Baker-Polito Administration to Embark on Israeli Economic Development Mission

Abstract Background In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines. Results First, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model. Conclusions Our results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced

Exploring the active mechanism of berberine against HCC by systematic pharmacology and experimental validation

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Berberine (BBR) is the main component of Coptidis rhizoma, the dried rhizome of Coptis chinensis and is a potential plant alkaloid used for the treatment of cancer due to its high antitumor activity. The present study examined the therapeutic potential and molecular mechanism of action of BBR against HCC, using systematic pharmacology combined with a molecular docking approach and experimental validation in vitro. Through systematic pharmacological analysis, it was found that BBR serves a significant role in inhibiting HCC by affecting multiple pathways, especially the PI3K/AKT signaling pathway. Furthermore, the docking approach indicated that the binding of BBR to AKT could lead to the suppression of AKT activity. The present study examined the inhibitory effect of BBR on the PI3K/AKT pathway in HCC and identified that BBR downregulated the expressions of phosphorylated AKT and PI3K in MHCC97‑H and HepG2 cells, inhibiting their growth, cell migration and invasion in a dose‑dependent manner. In addition, inhibition of the AKT pathway by BBR also contributed to cell apoptosis in MHCC97‑H and HepG2 cells. Taken together, the results of the present study suggested that BBR may be a promising antitumor drug for HCC that acts by inhibiting the PI3K/AKT pathway

Cheminformatics Approaches to Structure Based Virtual Screening: Methodology Development and Applications

Structure-based virtual screening (VS) using 3D structures of protein targets has become a popular in silico drug discovery approach. The success of VS relies on the quality of underlying scoring functions. Despite of the success of structure-based VS in several reported cases, target-dependent VS performance and poor binding affinity predictions are well-known drawbacks in structure-based scoring functions. The goal of my dissertation is to use cheminformatics approaches to address above problems of the existing structure-based scoring methods. In Aim 1, cheminformatics practices are applied to those problems which conventional structure-based scoring functions find difficult (anti-bacterial leads efflux study) or fail to address (AmpC β-lactamase study). Predictive binary classification QSAR models can be constructed to classify complex efflux properties (low vs. high) and to differentiate AmpC β-lactamase binders from binding decoys (i.e., the false positives generated by scoring functions). The above models are applied to virtual screening and many computational hits are experimentally confirmed. In Aim 2, novel statistical binding and pose scoring functions (or pose filter in Aim 3) are developed, to accurately predict protein-ligand binding affinity and to discriminate native-like poses of ligands from pose decoys respectively. In my approach, the proteinligand interface is represented at the atomic level resolution and transformed via a special computational geometry approach called Delaunay tessellation to a collection of atom quadruplet motifs. And individual atom members of the motifs are characterized by conceptual Density Functional Theory (DFT)-based atomic properties. The binding scoring function shows acceptable prediction accuracy towards Community Structure-Activity Resources (CSAR) data sets with diverse protein families. In Aim 3, a two-step scoring protocol for target-specific virtual screening is developed and validated using the challenging Directory of Useful Decoys (DUD) data sets. In the first step our target-specific pose (-scoring) filter developed in Aim 2 is used to filter out/penalize putative pose decoys for every compound. Then in the second step the remaining putative native-like poses are scored with MedusaScore, which is a conventional force-field-based scoring function. This novel screening protocol can consistently improve MedusaScore VS performance, suggesting it possible applications to practical pharmaceutically relevant targets

Prioritizing Small Molecules for Drug Discovery or Chemical Safety Assessments using Ligand- and Structure-based Cheminformatics Approaches

Recent growth in the experimental data describing the effects of chemicals at the molecular, cellular, and organism level has triggered the development of novel computational approaches for the prediction of a chemical's effect on an organism. The studies described in this dissertation research predict chemical activity at three levels of biological complexity: binding of drugs to a single protein target, selective binding to a family of protein targets, and systemic toxicity. Optimizing cheminformatics methods that examine diverse sources of experimental data can lead to novel insight into the therapeutic use and toxicity of chemicals. In the first study, a combinatorial Quantitative Structure-Activity Relationship (QSAR) modeling workflow was successfully applied to the discovery of novel bioactive compound against one specific protein target: histone deacetylase inhibitors (HDACIs). Four candidate molecules were selected from the virtual screening hits to be tested experimentally, and three of them were confirmed active against HDAC. Next, a receptor-based protocol was established and applied to discover target-selective ligands within a family of proteins. This protocol extended the concept of protein/ligand interaction-guided pose selection by employing a binary classifier to discriminate poses of interest from a calibration set. The resulting virtual screening tools were applied for enriching beta2-adrenergic receptor (β2AR) ligands that are selective against other subtypes in the βAR family (i.e. β1AR and β3AR). Moreover, some computational 3D protein structures used in this study have exhibited comparative or even better performance in virtual screening than X-ray crystal structures of β2AR, and therefore computational tools that use these computational structures could complement tools utilizing experimental structures. Finally, a two-step hierarchical QSAR modeling approach was developed to estimate in vivo toxicity effects of small molecules. Besides the chemical structural descriptors, the developed models utilized additional biological information from in vitro bioassays. The derived models were more accurate than traditional QSAR models utilizing chemical descriptors only. Moreover, retrospective analysis of the developed models helped to identify the most informative bioassays, suggesting potential applicability of this methodology in guiding future toxicity experiments. These studies contribute to the development of computational strategies for comprehensive analysis of small molecules' biological properties, and have the potential to be integrated into existing methods for modern rational drug design and discovery.Doctor of Philosoph

