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

Revealing druggable cryptic pockets in the Nsp1 of SARS-CoV-2 and other β-coronaviruses by simulations and crystallography

Non-structural protein 1 (Nsp1) is a main pathogenicity factor of α- and β-coronaviruses. Nsp1 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suppresses the host gene expression by sterically blocking 40S host ribosomal subunits and promoting host mRNA degradation. This mechanism leads to the downregulation of the translation-mediated innate immune response in host cells, ultimately mediating the observed immune evasion capabilities of SARS-CoV-2. Here, by combining extensive molecular dynamics simulations, fragment screening and crystallography, we reveal druggable pockets in Nsp1. Structural and computational solvent mapping analyses indicate the partial crypticity of these newly discovered and druggable binding sites. The results of fragment-based screening via X-ray crystallography confirm the druggability of the major pocket of Nsp1. Finally, we show how the targeting of this pocket could disrupt the Nsp1-mRNA complex and open a novel avenue to design new inhibitors for other Nsp1s present in homologous β-coronaviruses

Fundamentals of SARS-CoV-2 Biosensors

COVID-19 diagnostic strategies based on advanced techniques are currently essential topics of interest, with crucial roles in scientific research. This book integrates fundamental concepts and critical analyses that explore the progress of modern methods for the detection of SARS-CoV-2

Purification and characterization of a RNA binding protein, the severe acute respiratory syndrome coronavirus (SARS-CoV) nucleocapsid protein.

