An NMR-Guided Screening Method for Selective Fragment Docking and Synthesis of a Warhead Inhibitor

Selective hits for the glutaredoxin ortholog of Brucella melitensis are determined using STD NMR and verified by trNOE and (15)N-HSQC titration. The most promising hit, RK207, was docked into the target molecule using a scoring function to compare simulated poses to experimental data. After elucidating possible poses, the hit was further optimized into the lead compound by extension with an electrophilic acrylamide warhead. We believe that focusing on selectivity in this early stage of drug discovery will limit cross-reactivity that might occur with the human ortholog as the lead compound is optimized. Kinetics studies revealed that lead compound 5 modified with an ester group results in higher reactivity than an acrylamide control; however, after modification this compound shows little selectivity for bacterial protein versus the human ortholog. In contrast, hydrolysis of compound 5 to the acid form results in a decrease in the activity of the compound. Together these results suggest that more optimization is warranted for this simple chemical scaffold, and opens the door for discovery of drugs targeted against glutaredoxin proteins-a heretofore untapped reservoir for antibiotic agents

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

A Dance with Protein Assemblies : Analysis, Structure Prediction, and Design

Protein assemblies are some of the most complex molecular machines in nature. They facilitate many cellular functions, from DNA replication to molecular motion, energy production, and even the production of other proteins. In a series of 3 papers, we analyzed the structure, developed structure prediction tools, and design tools, for different protein assemblies. Many of the studies were centered around viral protein capsids. Viral capsids are protein coats found inside viruses that contain and protect the viral genome. In one paper, we studied the interfaces of these capids and their energy landscapes. We found that they differ from regular homomers in terms of the amino acid composition and size, but not in the quality of interactions. This contradicts existing experimental and theoretical studies that suggest that the interactions are weak. We hypothesise that the occlusion by our models of electrostatic and entropic contributions might be at play. In another paper, we developed methods to predict large cubic symmetrical protein assemblies, such as viral capsids, from sequence. This method is based upon AlphaFold, a new AI tool that has revolutionized protein structure prediction. We found that we can predict up to 50% of the structures of these assemblies. The method can quickly elucidate the structure of many relevant proteins for humans, and for understanding structures relevant to disease, such as the structures of viral capsids. In the final paper, we developed tools to design capsid-like proteins called cages – structures that can be used for drug delivery and vaccine design. A fundamental problem in designing cage structures is achieving different architectures and low porosity, goals that are important for vaccine design and the delivery of small drug molecules. By explicitly modelling the shapes of the subunits in the cage and matching the shapes with proteins from structural databases, we find that we can create structures with many different sizes, shapes, and porosities - including low porosities. While waiting for experimental validation, the design strategy described in the paper must be extended, and more designs must be tested

Computationally driven discovery of SARS-CoV-2 M pro inhibitors: From design to experimental validation

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (

Towards intelligent drug design system: Application of artificial dipeptide receptor library in QSAR-oriented studies

The pharmacophore properties of a new series of potential purinoreceptor (P2X) inhibitors determined using a coupled neural network and the partial least squares method with iterative variable elimination (IVE-PLS) are presented in a ligand-based comparative study of the molecular surface by comparative molecular surface analysis (CoMSA). Moreover, we focused on the interpretation of noticeable variations in the potential selectiveness of interactions of individual inhibitor-receptors due to their physicochemical properties; therefore, the library of artificial dipeptide receptors (ADP) was designed and examined. The resulting library response to individual inhibitors was arranged in the array, preprocessed and transformed by the principal component analysis (PCA) and PLS procedures. A dominant absolute contribution to PC1 of the Glu attached to heptanoic gating acid and Phe bonded to the linker m-phenylenediamine/triazine scaffold was revealed by the PCA. The IVE-PLS procedure indicated the receptor systems with predominant Pro bonded to the linker and Glu, Gln, Cys and Val directly attached to the gating acid. The proposed comprehensive ligand-based and simplified structure-based methodology allows the in-depth study of the performance of peptide receptors against the tested set of compounds.NC

Fully Flexible Binding of Taxane-Site Ligands to Tubulin via Enhanced Sampling MD Simulations

