Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Abstract

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation

“…First, the lowest Conformer Score (LCS) was taken as the final score to the ligand. Second, we computed a weighted average of Conformer Score which is inspired by the Boltzmann distribution (BS) 40,41 : $$\text{Boltzmann}-\text{Weighted}\text{Ligand}\text{Score}false(\mathrm{normalP},\mathrm{normalL}false)=\frac{true\sum _{\mathrm{normalC}}^{{\mathrm{N}}_{\text{conf}}}\text{PS}(\mathrm{P},\mathrm{C})\times expfalse[-\mathrm{normal\beta }\times \text{PS}false(\mathrm{normalP},\mathrm{normalC}false)false]}{true\sum _{\mathrm{normalC}}^{{\mathrm{N}}_{\text{conf}}}exp[-\mathrm{\beta }\times \text{PS}(\mathrm{P},\mathrm{C})]}$$…”

Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project

“…2.5 ns of MD simulations are subsequently performed for the ligand in solvent and in complex with the off‐target, using the representative binding poses as different starting configurations. Ensemble interaction energies of the ligand with its environment are gathered from the MD simulations and used for the prediction36,37 according to the iterative LIE approach 38. Ligand topologies are generated by the acpype tool43 and MD simulations are performed using GROMACS 4.5.7 44…”

“…A CYP 2D6 affinity model37 is based on a series of 8 aryloxypropanolamines45 and displayed a Root‐Mean‐Square Error ( RMSE ) of 3.2 kJ mol −1 (0.56 pK i units), R 2 of 0.8, and a Standard Deviation Error of Predictions ( SDEP ) of 5.2 kJ mol −1 (0.90 pK i units) using an external test set of 21 compounds. A CYP 1A2 affinity model (manuscript in preparation) is built on a set of 35 chemically diverse compounds46,47 and displayed a RMSE of 4.6 kJ mol −1 (0.80 p K i units), R 2 of 0.58, and a SDEP of 4.8 kJ mol −1 (0.83 p K i units) based on an external test set (9 compounds).…”

The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

“…The second major use case of the eTOXsys is the prediction of toxicological relevant properties of chemical compounds applying the in silico models developed in the framework of the project [ 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Although, as the current version does not yet include models making use of the legacy report data contributed, for the aforementioned reasons, strategies for utilising the in vivo data have been refined, and the modeling technology used by eTOXsys has been developed for a large number of toxicologically relevant endpoints using public data.…”