Potent PDZ-Domain PICK1 Inhibitors that Modulate Amyloid Beta-Mediated Synaptic Dysfunction.

Protein interacting with C kinase (PICK1) is a scaffolding protein that is present in dendritic spines and interacts with a wide array of proteins through its PDZ domain. The best understood function of PICK1 is regulation of trafficking of AMPA receptors at neuronal synapses via its specific interaction with the AMPA GluA2 subunit. Disrupting the PICK1-GluA2 interaction has been shown to alter synaptic plasticity, a molecular mechanism of learning and memory. Lack of potent, selective inhibitors of the PICK1 PDZ domain has hindered efforts at exploring the PICK1-GluA2 interaction as a therapeutic target for neurological diseases. Here, we report the discovery of PICK1 small molecule inhibitors using a structure-based drug design strategy. The inhibitors stabilized surface GluA2, reduced Aß-induced rise in intracellular calcium concentrations in cultured neurons, and blocked long term depression in brain slices. These findings demonstrate that it is possible to identify potent, selective PICK1-GluA2 inhibitors which may prove useful for treatment of neurodegenerative disorders.

Computational approach to de novo discovery of fragment binding for novel protein states.

In silico fragment-based drug discovery has become an integral component of the new fragment-based approach that has evolved over the past decade. Protein structure of high quality is essential in carrying out computational designs, and protein flexibility has been shown to impact prospective designs or docking experiments. Here we introduce methodology to calculate protein normal modes and protein molecular dynamics in torsion space which enable the development of multiple protein states to address the natural flexibility of proteins. We also present two fragment-based sampling methods, grand canonical Monte Carlo and systematic sampling, which are used to study protein-fragment interactions by generating fragment ensembles and we discuss the process by which these ensembles are linked to design ligands.

Transplant-insert-constrain-relax-assemble (TICRA): protein-ligand complex structure modeling and application to kinases.

We introduce TICRA (transplant-insert-constrain-relax-assemble), a method for modeling the structure of unknown protein-ligand complexes using the X-ray crystal structures of homologous proteins and ligands with known activity. We present results from modeling the structures of protein kinase-inhibitor complexes using p38 and Lck as examples. These examples show that the TICRA method may be used prospectively to create and refine models for protein kinase-inhibitor complexes with an overall backbone rmsd of less than 0.75 Å for the kinase domain, when compared to published X-ray crystal structures. Further refinement of the models of the kinase domains of p38 and Lck in complex with their cognate ligands from the published crystal structures was able to improve the rmsd's of the model complexes to below 0.5 Å. Our results show that TICRA is a useful approach to the problem of structure-based drug design in cases where little structural information is available for the target proteins and the binding mode of active compounds is unknown.

Discovery and SAR of a series of 4,6-diamino-1,3,5-triazin-2-ol as novel non-nucleoside reverse transcriptase inhibitors of HIV-1.

The discovery and SAR study of a series of 4,6-diamino-1,3,5-triazin-2-ol compounds as novel HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) are reported. The lead compounds in this series showed excellent activity against wild-type and drug-resistant RT enzymes and viral strains. In addition, compounds from this series demonstrated favorable pharmacokinetic profile in rat. A preliminary modeling study was conducted to understand the binding mode of this series of compounds.

Overview of artificial neural networks.

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Automated pharmacophore query optimization with genetic algorithms - a case study using the MC4R system.

Due to the recent availability of high quality small molecule databases, such as ZINC and PubChem,1,2 virtual screening is playing an even more important role in identifying biologically relevant molecules in drug discovery campaigns. The success of pharmacophore-based virtual screening (PBVS) relies largely on the accuracy and specificity of the pharmacophore query employed. Deriving a pharmacophore query from a single structure inevitably introduces uncertainty, and the derived query is unlikely to be optimal against every collection of input compounds, especially when it is desired to discriminate among compounds with similar chemical structures. In this study, we present an optimization approach empowered by genetic algorithms (GA) to enhance the accuracy and specificity of a primary pharmacophore query. The example utilized is the human melanocortin type 4 receptor (hMC4R), for which the pharmacophore query was built on the basis of the structure of a rigid cyclic peptide agonist.(3) The optimized query is shown to be capable of identifying 37 positive hMC4R agonists with no false positives from a training set containing 55 agonists and 51 nonagonists. This represents a significant improvement from the initial query which exhibited a 37/32 hit rate. The final, optimized query is challenged with a testing set comprising of 55 hMC4R agonists and 50 nonagonists and achieves a hit rate of 33/8, that improved from 40/31. The impact of GA controlling parameters, including mutation rate, crossover rate, fitness function, population size, and convergence criterion, on performance of optimization are examined and discussed.

Statistical theory for protein ensembles with designed energy landscapes.

Combinatorial protein libraries provide a promising route to investigate the determinants and features of protein folding and to identify novel folding amino acid sequences. A library of sequences based on a pool of different monomer types are screened for folding molecules, consistent with a particular foldability criterion. The number of sequences grows exponentially with the length of the polymer, making both experimental and computational tabulations of sequences infeasible. Herein a statistical theory is extended to specify the properties of sequences having particular values of global energetic quantities that specify their energy landscape. The theory yields the site-specific monomer probabilities. A foldability criterion is derived that characterizes the properties of sequences by quantifying the energetic separation of the target state from low-energy states in the unfolded ensemble and the fluctuations of the energies in the unfolded state ensemble. For a simple lattice model of proteins, excellent agreement is observed between the theory and the results of exact enumeration. The theory may be used to provide a quantitative framework for the design and interpretation of combinatorial experiments.