Microtubules (MTs) are cytoskeleton components involved in a plenty of cellular functions such as transport, motility, and mitosis. Being polymers made up of α/β-tubulin heterodimers, in order to accomplish these functions, they go through large variations in their spatial arrangement switching between polymerization and depolymerization phases. Because of their role in cellular division, interfering with MTs dynamic behavior has been proven to be suitable for anticancer therapy as tubulin-binding agents induce mitotic arrest and cell death by apoptosis. However, how microtubule-stabilizing agents like taxane-site ligands act to promote microtubule assembly and stabilization is still argument of debate. As in the case of tubulin, traditional docking techniques lack the necessary capabilities of treating protein flexibility that are central to certain binding processes. For this reason, the aim of this project is to put in place a protocol for dynamic docking of taxane-site ligands to β-tubulin by means of enhanced sampling MD simulation techniques. Firstly, the behavior of the binding pocket has been investigated with classical MD simulations. It has been observed that the most flexible part of the taxane site is the so-called “M-loop”, the one involved into the lateral associations of tubulin heterodimers and that is supposed to be stabilized by taxane-site ligands. Secondly, the protocol for the dynamic docking has been put in place using the MD-Binding technique developed by BiKi Technologies. It showed to be effective in reproducing the binding mode of epothilone A and discodermolide as in their X-ray crystal structures. Finally, the protocol has been tested against paclitaxel, a drug for which no X-ray crystal structure is currently available. These results showed the potential of such an approach and strengthen the belief that in the future dynamic docking will replace traditional static docking in the drug discovery and development process

Proline-based carbamates as cholinesterase inhibitors

Series of twenty-five benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates was prepared and completely characterized. All the compounds were tested for their in vitro ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the selectivity of compounds to individual cholinesterases was determined. Screening of the cytotoxicity of all the compounds was performed using a human monocytic leukaemia THP-1 cell line, and the compounds demonstrated insignificant toxicity. All the compounds showed rather moderate inhibitory effect against AChE; benzyl (2S)-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (IC50 = 46.35 M) was the most potent agent. On the other hand, benzyl (2S)-2-[(4-bromophenyl)-] and benzyl (2S)-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylates expressed anti-BChE activity (IC50 = 28.21 and 27.38 M, respectively) comparable with that of rivastigmine. The ortho-brominated compound as well as benzyl (2S)-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate demonstrated greater selectivity to BChE. The in silico characterization of the structure–inhibitory potency for the set of proline-based carbamates considering electronic, steric and lipophilic properties was provided using comparative molecular surface analysis (CoMSA) and principal component analysis (PCA). Moreover, the systematic space inspection with splitting data into the training/test subset was performed to monitor the statistical estimators performance in the effort to map the probability-guided pharmacophore pattern. The comprehensive screening of the AChE/BChE profile revealed potentially relevant structural and physicochemical features that might be essential for mapping of the carbamates inhibition efficiency indicating qualitative variations exerted on the reaction site by the substituent in the 30-/40-position of the phenyl ring. In addition, the investigation was completed by a molecular docking study of recombinant human AChE

Targeting the Poly (ADP-Ribose) Polymerase-1 Catalytic Pocket Using AutoGrow4, a Genetic Algorithm for De Novo Design

AutoGrow4 is a free and open-source program for de novo drug design that uses a genetic algorithm (GA) to create novel predicted small-molecule ligands for a given protein target without the constraints of a finite, pre-defined virtual library. By leveraging recent computational and cheminformatic advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. Features such as docking-software compatibility, chemical filters, multithreading options, and selection methods have been expanded to support a wide range of user needs. This dissertation will cover the development and validation of AutoGrow4, as well as its application to poly (ADP-ribose) polymerase-1 (PARP-1). PARP-1 is a well-characterized DNA-damage recognition protein, and PARP-1 inhibition is an effective treatment for ovarian and breast cancers that are homologous-recombination (HR) deficient1–5. As a well-studied protein, PARP-1 is also an excellent drug target with which to validate AutoGrow4. Multiple crystallographic structures of PARP-1 bound to various PARP-1 inhibitors (PARPi) serve as positive controls for assessing the quality of AutoGrow4-generated compounds in terms of predicted binding affinity, chemical structure, and predicted protein-ligand interactions. This dissertation describes how I (1) generated novel potential PARPi with predicted binding affinities that surpass those of known PARPi; (2) validated AutoGrow4 as a tool for de novo drug design, lead optimization, and hypothesis generation, using PARP-1 as a test target; (3) contributed support to the growing notion that there is a need for HR-deficient cancer chemotherapies that do not rely on the same set of protein-ligand interactions typical of current PARPi; (4) generated novel potential PARPi that are predicted to bind to PARP-1 independent of a post-translational modification that is known to cause PARPi resistance; and (5) generated novel potential PARPi that are predicted to bind a secondary PARP-1 pocket that is distant from the primary catalytic site