Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening (original) (raw)
Related papers
2012
Accounting for protein flexibility is an essential yet challenging component of structure-based virtual screening. Whereas an ideal approach would account for full protein and ligand flexibility during the virtual screening process, this is currently intractable using available computational resources. An alternative is ensemble docking, where calculations are performed on a set of individual rigid receptor conformations and the results combined. The primary challenge associated with this approach is the choice of receptor structures to use for the docking calculations. In this work, we show that selection of a small set of structures based on clustering on binding site volume overlaps provides an efficient and effective way to account for protein flexibility in virtual screening. We first apply the method to crystal structures of cyclin-dependent kinase 2 and HIV protease and show that virtual screening for ensembles of four cluster representative structures yields consistently high enrichments and diverse actives. We then apply the method to a structural ensemble of the androgen receptor generated with molecular dynamics and obtain results that are in agreement with those from the crystal structures of cyclin-dependent kinase 2 and HIV protease. This work provides a step forward in the incorporation of protein flexibility into structurebased virtual screening.
Molecules (Basel, Switzerland), 2015
Structure-based virtual screening for selecting potential drug candidates is usually challenged by how numerous false positives in a molecule library are excluded when receptor plasticity is considered. In this study, based on the binding energy landscape theory, a hypothesis that a true inhibitor can bind to different conformations of the binding site favorably was put forth, and related strategies to defeat this challenge were devised; reducing false positives when receptor plasticity is considered. The receptor in the study is the influenza A nucleoprotein, whose oligomerization is a requirement for RNA binding. The structural flexibility of influenza A nucleoprotein was explored by molecular dynamics simulations. The resultant distinctive structures and the crystal structure were used as receptor models in docking exercises in which two binding sites, the tail-loop binding pocket and the RNA binding site, were targeted with the Otava PrimScreen1 diversity-molecule library using ...
Current Opinion in Structural Biology, 2013
The recently developed field of ligand homology modeling (LHM) that extends the ideas of protein homology modeling to the prediction of ligand binding sites and for use in virtual ligand screening has emerged as a powerful new approach. Unlike traditional docking methodologies, LHM can be applied to low-to-moderate resolution predicted as well as experimental structures with little if any diminution in performance; thereby enabling $75% of an average proteome to have potentially significant virtual screening predictions. In large scale benchmarking, LHM is able to predict off-target ligand binding. Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction.
Journal of Molecular Graphics and Modelling, 2011
G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β 2 -Adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs.
2000
We describe a 'virtual NMR screening' method to assist in the design of inhibitors that occupy different sites within a target. We dock small molecules into the active site of an enzyme and score them. Keeping the tightest-binding lead fixed in space, we dock and score other small molecules in its presence. Using this approach, linker groups are used to join the compounds together to form a high-affinity inhibitor. We present validation of our computational approach by reproducing experimental results for FKBP and stromelysin. Docking simulations are not subject to experimental problems such as proteolysis, protein or compound insolubility, or enzyme size. Because docking is fast and our scoring method can distinguish between high-and low-affinity inhibitors, this docking procedure shows promise as integral part of a drug-design strategy.
Mining flexible-receptor docking experiments to select promising protein receptor snapshots
BMC Genomics, 2010
Background: Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts. Results: We previously docked four ligands (NADH, TCL, PIF and ETH) to 3,100 conformations of the InhA receptor from M. tuberculosis. Based on the receptor residues-ligand distances we preprocessed all docking results to generate appropriate input to mine data. Data preprocessing was done by calculating the shortest interatomic distances between the ligand and the receptor's residues for each docking result. They were the predictive attributes. The target attribute was the estimated free-energy of binding (FEB) value calculated by the AutodDock3.0.5 software. The mining inputs were submitted to the M5P model tree algorithm. It resulted in short and understandable trees. On the basis of the correlation values, for NADH, TCL and PIF we obtained more than 95% correlation while for ETH, only about 60%. Post processing the generated model trees for each of its linear models (LMs), we calculated the average FEB for their associated instances. From these values we considered a LM as representative if its average FEB was smaller than or equal the average FEB of the test set. The instances in the selected LMs were considered the most promising snapshots. It totalized 1,521, 1,780, 2,085 and 902 snapshots, for NADH, TCL, PIF and ETH respectively. Conclusions: By post processing the generated model trees we were able to propose a criterion of selection of linear models which, in turn, is capable of selecting a set of promising receptor conformations. As future work we intend to go further and use these results to elaborate a strategy to preprocess the receptors 3-D spatial conformation in order to predict FEB values. Besides, we intend to select other compounds, among the million catalogued, that may be promising as new drug candidates for our particular protein receptor target.
2019
MotivationNowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα).ResultsVS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to help characterizing secondary targets of xenobiotics (including drugs and pollutants). In this study, we propose an integrated approach using ligand docking based on multiple structural en-sembles to reflect the conformational flexibility of the receptor. Then, we investigate the impact of the two different types of features (structure-based docking descriptors and ligand-based molecular descriptors) for affinity predictions based on a random forest algorithm. We find that ligand-based features h...
Systematic exploitation of multiple receptor conformations for virtual ligand screening
PloS one, 2011
The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not o...
Outstanding challenges in protein-ligand docking and structure-based virtual screening
Wiley Interdisciplinary Reviews: Computational Molecular Science, 2011
With an ever-increasing number of protein structures being solved by X-ray crystallography, the use of protein-ligand docking algorithms to assess candidate ligands for a binding site has become commonplace. In particular, over the last decade, high-throughput docking has been widely applied to the virtual screening of large chemical databases for supporting hit-finding programs in drug discovery. However, the techniques and practice of protein-ligand docking in general, and of structure-based virtual screening in particular, are still evolving and significant limitations remain to be addressed. In this review, we seek to highlight some of the active areas of research and debate in this promising, but challenging, field.
Recent years have witnessed rapid developments of computer-aided drug design methods, which have reached accuracy that allows their routine practical applications in drug discovery campaigns. Protein structure-based methods are useful for the prediction of binding modes of small molecules and their relative affinity. The high-throughput docking of up to 10 6 small molecules followed by scoring based on implicitsolvent force field can robustly identify micromolar binders using a rigid protein target. Molecular dynamics with explicit solvent is a low-throughput technique for the characterization of flexible binding sites and accurate evaluation of binding pathways, kinetics, and thermodynamics. In this review we highlight recent advancements in applications of ligand docking tools and molecular dynamics simulations to ligand identification and optimization.