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Bacterial resistance to antibiotics has become a serious problem. This is due to the remarkable ability of bacteria to respond and rapidly adapt to environmental changes. Integrons are elements with the capacity for gene capture by an integron-encoded site-specific recombinase called IntI. IntI binds and acts at the recombination sites, attI and attC resulting in excision and integration of short DNA elements called gene cassettes carrying an attC site in the 3’ end. Several families of antibiotic resistance genes are borne on gene cassettes in integrons connected to mobile elements. Other cassettes reside in the larger and ancestral superintegrons located on chromosomes in both pathogenic and environmental bacteria. Due to their close connection with lateral gene transfer systems, it is possible that integrons are functionally dependent on those networks. This work presents arguments for such connections. The attC of the aadA1-qacE cassette junction in Tn21 was characterized in detail. Like other attC sites, it contains two pairs of inverted repeats and is almost palindromic. By using electrophoretic mobility shift assays, this study showed that IntI1 binds only to the bottom strand of attC. Upon folding the strand into a hairpin, a few chiral hairpin distortions define both the strand choice and also the appropriate orientation of the highly symmetrical site. Structural recognition also explains the wide sequence variation among attC sites. We have documented the initial cleavage step in recombination in IntI extracts and integrase levels in extracts were evaluated by a new method. Mutagenesis and homology modelling were performed to find amino acid residues in IntI1 that are important for recognition of attC hairpin-DNA. Comparisons were made with other tyrosine family members to explain how integron integrases differ in site-recognition and also in their mechanism of strand exchange.

Accurate predictions of binding free energies from computer simulations are an invaluable resource for understanding biochemical processes and drug action. The primary aim of the work described in the thesis was to predict and understand ligand binding to several proteins of major pharmaceutical importance using computational methods.

We report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 G-protein coupled receptor and a series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones. Site-directed mutagenesis, homology modelling and docking were further used to characterize agonist binding to the human neuropeptide Y2 receptor, which is important in feeding behavior and an obesity drug target.  In a separate project, homology modelling was also used for rationalization of mutagenesis data for an integron integrase involved in antibiotic resistance.

Blockade of the hERG potassium channel by various drug-like compounds, potentially causing serious cardiac side effects, is a major problem in drug development. We have used a homology model of hERG to conduct molecular docking experiments with a series of channel blockers, followed by molecular dynamics simulations of the complexes and evaluation of binding free energies with the linear interaction energy method. The calculations are in good agreement with experimental binding affinities and allow for a rationalization of three-dimensional structure-activity relationships with implications for design of new compounds. Docking, scoring, molecular dynamics, and the linear interaction energy method were also used to predict binding modes and affinities for a large set of inhibitors to HIV-1 reverse transcriptase. Good agreement with experiment was found and the work provides a validation of the methodology as a powerful tool in structure-based drug design. It is also easily scalable for higher throughput of compounds.