We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

Abstract. Background

Aberrant epigenetic modifications are involved in cancer development. Jumonji C domain-containing histone lysine demethylases (KDMs) are found mainly up-regulated in breast, prostate, and colon cancer. Currently, growing interest is focusing on the identification and development of new inhibitors able to block the activity of KDMs and thus reduce tumor progression. KDM4A is known to play a role in several cellular physiological processes, and was recently found overexpressed in a number of pathological states, including cancer. In this work, starting from the structure of purpurogallin 9aa, previously identified as a natural KDM4A inhibitor, we synthesized two main sets of compound derivatives in order to improve their inhibitory activity against KDM4A in vitro and in cells, as well as their antitumor action. Based on the hypothetical biogenesis of the 5-oxo-5H-benzo[7]annulene skeleton of the natural product purpurogallin (Salfeld, 1960; Horner et al., 1961; Dürckheimer and Paulus, 1985; Tanaka et al., 2002; Yanase et al., 2005) the pyrogallol and catechol units were first combined with structural modifications at different positions of the aryl ring using enzyme-mediated oxidative conditions, generating a series of benzotropolone analogs. Two of the synthetic analogs of purpurogallin, 9ac and 9bc, showed an efficient inhibition (50 and 80%) of KDM4A in enzymatic assays and in cells by increasing levels of its specific targets, H3K9me3/2 and H3K36me3. However, these two compounds/derivatives did not induce cell death. We then synthesized a further set of analogs of these two compounds with greater structural diversification. The most potent of these analogs, 9bf, displayed the highest KDM4A inhibitory enzymatic activity in vitro (IC 50 of 10.1 and 24.37 µM) in colon cancer cells, and the strongest antitumor action in several solid and hematological human cancer cell lines with no toxic effect in normal cells. Our findings suggest that further development of this compound and its derivatives may lead to the identification of new therapeutic antitumor agents acting through inhibition of KDM4A.

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

A new series of imidazo[2,1-b][1,3,4]thiadiazole derivatives was efficiently synthesized and screened for their in vitro antiproliferative activity on a panel of pancreatic ductal adenocarcinoma (PDAC) cells, including SUIT-2, Capan-1 and Panc-1. Compounds 9c and 9l, showed relevant in vitro antiproliferative activity on all three pre-clinical models with half maximal inhibitory concentration (IC50) ranging from 5.11 to 10.8 µM, while the compounds 9e and 9n were active in at least one cell line. In addition, compound 9c significantly inhibited the migration rate of SUIT-2 and Capan-1 cells in the scratch wound-healing assay. In conclusion, our results will support further studies to increase the library of imidazo [2,1-b][1,3,4] thiadiazole derivatives for deeper understanding of the relationship between biological activity of the compounds and their structures in the development of new antitumor compounds against pancreatic diseases.

Molecular dynamics (MD) simulations can be used, prior to virtual screening, to add flexibility to proteins and study them in a dynamic way. Furthermore, the use of multiple crystal structures of the same protein containing different co‐crystallized ligands can help elucidate the role of the ligand on a protein′s active conformation, and then explore the most common interactions between small molecules and the receptor. In this work, we evaluated the contribution of the combined use of MD on crystal structures containing the same protein but different ligands to examine the crucial ligand–protein interactions within the complexes. The study was carried out on peroxisome proliferator‐activated receptor α (PPARα). Findings derived from the dynamic analysis of interactions were then used as features for pharmacophore generation and constraints for generating the docking grid for use in virtual screening. We found that information derived from short multiple MD simulations using different molecules within the binding pocket of the target can improve the early enrichment of active ligands in the virtual screening process for this receptor. In the end we adopted a consensus scoring based on docking score and pharmacophore alignment to rank our dataset. Our results showed an improvement in virtual screening performance in early recognition when screening was performed with the Molecular dYnamics SHAred PharmacophorE (MYSHAPE) approach.

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