The conformational flexibility of target proteins is a major challenge in understanding and modeling protein-drug interactions. A fundamental issue, yet to be clarified, is whether the observed conformational changes are controlled by the protein, or induced by the inhibitor. While the concept of induced fit has been widely adopted for describing the structural changes that accompany ligand binding, there is growing evidence in support of the dominance of proteins intrinsic dynamics, which has been evolutionarily optimized to accommodate its functional interactions. The wealth of structural data for target proteins in the presence of different ligands now permits us to make a critical assessment of the balance between these two effects in selecting the bound forms. We focused on three widely studied drug targets, HIV-1 reverse transcriptase, p38 MAP kinase, and cyclin-dependent kinase 2. A total of 292 structures determined for these enzymes in the presence of different inhibitors as well as unbound form permitted us to perform an extensive comparative analysis of the conformational space accessed upon ligand binding, and its relation to the intrinsic dynamics prior to ligand binding as predicted by elastic network model analysis. Further, we analyzed NMR ensembles of ubiquitin and calmodulin representing their microseconds range solution dynamics. Our results show that the ligand selects the conformer that best matches its structural and dynamic properties amongst the conformers intrinsically accessible to the protein in the unliganded form. The results suggest that simple but robust rules encoded in the protein structure play a dominant role in pre-defining the mechanisms of ligand binding, which may be advantageously exploited in designing inhibitors. We apply these lessons to the study of MAP kinase phosphatases (MKPs), which are therapeutically relevant but challenging signaling enzymes. Our study provides insights into the interactions and selectivity of MKP inhibitors and shows how an allosteric inhibition mechanism holds for a recently discovered inhibitor of MKP-3. We also provide evidence for the functional significance of the structure-encoded dynamics of rhodopsin and nicotinic acetylcholine receptor, members of two membrane proteins classes serving as targets for more than 40% of all current FDA approved drugs.

This thesis focuses on applying meta-analysis methods for combining genomic studies on biomarker detection and pathway enrichment analysis. DNA microarray technology has been maturely developed in the past decade and led to an explosion on publicly available microarray data sets. However, the noisy nature of DNA microarray technology results in low reproducibility across microarray studies. Therefore, it is of interest to apply meta-analysis to microarray data to increase the reliability and robustness of results from individual studies. Currently most meta-analysis methods for combining genomic studies focus on biomarker detection, and meta-analysis for pathway analysis has not been systematically pursued. We investigated two natural approaches of meta-analysis for pathway enrichment (MAPE) by combining statistical significance across studies at the gene level (MAPE_G) or at the pathway level (MAPE_P). Simulation results showed increased statistical power of both approaches and their complementary advantages under different scenarios. We also developed an integrated method (MAPE_I) that incorporates advantages of both approaches. Applications to real data on drug response of a breast cancer cell line, lung and prostate cancer tissues were evaluated to compare the performance of the different methods. MAPE_P has the general advantage of not requiring gene matching across studies. When MAPE_G and MAPE_P show complementary advantages, the integrated version MAPE_I is recommended. A software package named MetaPath, was implemented to perform the MAPE analysis. In addition to developing MAPE methods, we also applied meta-analysis approach to chemotherapy research to discover robust biomarkers and multi-drug response genes, which have prognostic value and the potential of identifying new therapeutic targets.

Protein-protein interactions (PPIs) constitute an emerging class of targets for pharmaceutical intervention pursued by both industry and academia. Despite their fundamental role in many biological processes and diseases such as cancer, PPIs are still largely underrepresented in todays drug discovery. This dissertation describes novel computational approaches developed to facilitate the discovery/design of small-molecule inhibitors of PPIs, using the oncogenic c-Myc/Max interaction as a case study. First, we critically review current approaches and limitations to the discovery of small-molecule inhibitors of PPIs and we provide examples from the literature. Second, we examine the role of protein flexibility in molecular recognition and binding, and we review recent advances in the application of Elastic Network Models (ENMs) to modeling the global conformational changes of proteins observed upon ligand binding. The agreement between predicted soft modes of motions and structural changes experimentally observed upon ligand binding supports the view that ligand binding is facilitated, if not enabled, by the intrinsic (pre-existing) motions thermally accessible to the protein in the unliganded form. Third, we develop a new method for generating models of the bioactive conformations of molecules in the absence of protein structure, by identifying a set of conformations (from different molecules) that are most mutually similar in terms of both their shape and chemical features. We show how to solve the problem using an Integer Linear Programming formulation of the maximum-edge weight clique problem. In addition, we present the application of the method to known c-Myc/Max inhibitors. Fourth, we propose an innovative methodology for molecular mimicry design. We show how the structure of the c-Myc/Max complex was exploited to designing compounds that mimic the binding interactions that Max makes with the leucine zipper domain of c-Myc. In summary, the approaches described in this dissertation constitute important contributions to the fields of computational biology and computer-aided drug discovery, which combine biophysical insights and computational methods to expedite the discovery of novel inhibitors of PPIs.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2009. / Härtill 5 uppsatser.

