Drug efficiency indices for improvement of molecular docking scoring functions

Abstract

A dataset of protein-drug complexes with experimental binding energy and crystal structure were analyzed and the performance of different docking engines and scoring functions (as well as components of these) for predicting the free energy of binding and several ligand efficiency indices were compared. The aim was not to evaluate the best docking method, but to determine the effect of different efficiency indices on the experimental and predicted free energy. Some ligand efficiency indices, such as DeltaG/W (Wiener index), DeltaG/NoC (number of carbons), and DeltaG/P (partition coefficient), improve the correlation between experimental and calculated values. This effect was shown to be valid across the different scoring functions and docking programs. It also removes the common bias of scoring functions in favor of larger ligands. For all scoring functions, the efficiency indices effectively normalize the free energy derived indices, to give values closer to experiment. Compound collection filtering can be done prior or after docking, using pharmacokinetic as well as pharmacodynamic profiles. Achieving these better correlations with experiment can improve the ability of docking scoring functions to predict active molecules in virtual screening.

Consensus virtual screening approaches to predict protein ligands

“…The rescoring of the binding energies ΔG with the water/butanol partition coefficient P according to log(-ΔG/P) as suggested by Garcia-Sosa et al (2010), did not lead to a significant improvement. The authors of this study reported an improvement of correlation coefficient between calculated and experimental binding energies from 0.347 for the AutoDock calculated binding energies to 0.996 after rescoring.…”

“…AutoDock Vina (Trott & Olson, 2010) version 1.02 was employed with default parameters and GemDock (Yang & Chen, 2004) parameters were adjusted to a population size of 300, 80 generations and 5 solutions resulting in a similar docking time/CPU than AutoDock. Various strategies of improving the recall of known ligands in the top 2% of the ranked ligand databases were employed such as 1) the rescoring of the ranked list with the computed water/octanol partition coefficient (Cheng et al, 2007) P according to the equation new_score = log 10 (-score/P) as suggested by Garcia-Sosa et al (Garcia-Sosa et al, 2010); 2) the combination of two or three ranked ligand lists by choosing the ligands that are common among the top n ligands of each list, whereby n is counted in steps of five to the maximum number of ligands (CommonTop); 3) a simple joining of ranked ligand list by choosing the top n (n = 1, 2, 3, …) ligand from each list , if it was not chosen previously; 4) a consensus scoring method, where for each ligand a weighted average rank was computed from two or more rank lists. The weight was decreased linearly from 1.0 to 0.0 based on the position of the ligand in the rank list.…”

“…In addition, various strategies of combining the results of two or more docking algorithms were developed and and the utility of recently published ligand efficiency indices (Garcia-Sosa et al, 2010) was evaluated. DUD contains protein targets with known ligands and decoys that have similar physico-chemical properties to known ligands; thus it provides a challenging test case for virtual screening methods.…”

Do adsorbed drugs onto P-glycoprotein influence its efflux capability?

“…While nonsubstrates interaction energies were similar within the group, for both substrates and modulators the interaction energy decreased almost linearly with increasing molecular size, in agreement other published reports. 105,106 Thus, an efficiency index (EI) was calculated by dividing each interaction energy by the molecules' molecular weight (MW) to assess possible differences between charged and neutral molecules (Table 2). Statistical significance was determined with t-Tests ( a , p < 0.05 between charged and neutral molecules within the same class; b , p < 0.05 with all groups except charged substrates).…”

Anti-Aggregating Effect of the Naturally Occurring Dipeptide Carnosine on Aβ1-42 Fibril Formation

“…: molecular weight (MW), number of heavy atoms (NHA), number of carbons (NoC) and Wiener index (W, a topological index defined as the sum of the edges in the shortest paths between all the heavy atoms)] of ligands were calculated using the Marvin Calculator Plugin (Version 5.10.3; http://www.chemaxon.com). The ligand efficiency indices were calculated as previously described [72], [73] by normalizing the Autodock Vina predicted free energy of binding of the ligand with respect to the different size descriptors.…”