Kinetic model construction using chemoinformatics

Kinetic models of chemical processes not only provide an alternative to costly experiments; they also have the potential to accelerate the pace of innovation in developing new chemical processes or in improving existing ones. Kinetic models are most powerful when they reflect the underlying chemistry by incorporating elementary pathways between individual molecules. The downside of this high level of detail is that the complexity and size of the models also steadily increase, such that the models eventually become too difficult to be manually constructed. Instead, computers are programmed to automate the construction of these models, and make use of graph theory to translate chemical entities such as molecules and reactions into computer-understandable representations. This work studies the use of automated methods to construct kinetic models. More particularly, the need to account for the three-dimensional arrangement of atoms in molecules and reactions of kinetic models is investigated and illustrated by two case studies. First of all, the thermal rearrangement of two monoterpenoids, cis- and trans-2-pinanol, is studied. A kinetic model that accounts for the differences in reactivity and selectivity of both pinanol diastereomers is proposed. Secondly, a kinetic model for the pyrolysis of the fuel “JP-10” is constructed and highlights the use of state-of-the-art techniques for the automated estimation of thermochemistry of polycyclic molecules. A new code is developed for the automated construction of kinetic models and takes advantage of the advances made in the field of chemo-informatics to tackle fundamental issues of previous approaches. Novel algorithms are developed for three important aspects of automated construction of kinetic models: the estimation of symmetry of molecules and reactions, the incorporation of stereochemistry in kinetic models, and the estimation of thermochemical and kinetic data using scalable structure-property methods. Finally, the application of the code is illustrated by the automated construction of a kinetic model for alkylsulfide pyrolysis

Cheminformatics Tools to Explore the Chemical Space of Peptides and Natural Products

Cheminformatics facilitates the analysis, storage, and collection of large quantities of chemical data, such as molecular structures and molecules' properties and biological activity, and it has revolutionized medicinal chemistry for small molecules. However, its application to larger molecules is still underrepresented. This thesis work attempts to fill this gap and extend the cheminformatics approach towards large molecules and peptides. This thesis is divided into two parts. The first part presents the implementation and application of two new molecular descriptors: macromolecule extended atom pair fingerprint (MXFP) and MinHashed atom pair fingerprint of radius 2 (MAP4). MXFP is an atom pair fingerprint suitable for large molecules, and here, it is used to explore the chemical space of non-Lipinski molecules within the widely used PubChem and ChEMBL databases. MAP4 is a MinHashed hybrid of substructure and atom pair fingerprints suitable for encoding small and large molecules. MAP4 is first benchmarked against commonly used atom pairs and substructure fingerprints, and then it is used to investigate the chemical space of microbial and plants natural products with the aid of machine learning and chemical space mapping. The second part of the thesis focuses on peptides, and it is introduced by a review chapter on approaches to discover novel peptide structures and describing the known peptide chemical space. Then, a genetic algorithm that uses MXFP in its fitness function is described and challenged to generate peptide analogs of peptidic or non-peptidic queries. Finally, supervised and unsupervised machine learning is used to generate novel antimicrobial and non-hemolytic peptide sequences

Metabolic engineering strategies for high-level production of aromatic amino acid pathway derivatives in Saccharomyces cerevisiae

Due to its robustness, genetic tractability, and industrial relevance, the budding yeast Saccharomyces cerevisiae was selected as study model of the aromatic amino acid biosynthetic pathway. This pathway houses a wide diversity of economically important metabolites ranging from polymer precursors to pain-management drugs, whose productions have been highly sought-after in biotechnological research. However, tight regulations at the transcriptional, translational, and allosteric levels surround the aromatic amino acid pathway, protecting the microbial factories (e.g. S. cerevisiae) from unnecessary energy expenditures. By making use of computational metabolic engineering tools such as Flux Balance Analysis and Metabolic Flux Analysis, together with fast and reliable synthetic biology techniques, the flux into the aromatic amino acid pathway was exploited. Initially, the flux distribution in the central carbon metabolism was studied through 13C-metabolic flux analysis and carbon tracing experiments. Important insights regarding the partition between glycolysis and the pentose phosphate pathway were obtained and correlated with the production of aromatic amino acid derivatives. For the first time, the pentafunctional enzyme, ARO1, composing the core of the shikimic acid pathway was subjected to site-directed mutagenesis to reveal its active domains. This resulted in the development of new variants with disrupted activities specifically designed for increasing production of the two target molecules, namely, muconic acid and shikimic acid. Further analysis with OptForce simulations revealed that overexpressing the ribose-5-phosphate ketol-isomerase gene, RKI1, can enhance carbon funneling into the aromatic amino acid pathway. A multilevel engineering strategy was established to explore novel transcriptional regulators that tightly control the carbon flux into the pathway. Deleting the gene RIC1, involved in efficient protein localization of trans-Golgi network proteins, increased the titers of shikimic acid and muconic acid. These non-intuitive interventions, in combination with the previous genetic platforms, increased the production titers over 3-fold compared to the base strains. The shikimic acid strains produced 1.9 g L-1, while muconic acid and intermediates were accumulated up to 1.6 g L-1, both being the highest reported in S. cerevisiae, in batch fermentations. Future research should focus on devising more dynamic genome engineering strategies that rely on modulating the activity of essential genes while ensuring a good compromise with biomass formation

