<p>In this dissertation a novel approach to protein-ligand docking is presented. First an existing method to predict putative active sites is employed. These predictions are then used to cut down the search space of an algorithm that uses the fast Fourier transform to calculate the geometrical and electrostatic complementarity between a protein and a small organic ligand. A simplified hydrophobicity score is also calculated for each active site. The docking method could be applied either to dock ligands in a known active site or to rank several putative active sites according to their biological feasibility. The method was evaluated on a set of 310 protein-ligand complexes. The results show that with respect to docking the method with its initial parameter settings is too coarse grained. The results also show that with respect to ranking of putative active sites the method works quite well.</p>

In this dissertation a novel approach to protein-ligand docking is presented. First an existing method to predict putative active sites is employed. These predictions are then used to cut down the search space of an algorithm that uses the fast Fourier transform to calculate the geometrical and electrostatic complementarity between a protein and a small organic ligand. A simplified hydrophobicity score is also calculated for each active site. The docking method could be applied either to dock ligands in a known active site or to rank several putative active sites according to their biological feasibility. The method was evaluated on a set of 310 protein-ligand complexes. The results show that with respect to docking the method with its initial parameter settings is too coarse grained. The results also show that with respect to ranking of putative active sites the method works quite well.

This dissertation presents research in the development and use of a multi-modal visual and haptic virtual model in higher education. The model, named Chemical Force Feedback (CFF), represents molecular recognition through the example of protein-ligand docking, and enables students to simultaneously see and feel representations of the protein and ligand molecules and their force interactions. The research efforts have been divided between educational research aspects and development of haptic feedback techniques. The CFF model was evaluated in situ through multiple data-collections in a university course on molecular interactions. To isolate possible influences of haptics on learning, half of the students ran CFF with haptics, and the others used the equipment with force feedback disabled. Pre- and post-tests showed a significant learning gain for all students. A particular influence of haptics was found on students reasoning, discovered through an open-ended written probe where students' responses contained elaborate descriptions of the molecular recognition process. Students' interactions with the system were analyzed using customized information visualization tools. Analysis revealed differences between the groups, for example, in their use of visual representations on offer, and in how they moved the ligand molecule. Differences in representational and interactive behaviours showed relationships with aspects of the learning outcomes. The CFF model was improved in an iterative evaluation and development process. A focus was placed on force model design, where one significant challenge was in conveying information from data with large force differences, ranging from very weak interactions to extreme forces generated when atoms collide. Therefore, a History Dependent Transfer Function (HDTF) was designed which adapts the translation of forces derived from the data to output forces according to the properties of the recently derived forces. Evaluation revealed that the HDTF improves the ability to haptically detect features in volumetric data with large force ranges. To further enable force models with high fidelity, an investigation was conducted to determine the perceptual Just Noticeable Difference (JND) in force for detection of interfaces between features in volumetric data. Results showed that JNDs vary depending on the magnitude of the forces in the volume and depending on where in the workspace the data is presented.

In recent years, the potential benefits of high-throughput virtual screening to the drug discovery community have been recognized, bringing an increase in the number of tools developed for this purpose. These programs have to process large quantities of data, searching for an optimal solution in a vast combinatorial range. This is particularly the case for protein-ligand docking, since proteins are sophisticated structures with complicated interactions for which either molecule might reshape itself. Even the very limited flexibility model to be considered here, using ligand conformation ensembles, requires six dimensions of exploration - three translations and three rotations - per rigid conformation. The functions for evaluating pose suitability can also be complex to calculate. Consequently, the programs being written for these biochemical simulations are extremely resource-intensive. This work introduces a pure computer science approach to the field, developing techniques to improve the effectiveness of such tools. Their architecture is generalized to an abstract pattern of nested layers for discussion, covering scoring functions, search methods, and screening overall. Based on this, new stratagems for molecular docking software design are described, including lazy or partial evaluation, geometric analysis, and parallel processing implementation. In addition, a range of novel algorithms are presented for applications such as active site detection with linear complexity (PIES) and small molecule shape description (PASTRY) for pre-alignment of ligands. The various stratagems are assessed individually and in combination, using several modified versions of an existing docking program, to demonstrate their benefit to virtual screening in practical contexts. In particular, the importance of appropriate precision in calculations is highlighted.