ABSTRACT
Botulinum neurotoxin A (BoNT/A) is the most potent toxin known. Unfortunately, it is also a potential bioweapon in terrorism, which is without an approved therapeutic treatment once cellular intoxication takes place. Previously, we reported how hydroxamic acid prodrug carbamates increased cellular uptake, which translated to successful inhibition of this neurotoxin. Building upon this research, we detail BoNT/A protease molecular modeling studies accompanied by the construction of small library of hydroxamic acids based on 2,4-dichlorocinnamic hydroxamic acid scaffold and their carbamate prodrug derivatization along with the evaluation of these molecules in both enzymatic and cellular models.
Subject(s)
Botulinum Toxins, Type A/antagonists & inhibitors , Hydroxamic Acids/chemistry , Prodrugs/chemistry , Prodrugs/pharmacology , Protease Inhibitors/pharmacology , Small Molecule Libraries/chemistry , Small Molecule Libraries/pharmacology , Chemistry Techniques, Synthetic , Hydroxamic Acids/chemical synthesis , Hydroxamic Acids/pharmacology , Models, Molecular , Prodrugs/chemical synthesis , Protease Inhibitors/chemistry , Small Molecule Libraries/chemical synthesisABSTRACT
Botulinum neurotoxins (BoNTs) are among the most deadly poisons known though ironically, they also are of great therapeutic utility. A number of research programs have been initiated to discover small molecule inhibitors of BoNTs metalloprotease activity. Many, though not all of these programs have screened against a truncated and more stable form of the enzyme, that possess comparable catalytic properties to the full length enzyme. Interestingly, several classes of inhibitors notably the hydroxamates, display a large shift in potency between the two enzyme forms. In this report we compare the kinetics of active-site, alpha-exosite and beta-exosite inhibitors versus truncated and full length enzyme. Molecular dynamics simulations conducted with the truncated and homology models of the fully length BoNT LC/A indicate the flexibility of the C-terminus of the full length enzyme is responsible for the potency shifts of active-site proximally binding inhibitors while distal binding (alpha-exosite) inhibitors remain equipotent.
ABSTRACT
In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC50) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.
Subject(s)
Aryl Hydrocarbon Hydroxylases/antagonists & inhibitors , Computer Simulation , Drug Discovery , Models, Molecular , Cytochrome P-450 CYP2C9 , Enzyme Activation/drug effects , Enzyme Inhibitors/pharmacology , Flurbiprofen/chemistry , Flurbiprofen/pharmacology , Humans , Inhibitory Concentration 50 , Molecular Structure , Protein Binding/drug effects , Warfarin/chemistry , Warfarin/pharmacologyABSTRACT
Computer aided drug design is progressing and playing an increasingly important role in drug discovery. Computational methods are being used to evaluate larger and larger numbers of real and virtual compounds. New methods based on molecular simulations that take protein and ligand flexibility into account also contribute to an ever increasing need for compute time. Computational grids are therefore becoming a critically important tool for modern drug discovery, but can be expensive to deploy and maintain. Here, we describe the low cost implementation of a 165 node, computational grid at Anadys Pharmaceuticals. The grid makes use of the excess computing capacity of desktop computers deployed throughout the company and of outdated desktop computers which populate a central computing grid. The performance of the grid grows automatically with the size of the company and with advances in computer technology. To ensure the uniformity of the nodes in the grid, all computers are running the Linux operating system. The desktop computers run Linux inside MS Windows using coLinux as virtualization software. HYDRA has been used to optimize computational models, for virtual screening and for lead optimization.