by Chan Wai Ling.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 170-185).Abstracts in English and Chinese.Acknowledgements --- p.iAbstract --- p.iii摘要 --- p.vTable of Content --- p.viiAbbreviations --- p.xiifor Nucleotides --- p.xiifor Amino acids --- p.xiifor Standard genetic codes --- p.xiiifor Units --- p.xiiifor Prefixes of units --- p.xivfor Terms commonly used in the report --- p.xivList of Figures --- p.xviiList of Tables --- p.xxiiiChapter Chapter I --- Introduction --- p.1Chapter 1.1 --- Epidemiology of the Severe Acute Respiratory Syndrome --- p.1Chapter 1.2 --- The SARS Coronavirus --- p.3Chapter 1.3 --- Cell Biology of Coronavirus Infection and Replication and the Role of Nucleocapsid Protein --- p.9Chapter 1.4 --- Recent Advances in the SARS-CoV Nucleocapsid Protein --- p.16Chapter 1.5 --- The Sumoylation System --- p.24Chapter 1.6 --- Objectives of the Present Study --- p.28Chapter Chapter II --- SARS-CoV N protein and Fragment Purification --- p.29Chapter 2.1 --- INTRODUCTION --- p.29Chapter 2.2 --- METHODOLOGY --- p.31Materials --- p.31Methods --- p.39Chapter 2.2.1 --- Construction of the pMAL-c2P vector --- p.39Chapter 2.2.2 --- Sub-cloning of the N protein into expression vectors --- p.42Chapter 2.2.2.1 --- Design of primers for the cloning of N protein --- p.43Chapter 2.2.2.2 --- DNA amplification using Polymerase Chain Reaction (PCR) --- p.44Chapter 2.2.2.3 --- DNA extraction from agarose gel --- p.45Chapter 2.2.2.4 --- Restriction digestion of purified PCR product and vectors --- p.46Chapter 2.2.2.5 --- Ligation of N protein into expression vectors --- p.47Chapter 2.2.2.6 --- Preparation of competent cells --- p.48Chapter 2.2.2.7 --- Transformation of plasmids into competent Escherichia coli --- p.49Chapter 2.2.2.8 --- Preparation of plasmid DNA --- p.49Chapter 2.2.2.8.1 --- Mini-preparation of plasmid DNA --- p.49Chapter 2.2.2.8.2 --- Midi-preparation of plasmid DNA --- p.51Chapter 2.2.3 --- Expression of tagged and untagged N protein --- p.53Chapter 2.2.3.1 --- Preparation of E. coli competent cells for protein expression --- p.53Chapter 2.2.3.2 --- Expression of N protein --- p.53Chapter 2.2.3.3 --- Solubility tests on the fusion proteins expressed --- p.54Chapter 2.2.4 --- Purification of N protein Chromatographic methods --- p.55Chapter 2.2.4.1 --- Affinity chromatography --- p.55Chapter 2.2.4.1.1 --- Ni-NTA affinity chromatography --- p.55Chapter 2.2.4.1.2 --- Glutathione affinity chromatography --- p.56Chapter 2.2.4.1.3 --- Amylose affinity chromatography --- p.56Chapter 2.2.4.2 --- Ion exchange chromatography --- p.57Chapter 2.2.4.2.1 --- Cation exchange chromatography --- p.57Chapter 2.2.4.2.2 --- Anion exchange chromatography --- p.58Chapter 2.2.4.3 --- Heparin affinity chromatography --- p.58Chapter 2.2.4.4 --- Size exclusion chromatography Purification strategies --- p.60Chapter 2.2.4.5 --- Purification of His6-tagged N proteins --- p.60Chapter 2.2.4.6 --- Purification of MBP-tagged N proteins --- p.60Chapter 2.2.4.7 --- Purification of GST-tagged N proteins --- p.61Chapter 2.2.4.8 --- Purification of untagged N proteins --- p.61Chapter 2.2.5 --- Trypsin digestion assay for the design of stable fragment --- p.64Chapter 2.2.6 --- Partial purification of the N protein amino acid residue 214-422 fragment --- p.65Chapter 2.2.7 --- Sumoylation of the SARS-CoV N protein --- p.67Chapter 2.2.7.1 --- In vitro sumoylation assay --- p.67Chapter 2.2.7.2 --- Sample preparation for mass spectrometric analysis --- p.68Chapter 2.3 --- RESULTS --- p.70Chapter 2.3.1 --- Construction of the vector pMAL-c2P --- p.70Chapter 2.3.2 --- "Construction of recombinant N protein-pAC28m, N-protein- pGEX-6P-l,N protein-pMAL-c2E and N protein-pMAL-c2P plasmids" --- p.72Chapter 2.3.3 --- Optimization of expression conditions --- p.79Chapter 2.3.4 --- Screening of purification strategies --- p.82Chapter 2.3.4.1 --- Purification of His6-N protein --- p.82Chapter 2.3.4.2 --- Purification of MBP-N protein --- p.84Chapter 2.3.4.3 --- Purification of GST-N protein --- p.85Chapter 2.3.4.4 --- Purification of untagged N protein --- p.87Chapter 2.3.5 --- Limited trypsinolysis for the determination of discrete structural unit --- p.91Chapter 2.3.6 --- Partial purification of the N protein 214-422 fragment --- p.94Chapter 2.3.7 --- Sumoylation of N protein --- p.97Chapter 2.2.7.1 --- Sumoylation site prediction --- p.97Chapter 2.2.7.2 --- In vitro sumoylation assay --- p.99Chapter 2.2.7.3 --- Mass spectrometric identification of sumoylated SARS-CoV N protein --- p.103Chapter 2.4 --- DISCUSSION --- p.109Chapter Chapter III --- Characterization of the Nucleic Acid Binding Ability of N protein --- p.119Chapter 3.1 --- INTRODUCTION --- p.119Chapter 3.2 --- METHODOLOGY --- p.120Materials --- p.120Methods --- p.124Chapter 3.2.1 --- Spectrophotometric Measurement of ratio OD260/ OD280 --- p.124Chapter 3.2.2 --- Native gel electrophoresis --- p.124Chapter 3.2.3 --- Quantitative determination of nucleic acids content --- p.125Chapter 3.2.3.1 --- Dische assay - quantitative determination of DNA content --- p.125Chapter 3.2.3.2 --- Orcinol assay - quantitative determination of RNA content --- p.126Chapter 3.2.4 --- RNase digestion of the N protein-bound RNA --- p.128Chapter 3.2.5 --- Isolation of RNA from purified GST-N proteins --- p.128Chapter 3.2.6 --- In vitro transcription of SARS-CoV genomic RNA fragment --- p.129Chapter 3.2.7 --- Vero E6 cell line maintenance and total RNA extraction --- p.131Chapter 3.2.8 --- Electrophoretic mobility shift assay (EMSA) --- p.131Chapter 3.3 --- RESULTS --- p.133Chapter 3.3.1 --- Detection of nucleic acids in the purified N proteins byspectrophotometric Measurement of ratio OD260/ OD280 --- p.133Chapter 3.3.2 --- Native gel electrophoresis --- p.135Chapter 3.3.3 --- Quantitative determination of nucleic acids content in purified GST-N proteins --- p.136Chapter 3.3.3.1 --- Dische assay for the determination of DNA --- p.136Chapter 3.3.3.2 --- Orcinol assay for the determination of RNA --- p.138Chapter 3.3.4 --- RNase digestion treatment --- p.139Chapter 3.3.5 --- Extraction of RNA from GST-N proteins --- p.140Chapter 3.3.6 --- In vitro transcription of SARS-CoV genomic RNA fragment --- p.142Chapter 3.3.7 --- Electrophoretic mobility shift assay (EMSA) --- p.144Chapter 3.4 --- DISCUSSION --- p.147Chapter Chapter IV --- Discussion --- p.154Chapter 4.1 --- "Purity, Aggregation and RNA Binding Property of the SARS-CoV Nucleocapsid Protein" --- p.154Chapter 4.2 --- Future perspectives --- p.156Chapter 4.2.1 --- Structural study of the SARS-CoV N protein through x-ray crystallography --- p.156Chapter 4.2.2 --- Mapping the RNA binding domain in the SARS-CoV N protein --- p.156Chapter 4.2.3 --- Determination of aggregation state by lateral turbidimetry analysis --- p.156Chapter 4.2.4 --- Exploring protein interacting partners that enhance RNA binding specificity --- p.157Appendix --- p.159Chapter I. --- Sequence of the SARS-CoV N protein --- p.159Chapter II. --- Sequence of the SARS-CoV genome fragment used for RNA binding assay in section 3.37.1 --- p.161Chapter III. --- Vector maps --- p.161Chapter a) --- Vector map of pACYC177 --- p.161Chapter b) --- Vector map and MCS of pET28a --- p.163Chapter c) --- Vector map and MCS of pAC28 --- p.164Chapter d) --- Vector map and MCS of pGEX-6P-1Chapter e) --- Vector map of pMAL-c2X and MCS of pMAL-c2EChapter IV. --- Electrophoresis markers --- p.166Chapter V. --- SDS-PAGE gel parathion protocol --- p.169References --- p.17

