The main topic of this thesis is the development of computational search methods that are useful in drug design applications. The emphasis is on exhaustiveness of the search method such that it can guarantee a certain level of geometric accuracy. In particular, the following two problems are addressed: (i) Prediction of binding mode of a drug molecule to a receptor and (ii) prediction of crystal structures of drug molecules. Predicting the binding mode(s) of a drug molecule to a target receptor is pivotal in structure-based rational drug design. In contrast to most approaches to solve this problem, the idea in this work is to analyze the search problem from a computational perspective. By building on top of an existing docking tool, new methods are proposed and relevant computational results are proven. These methods and results are applicable for other place-and-join frameworks as well. A fast approximation scheme for the docking of rigid fragments is described that guarantees certain geometric approximation factors. It is also demonstrated that this can be translated into an energy approximation for simple scoring functions. A polynomial time algorithm is developed for the matching phase of the docked rigid fragments. It is demonstrated that the generic matching problem is NP-hard. At the same time the optimality of the proposed algorithm is proven under certain scoring function conditions. The matching results are also applicable for some of the fragment-based de novo design methods. On the practical side, the proposed method is tested on 829 complexes from the PDB. The results show that the closest predicted pose to the native structure has the average RMS deviation of 1.06 °A. The prediction of crystal structures of small organic molecules has significantly improved over the last two decades. Most of the new developments, since the first blind test held in 1999, have occurred in the lattice energy estimation subproblem. In this work, a new efficient systematic search method that avoids random moves is proposed. It systematically searches through the space of possible crystal structures and conducts search space cuts based on statistics collected from the structural databases. It is demonstrated that the fast search method for rigid molecules can be extended to include flexible molecules as well. Also, the results of some prediction experiments are provided showing that in most cases the systematic search generates a structure with less than 1.0°A RMSD from the experimental crystal structure. The scoring function that has been developed for these experiments is described briefly. It is also demonstrated that with a more accurate lattice energy estimation function, better results can be achieved with the proposed robust search method.

The main topic of this thesis is the development of computational search methods that are useful in drug design applications. The emphasis is on exhaustiveness of the search method such that it can guarantee a certain level of geometric accuracy. In particular, the following two problems are addressed: (i) Prediction of binding mode of a drug molecule to a receptor and (ii) prediction of crystal structures of drug molecules. Predicting the binding mode(s) of a drug molecule to a target receptor is pivotal in structure-based rational drug design. In contrast to most approaches to solve this problem, the idea in this work is to analyze the search problem from a computational perspective. By building on top of an existing docking tool, new methods are proposed and relevant computational results are proven. These methods and results are applicable for other place-and-join frameworks as well. A fast approximation scheme for the docking of rigid fragments is described that guarantees certain geometric approximation factors. It is also demonstrated that this can be translated into an energy approximation for simple scoring functions. A polynomial time algorithm is developed for the matching phase of the docked rigid fragments. It is demonstrated that the generic matching problem is NP-hard. At the same time the optimality of the proposed algorithm is proven under certain scoring function conditions. The matching results are also applicable for some of the fragment-based de novo design methods. On the practical side, the proposed method is tested on 829 complexes from the PDB. The results show that the closest predicted pose to the native structure has the average RMS deviation of 1.06 °A. The prediction of crystal structures of small organic molecules has significantly improved over the last two decades. Most of the new developments, since the first blind test held in 1999, have occurred in the lattice energy estimation subproblem. In this work, a new efficient systematic search method that avoids random moves is proposed. It systematically searches through the space of possible crystal structures and conducts search space cuts based on statistics collected from the structural databases. It is demonstrated that the fast search method for rigid molecules can be extended to include flexible molecules as well. Also, the results of some prediction experiments are provided showing that in most cases the systematic search generates a structure with less than 1.0°A RMSD from the experimental crystal structure. The scoring function that has been developed for these experiments is described briefly. It is also demonstrated that with a more accurate lattice energy estimation function, better results can be achieved with the proposed robust search method.

<p>Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.</p>

<p>A novel approach is proposed for modeling of structurally variable regions in proteins. In this approach, a prerequisite sequence-structure alignment is examined for regions where the target sequence is not covered by the structural template. These regions, extended with a number of residues from adjacent stem regions, are submitted to fold recognition. The alignments produced by fold recognition are integrated into the initial alignment to create a multiple alignment where gaps in the main structural template are covered by local structural templates. This multiple alignment is used to create a protein model by existing protein modeling techniques.</p><p>Several alternative parameters are evaluated using a set of ten proteins. One set of parameters is selected and evaluated using another set of 31 proteins. The most promising result is for loop regions not located at the C- or N-terminal of a protein, where the method produces an average RMSD 12% lower than the loop modeling provided with the program MODELLER. This improvement is shown to be statistically significant.</p>

