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

Pharmacophore Models Derived From Molecular Dynamics Simulations of Protein-Ligand Complexes: A Case Study.

A single, merged pharmacophore hypothesis is derived combining 2000 pharmacophore models obtained during a 20 ns molecular dynamics simulation of a protein-ligand complex with one pharmacophore model derived from the initial PDB structure. This merged pharmacophore model contains all features that are present during the simulation and statistical information about the dynamics of the pharmacophore features. Based on the dynamics of the pharmacophore features we derive two distinctive feature patterns resulting in two different pharmacophore models for the analyzed system – the first model consists of features that are obtained from the PDB structure and the second uses two features that can only be derived from the molecular dynamics simulation. Both models can distinguish between active and decoy molecules in virtual screening. Our approach represents an objective way to add/remove features in pharmacophore models and can be of interest for the investigation of any naturally occurring system that relies on ligand-receptor interactions for its biological activity

A rational design of α-helix-shaped peptides employing the hydrogen-bond surrogate approach: A modulation strategy for Ras-RasGRF1 interaction in neuropsychiatric disorders

In the last two decades, abnormal Ras (rat sarcoma protein)–ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation has been proposed as a potential target mechanism to attenuate ERK signalling in the brain. Previously, we showed that a cell penetrating peptide, RB3, was able to inhibit downstream signalling by preventing RasGRF1 (Ras guanine nucleotide-releasing factor 1), a neuronal specific GDP/GTP exchange factor, to bind Ras proteins, both in brain slices and in vivo, with an IC50 value in the micromolar range. The aim of this work was to mutate and improve this peptide through computer-aided techniques to increase its inhibitory activity against RasGRF1. The designed peptides were built based on the RB3 peptide structure corresponding to the α-helix of RasGRF1 responsible for Ras binding. For this purpose, the hydrogen-bond surrogate (HBS) approach was exploited to maintain the helical conformation of the designed peptides. Finally, residue scanning, MD simulations, and MM-GBSA calculations were used to identify 18 most promising α-helix-shaped peptides that will be assayed to check their potential activity against Ras-RasGRF1 and prevent downstream molecular events implicated in brain disorders

Virtual Screening Strategy and In Vitro Tests to Identify New Inhibitors of the Immunoproteasome

Immunoproteasome inhibition is a promising strategy for the treatment of hematological malignancies, autoimmune diseases, and inflammatory diseases. The design of non-covalent inhibitors of the immunoproteasome beta 1i/beta 5i catalytic subunits could be a novel approach to avoid the drawbacks of the known covalent inhibitors, such as toxicity due to off-target binding. In this work, we report the biological evaluation of thirty-four compounds selected from a commercially available collection. These hit compounds are the outcomes of a virtual screening strategy including a dynamic pharmacophore modeling approach onto the beta 1i subunit and a pharmacophore/docking approach onto the beta 5i subunit. The computational studies were first followed by in vitro enzymatic assays at 100 mu M. Only compounds capable of inhibiting the enzymatic activity by more than 50% were characterized in detail using Tian continuous assays, determining the dissociation constant (K-i) of the non-covalent complex where K-i is also the measure of the binding affinity. Seven out of thirty-four hits showed to inhibit beta 1i and/or beta 5i subunit. Compound 3 is the most active on the beta 1i subunit with K-i = 11.84 +/- 1.63 mu M, and compound 17 showed K-i = 12.50 +/- 0.77 mu M on the beta 5i subunit. Compound 2 showed inhibitory activity on both subunits (K-i = 12.53 +/- 0.18 and K-i = 31.95 +/- 0.81 on the beta 1i subunit and beta 5i subunit, respectively). The induced fit docking analysis revealed interactions with Thr1 and Phe31 of beta 1i subunit and that represent new key residues as reported in our previous work. Onto beta 5i subunit, it interacts with the key residues Thr1, Thr21, and Tyr169. This last hit compound identified represents an interesting starting point for further optimization of beta 1i/beta 5i dual inhibitors of the immunoproteasome

