No description available.

The pathogenicity and intractable nature of the microorganism Staphylococcus aureus (SA) has been long documented and highlighted by many health care agencies, with emphasis on its ability to exploit the human coagulation system to deadly effect. Two drugs from a class of inhibitors known as Direct Thrombin Inhibitors (DTI) have been shown to have a substantial effect on the enzyme secreted by SA known as Staphylocoagulase (SC), but up until now the application of this potential treatment has been limited. This paper strives to supply an overview of these clinical studies and propose a novel protocol for testing DTI's on SA in an in vitro setting. Three DTIs have been identified, including two already tested in clinical trials, and computational molecular docking simulations have been applied to elucidate the mechanisms of action for the inhibition. An additional DTI has been developed using these mechanisms as principles and shows promise for future development. After conducting this preliminary protocol, it has been found that running a minimum inhibitory concentration test across several tubes with varying degrees of these DTIs demonstrated varying levels of coagulation consistent with the findings of clinical research papers. It is fair to conclude, then, that after development or discovery of new coagulase inhibitors, they can be quickly and accurately tested against existent DTIs to gauge their efficacy.

The rationalisation of drug potency using three-dimensional structures of protein-ligand complexes is a central paradigm in medicinal research. For over two decades, a major goal has been to find the rules that accurately relate the structure of any protein-ligand complex to its affinity. Addressing this problem is of great concern to the pharmaceutical industry, which uses virtual screens to computationally assay up to many millions of compounds against a protein target. A fast and trustworthy affinity estimator could potentially streamline the drug discovery process, reducing reliance on expensive wet lab experiments, speeding up the discovery of new hits and aiding lead optimization. Water plays a critical role in drug-protein interactions. To address the often ambiguous nature of water in binding sites, a water placement method was developed and found to be in good agreement with X-ray crystallography, neutron diffraction data and molecular dynamics simulations. The method is fast and has facilitated a large scale study of the statistics of water in ligand binding sites, as well as the creation of models pertaining to water binding free energies and displacement propensities, which are of particular interest to medicinal chemistry. Structure-based scoring functions employing the explicit water models were developed. Surprisingly, these attempts were no more accurate than the current state of the art, and the models suffered from the same inadequacies which have plagued all previous scoring functions. This suggests a unifying cause behind scoring function inaccuracy. Accordingly, mathematical analyses on the fundamental uncertainties in structure-based modelling were conducted. Using statistical learning theory and information theory, the existence of inherent errors in empirical scoring functions was proven. Among other results, it was found that even the very best generalised structure-based model is significantly limited in its accuracy, and protein-specific models are always likely to be better. The theoretical framework developed herein hints at modelling strategies that operate at the leading edge of achievable accuracy.

Macromolecular transitions such as conformational changes and protein-protein association underlie many biological processes. Conformational changes in the N-terminal domain of the transmembrane protein DsbD (nDsbD) were studied by NMR and molecular dynamics (MD) simulations. nDsbD supplies reductant to biosynthetic pathways in the oxidising periplasm of Gram-negative bacteria after receiving reductant from the C-terminal domain of DsbD (cDsbD). Reductant transfer in the DsbD pathway happens via protein-protein association and subsequent thiol-disulphide exchange reactions. The cap loop shields the active-site cysteines in nDsbD from non-cognate oxidation, but needs to open when nDsbD bind its interaction partners. The loop was rigid in MD simulations of reduced nDsbD. More complicated dynamics were observed for oxidised nDsbD, as the disulphide bond introduces frustration which led to loop opening in some trajectories. The simulations of oxidised and reduced nDsbD agreed well with previous NMR spin-relaxation and residual dipolar coupling measurements as well as chemical shift-based torsion angle predictions. NMR relaxation dispersion experiments revealed that the cap loop of oxidised nDsbD exchanges between a major and a minor conformation. The differences in their conformational dynamics may explain why oxidised nDsbD binds its physiological partner cDsbD much tighter than reduced nDsbD. The redox-state dependent interaction between cDsbD and nDsbD is thought to enhance turnover. NMR relaxation dispersion experiments gave insight into the kinetics of the redox-state dependent interaction. MD simulations identified dynamic encounter complexes in the association of nDsbD with cDsbD. The mechanism of the conformational changes in the transport cycle of LacY were also investigated. LacY switches between periplasmic open and cytoplasmic open conformations to transport sugars across the cell membrane. Two mechanisms have been proposed for the conformational change, a rocker-switch mechanism based on rigid body motions and an “airlock” like mechanism in which the transporter would switch conformation via a fully occluded structure. In MD simulations using the novel dynamics importance sampling approach such a fully occluded structure was found. The simulations argued against a strict “rocker-switch” mechanism.

