Solvent Accessible Surface Area-Based Hot-Spot Detection Methods for Protein–Protein and Protein–Nucleic Acid Interfaces

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein–protein and protein–nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein–protein systems) and a more complex multi-objective genetic algorithm–support vector machine algorithms (protein–nucleic acid systems). The best models for these interactions have been implemented in two free Web tools

Doctor of Philosophy

dissertationThe dysregulation of proteinâ€"protein interaction (PPI) networks has been implicated in many diseases. Designing therapeutic small-molecule inhibitors of these interactions is a challenging field for medicinal chemistry. This work advances the techniques for discovering more potent PPI inhibitors through integration of computational and biochemical techniques. High-throughput screening using fluorescence polarization and AlphaScreen assays identified an acyl hydrazone-containing inhibitor of the β-catenin/Tcf4 PPI, a key mediator of the canonical Wnt signaling pathway. By removing the undesirable acyl hydrazone moiety, a new compound, 4-(5H-[1,2,5]oxadiazolo[3',4':5,6]pyrazino[2,3-b]indol-5-yl)butanoic acid, was developed to selectively inhibit the β-catenin/Tcf4 interaction. The ethyl ester of this compound was tested in zebrafish embryos and shown to inhibit Wnt signaling in vivo at 2 and 10 μM concentrations. Differences between the PPI interface and the active site of traditional targets add to the difficulty of discovering PPI inhibitors. Herein, the relationship between inhibitor potency and ligand burialâ€"defined as the fraction of the solvent accessible surface areas of the bound over unbound ligand, θlâ€"in the PPI surface was evaluated. A positive correlation between θl and inhibitor potency was discovered. However, this correlation was secondary to the strong nonbonding interactions. A study of five PPI targets with corresponding inhibitor-bound crystal structures also revealed that empirical scoring functions were slightly better at identifying known inhibitors out of the putatively inactive test set, and the Lamarckian genetic algorithm was more successful at pose prediction. Due to the nature of the PPI surface, directly targeting the binding site may be difficult. A novel combination of computational methods explored the druggability, selectivity, and potential allosteric regulation of PPIs. Solvent mapping confirmed that Tcf4, E-cadherin, APC and axin use the same binding site on β-catenin in different ways. Evolutionary trace analysis indicated that the region surrounding W504 of β-catenin might be a potentially allosteric site. Site-directed mutagenesis testing results for a W504I β-catenin mutant resulted in three-fold increased binding of Tcf4 to β-catenin over the wild-type. This new site is promising for the discovery of future allosteric inhibitors of the β-catenin/Tcf4 PPI. The combined results from these studies reveals ways to better design PPI inhibitors

Probing Local Atomic Environments to Model RNA Energetics and Structure

Ribonucleic acids (RNA) are critical components of living systems. Understanding RNA structure and its interaction with other molecules is an essential step in understanding RNA-driven processes within the cell. Experimental techniques like X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and chemical probing methods have provided insights into RNA structures on the atomic scale. To effectively exploit experimental data and characterize features of an RNA structure, quantitative descriptors of local atomic environments are required. Here, I investigated different ways to describe RNA local atomic environments. First, I investigated the solvent-accessible surface area (SASA) as a probe of RNA local atomic environment. SASA contains information on the level of exposure of an RNA atom to solvents and, in some cases, is highly correlated to reactivity profiles derived from chemical probing experiments. Using Bayesian/maximum entropy (BME), I was able to reweight RNA structure models based on the agreement between SASA and chemical reactivities. Next, I developed a numerical descriptor (the atomic fingerprint), that is capable of discriminating different atomic environments. Using atomic fingerprints as features enable the prediction of RNA structure and structure-related properties. Two detailed examples are discussed. Firstly, a classification model was developed to predict Mg$^{2+}$ ion binding sites. Results indicate that the model could predict Mg$^{2+}$ binding sites with reasonable accuracy, and it appears to outperform existing methods. Secondly, a set of models were developed to identify cavities in RNA that are likely to accommodate small-molecule ligands. The models were also used to identify bound-like conformations from an ensemble of RNA structures. The frameworks presented here provide paths to connect the local atomic environment to RNA structure, and I envision they will provide opportunities to develop novel RNA modeling tools.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163135/1/jingrux_1.pd