Computational modeling approaches have lately been earning their place as viable tools in drug discovery. Research efforts more often include computational component and the usage of the scientific software is commonplace at more stages of the drug discovery pipeline. However, as software takes on more responsibility and the computational methods grow more involved, the gap grows between research entities that have the means to maintain the necessary computational infrastructure and those that lack the technical expertise or financial means to obtain and include computational component in their scientific efforts. To fill this gap and to meet the need of many, mainly academic, labs numerous community contributions collectively known as open source projects play an increasingly important role. This work describes design, implementation and application of a set of drug discovery workflows based on the CHARMMing (CHARMM interface and graphics) web-server. The protocols described herein include docking, virtual target screening, de novo drug design, SAR/QSAR modeling as well as chemical education. The performance of the newly developed workflows is evaluated by applying them to a number of scientific problems that include reproducibility of crystal poses of small molecules in protein-ligand systems, identification of potential targets of a library of natural compounds as well as elucidating molecular targets of a vitamin. The results of these inquiries show that protocols developed as part of this effort perform comparably to commercial products, are able to produce results consistent with the experimental data and can substantially enrich the research efforts of labs with otherwise little or no computational component.

Pharmaceuticals are environmental contaminants causing steadily increasing concern due to their high usage, ubiquitous distribution in the aquatic environment, and potential to exert adverse effects on the ecosystems. After being discharged from wastewater treatment plants (WWTPs), pharmaceuticals can undergo transformation processes in surface waters, of which microbial degradation in river sediments is considered highly significant. In spite of a substantial number of studies on the occurrence of pharmaceuticals in aquatic systems, a comprehensive understanding of their environmental fate is still limited. First of all, very few consistent datasets from lab-based experiments to field studies exist to allow for a straightforward comparison of observations. Secondly, data on the identity and occurrence of transformation products (TPs) is insufficient and the relation of the behavior of TPs to that of their parent compounds (PCs) is poorly understood. In this thesis, these knowledge gaps were addressed by integrating the TP identification using suspect/non-target screening approaches and PC/TP fate determination. The overarching objective was to improve the understanding of the fate of pharmaceuticals in rivers, with a specific focus on water-sediment interactions, and formation and behavior of TPs. In paper I, 11 pharmaceutical TPs were identified in water-sediment incubation experiments using non-target screening. Bench-scale flume experiments were conducted in paper II to simultaneously investigate the behavior of PCs and TPs in both water and sediment compartments under more complex and realistic hydraulic conditions. The results illustrate that water-sediment interactions play a significant role for efficient attenuation of PCs, and demonstrate that TPs are formed in sediment and released back to surface water. In paper III the environmental behavior of PCs along stretches of four wastewater-impacted rivers was related to that of their TPs. The attenuation of PCs is highly compound and site specific. The highest attenuation rates of the PCs were observed in the river with the most efficient river water-pore water exchange. This research also indicates that WWTPs can be a major source of TPs to the receiving waters. In paper IV, suspect screening with a case-control concept was applied on water samples collected at both ends of the river stretches, which led to the identification of several key TPs formed along the stretches. The process-oriented strategies applied in this thesis provide a basis for prioritizing and identifying the critical PCs and TPs with respect to environmental relevance in future fate studies. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 4: Manuscript.</p>