A range of simulations have been conducted to investigate the behaviour of a diverse set of complex biological membrane systems. The processes of interest have required simulations over extended time and length scales, but without sacrifice of molecular detail. For this reason, the primary technique used has been coarse-grained molecular dynamics (CG MD) simulations, in which small groups of atoms are combined into lower-resolution CG particles. The increased computational efficiency of this technique has allowed simulations with time scales of microseconds, and length scales of hundreds of nm. The membrane-permeabilizing action of the antimicrobial peptide maculatin 1.1 was investigated. This short α-helical peptide is thought to kill bacteria by permeabilizing the plasma membrane, but the exact mechanism has not been confirmed. Multiscale (CG and atomistic) simulations show that maculatin can insert into membranes to form disordered, water-permeable aggregates, while CG simulations of large numbers of peptides resulted in substantial deformation of lipid vesicles. The simulations imply that both pore-forming and lytic mechanisms are available to maculatin 1.1, and that the predominance of either depends on conditions such as peptide concentration and membrane composition. A generalized study of membrane protein aggregation was conducted via CG simulations of lipid bilayers containing multiple copies of model transmembrane proteins: either α-helical bundles or β-barrels. By varying the lipid tail length and the membrane type (planar bilayer or spherical vesicle), the simulations display protein aggregation ranging from negligible to extensive; they show how this biologically important process is modulated by hydrophobic mismatch, membrane curvature, and the structural class or orientation of the protein. The association of influenza hemagglutinin (HA) with putative lipid rafts was investigated by simulating aggregates of HA in a domain-forming membrane. The CG MD study addressed an important limitation of model membrane experiments by investigating the influence of high local protein concentration on membrane phase behaviour. The simulations showed attenuated diffusion of unsaturated lipids within HA aggregates, leading to spontaneous accumulation of raft-type lipids (saturated lipids and cholesterol). A CG model of the entire influenza viral envelope was constructed in realistic dimensions, comprising the three types of viral envelope protein (HA, neuraminidase and M2) inserted into a large lipid vesicle. The study represents one of the largest near-atomistic simulations of a biological membrane to date. It shows how the high concentration of proteins found in the viral envelope can attenuate formation of lipid domains, which may help to explain why lipid rafts do not form on large scales in vivo.