Drug Repurposing

This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Dynamics of bat-coronavirus interactions: role of innate antiviral responses

Bats are speculated to be reservoirs of several emerging viruses including coronaviruses (CoVs) that cause severe acute respiratory syndrome (SARS), Middle-East respiratory syndrome (MERS), porcine epidemic diarrhea and swine acute diarrhea syndrome. These viruses cause significant disease in humans and agricultural animals. MERS-CoV causes serious disease in humans with a thirty-five percent mortality and has evolved proteins that can effectively suppress an innate antiviral response in human cells. Bats that are naturally or experimentally infected with these or similar viruses do not show apparent signs of disease and the molecular mechanisms of protection are not yet known. My doctoral thesis tested the hypothesis that big brown bat cells have unique adaptations in innate antiviral signaling pathways involved in the control of virus replication and coronavirus-induced inflammatory cytokines. To test this hypothesis, we generated the first commercially available North American bat (Eptesicus fuscus; big brown bat) kidney epithelial cell line. Using this cell line, we were able to demonstrate that big brown bat cells have evolved a unique repressor molecule, c-Rel that can effectively suppress double-stranded RNA (poly(I:C)) mediated expression of a key inflammatory cytokine, tumor necrosis factor alpha (TNF). MERS-CoV is thought to have evolved in insectivorous bats before spilling over to camels and eventually to humans. To further our understanding about bat-coronavirus interactions, we demonstrated that big brown bat cells are resistant to MERS-CoV-mediated subversion of antiviral responses. We determined that interferon regulatory factor 3 (IRF3) plays a critical role in controlling MERS-CoV propagation in big brown bat epithelial cells. Indeed, my doctoral thesis has identified two unique adaptations in big brown bat cells that might allow these bats, and probably other species of bats to successfully co-exist with coronaviruses. My thesis supports the hypothesis that bats function as global reservoirs for emerging coronaviruses by providing definitive examples of adaptations that would allow bats to co-exist with these viruses. Future work from my thesis will focus on adapting some of these antiviral strategies in human cells to control coronavirus-mediated disease in humans

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P