A computer-based methodology to design non-standard peptides potentially able to prevent HOX-PBX1-associated cancer diseases

In the last decades, HOX proteins have been extensively studied due to their pivotal role in transcriptional events. HOX proteins execute their activity by exploiting a cooperative binding to PBX proteins and DNA. Therefore, an increase or decrease in HOX activity has been associated with both solid and haematological cancer diseases. Thus, inhibiting HOX-PBX interaction represents a potential strategy to prevent these malignancies, as demonstrated by the patented peptide HTL001 that is being studied in clinical trials. In this work, a computational study is described to identify novel potential peptides designed by employing a database of non-natural amino acids. For this purpose, residue scanning of the HOX minimal active sequence was performed to select the mutations to be further processed. According to these results, the peptides were point-mutated and used for Molecular Dynamics (MD) simulations in complex with PBX1 protein and DNA to evaluate complex binding stability. MM-GBSA calculations of the resulting MD trajectories were exploited to guide the selection of the most promising mutations that were exploited to generate twelve combinatorial peptides. Finally, the latter peptides in complex with PBX1 protein and DNA were exploited to run MD simulations and the ΔGbinding average values of the complexes were calculated. Thus, the analysis of the results highlighted eleven combinatorial peptides that will be considered for further assays

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

The Repurposing of Old Drugs or Unsuccessful Lead Compounds by in Silico Approaches: New Advances and Perspectives

Have you a compound in your lab, which was not successful against the designed target, or a drug that is no more attractive? The drug repurposing represents the right way to reconsider them. It can be defined as the modern and rationale approach of the traditional methods adopted in drug discovery, based on the knowledge, insight and luck, alias known as serendipity. This repurposing approach can be applied both in silico and in wet. In this review we report the molecular modeling facilities that can be of huge support in the repurposing of drugs and/or unsuccessful lead compounds. In the last decades, different methods were proposed to help the scientists in drug design and in drug repurposing. The steps strongly depend on the approach applied. It could be a ligand or a structure based method, correlated to the use of specific means. These processes, starting from a compound with potential therapeutic properties and a sizeable number of toxicity passed tests, can successfully speed up the very slow development of a molecule from bench to market. Herein, we discuss the facilities available to date, classifying them by methods and types. We have reported a series of databases, ligand and structure stand-alone software, and of web-based tools, which are free accessible to scientific community. This review does not claim to be exhaustive, but can be of interest to help in drug repurposing through in silico methods, as a valuable tool for the medicinal chemistry community

Conf-VLKA: A structure-based revisitation of the Virtual Lock-and-key Approach

In a previous work, we developed the in house Virtual Lock-and-Key Approach (VLKA) in order to evaluate target assignment starting from molecular descriptors calculated on known inhibitors used as an information source. This protocol was able to predict the correct biological target for the whole dataset with a good degree of reliability (80%), and proved experimentally, which was useful for the target fishing of unknown compounds. In this paper, we tried to remodel the previous in house developed VLKA in a more sophisticated one in order to evaluate the influence of 3D conformation of ligands on the accuracy of the prediction. We applied the same previous algorithm of scoring and ranking but, this time, combining it with a structure-based approach as docking. For this reason, we retrieved from the RCSB Protein Data Bank (PDB), the available 3D structures of the biological targets included into the previous work, and we used them to calculate poses of the 7352 dataset compounds in the VLKA biological targets. First, docking protocol has been used to retrieve docking scores, then, from the docked poses of each molecule, 3D-descriptors were calculated (Conf-VLKA), While the use of the simple docking scores proved to be inadequate to improve compounds classification, the Conf-VLKA showed some interesting variations compared to the original VLKA, especially for targets whose ligands present a high number of rotamers. This work represent a first preliminary study to be completed using other techniques such as induced fit docking or molecular dynamics structure clustering to take into account the protein side chains adaptation to ligands structures