In this thesis, we have employed two computational methods, molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) MD simulations with umbrella sampling (US), to gain insights into the molecular mechanism governing the molecular recognition and reactivity in several protein-ligand complexes. Three systems involving protein-ligand interactions are examined in this dissertation utilizing well-established computational methodologies and mathematical modeling. The three proteins studied here are acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1). These enzymes are known to interact with a variety of ligands. AChE dysfunction caused by organophosphorus (OP) chemicals is a severe hazard since AChE is a critical enzyme in neurotransmission. Oximes are chemical compounds that can reactivate inhibited AChE; hence in the development of better oximes, it is critical to understand the mechanism through which OPs block AChE. We have described the covalent inhibition mechanism between AChE and the OP insecticide phorate oxon and its more potent metabolites and established their free energy profiles using QM/MM MD-US for the first time. Our results suggest a concerted mechanism and provide insights into the challenges in reactivating phorate oxon inhibited AChE. Reactivating BChE is another therapeutic approach to detoxifying circulating OP molecules before reaching the target AChE. We explored the covalent modification of BChE with phorate oxon and its metabolites using hybrid quantum mechanics/molecular mechanics (QM/MM) umbrella sampling simulations (PM6/ff14SB) for the inhibition process. Our results reveal that the mechanism is distinct between the inhibitors. The PM6 methodology is a good predictor of these compounds' potency, which may efficiently help study OPs like phorate oxon with larger leaving groups. Finally, we investigated the interactions between Peptidyl-prolyl isomerase (PPIase), which consists of a peptidyl isomerase (PPIase) domain flexibly tethered to a smaller Trp-Trp (WW) protein-binding domain, and chimeric peptides based on the human histone H1.4 sequence (KATGAApTPKKSAKW), as well as the effects on inter-domain dynamics. Using explicit solvent MD simulations, simulated annealing, and native contact analysis, our modeling sugget that the residues in the N-terminal immediate to the pSer/Thr Pro site connect the PPIase and WW domains via a series of hydrogen bonds and native contacts.

No description available.

The biological membrane, which is composed of a lipid bilayer embedded with numerous proteins, defines the cell boundary, separating the cell interior from the external environment. It serves as a gatekeeper and entry point for various molecular and ionic species. This thesis describes experimental and simulation studies of the interactions of carbon nanotubes (CNTs) with model membranes (lipid bilayers). The unique properties of CNTs make them ideal candidates for many nanotechnological applications. They can, however, pose a potential risk as toxins. While research into the positive benefits of CNTs continues, very little is known about their basic interactions with cellular components. It is particularly important to understand the interaction of CNTs with biological membranes, which form the primary physical barrier surrounding a cell. Coarse grained molecular dynamics (MD) simulations and atomic force microscopy (AFM) have been used to study the interactions of CNTs and lipid bilayers. They are investigated in a controlled manner using MD simulations, while AFM has allowed the controlled approach-to-contact and insertion of CNTs into bilayers. A number of effects are reported, including lipid creep along the CNT and bilayer thickening upon contact. The robustness of this response is established using different force fields and lipid species. The experimental results show an unusual reaction to mechanical indentation, and are further backed by MD simulations. The lipid bilayer response to multiple CNTs is studied and the effects of CNTs on bilayer conformation and lipid diffusion are reported. CNT internalisation from the solvent is observed in the simulations. Indeed, many of the observed phenomena are reminiscent of those known from the field of membrane protein. This project focuses on understanding the basic molecular interactions of CNTs with lipid bilayers and addresses the gap between experimental and computational work.

