Lead Finder: An Approach To Improve Accuracy of Protein−Ligand Docking, Binding Energy Estimation, and Virtual Screening

Abstract

An innovative molecular docking algorithm and three specialized high accuracy scoring functions are introduced in the Lead Finder docking software. Lead Finder's algorithm for ligand docking combines the classical genetic algorithm with various local optimization procedures and resourceful exploitation of the knowledge generated during docking process. Lead Finder's scoring functions are based on a molecular mechanics functional which explicitly accounts for different types of energy contributions scaled with empiric coefficients to produce three scoring functions tailored for (a) accurate binding energy predictions; (b) correct energy-ranking of docked ligand poses; and (c) correct rank-ordering of active and inactive compounds in virtual screening experiments. The predicted values of the free energy of protein-ligand binding were benchmarked against a set of experimentally measured binding energies for 330 diverse protein-ligand complexes yielding rmsd of 1.50 kcal/mol. The accuracy of ligand docking was assessed on a set of 407 structures, which included almost all published test sets of the following programs: FlexX, Glide SP, Glide XP, Gold, LigandFit, MolDock, and Surflex. rmsd of 2 A or less was observed for 80-96% of the structures in the test sets (80.0% on the Glide XP and FlexX test sets, 96.0% on the Surflex and MolDock test sets). The ability of Lead Finder to distinguish between active and inactive compounds during virtual screening experiments was benchmarked against 34 therapeutically relevant protein targets. Impressive enrichment factors were obtained for almost all of the targets with the average area under receiver operator curve being equal to 0.92.

Novel Antitumor L‐Arabinose Derivative of Indolocarbazole with High Affinity to DNA

“…All mentioned models revealed rmsd < 2 , which indicates their appropriateness for the molecular docking studies. Docking of AIC to the prepared models of CDKs and binding energy calculations were performed with the Lead Finder v 1.1.10 software [30] by using default configuration parameters. The energy grid box for ligand docking was set to span the default value 6 in each direction from the cognate ligand of the corresponding PDB structure.…”

In Silico Engineering of Proteins That Recognize Small Molecules

“…Some of the most popular programs like AutoDock (Morris et al, 1998), GOLD (Jones et al, 1995(Jones et al, , 1997, and Lead finder (Stroganov et al, 2008) include GA or hybrid approaches to find the optimal orientation of the ligand.…”

Hit clustering can improve virtual fragment screening: CDK2 and PARP1 case studies

“…The full-atom models of all proteins used in the current study were prepared from the raw PDB structures by adding hydrogen atoms and assigning ionization states of the amino acids with the Model Builder (build_model) program of the Lead Finder software package v 1.1.13 [21]. Each model was validated by docking its cognate ligand (as extracted from the corresponding PDB structure) and measuring the root mean square deviation (RMSD) of docked ligand pose from its crystallographic position.…”

“…The structural filtration of the docked complexes obtained in virtual screening experiments was performed by the structure_filter module that is part of the Lead Finder software package v 1.1.13 [21].…”