In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process

Gene expression of xenobiotic metabolising enzymes in rat liver and kidney: differential effects of rooibos and honeybush herbal teas

Magister Scientiae (Medical Bioscience) - MSc(MBS)Laboratory studies, epidemiological investigations and human clinical trials indicate that flavonoids have important effects on cancer chemoprevention and therapy. Flavonoids may interfere in several steps that lead to cancer development but may also lead to toxicity as the inhibition of carcinogen-activating enzymes may also cause potential toxic flavonoid-drug interactions. However, the potential toxicity of these dietary components has not been well studied. The use of polyphenol enriched supplements prepared from South African herbal teas, rooibos(Aspalathus linearis)and honeybush (Cyclopia spp.) are being marketed to alleviate symptoms that are known to be “cured” by the herbal teas. However, there is a lack of information regarding the possible health promoting effects of these polyphenol-enriched extracts on xenobiotic metabolism. In the present study, the modulating effects of aspalathinenriched rooibos and mangiferin-enriched C. genistoides and C. subternata extracts on the gene expression of xenobiotic metabolising enzymes (XMEs) were investigated in vivo in the rat liver and kidneys. An in vitro study, utilising a primary rat hepatocyte cell model, was included to further evaluate changes in the expression of selected XMEs by the herbal tea extracts, including their major polyphenolic constituents, aspalathin and mangiferin. The use of the in vitro primary hepatocytes assay as a predictive cell model for the modulation of the expression of XMEs genes by the herbal tea extracts in vivo was critically evaluated.In the liver and kidneys, the C. subternata polyphenol-enriched herbal tea extract effected the majority of changes regarding the expression of XMEs genes when compared to the rooibos and C. genistoides. Variations in the modulation of gene expression of the XMEs by the herbal tea extracts were related to differences in their polyphenol constituents, although non-polyphenolic constituent could also be involved.Overall the herbal teas regulated alcohol,energy, drug and steroid metabolism in the liver, whereas in the kidneys the gene expression of phase I, phase II, steroid metabolising enzymes, as well as drug transporters were modulated. It would appear that the herbal teas are likely to exhibit both beneficial and adverse effects in vivo,depending on the specific organ, the xenobiotic and/or drug that are involved. The primary rat hepatocytes model display varying effects with respect to modulating gene expression of specific XMEs by the polyphenol-enriched herbal tea extracts. Apart from the reduction in 17 -hydroxysteroid dehydrogenase gene expression care should be taken to directly extrapolate the in vitro findings to changes that prevail in vivo.However, interesting results regarding the masking effect of complex mixture on the modulation of XME gene expression of individual polyphenols were encountered. In addition differences in the dose and duration of exposure between the in vitro and in vivo studies were not comparable and should be further explored to validate the in vitro primary hepatocytes model to predict changes in vivo. Future studies should investigate the effects of the herbal tea extracts, its polyphenols and metabolites on XME induction at a protein level as well as varying herb-drug-enzyme interactions