Precise regulation of cell cycle events by the Cdk-control network is essential for cell proliferation and the perpetuation of life. The unidirectionality of cell cycle progression is governed by several critical irreversible transitions: the G1-to-S transition, the G2-to-M transition, and the M-to-G1 transition. Recent experimental and theoretical evidence has pulled into question the consensus view that irreversible protein degradation causes the irreversibility of those transitions. A new view has started to emerge, which explains the irreversibility of cell cycle transitions as a consequence of systems-level feedback rather than of proteolysis. This thesis applies mathematical modelling approaches to test this proposal for the Mto- G1 transition, which consists of two consecutive irreversible substeps: the metaphase-to-anaphase transition, and mitotic exit. The main objectives of the present work were: (i) to develop deterministic models to identify the essential molecular feedback loops and to examine their roles in the irreversibility of the M-to-G1 transition; (ii) to present a straightforward and reliable workflow to translate deterministic models of reaction networks into stochastic models; (iii) to explore the effects of noise on the cell cycle transitions using stochastic models, and to compare the deterministic and the stochastic approaches. In the first part of this thesis, I constructed a simplified deterministic model of the metaphase-to-anaphase transition, which is mainly regulated by the spindle assembly checkpoint (the SAC). Based on the essential feedback loops causing the bistability of the transition, this deterministic model provides explanations for three open questions regarding the SAC: Why is the SAC not reactivated when the kinetochore tension decreases to zero at anaphase onset? How can a single unattached kinetochore keep the SAC active? How is the synchronized and abrupt destruction of cohesin triggered? This deterministic model was then translated into a stochastic model of the SAC by treating the kinetochore microtubule attachment at prometaphase as a noisy process. The stochastic model was analyzed and simulation results were compared to the experimental data, with the aim of explaining the mitotic timing regulation by the SAC. Our model works remarkably well in qualitatively explaining experimental key findings and also makes testable predictions for different cell lines with very different number of chromosomes. The noise generated from the chemical interactions was found to only perturb the transit timing of the mitotic events, but not their ultimate outcomes: all cells eventually undergo anaphase, however, the time required to satisfy the SAC differs between cells due to stochastic effects. In the second part of the thesis, stochastic models of mitotic exit were created for two model organisms, budding yeast and mammalian cells. I analyzed the role of noise in mitotic exit at both the single-cell and the population level. Stochastic time series simulations of the models are able to explain the phenomenon of reversible mitotic exit, which is observed under specific experimental conditions in both model organisms. In spite of the fact that the detailed molecular networks of mitotic exit are very different in budding yeast and mammalian cells, their dynamic properties are similar. Importantly, bistability of the transitions is successfully captured also in the stochastic models. This work strongly supports the hypothesis that uni-directional cell cycle progression is a consequence of systems-level feedback in the cell cycle control system. Systems-level feedback creates alternative steady states, which allows cells to accomplish irreversible transitions, such as the M-to-G1 transition studied here. We demonstrate that stochastic models can serve as powerful tools to capture and study the heterogeneity of dynamical features among individual cells. In this way, stochastic simulations not only complement the deterministic approach, but also help to obtain a better understanding of mechanistic aspects. We argue that the effects of noise and the potential needs for stochastic simulations should not be overlooked in studying dynamic features of biological systems.

Mutual Information (MI) based methods are used to predict contact residues within proteins and between interacting proteins. There have been many high impact papers citing the successful use of MI for determining contact residues in a particular protein of interest, or in certain types of proteins, such as homotrimers. In this dissertation we have carried out a systematic study to assess if this popularly employed contact prediction tool is useful on a global scale. After testing original MI and leading MI based methods on large, cross-species datasets we found that in general the performance of these methods for predicting contact residues both within (intra-protein) and between proteins (inter-protein) is weak. We observe that all MI variants have a bias towards surface residues, and therefore predict surface residues instead of contact residues. This finding is in contrast to the relatively good performance of i-Patch (Hamer et al. [2010]), a statistical scoring tool for inter-protein contact prediction. i-Patch uses as input surface residues only, groups amino acids by physiochemical properties, and assumes the existence of patches of contact residues on interacting proteins. We examine whether using these ideas would improve the performance of MI. Since inter-protein contact residues are only on the surface of each protein, to disentangle surface from contact prediction we filtered out the confounding buried residues. We observed that considering surface residues only does indeed improve the interprotein contact prediction ability of all tested MI methods. We examined a specific "successful" case study in the literature and demonstrated that here, even when considering surface residues only, the most accurate MI based inter-protein contact predictor,MIc, performs no better than random. We have developed two novel MI variants; the first groups amino acids by their physiochemical properties, and the second considers patches of residues on the interacting proteins. In our analyses these new variants highlight the delicate trade-off between signal and noise that must be achieved when using MI for inter-protein contact prediction. The input for all tested MI methods is a multiple sequence alignment of homologous proteins. In a further attempt to understand why the MI methods perform poorly, we have investigated the influence of gaps in the alignment on intra-protein contact prediction. Our results suggest that depending on the evaluation criteria and the alignment construction algorithm employed, a gap cutoff of around 10% would maximise the performance of MI methods, whereas the popularly employed 0% gap cutoff may lead to predictions that are no better than random guesses. Based on the insight we have gained through our analyses, we end this dissertation by identifying a number of ways in which the contact residue prediction ability of MI variants may be improved, including direct coupling analysis.