Improved pose and affinity predictions using different protocols tailored on the basis of data availability

Abstract

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein-ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).

Metadynamics for Perspective Drug Design: Computationally Driven Synthesis of New Protein–Protein Interaction Inhibitors Targeting the EphA2 Receptor

“…On the other hand, the poor physicochemical profile of 16 has so far hampered the atomic resolution of the EphA2–compound 16 complex by X-ray and NMR spectroscopy. Therefore, the combination of free-energy simulations, , either based on metadynamics (META-D) or other enhanced sampling methods, , and extensive structure–activity relationship (SAR) analysis − appears as an alternative, viable approach to propose a reasonable model of interaction, exploitable in prospective drug design. , META-D is currently emerging as a powerful enhanced sampling method for the efficient and rapid computation of multidimensional free-energy surfaces (FES) describing the protein–ligand binding process. , This FES provides information on the position and energetics of minima such as the bound and the unbound states of a protein–ligand system, allowing retrieval of the equilibrium binding free energy (Δ A bind ) which is related to the affinity of a ligand for its target. Moreover, this FES allows to identify minimum binding geometries of the protein–ligand complex that can be used to design novel compounds for the target under investigation.…”

Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

“…Recently several major docking competitions were held, namely CSAR 2013 [5], CSAR 2014 [6] and D3R 2015-2016 [7], which became an opportunity for many teams to assess their protein-ligand pose and binding affinity prediction algorithms and protocols. In the course of these competitions various methods were used including classical docking methods [8][9][10][11][12][13][14][15][16][17][18][19][20][21], QSAR models [15,22,23], targetspecific scoring functions [23][24][25], and sometimes combinations of these with more computationally expensive molecular dynamics-based methods [26][27][28][29]. The CSAR 2013 exercise also involved homol-ogy modeling to obtain proper receptors from the given sequences, while in CSAR 2014 protein structures were provided.…”

PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS

“…Inspired by those successful uses of the IFP, ,− , we developed a similar tool in Python programming language called PyPLIF, an abbreviation of Python-based Protein–Ligand Interaction Fingerprinting. , Unlike the IFP of Marcou and Rognan, PyPLIF uses an open source library from Open Babel to increase access to this fingerprinting tool. Notably, PyPLIF has played an important role in some drug discovery-related research projects. ,− PyPLIF was developed as a rescoring tool for results of PLANTS docking software, , since PLANTS was the main docking software in our in-house laboratory those days. Nevertheless, the popularity increase of another free and open source docking software AutoDock Vina , has created demands to update PyPLIF by appending its ability to analyze the results of AutoDock Vina.…”