Characterization and Analysis of Biosynthetic Systems from Nostoc sp. ATCC 53789 and Selected Fungal Natural Product Pathways.

Complex secondary metabolites display diverse biological activities and together with their derivatives have provided over two-thirds of new pharmaceutical agents introduced during the past two decades. However, limitations in isolation and in rapid structural determination continue to be inherent hurdles for using natural products as leads in drug discovery and design. My dissertation research focused on selected biosynthetic pathways with the hope to overcome some of these limitations. Three projects are described in this dissertation thesis. The first project demonstrates my efforts to generate natural product analogs using the biocatalysts, a strategy that provides significant advantages in catalytic specificity, efficiency, and impacts on the environment. Several natural and synthetic anticancer agent analogs were produced with a single P450 epoxidase and an excised thioesterase involved in the production of cryptophycin in Nostoc sp. Moreover, the homotropic and heterotropic cooperativity of the bacterial P450 epoxidase toward its substrates was characterized in details. This enzyme may serve as a more operable model to study the same features in several human P450s involving in xenobiotcs metabolism. The second project describes how unique prenylated indole alkaloids are biosynthesized in various fungal genera. These biosynthetic pathways were extensively investigated by isolation and characterization of several key biosynthetic intermediates from Penicillium, Aspergillus, and Malbranchea sp. Subsequently, these pathways were examined at the first time through the elucidation of the biosynthetic gene cluster for stephacidin/notoamide from a marine Aspergillus strain and biochemical characterization of two critical aromatic prenyltransferases catalyzing two committed steps. Finally, one trichothecene macrolide gene cluster was cloned from a marine Myrothecium verrucaria strain and validated with biochemical characterization of a sesquiterpene synthase and a multifunctional P450, representing the latest understanding of the biosynthesis of structurally complex mycotoxins. With the identification and characterization of natural product gene clusters, more new fungal secondary metabolite analogs may be generated through metabolic engineering and heterologous production.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/76020/1/dingyous_1.pd

Chemoenzymatic Synthesis of Cryptophycin Anticancer Agents by an Ester Bond-Forming Non-ribosomal Peptide Synthetase Module

Cryptophycins (Crp) are a group of cyanobacterial depsipeptides with activity against drug-resistant tumors. Although they have been shown to be promising, further efforts are required to return these highly potent compounds to the clinic through a new generation of analogues with improved medicinal properties. Herein, we report a chemosynthetic route relying on themultifunctional enzyme CrpD-M2 that incorporates a 2-hydroxy acid moiety (unit D) into Crp analogues. CrpD-M2 is a unique nonribosomal peptide synthetase (NRPS) module comprised of condensation-adenylation-ketoreduction-thiolation (C-A-KR-T) domains. We interrogated A-domain 2-keto and 2-hydroxy acid activation and loading, and KR domain activity in the presence of NADPH and NADH. The resulting 2-hydroxy acid was elongated with three synthetic Crp chain elongation intermediate analogues through ester bond formation catalyzed by CrpD-M2 C domain. Finally, the enzyme-bound seco-Crp products were macrolactonized by the Crp thioesterase. Analysis of these sequential steps was enabled through LC-FTICR-MS of enzyme-bound intermediates and products. This novel chemoenzymatic synthesis of Crp involves four sequential catalytic steps leading to the incorporation of a 2-hydroxy acid moiety in the final chain elongation intermediate. The presented work constitutes the first example where a NRPS-embedded KR domain is employed for assembly of a fully elaborated natural product, and serves as a proof-of-principle for chemoenzymatic synthesis of new Crp analogues

Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China.

An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV

Morphological Profiling for Drug Discovery in the Era of Deep Learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.Comment: 44 pages, 5 figure, 5 table

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe