Maximum Margin Clustering for State Decomposition of Metastable Systems

When studying a metastable dynamical system, a prime concern is how to decompose the phase space into a set of metastable states. Unfortunately, the metastable state decomposition based on simulation or experimental data is still a challenge. The most popular and simplest approach is geometric clustering which is developed based on the classical clustering technique. However, the prerequisites of this approach are: (1) data are obtained from simulations or experiments which are in global equilibrium and (2) the coordinate system is appropriately selected. Recently, the kinetic clustering approach based on phase space discretization and transition probability estimation has drawn much attention due to its applicability to more general cases, but the choice of discretization policy is a difficult task. In this paper, a new decomposition method designated as maximum margin metastable clustering is proposed, which converts the problem of metastable state decomposition to a semi-supervised learning problem so that the large margin technique can be utilized to search for the optimal decomposition without phase space discretization. Moreover, several simulation examples are given to illustrate the effectiveness of the proposed method

Robust techniques and applications in fuzzy clustering

This dissertation addresses issues central to frizzy classification. The issue of sensitivity to noise and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM memberships of data points based on the distance of the points from the cluster centers. Scaling done on outliers reduces their membership in true clusters. This scheme, known as the Mega-clustering, defines a conceptual mega-cluster which is a collective cluster of all data points but views outliers and good points differently (as opposed to the concept of Dave\u27s Noise cluster). The scheme is presented and validated with experiments and similarities with Noise Clustering (NC) are also presented. The other scheme is based on the feasible solution algorithm that implements the Least Trimmed Squares (LTS) estimator. The LTS estimator is known to be resistant to noise and has a high breakdown point. The feasible solution approach also guarantees convergence of the solution set to a global optima. Experiments show the practicability of the proposed schemes in terms of computational requirements and in the attractiveness of their simplistic frameworks. The issue of validation of clustering results has often received less attention than clustering itself. Fuzzy and non-fuzzy cluster validation schemes are reviewed and a novel methodology for cluster validity using a test for random position hypothesis is developed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by the partitioning algorithm. The Hopkins statistic is used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here. A unique feature selection procedure for use with large molecular conformational datasets with high dimensionality is also developed. The intelligent feature extraction scheme not only helps in reducing dimensionality of the feature space but also helps in eliminating contentious issues such as the ones associated with labeling of symmetric atoms in the molecule. The feature vector is converted to a proximity matrix, and is used as an input to the relational fuzzy clustering (FRC) algorithm with very promising results. Results are also validated using several cluster validity measures from literature. Another application of fuzzy clustering considered here is image segmentation. Image analysis on extremely noisy images is carried out as a precursor to the development of an automated real time condition state monitoring system for underground pipelines. A two-stage FCM with intelligent feature selection is implemented as the segmentation procedure and results on a test image are presented. A conceptual framework for automated condition state assessment is also developed

Ligand-based design of dopamine reuptake inhibitors : fuzzy relational clustering and 2-D and 3-D QSAR modleing

As the three-dimensional structure of the dopamine transporter (DAT) remains undiscovered, any attempt to model the binding of drug-like ligands to this protein must necessarily include strategies that use ligand information. For flexible ligands that bind to the DAT, the identification of the binding conformation becomes an important but challenging task. In the first part of this work, the selection of a few representative structures as putative binding conformations from a large collection of conformations of a flexible GBR 12909 analogue was demonstrated by cluster analysis. Novel structurebased features that can be easily generalized to other molecules were developed and used for clustering. Since the feature space may or may not be Euclidean, a recently-developed fuzzy relational clustering algorithm capable of handling such data was used. Both superposition-dependent and superposition-independent features were used along with region-specific clustering that focused on separate pharmacophore elements in the molecule. Separate sets of representative structures were identified for the superpositiondependent and superposition-independent analyses. In the second part of this work, several QSAR models were developed for a series of analogues of methylphenidate (MP), another potent dopamine reuptake inhibitor. In a novel method, the Electrotopological-state (B-state) indices for atoms of the scaffold common to all 80 compounds were used to develop an effective test set spanning both the structure space as well as the activity space. The utility of B-state indices in modeling a series of analogues with a common scaffold was demonstrated. Several models were developed using various combinations of 2-D and 3-D descriptors in the Molconn-Z and MOE descriptor sets. The models derived from CoMFA descriptors were found to be the most predictive and explanatory. Progressive scrambling of all models indicated several stable models. The best models were used to predict the activity of the test set analogues and were found to produce reasonable residuals. Substitutions in the phenyl ring of MP, especially at the 3- and 4-positions, were found to be the most important for DATbinding. It was predicted that for better DAT-binding the substituents at these positions should be relatively bulky, electron-rich atoms or groups

Molecular Mechanisms for Inhibition of Regulators of G-protein Signaling by Small Molecules.

Regulator of G-protein signaling (RGS) proteins potently suppress G-protein coupled receptor (GPCR) signal transduction by accelerating GTP hydrolysis on activated heterotrimeric Gα proteins. RGS proteins have become attractive targets for the purpose of manipulating GPCR-mediated cellular responses. The RGS family comprises thirty-seven proteins with differential expression patterns throughout the body. RGS4 is enriched in the CNS and has been proposed as a therapeutic target for treatment of neurological disorders including epilepsy and Parkinson's disease. Therefore, our lab has focused on the identification of small molecule inhibitors of RGS4. To date, all small molecule inhibitors of RGS proteins function through covalent modification of cysteine residues, yet substantial specificity has been observed for RGS4 over other closely related homologs. The work in this thesis details the molecular mechanism of inhibition by a potent RGS4 inhibitor (CCG-50014; IC50 = 30 nM), and reveals the importance of RGS4 dynamics in the exposure of key cysteine residues that upon binding the inhibitor prevent the protein from reaching native conformations. Elucidating this mechanism has allowed us to propose a novel cryptic site (C-site) that is formed around the buried cysteine residues, and may be more druggable than previously proposed sites on RGS4. In addition, new chemical scaffolds have been identified using a cell-based high-throughput screen that also inhibit RGS4 through a cysteine-dependent mechanism, but are significantly reversible in contrast to the first cell-active RGS4 inhibitors. Furthermore, I employed IP6 as a derivative of an endogenous negative regulator, PIP3, to map the binding site on RGS4 and the corresponding effects on protein stability and function. This study shows the direct interactions of a non-covalent small molecule with an allosteric site on the RGS4 structure, and provides insight into the mechanism of endogenous regulation of RGS4. In conclusion, the studies described within this thesis provide new pharmacological tools for the study of RGS function in a cellular context, and describe in detail the molecular interactions of both endogenous and pharmacological modulators of RGS4. These results provide the theoretical framework to pursue drug discovery strategies that are expected to significantly advance the field of RGS drug discovery.PHDPharmacologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107061/1/storaska_1.pd