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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/24987
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dc.contributor.advisorAyers, Paul W.-
dc.contributor.authorGarner, Jennifer H.-
dc.date.accessioned2019-10-07T14:04:15Z-
dc.date.available2019-10-07T14:04:15Z-
dc.date.issued2019-
dc.identifier.urihttp://hdl.handle.net/11375/24987-
dc.descriptionThis thesis discusses Kaplan, a free conformer searching package, available at github.com/PeaWagon/Kaplanen_US
dc.description.abstractConformer searching algorithms find minima in the Potential Energy Surface (PES) of a molecule, usually by following a torsion-driven approach. The minima represent conformers, which are interchangeable via free rotation around bonds. Conformers can be used as input to computational analyses, such as drug design, that can convey molecular reactivity, structure, and function. With an increasing number of rotatable bonds, finding optima in the PES becomes more complicated, as the dimensionality explodes. Kaplan is a new, free and open-source software package written by the author that uses a ring-based Evolutionary Algorithm (EA) to find conformers. The ring, which contains population members (or pmems), is designed to allow initial PES exploration, followed by exploitation of individual energy wells, such that the most energetically-favourable structures are returned. The strengths and weaknesses of existing publicly available conformer searchers are discussed, including Balloon, RDKit, Openbabel, Confab, Frog2, and Kaplan. Since RDKit is usually considered to be the best free package for conformer searching, its conformers for the amino acids were optimised using the MMFF94 forcefield and compared to the conformers generated by Kaplan. Amino acid conformers are well characterised, and provide insight for protein substructure. Of the 20 molecules, Kaplan found a lower energy minima for 12 of the structures and tied for 5 of them. Kaplan allows the user to specify which dihedrals (by atom indices) to optimise and angles to use, a feature that is not offered by other programs. The results from Kaplan were compared to a known dataset of amino acid conformers. Kaplan identified all 57 conformers of methionine to within 1.2Å, and found identical conformers for the 5 lowest-energy structures (i.e. within 0.083Å), following forcefield optimisation.en_US
dc.language.isoenen_US
dc.subjectconformersen_US
dc.subjectevolutionary algorithmsen_US
dc.subjectamino acidsen_US
dc.subjectpotential energy surfaceen_US
dc.subjectsoftwareen_US
dc.subjectKaplanen_US
dc.subjectstructure searchen_US
dc.subjectRMSDen_US
dc.subjectforcefielden_US
dc.titleConformer Searchingen_US
dc.title.alternativeConformer Searching using an Evolutionary Algorithmen_US
dc.typeThesisen_US
dc.contributor.departmentChemistry and Chemical Biologyen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Science (MSc)en_US
dc.description.layabstractA conformer search affords the low-energy arrangements of atoms that can be obtained via rotation around bonds. Conformers provide insight about the chemical reactivity and physical properties of a molecule. With increasing molecule size, the number of possible conformers increases exponentially. To search the space of possible conformers, this thesis presents Kaplan, which is a software package that implements a novel directed, stochastic, sampling technique based on an Evolutionary Algorithm (EA). Kaplan uses a special type of EA that stores sets of conformers in a ring-based structure. Unlike other conformer-specific packages, Kaplan provides the means to analyse and interact with found conformers. Known conformers of amino acids are used to verify Kaplan. Other tools for generating conformers are discussed, including a comparison of freely available software. Kaplan effectively finds the conformers of small molecules, but requires additional parametrisation to find the conformers of mid-sized molecules, such as Penta-Alanine.en_US
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