Title
ALiBERO: Evolving a Team of Complementary Pocket Conformations Rather than a Single Leader.
Abstract
Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.
Year
DOI
Venue
2012
10.1021/ci3001088
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Keywords
Field
DocType
thermodynamics,estrogen receptor alpha,binding sites,monte carlo method,drug design,ligands,protein binding,protein conformation,algorithms
Monte Carlo method,Ligand (biochemistry),Docking (dog),Searching the conformational space for docking,Molecular Docking Simulation,Chemistry,Bioinformatics,Virtual screening,Combinatorial search,Protein structure
Journal
Volume
Issue
ISSN
52
10
1549-9596
Citations 
PageRank 
References 
10
0.67
17
Authors
3
Name
Order
Citations
PageRank
Manuel Rueda1948.18
Maxim Totrov226931.59
Ruben Abagyan343055.44