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

Development and optimisation of computational tools for drug discovery

The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds.The aim of my PhD project was the development, optimisation, and implementation of new in silico virtual screening protocols. Specifically, this thesis manuscript is divided into three main parts, presenting some of the papers published during my doctoral work. The first one, here named CHEMOMETRIC PROTOCOLS IN DRUG DISCOVERY, is about the optimisation and application of an in house developed chemometric protocol. This part has been entirely developed at the University of Palermo - STEBICEF Department - under the guide of my supervisors. During the development of this part I have personally worked on the tuning and optimisation of the algorithm and on the docking campaigns to obtain molecule conformaitons. The second part, THE APPLICATION OF MOLECULAR DYNAMICS TO VIRTUAL SCREENING, presents a new approach to virtual screening, in particular the attention is focused on different approaches to the application of protein flexibility and dynamics to virtual screening. This part, has been carried out in cooperation with the University of Vienna - Department of Pharmaceutical Chemistry. For these works I have worked in the development of the general workflow, to a lesser extent to the programming (coding) part of the applications used and I mainly focused on the realisation of the screening campaigns and results interpretation. The third and last part, COMPUTATIONAL CHEMISTRY IN POLY-PHARMACOLOGY AND DRUG REPURPOSING, concerns the study of the in silico methods applied to two main topics of the drug discovery process, such as the drug repurposing and the polypharmacology. In this part I will briefly describe what published in two reviews dealing to the above mentioned topics. In conclusion during this doctoral project, I have demonstrated how the use of in silico tools can be useful in the drug discovery process. The Chemometric protocols developed and optimised represent in fact a helpful strategy to use for target fishing. Whereas, the application of molecular dynamics to virtual screening, especially for pharmacophore modelling, is a new way to deepen crucial features to be adopted in the search of new putative active compounds

IN SILICO APPROACHES IN DRUG DESIGN AND DEVELOPMENT: APPLICATIONS TO RATIONAL LIGAND DESIGN AND METABOLISM PREDICTION

In the last decades, the applications of computational methods in medicinal chemistry have experienced significant changes which have incredibly expanded their approaches, and more importantly their objectives. The overall aim of the present research project is to explore the different fields of the modelling studies by using well-known computational methods as well as different and innovative techniques. Indeed, computational methods traditionally consisted in ligand-based and the structure-based approaches substantially aimed at optimizing the ligand structure in terms of affinity, potency and selectivity. The studies concerning the muscarinic receptors in the present thesis applied these approaches for the rational design of novel improved bioactive molecules, interacting both in the orthosteric (e.g., 1,4-dioxane agonist) and in the allosteric sites. The research includes also the application of a novel method for target optimization, which consists in the generation of the so-called conformational chimeras to explore the flexibility of the modelled GPCR structures. In parallel, computational methods are finding successful applications in the research phase which precedes the ligand design and which is focused on a detailed validation and characterization of the biological target. A proper example of this kind of studies is given by the study regarding the purinergic receptors, which is aimed at the identification and characterization of potential allosteric binding pockets for the already reported inhibitors, exploiting also innovative approaches for binding site predictions (e.g., PELE, SPILLO-PBSS). Over time, computational applications felt a rich extension of their objectives and one of the clearest examples is represented by the ever increasing attempts to optimize the ADME/Tox profile of the novel compounds, so reducing the marked attrition in drug discovery caused by unsuitable pharmacokinetic profiles. Coherently, the first and main project of the present thesis regards the field of metabolism prediction and is founded on the meta-analysis and the corresponding database called MetaSar, manually collected from the recent specialized literature. This ongoing extended project includes different studies which are overall aimed at developing a comprehensive method for metabolism prediction. In detail, this Thesis reports an interesting application of the database which exploits an innovative predictive technique, the Proteochemometric modelling (PCM). This approach is indeed at the forefront of the latest modelling techniques, as it perfectly fits the growing request of new solutions to deal with the incredibly huge amount of data recently produced by the \u201comics\u201d disciplines. In this context, MetaSar represents an alternative and still appropriate source of data for PCM studies, which also enables the extension of its fields of application to a new avenue, such as the prediction of metabolism biotransformation. In the present thesis, we present the first example of these applications, which involves the building of a classification model for the prediction of the glucuronidation reaction. The field of glucuronidation reactions is exhaustively explored also through an homology modelling study aimed at defining the complete three-dimensional structure of the enzyme UGT2B7, the main isoform of glucuronidation enzymes in humans, in complex with the cofactor UDPGA and a typical substrate, such as Naproxen. The paths of the substrate entering to the binding site and the egress of the product have been investigated by performing Steered Molecular Dynamics (SMD) simulations, which were also useful to gain deeper insights regarding the full mechanism of action and the movements of the cofactor