Laboratory directed research and development program, FY 1996

The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) Laboratory Directed Research and Development Program FY 1996 report is compiled from annual reports submitted by principal investigators following the close of the fiscal year. This report describes the projects supported and summarizes their accomplishments. It constitutes a part of the Laboratory Directed Research and Development (LDRD) program planning and documentation process that includes an annual planning cycle, projection selection, implementation, and review. The Berkeley Lab LDRD program is a critical tool for directing the Laboratory`s forefront scientific research capabilities toward vital, excellent, and emerging scientific challenges. The program provides the resources for Berkeley Lab scientists to make rapid and significant contributions to critical national science and technology problems. The LDRD program also advances the Laboratory`s core competencies, foundations, and scientific capability, and permits exploration of exciting new opportunities. Areas eligible for support include: (1) Work in forefront areas of science and technology that enrich Laboratory research and development capability; (2) Advanced study of new hypotheses, new experiments, and innovative approaches to develop new concepts or knowledge; (3) Experiments directed toward proof of principle for initial hypothesis testing or verification; and (4) Conception and preliminary technical analysis to explore possible instrumentation, experimental facilities, or new devices

A treatment of stereochemistry in computer aided organic synthesis

This thesis describes the author’s contributions to a new stereochemical processing module constructed for the ARChem retrosynthesis program. The purpose of the module is to add the ability to perform enantioselective and diastereoselective retrosynthetic disconnections and generate appropriate precursor molecules. The module uses evidence based rules generated from a large database of literature reactions. Chapter 1 provides an introduction and critical review of the published body of work for computer aided synthesis design. The role of computer perception of key structural features (rings, functions groups etc.) and the construction and use of reaction transforms for generating precursors is discussed. Emphasis is also given to the application of strategies in retrosynthetic analysis. The availability of large reaction databases has enabled a new generation of retrosynthesis design programs to be developed that use automatically generated transforms assembled from published reactions. A brief description of the transform generation method employed by ARChem is given. Chapter 2 describes the algorithms devised by the author for handling the computer recognition and representation of the stereochemical features found in molecule and reaction scheme diagrams. The approach is generalised and uses flexible recognition patterns to transform information found in chemical diagrams into concise stereo descriptors for computer processing. An algorithm for efficiently comparing and classifying pairs of stereo descriptors is described. This algorithm is central for solving the stereochemical constraints in a variety of substructure matching problems addressed in chapter 3. The concise representation of reactions and transform rules as hyperstructure graphs is described. Chapter 3 is concerned with the efficient and reliable detection of stereochemical symmetry in both molecules, reactions and rules. A novel symmetry perception algorithm, based on a constraints satisfaction problem (CSP) solver, is described. The use of a CSP solver to implement an isomorph‐free matching algorithm for stereochemical substructure matching is detailed. The prime function of this algorithm is to seek out unique retron locations in target molecules and then to generate precursor molecules without duplications due to symmetry. Novel algorithms for classifying asymmetric, pseudo‐asymmetric and symmetric stereocentres; meso, centro, and C2 symmetric molecules; and the stereotopicity of trigonal (sp2) centres are described. Chapter 4 introduces and formalises the annotated structural language used to create both retrosynthetic rules and the patterns used for functional group recognition. A novel functional group recognition package is described along with its use to detect important electronic features such as electron‐withdrawing or donating groups and leaving groups. The functional groups and electronic features are used as constraints in retron rules to improve transform relevance. Chapter 5 details the approach taken to design detailed stereoselective and substrate controlled transforms from organised hierarchies of rules. The rules employ a rich set of constraints annotations that concisely describe the keying retrons. The application of the transforms for collating evidence based scoring parameters from published reaction examples is described. A survey of available reaction databases and the techniques for mining stereoselective reactions is demonstrated. A data mining tool was developed for finding the best reputable stereoselective reaction types for coding as transforms. For various reasons it was not possible during the research period to fully integrate this work with the ARChem program. Instead, Chapter 6 introduces a novel one‐step retrosynthesis module to test the developed transforms. The retrosynthesis algorithms use the organisation of the transform rule hierarchy to efficiently locate the best retron matches using all applicable stereoselective transforms. This module was tested using a small set of selected target molecules and the generated routes were ranked using a series of measured parameters including: stereocentre clearance and bond cleavage; example reputation; estimated stereoselectivity with reliability; and evidence of tolerated functional groups. In addition a method for detecting regioselectivity issues is presented. This work presents a number of algorithms using common set and graph theory operations and notations. Appendix A lists the set theory symbols and meanings. Appendix B summarises and defines the common graph theory terminology used throughout this thesis