Includes bibliographical references. / The South Africa Coloured population of the Western Cape is the result of unions between Europeans, Africans (Bantu and Khoisan), and various other populations (Malaysian or Indonesian descent). The world-wide burden of tuberculosis remains an enormous problem, and is particularly severe in this population. In general, admixed populations that have arisen in historical times can make an important contribution to the discovery of disease susceptibility genes if the parental populations exhibit substantial variation in susceptibility. Despite numerous successful genome-wide association studies, detecting variants that have low disease risk still poses a challenge. Furthermore, admixture association studies for multi-way admixed populations pose constant challenges, including the choice of an accurate ancestral panel to infer ancestry and for imputing missing genotypes to identify possible genetic variants causing susceptibility to disease. This thesis addresses some of these challenges. We first developed PROXYANC, an approach to select the best proxy ancestral populations for admixed populations. From the simulation of a multi-way admixed population, we demonstrated the ability and accuracy of PROXYANC in selecting the best proxy ancestry and illustrated the importance of the choice of ancestries in both estimating admixture proportions and imputing missing genotypes. We applied this approach to the South African Coloured population, to refine both the choice of ancestral populations and their genetic contributions. We also demonstrated that the ancestral allele frequency differences correlated with increased linkage disequilibrium in the SAC, and that the increased LD originates from admixture events rather than population bottlenecks. Secondly, we conducted a study to determine whether ancestry-specific genetic contributions affect tuberculosis risk. We additionally conducted imputation genome-wide association studies and a meta-analysis incorporating previous genome-wide association studies of tuberculosis.

Includes abstract. / Includes bibliographical references (leaves 126-150). / Mycobacterium tuberculosis is a bacterial pathogen that causes tuberculosis, a leading cause of human death worldwide from infectious diseases, especially in Africa. Despite enormous advances achieved in recent years in controlling the disease, tuberculosis remains a public health challenge. The contribution of existing drugs is of immense value, but the deadly synergy of the disease with Human Immunodeficiency Virus (HIV) or Acquired Immunodeficiency Syndrome (AIDS) and the emergence of drug resistant strains are threatening to compromise gains in tuberculosis control. In fact, the development of active tuberculosis is the outcome of the delicate balance between bacterial virulence and host resistance, which constitute two distinct and independent components. Significant progress has been made in understanding the evolution of the bacterial pathogen and its interaction with the host. The end point of these efforts is the identification of virulence factors and drug targets within the bacterium in order to develop new drugs and vaccines for the eradication of the disease.

Genetic variation is shaped by admixture between populations in an evolutionary process. The mixture dynamic between groups of populations results in a mosaic of chromosomal segments inherited from multiple ancestral populations. The distribution of ancestral chromosomal segments and the recombination breakpoints in an admixed genome provide information about the time of admixture. Studying populations with particular ancestries has become a major interest in population genetics because of medical and evolutionary impacts of the patterns of single nucleotide polymorphisms. It provides a better understanding of the impact of population migrations and helps us uncover interactions between several populations. Most of the research on admixed population dating has focused on a single interaction between two populations using various approaches. Some have extended this to mixing of three populations based on assumptions and approaches which differ from one tool to another. However, the inference of distinct ancestral proportions along the genome of an admixed individual and plausible dates of admixture, still remain a challenge in the case of multi-way admixed populations. This dissertation consists of three research initiatives. First, provide a succinct review of current methods for dating the admixture events. We accomplish this by providing a comprehensive review and comparison of current methods pertinent to date admixture event. Second, we assess various admixture dating tools which estimate the time of admixture between two parental populations. We do so by performing various simulations assuming a particular number of generations and use these to evaluate the tools. Third, we apply the top three assessed methods to some admixed populations from the 1000 Genomes project. Despite MALDER shows improvement and produces reasonable date estimates over other current methods, the results from both simulation and real data suggest that dating ancient admixture events accounting for the effect of other admixtures remains a challenge. Our results suggest the need for developing a new approach to date ancient and complex admixture events in multi-way admixed populations.

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Prediction of functions of genes in a genome is a key step in all genome sequencing projects. Sequences that carry out important functions are likely to be conserved between evolutionarily distant species and can be identified using cross-species comparisons. In the absence of completed genomes and the accompanying high-quality annotations, expressed sequence tags (ESTs) from random cDNA clones are the primary tools for functional genomics. EST datasets are fragmented and redundant, necessitating clustering of ESTs into groups that are likely to have been derived from the same genes. EST clustering helps reduce the search space for sequence homology searching and improves the accuracy of function predictions using EST datasets. This dissertation is a case study that describes clustering of Bos taurus and Sus scrofa EST datasets, and utilizes the EST clusters to make computational function predictions using a comparative genomics approach. We used a novel EST clustering method, TAMUClust, to cluster bovine ESTs and compare its performance to the bovine EST clusters from TIGR Gene Indices (TGI) by using bovine ESTs aligned to the bovine genome assembly as a gold standard. This comparison study reveals that TAMUClust and TGI are similar in performance. Comparisons of TAMUClust and TGI with predicted bovine gene models reveal that both datasets are similar in transcript coverage. We describe here the design and implementation of an annotation pipeline for predicting functions of the Bos taurus (cattle) and Sus scrofa (pig) transcriptomes. EST datasets were clustered into gene families using Ensembl protein family clusters as a framework. Following clustering, the EST consensus sequences were assigned predicted function by transferring annotations of the Ensembl vertebrate protein(s) they are grouped to after sequence homology searches and phylogenetic analysis. The annotations benefit the livestock community by helping narrow down the gamut of direct experiments needed to verify function.