Exhaustive Conformational Sampling of Complex Fused Ring Macrocycles Using Inverse Kinematics.

Synthetic macrocycles offer variety of biologically significant properties e.g. antibacterial, antifungal, anti-fertility, anticancerous, anti-inflammatory, anti-diabetic, anti-malarial, antiviral and antioxidant. DNA binding and cleaving agents are thoroughly used in area of medicinal chemistry. Host-guest chemistry is the one of most fascinating parts of macrocycles. Literature suggests various macrocycles with the potential therapeutic properties. Magnetic resonance imagining (MRI) contrast agent, dopamine detection, photochemical properties as sensor and catalytic activities of macrocycle are most attracting and growing fields of naturally mimic synthetic macrocycles. In recent years, interest has aroused on Schiff based macrocycles and synthetic strategies were employed to develop novel macrocycles with engrossed properties and applications.

Research in natural products (NPs) has gained interest as drug developers turn to nature to combat problems with drug resistance, drug delivery, and emerging diseases. Whereas NPs offer a tantalizing source of new pharmacologically active compounds, their structural complexity presents a challenge for analytical characterization and organic synthesis. Of particular concern is the characterization of cyclic-, polycyclic-, or macrocyclic compounds. One example of endogenous compounds as inspiration for NP development are cobalamins, like vitamin B12. An example of exogenous NPs is the class of macrolides that includes erythromycin. Both classes of macrocycles feature analogues with a range of modifications on their macrocyclic cores, but because of their cyclic nature, they are generally resistant to fragmentation by collision-induced dissociation (CID). In the present work, charge-transfer dissociation (CTD) was employed, with or without supplemental collisional activation, to produce radical-driven, high-energy fragmentation products of different macrocyclic precursors. With the assistance of collisional activation of CTnoD products, CTD frequently cleaved two covalent bonds within the macrocycle cores to reveal rich, informative spectra that helped identify sites of modification and resolve structural analogues. In a third example of macrocycle fragmentation, CTD enabled an impurity in a biological sample to be characterized as a cyclic polymer of nylon-6,6. In each example, CTD spectra are starkly different from CID and are highly reminiscent of other high-energy fragmentation techniques like extreme ultraviolet dissociative photoionization (XUV-DPI) and electron ionization-induced dissociation (EID). The results indicate that CTD-MS is a useful tool for the characterization of natural and synthetic macrocycles.

One of the challenges in natural product discovery is the re-isolation and identification of known compounds. Dereplication is the process of identifying known compounds, allowing time and effort involved in their isolation and structure elucidation to be saved, thus enabling focus to be directed solely on new compounds. While dereplication is mainly MS-based, the main tool for structure elucidation of natural products is NMR. These techniques are very different to each other, with the only current approach in which they can be correlated is by hyphenated techniques such as LC-NMR-MS, requiring specialist and expensive hardware that is not available in every lab. In this thesis we develop structure and molecular size based NMR methodology for analysis of complex mixtures, such as those encountered in natural product research. We have used Diffusion Ordered Spectroscopy (DOSY), an NMR technique that enables components of a mixture to be distinguished by their diffusion coefficients (D), corresponding to hydrodynamic size. Since DOSY has mostly been used for organometallic and polymer analysis, we have established a calibration curve consisting mostly of natural products that provides a correlation between D to molecular weight (MW), allowing MW prediction from experimental D. Since physicochemical properties such as hydrogen bonding, molar density and molecular shape have been shown to affect D and therefore MW prediction, we have generated two new MW prediction models. The first model takes hydrogen bonding into account by the chemical shift of the most acidic hydrogen bond donor (HBD), and the second model provides a more accurate MW prediction for RP HPLC fractions based on the percentage MeOH the compound elutes it. We have generated predicted D based on structural and chemical properties for a database of 220,817 natural products, allowing dereplication by use of a D filter based on the experimental D in conjunction with structural filters based on NMR data. These models have been validated by the dereplication of a mixture of two sesquiterpenes from the plant Tasmannia xerophila, and the dereplication of three known tropane alkaloids in a complex mixture from Darlingia ferruginea. 3D DOSY methodology was undertaken for the analysis of the D. ferruginea complex mixture, allowing molecular fragments to be constructed from DOSY-COSY data. We have also projected diffusion data derived from DOSY-HSQC data onto an HMBC spectrum to generate complex fragments containing non protonated carbons, and this has provided a powerful tool for dereplication. These molecular fragments allowed dereplication and the annotation of three new tropane alkaloids in a complex mixture. These new compounds, together with other known alkaloids were subsequently isolated, and extensive 2D NMR and ECD data allowed their total absolute structures to be assigned to the three new compounds and the structure revision of two of the known compounds, one previously reported as it is diastereomer and both originally wrongly assigned as their opposite enantiomers. In this thesis we report the chemical investigation of two bryozoan species, members of a marine invertebrate phylum that is one of the most understudied. These species were selected for investigation after their extracts were identified as possessing potent bioactivity during an antiplasmodial screening of 486 marine invertebrate crude extracts collected in Australia as active toward the Plasmodium falciparum parasite, the causative agent of the malaria disease. The bryozoan Orthoscuticella ventricosa has afforded five new β-carboline alkaloids, two of which contained were bis-β-carboline containing an uncommon 1,2-disubstituted cyclobutane moiety. These compounds displayed weak antiplasmodial activity against the chloroquine-sensitive strain (3D7) of the P. falciparum. The bryozoan Amathia lamourouxi has afforded six new brominated alkaloids. Two of these displayed sub-micromolar antiplasmodial activity against both chloroquine-sensitive (3D7) and chloroquine-resistant (Dd2) P. falciparum strains and good selectivity relative to the human cell line, marking these compounds as validated antimalarial hits.

