Kernel Methods in Computer-Aided Constructive Drug Design

A drug is typically a small molecule that interacts with the binding site of some target protein. Drug design involves the optimization of this interaction so that the drug effectively binds with the target protein while not binding with other proteins (an event that could produce dangerous side effects). Computational drug design involves the geometric modeling of drug molecules, with the goal of generating similar molecules that will be more effective drug candidates. It is necessary that algorithms incorporate strategies to measure molecular similarity by comparing molecular descriptors that may involve dozens to hundreds of attributes. We use kernel-based methods to define these measures of similarity. Kernels are general functions that can be used to formulate similarity comparisons. The overall goal of this thesis is to develop effective and efficient computational methods that are reliant on transparent mathematical descriptors of molecules with applications to affinity prediction, detection of multiple binding modes, and generation of new drug leads. While in this thesis we derive computational strategies for the discovery of new drug leads, our approach differs from the traditional ligandbased approach. We have developed novel procedures to calculate inverse mappings and subsequently recover the structure of a potential drug lead. The contributions of this thesis are the following: 1. We propose a vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our experiments have provided convincing comparative empirical evidence that our descriptor formulation in conjunction with kernel based regression algorithms can provide sufficient discrimination to predict various biological activities of a molecule with reasonable accuracy. 2. We present a new component selection algorithm KACS (Kernel Alignment Component Selection) based on kernel alignment for a QSAR study. Kernel alignment has been developed as a measure of similarity between two kernel functions. In our algorithm, we refine kernel alignment as an evaluation tool, using recursive component elimination to eventually select the most important components for classification. We have demonstrated empirically and proven theoretically that our algorithm works well for finding the most important components in different QSAR data sets. 3. We extend the VSMMD in conjunction with a kernel based clustering algorithm to the prediction of multiple binding modes, a challenging area of research that has been previously studied by means of time consuming docking simulations. The results reported in this study provide strong empirical evidence that our strategy has enough resolving power to distinguish multiple binding modes through the use of a standard k-means algorithm. 4. We develop a set of reverse engineering strategies for QSAR modeling based on our VSMMD. These strategies include: (a) The use of a kernel feature space algorithm to design or modify descriptor image points in a feature space. (b) The deployment of a pre-image algorithm to map the newly defined descriptor image points in the feature space back to the input space of the descriptors. (c) The design of a probabilistic strategy to convert new descriptors to meaningful chemical graph templates. The most important aspect of these contributions is the presentation of strategies that actually generate the structure of a new drug candidate. While the training set is still used to generate a new image point in the feature space, the reverse engineering strategies just described allows us to develop a new drug candidate that is independent of issues related to probability distribution constraints placed on test set molecules

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Carbon isotope fractionation in autotrophic Chromatium

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Not availabl

Carbon isotope fractionation by marine phytoplankton

Vita.Stable carbon isotope fractionations by seventeen species of marine phytoplankton, representing the classes of Bacillariophyceae, Chlorophyceae, Prasinophyceae, Chrysophyceae, Haptophyceae and Dinophyceae, have been determined in laboratory culture experiments using bicarbonate enriched artificial sea water. The Δ¹³C values (Δ¹³C = δ¹³C of plant HCO₃) range from -22.1 0/00 to -35.5 °/oo Nitzschia closterium shows the smallest fractionation of -22.1 °/oo and Isochrysis galbana, the greatest of -35.5 °/oo. Within the Bacillariophyceae, the Δ¹³C values vary from -22.1 °/oo (Nitzschia closterium) to -29.9 °/oo (Skeletonema costatum). With the exception of Cyclotella sp., the centric diatoms seem to fractionate more than the pennate diatoms. Haptophyceae and Chrysophyceae are always more depleted in ¹³C than other marine microalgae. For Prasinophyceae, Chlorophyceae and Dinophyceae, Δ¹³C values of -28.9 °/oo (Platymonas sp.), -31.9 °/oo (Dunaliella sp.), -29.1 °/oo (Chlorococcum sp.) and -27.2 °/oo (Glenodinium foliaceum) have been measured. Since these algae were cultured under identical laboratory conditions, the wide range of Δ¹³C values are indicative that fractionating processes other than RuDP and/or PEP carboxylases are operable in these organisms. Differential accumulation of photosynthetic products by marine algae has been suggested to be a possible explanation for the variation of Δ¹³C in these organisms.

Carbon isotope fractionation by marine phytoplankton

Vita.Stable carbon isotope fractionations by seventeen species of marine phytoplankton, representing the classes of Bacillariophyceae, Chlorophyceae, Prasinophyceae, Chrysophyceae, Haptophyceae and Dinophyceae, have been determined in laboratory culture experiments using bicarbonate enriched artificial sea water. The Δ¹³C values (Δ¹³C = δ¹³C of plant HCO₃) range from -22.1 0/00 to -35.5 °/oo Nitzschia closterium shows the smallest fractionation of -22.1 °/oo and Isochrysis galbana, the greatest of -35.5 °/oo. Within the Bacillariophyceae, the Δ¹³C values vary from -22.1 °/oo (Nitzschia closterium) to -29.9 °/oo (Skeletonema costatum). With the exception of Cyclotella sp., the centric diatoms seem to fractionate more than the pennate diatoms. Haptophyceae and Chrysophyceae are always more depleted in ¹³C than other marine microalgae. For Prasinophyceae, Chlorophyceae and Dinophyceae, Δ¹³C values of -28.9 °/oo (Platymonas sp.), -31.9 °/oo (Dunaliella sp.), -29.1 °/oo (Chlorococcum sp.) and -27.2 °/oo (Glenodinium foliaceum) have been measured. Since these algae were cultured under identical laboratory conditions, the wide range of Δ¹³C values are indicative that fractionating processes other than RuDP and/or PEP carboxylases are operable in these organisms. Differential accumulation of photosynthetic products by marine algae has been suggested to be a possible explanation for the variation of Δ¹³C in these organisms.

EBNA1-targeted probe for the imaging and growth inhibition of tumours associated with the Epstein–Barr virus

Epstein–Barr nuclear antigen 1 (EBNA1), a dimeric oncoprotein of the Epstein–Barr virus (EBV), is essential for both viral-genome maintenance and the survival of infected cells. Despite EBNA1’s potential as a therapeutic target, tools for the direct monitoring of EBNA1 in vitro and in vivo are lacking. Here, we show that a peptide-based inhibitor that luminesces when bound to EBNA1 inside the nucleus of EBV+ cells can regulate EBNA1 homodimer formation and selectively inhibit the growth of EBV+ tumours of nasopharyngeal carcinoma cells (C666-1 and NPC43) and Burkitt’s lymphoma Raji cells. We also show that the peptide-based probe leads to 93% growth inhibition of EBV+ tumours in mice. Our findings support the hypothesis that selective inhibition of EBNA1 dimerization can be used to afford better EBV-related cancer differentiation, and highlight the potential application of the probe as a new generation of biotracers for investigating the fundamental biological function of EBNA1 and for exploring its application as a therapeutic target