Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

Abstract

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.

Large-scale prediction of binding affinity in protein–small ligand complexes: the PRODIGY-LIG web server

“…Our predictor was successfully used to predict the binding affinity of 102 protein-ligand targets during the blind D3R Grand Challenge 2-Stage 2. Using exclusively docked models, it reached a correlation score (Kendall's Tau) of 0.37, placing our approach as the ninth best predictor out of over 82 submissions (Kurkcuoglu et al, 2018). However, if the crystal structures made available at the second stage of the D3R challenge would have been used, our approach would have reached a correlation of 0.43, making it the third best ranking method, as reported by the organizers of the Challenge (see page 10 in Gaieb et al, 2018).…”

“…Our predictive method, adapted from PRODIGY to address systems with small ligands, makes use of atomic instead of residue contacts. It has been successfully applied for the blind prediction during the D3R Grand Challenge 2 (Gaieb et al, 2018;Kurkcuoglu et al, 2018). In Kurkcuoglu et al 2018, we trained PRODIGY-LIG on 200 protein-small ligand complexes with known experimental binding affinity and structure, retrieved from the 2P2I dataset (Basse et al, 2016).…”

Structural characterization and in-silico analysis of Momordica charantia 7S globulin for stability and ACE inhibition

“…Further, Expasy peptide cutter server was utilized to check the digestion site in Mc7S. 'Valine Phenylalanine Lysine-VFK' a tri-peptide fragment confirmed by BIOPEP server was considered for molecular docking using HADDOCK (High Ambiguity Driven protein-protein DOcking) 56,57 . Best generated conformations were analysed and visualized in PyMOL 53 .…”

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

“…Protein flexibility is still a great challenge for docking programs and scoring functions ( Cavasotto and Singh, 2008 ; Tuffery and Derreumaux, 2012 ; Buonfiglio et al, 2015 ; Spyrakis and Cavasotto, 2015 ; Kurkcuoglu et al, 2018 ). Most docking methodologies adopt a single, rigid conformation of the receptor, due to the high computational cost and methodological limitations proportional to the increase in the degree of flexibility.…”