RNA molecules form complex higher-order structures which are essential to perform their biological activities. The accurate prediction of an RNA secondary structure and other higher-order structural constraints will significantly enhance the understanding of RNA molecules and help interpret their functions. Covariation analysis is the predominant computational method to accurately predict the base pairs in the secondary structure of RNAs. I developed a novel and powerful covariation method, Phylogenetic Events Count (PEC) method, to determine the positional covariation. The application of the PEC method onto a bacterial 16S rRNA sequence alignment proves that it is more sensitive and accurate than other mutual information based method in the identification of base-pairs and other structural constraints of the RNA structure. The analysis also discoveries a new type of structural constraint – neighbor effect, between sets of nucleotides that are in proximity in the three dimensional RNA structure with weaker but significant covariation with one another. Utilizing these covariation methods, a proposed secondary structure model of an entire HIV-1 genome RNA is evaluated. The results reveal that vast majority of the predicted base pairs in the proposed HIV-1 secondary structure model do not have covariation, thus lack the support from comparative analysis. Generating the most accurate multiple sequence alignment is fundamental and essential of performing high-quality comparative analysis. The rapid determination of nucleic acid sequences dramatically increases the number of available sequences. Thus developing the accurate and rapid alignment program for these RNA sequences has become a vital and challenging task to decipher the maximum amount of information from the data. A template-based RNA sequence alignment system, CRWAlign-2, is developed to accurately align new sequences to an existing reference sequence alignment based on primary and secondary structural similarity. A comparison of CRWAlign-2 with eight alternative widely-used alignment programs reveals that CRWAlign-2 outperforms other programs in aligning new sequences with higher accuracy. In addition to aligning sequences accurately, CRWAlign-2 also creates secondary structure models for each sequence to be aligned, which provides very useful information for the comparative analysis of RNA sequences and structures. The CRWAlign-2 program also provides opportunities for multiple areas including the identification of chimeric 16S rRNA sequences generated in microbiome sequencing projects. / text

Inference for dynamic systems and conformational sampling for protein folding are two problems motivated by applied data, which pose computational challenges from a statistical perspective. Dynamic systems are often described by a set of coupled differential equations, and methods of parametric estimation for these models from noisy data can require repeatedly solving the equations numerically. Many of these models also lead to rough likelihood surfaces, which makes sampling difficult. We introduce a method for Bayesian inference on these models, using a multiple chain framework that exploits the underlying mathematical structure and interpolates the posterior to improve efficiency. In protein folding, a large conformational space must be searched for low energy states, where any energy function constructed on the states is at best approximate. We propose a method for sampling fragment conformations that accounts for geometric and energetic constraints, and explore ideas for folding entire proteins that account for uncertain energy landscapes and learning from data more effectively. These ingredients are combined into a framework for tackling the problem of generating improvements to protein structure predictions. / Statistics

No description available.

Comparing protein structures based on their contact maps is an important problem in structural proteomics. Building a system for reconstructing protein tertiary structures from their contact maps is one of the motivations for devising novel contact map comparison algorithms. Several methods that address the contact map comparison problem have been designed which are briefly discussed in this thesis. However, they suggest scoring schemes that do not satisfy the two characteristics of “metricity” and “universality”. In this research we investigate the applicability of the Universal Similarity Metric (USM) to the contact map comparison problem. The USM is an information theoretical measure which is based on the concept of Kolmogorov complexity. The ultimate goal of this research is to use the USM in case-based reasoning system to predict protein structures from their predicted contact maps. The fact that the contact maps that will be used in such a system are the ones which are predicted from the protein sequences and are not noise-free, implies that we should investigate the noise-sensitivity of the USM. This is the first attempt to study the noise-tolerance of the USM. In this research, as the first implementation of the USM we converted the two-dimensional data structures (contact maps) to one-dimensional data structures (strings). The results of this implementation motivated us to circumvent the dimension reduction in our second attempt to implement the USM. Our suggested method in this thesis has the advantage of obtaining a measure which is noise tolerant. We assess the effectiveness of this noise tolerance by testing different USM implementation schemes against noise-contaminated versions of distinguished data-sets. / Thesis (Master, Computing) -- Queen's University, 2008-09-27 05:53:31.988

Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers.

The recent determination of several crystal structures of voltage-gated ion channels has catalyzed computational efforts of studying these remarkable molecular machines that are able to conduct ions across biological membranes at extremely high rates without compromising the ion selectivity. Starting from the open crystal structures, we have studied the gating mechanism of these channels by molecular modeling techniques. Firstly, by applying a membrane potential, initial stages of the closing of the channel were captured, manifested in a secondary-structure change in the voltage-sensor. In a follow-up study, we found that the energetic cost of translocating this 310-helix conformation was significantly lower than in the original conformation. Thirdly, collaborators of ours identified new molecular constraints for different states along the gating pathway. We used those to build new protein models that were evaluated by simulations. All these results point to a gating mechanism where the S4 helix undergoes a secondary structure transformation during gating. These simulations also provide information about how the protein interacts with the surrounding membrane. In particular, we found that lipid molecules close to the protein diffuse together with it, forming a large dynamic lipid-protein cluster. This has important consequences for the understanding of protein-membrane interactions and for the theories of lateral diffusion of membrane proteins. Further, simulations of the simple ion channel antiamoebin were performed where different molecular models of the channel were evaluated by calculating ion conduction rates, which were compared to experimentally measured values. One of the models had a conductance consistent with the experimental data and was proposed to represent the biological active state of the channel. Finally, the underlying methods for simulating molecular systems were probed by implementing the CHARMM force field into the GROMACS simulation package. The implementation was verified and specific GROMACS-features were combined with CHARMM and evaluated on long timescales. The CHARMM interaction potential was found to sample relevant protein conformations indifferently of the model of solvent used. / At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.