Continuum robots, characterized by their hyper-redundant and flexible structures, have gained significant attention in fields such as minimally invasive surgery, remote inspection, and soft manipulation. Their complex design and highly nonlinear kinematic behavior present substantial modeling and control challenges. This paper presents a complete framework encompassing the design, modeling, and inverse kinematics solution for a novel two-segment, tendon-driven continuum robot utilizing an elastic spring backbone for enhanced compliance and structural simplicity. A constant curvature forward kinematic model is presented. Subsequently, an efficient numerical approach for solving the challenging inverse kinematics problem is introduced by adapting the Jacobian-based Newton-Raphson method and incorporating the Moore-Penrose Pseudoinverse. This strategy effectively manages the robot's redundancy, ensuring smooth and reliable trajectory generation. Experimental verification confirms the robot's feasibility, demonstrating that the system successfully navigates along the trajectories computed by the inverse kinematics, thus validating the reasonableness of the kinematic model and ensuring seamless and smooth operation. These findings provide a robust foundation for improving motion planning of simple continuum robot platforms in practical applications.

The location of naphthalene in the zeolite ZSM-5 has been determined from solid-state 1H/29Si cross-polarization (CP) magic-angle-spinning (MAS) NMR data alone. With the peaks in the 29Si spectrum assigned to the inequivalent Si sites in the zeolite from a two-dimensional INADEQUATE spectrum, the rates of cross polarization between the 1H nuclei of the guest sorbate molecules and the 29Si nuclei of the zeolite framework were used to determine the location of the naphthalene molecules by exploiting the proportional relationship between cross-polarization rate constants and 1H/29Si dipolar coupling second moments. The NMR structure determination was carried out on three different selectively deuterium-labeled naphthalene molecules (naphthalene-d0, α-naphthalene-d4, and β-naphthalene-d4). The average of the molecule locations in agreement with all three sets of NMR data was found to be in excellent agreement with an existing single crystal XRD structure of the naphthalene–ZSM-5 complex.Key words: solid-state NMR, cross polarization, zeolites, host–guest complexes, structure determination.

We present a computational environment for Fast Analysis of multidimensional NMR DAta Sets (FANDAS) that allows assembling multidimensional data sets from a variety of input parameters and facilitates comparing and modifying such "in silico" data sets during the various stages of the NMR data analysis. The input parameters can vary from (partial) NMR assignments directly obtained from experiments to values retrieved from in silico prediction programs. The resulting predicted data sets enable a rapid evaluation of sample labeling in light of spectral resolution and structural content, using standard NMR software such as Sparky. In addition, direct comparison to experimental data sets can be used to validate NMR assignments, distinguish different molecular components, refine structural models or other parameters derived from NMR data. The method is demonstrated in the context of solid-state NMR data obtained for the cyclic nucleotide binding domain of a bacterial cyclic nucleotide-gated channel and on membrane-embedded sensory rhodopsin II. FANDAS is freely available as web portal under WeNMR ( http://www.wenmr.eu/services/FANDAS ).

G protein-coupled receptors (GPCRs) are a superfamily of transmembrane proteins that play crucial roles in cell physiology. Being key drug targets in many diseases, drug discovery efforts harness structural information about these receptors to rationally design drugs in the field of structure-based drug design (SBDD). This thesis presents the development of computational tools to manage and assess large GPCR structural model datasets, applies these tools to define key features linked with the best performing models in SBDD and integrates this knowledge into a novel computational workflow that predicts new GPCR models, which may offer new opportunities in SBDD programs.

