐wide experiment. This sustained performance is due, in part, to its ability to integrate experimental data and/or bioinformatics information into the modelling process, and also to the overall robustness of the scoring function used to assess and rank the predictions. In the CASP‐CAPRI Round 1 scoring experiment we successfully selected acceptable/medium quality models for 18/14 of the 25 targets – a top‐ranking performance among all scorers. Considering that for only 20 targets acceptable models were generated by the community, our effective success rate reaches as high as 90% (18/20). This was achieved using the standard HADDOCK scoring function, which, thirteen years after its original publication, still consists of a simple linear combination of intermolecular van der Waals and Coulomb electrostatics energies and an empirically derived desolvation energy term. Despite its simplicity, this scoring function makes sense from a physico‐chemical perspective, encoding key aspects of biomolecular recognition. In addition to its success in the scoring experiment, the HADDOCK server takes the first place in the server prediction category, with 16 successful predictions. Much like our scoring protocol, because of the limited time per target, the predictions relied mainly on either an ab initio center‐of‐mass and symmetry restrained protocol, or on a template‐based approach whenever applicable. These results underline the success of our simple but sensible prediction and scoring scheme. Proteins 2017; 85:417–423. © 2016 Wiley Periodicals, Inc.