Predicting Binding Affinity of CSAR Ligands Using Both Structure-Based and Ligand-Based Approaches

We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly-available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds towards the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in top-10% among all the participants. In parallel, MedusaDock designed to predict reliable docking poses was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock and QSAR predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus

Predicting Binding Affinity of CSAR Ligands Using Both Structure-Based and Ligand-Based Approaches

We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly-available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds towards the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in top-10% among all the participants. In parallel, MedusaDock designed to predict reliable docking poses was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock and QSAR predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus

IMPROVING RATIONAL DRUG DESIGN BY INCORPORATING NOVEL BIOPHYSICAL INSIGHT

Computer-aided drug design is a valuable and effective complement to conventional experimental drug discovery methods. In this thesis, we will discuss our contributions to advancing a number of outstanding challenges in computational drug discovery: understanding protein flexibility and dynamics, the role of water in small molecule binding and using and understanding large amounts of data. First, we describe the molecular steps involved in the induced-fit binding mechanism of p53 and MDM2. We use molecular dynamics simulations to understand the key chemistry responsible for the dynamic transition between the apo and holo structures of MDM2. This chemistry involves not only the indole side chain of the anchor residue of p53, Trp23, but surprisingly, the beta-carbon as well. We demonstrate that this chemistry plays a key role in opening the binding site by coordinating the position and orientation of MDM2 residues, Val93 and His96, through a previously undescribed transition state. We confirm these findings by observing that this chemistry is preserved in all available inhibitor-bound MDM2 co-crystal structures. Second, we discuss our advances in understanding water molecules in ligand binding sites by data mining the structural information of water molecules found in X-ray crystal structures. We examine a large set of paired bound and unbound proteins and compare the water molecules found in the binding site of the unbound structure to the functional groups on the ligand that displace them upon binding. We identify a number of generalized functional groups that are associated with characteristic clusters of water molecules. This information has been utilized in several successful and ongoing virtual screens. Third, we discuss software that we have developed that allows for very efficient exploration and selection of virtual screening results. Implemented as a PyMOL plugin, ClusterMols clusters compounds based on a user-defined level of chemical similarity. The software also provides advanced visualization tools and a number of controls for quickly navigating and selecting compounds of interest, as well as the ability to check online for available vendors. Finally, we present several published examples of modeling protein-lipid and protein-small molecules interactions for a number of important targets including ABL, c-Src and 5-LOX