Use of Decoys to Optimize an All-Atom Force Field Including Hydration

We describe an active contour based local energy minimization point distribution, behavior of orientation, relaxation iterative algorithm that estimates feature points of static target in image sequence. In our approach, we build a contour model of a target to get some of low-energy points kernels. The use of snake based line model results in more reliable convergence of the point local energy minimization. The algorithm uses auto-relation relation to give the behavior of orientation in a local kernel's window. It uses local window-based probability method to refine the current corresponding relations of scene kernels. Results are illustrated on real outdoor image sequence.

Structural studies of vinblastine alkaloids by exciton coupled circular dichroism

Computational approaches are widely used to help discover and develop new drugs, in particular, to understand how these molecules interact with their biomolecular targets. There are well-optimised and validated force field parameters available to describe interactions of common biomolecules, such as proteins, lipids and nucleotides. However, these force fields are not designed to represent heteromolecular ligands such as substrates, inhibitors, co-factors and potential drug molecules. Errors in parameters may result in incorrect predictions of ligand structure, orientation and conformation, which in turn can lead to the failure of computational drug design efforts. With over 25% of all the structures in the Protein Data Bank (PDB) containing ligands and over a million ligand molecules of potential interest in drug design in other databases, ligand validation and parameterisation represents a significant scientific challenge. While several automated parameterisation protocols have been proposed to generate the parameters, none of the current procedures are properly validated. As a response to the high demand for interaction parameters for ligands compatible with the GROMOS force field, a web accessible Automated force field Topology Builder (ATB; http://atb.uq.edu.au/) and Repository was developed. The ATB and Repository is intended to facilitate the development of molecular force fields and generates parameters that can be used in X-ray refinement, structure-based drug design and study of biomolecule-ligand complexes. In this thesis, molecular dynamics (MD) simulations were used to calculate the thermodynamic properties for the validation and refinement of ATB force fields parameters. A fully automated validation protocol of the ATB force field parameters for small organic molecules based on thermodynamic and structural information was developed and incorporated into the ATB. A novel integration and convergence protocol which increases the efficiency of the TI method for free energy calculations was also proposed. The validation has shown good overall agreement between the experimental and computed values and indicated problematic functional groups with parameters to be refined. The generation of parameters for novel molecules that are compatible with a given biomolecular force field can be tedious, time-consuming and error-prone. Here, a novel method to refine parameters in classical force fields in an automated manner that can be extended to the parameterisation of all other atom types is also presented. Single-step perturbation protocols were developed for small halogenated molecules to establish alternative van der Waals parameters which describe the interactions of these molecules with high precision. Parameters were successfully refined against experimental hydration free energies, densities and heats of vaporisation. As a result, alternative van der Waals interactions parameters for chlorine and bromine were proposed. Properties calculated with these proposed parameters closely matched the experiment data. The quality of the ATB force field parameters in validating the X-ray structures and investigating binding of ligands to the protein endothiapepsin was also investigated. Analysis of the MD trajectories showed that the protein structure remained relatively stable after 10 ns of simulations. The results for the validation of the ligands were mixed. While in some cases the simulations reproduced the binding mode and the interactions between the protein and the ligand with high accuracy suggesting the ATB parameters performed well, in other cases the complexes were unstable. This was despite the fact that the electron density for all ligands was well defined and the parameters for the ligand were generated using the same procedure. Endothiapepsin contains a catalytic dyad consisting of two aspartates that are generally assumed to share a proton. We demonstrated that the protonation of both the ligand and the residues with the binding site were critical to the ability to reproduce the crystal complex. To conclude, the work presented in this thesis provided insight into the development and validation of the force field parameters for drug-like molecules and their applications in structure-based drug design.

Chikungunya virus (CHIKV) is one of the re-emerging “neglected” tropical diseases whose recent outbreak affected not only Africa and South-East Asia but also several parts of America and Europe. To date, despite its serious nature, no antivirals or vaccines were developed in order to counter this resurgent infectious disease. The recent advancement in crystallography and availability of crystal structures of certain domains of CHIKV initiates the development of anti-CHIKV agents using structure-based drug design or synthetic medicinal chemistry approach. Despite the fact that almost 50 % of the new chemical entities against several biological targets were either obtained from natural products or natural product analogues, a very humble effort was directed towards identification of novel CHIKV inhibitors from natural products. In this review, besides a brief overview on CHIKV as well as the nature as a source of medicines, we highlight the current progress and future steps towards the discovery of CHIKV inhibitors from natural products. This report could pave the road towards the design of novel semi-synthetic derivatives with enhanced anti-viral activities.Graphical

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Recent advances in unsteady flow visualization

Interactive out-of-core isosurface visualisation in time-varying data sets