The scope of this work focuses on computationally modeling compounds with protein structures. While the impetus of drug discovery is the innovation of new therapeutic molecules, it also involves distinguishing molecules that would not be an effective drug. This can be achieved by inventing new tools or by refining old tools. Virtual screening (VS, also called docking), the computational modeling of a molecule in a receptor structure, is a staple in predicting a molecule's affinity for an intended target. In our Virtual Target Screening system (also called inverse-docking), VS is used to find high-affinity targets, which can potentially explain absorption, distribution, metabolism, and excretion (ADME) of a molecule of interest in the human body. The next project, low-mode docking (LD), attempts to improve VS by incorporating protein flexibility into traditional docking where a static receptor structure has potential to produce poor results due to incorrectly predicted ligand poses. Finally, VS, performed mostly on small molecules, is scaled up to cyclic peptides by employing Monte Carlo simulations and molecular dynamics to mimic the steps of small molecule VS. The first project discussed is Virtual Target Screening (also called inverse-docking) where a small molecule is virtually screened against a library of protein structures. Predicting receptors to which a synthesized compound may bind would give insights to drug repurposing, metabolism, toxicity, and lead optimization. Our protocol calibrates each protein entry with a diverse set of small molecule structures, the NCI Diversity Set I. Our test set, 20 kinase inhibitors, was predicted to have a high percentage of kinase "hits" among approximately 1500 protein structures. Further, approved drugs within the test set generally had better rates of kinase hits. Next, normal mode analysis (NMA), which can computationally describe the fundamental motions of a receptor structure, is utilized to approach the rigid body bias problem in traditional docking techniques. Traditional docking involves the selection of a static receptor structure for VS; however, protein structures are dynamic. Simulation of the induced fit effect in protein-ligand binding events is modeled by full articulation of the approximated large-scale low-frequency normal modes of vibration, or "low-modes," coupled with the docking of a ligand structure. Low-mode dockings of 40 cyclin dependent 2 (CDK2) inhibitors into 54 low-modes of CDK2 yielded minimum root-mean-square deviation (RMSD) values of 1.82 – 1.20 Å when compared to known coordinate data. The choice of pose is currently limited to docking score, however, with ligand pose RMSD values of 3.87 – 2.07 Å. When compared to corresponding traditional dockings with RMSD values of 5.89 – 2.33 Å, low-mode docking was more accurate. The last discussion involves the rational docking of a cyclic peptide to the murine double minute 2 (MDM2) oncoprotein. The affinity for a cyclic peptide (synthesized by Priyesh Jain, McLaughin Lab, University of South Florida), PJ-8-73, in MDM2 was found to be within an order of magnitude of a cyclic peptide from the Robinson Lab at the University of Zurich in Switzerland. Both are Β-hairpin cyclic peptides with IC50 values of 650 nm and 140 nm, respectively. Using the co-crystalized structure of the Robinson peptide (PDB 2AXI), we modeled the McLaughlin peptide based on an important interaction of the 6-chloro-tryptophan residue of the Robinson peptide occupying the same pocket in MDM2 as the tryptophan residue by the native p53 transactivation helical domain. By preserving this interaction in initial cyclic peptide poses, the resulting pose of PJ-8-73 structure in MDM2 possessed comparable active site residue contacts and surface area. These protocols will aid medical research by using computer technology to reduce cost and time. VTS utilizes a unique structural and statistical calibration to virtually assay thousands of protein structures to predict high affinity binding. Determining unintended protein targets aids in creating more effective drugs. In low-mode docking, the accuracy of virtual screening was increased by including the fundamental motions of proteins. This newfound accuracy can decrease false negative results common in virtual screening. Lastly, docking techniques, usually for small molecules, were applied to larger peptide molecules. These modifications allow for the prediction of peptide therapeutics in protein-protein interaction modulation, a growing interest in medicine. Impactful in their own ways, these procedures contribute to the discovery of drugs, whether they are small molecules or cyclic peptides.

京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第22617号 / 地環博第196号 / 新制||地環||38(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 藤井 滋穂, 教授 梶井 克純, 准教授 田中 周平 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

Since the late 18th century, chemicals have been industrially produced, and used by consumers. Today, the number of registered chemicals are over 150 000 in North America and Europe alone, and the number is predicted to increase. Industrial or anthropogenic chemicals can, directly or indirectly, be released into the ecosystem during their lifetime, where they can cause harm to human health and the environment. Depending on their properties, chemicals can travel far away from its source, causing global contamination. Through this, the Arctic region becomes a sink for many different types of contaminants. Because of the danger certain chemicals pose, techniques to detect and identify them in environmental samples have evolved during recent years. In these cases, non-targeted screening methods are commonly used to characterise contaminants in samples.In this study, surface sediment samples were collected on three locations in the Hudson Bay (Canada). The samples were analysed using two different instruments: a comprehensive two-dimensional gas chromatograph coupled to a high resolution time-of-flight mass spectrometer (GC×GC-HR-ToF-MS) and a gas chromatograph coupled with a Orbitrap mass spectrometer (GC-Orbitrap-MS). After data acquisition and processing, certain components were identified in both datasets, and their semi-quantitative concentrations were calculated.Overall, 32 compounds were detected and identified in the Orbitrap dataset, and 17 of these were also detected in the GC×GC dataset. The concentration was determined semi-quantitively for the identified compounds and ranged from 0.005–333 ng/g dry weight (d.w.) for the Orbitrap dataset, and 0.013–278 ng/g d.w. for the GC×GC dataset, which was below, or in the lower half, of concentration ranges from previous studies. Overall, the data processing for Orbitrap data seems to be more advanced and evolved than for GC×GC data, causing differences between the results from the two instruments.