Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Abstract

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein–ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol–1), with good cross-validation statistics.

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

“…Average interaction energies for unbound ligands were obtained from separate duplicated 1 ns production simulations of the ligand solvated in (approximately 650 mol) TIP3P water molecules [ 32 ] using the same MD settings as for the protein–ligand complexes. Subsequently, interaction profiles between ligands and FXR as obtained from the simulations for all binding poses were analyzed using an in-house Python script, to identify protein ligand interaction types using rule-based protocols described in the supplementary material of [ 36 ].…”

Adequate prediction for inhibitor affinity of Aβ40protofibril using the linear interaction energy method

“…This is probably due to the lack of a specic binding site for Ab oligomers, and/or, as mentioned above, the loss of electrostatic interaction of ligand from the free to bound states, which is not observed in other protein complexes. [42][43][44][45][46][47][48][49] Because of the failure of these standard a and b parameters, a new set of parameters for Ab peptides is needed. Based on the average interaction energies of training set comprising of 20 inhibitors taken randomly listed in Table S4, † the a, b, and g parameters are calculated to have the values of 0.288, À0.049, and À5.880 kcal mol À1 , respectively (eqn (2)) giving a Pearson correlation R and a standard error of 0.79 and of 0.95 kcal mol À1 , respectively (Fig.…”

“…Last but not least, the end-point free energy calculation method, LIE, has also been successfully applied to various systems. [42][43][44][45][46][47][48][49] In this method, the binding free energy is calculated based on the average van der Waals and electrostatic interaction differences of ligand with its surrounding environments upon association, i.e. the free ligand in solvent (free statedenoted as subscript f) and the ligand in complex with solvated protein (bound statedenoted as subscript b).…”

How Good is Jarzynski’s Equality for Computer-Aided Drug Design?

“…7 − 9 The contribution from the electrostatic and van der Waals (vdW) interaction energy is obtained through the thermodynamic cycle. 10 In the past few decades, several groups have successfully applied the LIE method to predict new inhibitors of human dihydrofolate reductases 10 and to obtain the binding affinity of inhibitors of neuraminidase, 11 BACE-1, 12 cytochrome P450, 13 , 14 farnesoid X receptor, 15 and others. 16 Several improved versions of LIE were proposed.…”