Improving database enrichment through ensemble docking

The success of structure-based drug design relies on accurate protein modeling where one of the key issues is the modeling and refinement of loops. This study takes a critical look at modeled loops, determining the effect of re-sampling side-chains after the loop conformation has been generated. The results are evaluated in terms of backbone and side-chain conformations with respect to the native loop. While models can contain loops with high quality backbone conformations, the side-chain orientations could be poor, and therefore unsuitable for ligand docking and structure-based design. In this study, we report on the ability to model loop side-chains accurately using a variety of commercially available algorithms that include rotamer libraries, systematic torsion scans and knowledge-based methods.

Section: Discussionsupporting

confidence: 84%

Closing the side-chain gap in protein loop modeling

Rossi

Nayeem

Weigelt

et al. 2009

“…The single receptor docking displayed best AUC value of 0.722 as compared to AUC value of 0.620 when average docking scores are used and AUC value of 0.559 when lowest energy scores from all 14 receptors are used to prepare ranked list. Although single receptor docking performed fairly well as compared to multiple receptors The receiver operating characteristic (ROC) curve describing tradeoff between sensitivity and specificity for three different scoring procedures; single receptor docking, multiple receptor average (when average docking scores from all receptors are used to prepare rankings) and multiple receptor best (when best docking scores among all receptors are used to prepare rankings) for test library consisting of fragment like trypsin inhibitors and decoy molecules docking here, it is contrary to previous results where multiple receptors docking is shown to improve the virtual screening performance [46][47][48][49]. Single receptor docking is a good measure to evaluate a docking program's performance when information about the native ligand is available and enrichment of native ligand could be used to identify best receptor for virtual screening.…”

Section: Docking Of Pdbbind Setcontrasting

confidence: 63%

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

Kumar

Zhang

2012

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.

“…Other approaches that are more computationally expensive such as ensemble docking, that involves docking ligands to a small number of protein conformations, have been shown to give better enrichment [57] than using just a single structure. Induced fit docking methods [58] where protein is ''induced'' into the correct binding conformation for a given ligand also lead to better docking results when compared to rigid receptor docking and recently a combined molecular dynamics and decision tree [59] approach were found to be successful in improving the reliability of SoM predictions (80%) of known CYP2D6 substrates.…”

Section: Discussionmentioning

confidence: 99%

Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism

Unwalla

Cross

Salaniwal³

et al. 2010

CYP2D6 is an important enzyme that is involved in first pass metabolism and is responsible for metabolizing ~25% of currently marketed drugs. A homology model of CYP2D6 was built using X-ray structures of ligand-bound CYP2C5 complexes as templates. This homology model was used in docking studies to rationalize and predict the site of metabolism of known CYP2D6 substrates. While the homology model was generally found to be in good agreement with the recently solved apo (ligand-free) X-ray structure of CYP2D6, significant differences between the structures were observed in the B' and F-G helical region. These structural differences are similar to those observed between ligand-free and ligand-bound structures of other CYPs and suggest that these conformational changes result from induced-fit adaptations upon ligand binding. By docking to the homology model using Glide, it was possible to identify the correct site of metabolism for a set of 16 CYP2D6 substrates 85% of the time when the 5 top scoring poses were examined. On the other hand, docking to the apo CYP2D6 X-ray structure led to a loss in accuracy in predicting the sites of metabolism for many of the CYP2D6 substrates considered in this study. These results demonstrate the importance of describing substrate-induced conformational changes that occur upon binding. The best results were obtained using Glide SP with van der Waals scaling set to 0.8 for both the receptor and ligand atoms. A discussion of putative binding modes that explain the distribution of metabolic sites for substrates, as well as a relationship between the number of metabolic sites and substrate size, are also presented. In addition, analysis of these binding modes enabled us to rationalize the typical hydroxylation and O-demethylation reactions catalyzed by CYP2D6 as well as the less common N-dealkylation.