Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects

Watermelon: setup and validation of an in silico fragment-based approach

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

pH-sensitive niosomes for ATRA delivery: A promising approach to inhibit Pin1 in high-grade serous ovarian cancer

The peptidyl-prolyl cis/trans isomerase Pin1 positively regulates numerous cancer-driving pathways, and it is overexpressed in several malignancies, including high-grade serous ovarian cancer (HGSOC). The findings that all-trans retinoic acid (ATRA) induces Pin1 degradation strongly support that ATRA treatment might be a promising approach for HGSOC targeted therapy. Nevertheless, repurposing ATRA into the clinics for the treatment of solid tumors remains an unmet need mainly due to the insurgence of resistance and its ineffective delivery. In the present study, niosomes have been employed for improving ATRA delivery in HGSOC cell lines. Characterization of niosomes including hydrodynamic diameter, ζ-potential, morphology, entrapment efficiency and stability over time and in culture media was performed. Furthermore, pH-sensitiveness and ATRA release profile were investigated to demonstrate the capability of these vesicles to release ATRA in a stimuli-responsive manner. Obtained results documented a nanometric and monodispersed samples with negative ζ-potential. ATRA was efficiently entrapped, and a substantial release was observed in the presence of acidic pH (pH 5.5). Finally, unloaded niosomes showed good biocompatibility while ATRA-loaded niosomes significantly increased ATRA Pin1 inhibitory activity, which was consistent with cell growth inhibition. Taken together, ATRA-loaded niosomes might represent an appealing therapeutic strategy for HGSOC therapy.

Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors

Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.

New PIN1 inhibitors identified through a pharmacophore-driven, hierarchical consensus docking strategy

PIN1 is considered as a therapeutic target for a wide variety of tumours. However, most of known inhibitors are devoid of cellular activity despite their good enzyme inhibitory profile. Hence, the lack of effective compounds for the clinic makes the identification of novel PIN1 inhibitors a hot topic in the medicinal chemistry field. In this work, we reported a virtual screening study for the identification of new promising PIN1 inhibitors. A receptor-based procedure was applied to screen different chemical databases of commercial compounds. Based on the whole workflow, two compounds were selected and biologically evaluated. Both ligands, compounds VS1 and VS2, showed a good enzyme inhibitory activity and VS2 also demonstrated a promising antitumoral activity in ovarian cancer cells. These results confirmed the reliability of our in silico protocol and provided a structurally novel ligand as a valuable starting point for the development of new PIN1 inhibitors.

Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking

Molecular docking is one of the most widely used computational tools in structure-based drug design and is critically dependent on accuracy and robustness of the scoring function. In this work, we introduce a new scoring function Lin_F9, which is a linear combination of nine empirical terms, including a unified metal bond term to specifically describe metal-ligand interactions. Parameters in Lin_F9 are obtained with a multistage fitting protocol using explicit water-included structures. For the CASF-2016 benchmark test set, Lin_F9 achieves the top scoring power among all 34 classical scoring functions for both original crystal poses and locally optimized poses with Pearson correlation coefficients (R) of 0.680 and 0.687, respectively. Meanwhile, in comparison with Vina, Lin_F9 achieves consistently better scoring power and ranking power with various types of protein-ligand complex structures that mimic real docking applications, including end-to-end flexible docking for the CASF-2016 benchmark test set using a single or an ensemble of protein receptor structures, as well as for D3R Grand Challenge (GC4) test sets. Lin_F9 has been implemented in a fork of Smina as an optional built-in scoring function that can be used for docking applications as well as for further improvement of scoring functions and docking protocols. Lin_F9 is accessible through https://yzhang.hpc.nyu.edu/Lin_F9/.

Ubiquitination Regulators Discovered by Virtual Screening for the Treatment of Cancer

The ubiquitin-proteasome system oversees cellular protein degradation in order to regulate various critical processes, such as cell cycle control and DNA repair. Ubiquitination can serve as a marker for mutation, chemical damage, transcriptional or translational errors, and heat-induced denaturation. However, aberrant ubiquitination and degradation of tumor suppressor proteins may result in the growth and metastasis of cancer. Hence, targeting the ubiquitination cascade reaction has become a potential strategy for treating malignant diseases. Meanwhile, computer-aided methods have become widely accepted as fast and efficient techniques for early stage drug discovery. This review summarizes ubiquitination regulators that have been discovered via virtual screening and their applications for cancer treatment.

Improving structure-based virtual screening performance via learning from scoring function components

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

Discovery of a new ATP-citrate lyase (ACLY) inhibitor identified by a pharmacophore-based virtual screening study

ATP citrate lyase (ACLY) is an important enzyme that catalyzes the conversion of citrate to acetyl-CoA in normal cells, facilitating the de novo fatty acid synthesis. Lipids and fatty acids were found to be accumulated in different types of tumors, such as brain, breast, rectal and ovarian cancer, representing a great source of energy for cancer cell growth and metabolism. Since ACLY-mediated conversion of citrate to acetyl-CoA constitutes the basis for fatty acid synthesis, ACLY seems to be quite an unexplored and promising therapeutic target for anticancer drug design. A pharmacophore-based virtual screening (VS) protocol with the aid of hierarchical docking, consensus docking (CD), molecular dynamics (MD) simulations and ligand-protein binding free energy calculations led to the identification of compound VS1, which showed a moderate but promising inhibitory activity, demonstrating to be 2.5 times more potent than reference inhibitor 2-hydroxycitrate. These results validate the reliability of our VS workflow and pave the way for the design of novel and more potent ACLY inhibitors.Communicated by Ramaswamy H. Sarma.

Application of MM-PBSA Methods in Virtual Screening

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

Strategies for Delivery of siRNAs to Ovarian Cancer Cells

The unmet need for novel therapeutic options for ovarian cancer (OC) deserves further investigation. Among the different novel drugs, small interfering RNAs (siRNAs) are particularly attractive because of their specificity of action and efficacy, as documented in many experimental setups. However, the fragility of these molecules in the biological environment necessitates the use of delivery materials able to protect them and possibly target them to the cancer cells. Among the different delivery materials, those based on polymers and lipids are considered very interesting because of their biocompatibility and ability to carry/deliver siRNAs. Despite these features, polymers and lipids need to be engineered to optimize their delivery properties for OC. In this review, we concentrated on the description of the therapeutic potential of siRNAs and polymer-/lipid-based delivery systems for OC. After a brief description of OC and siRNA features, we summarized the strategies employed to minimize siRNA delivery problems, the targeting strategies to OC, and the preclinical models available. Finally, we discussed the most interesting works published in the last three years about polymer-/lipid-based materials for siRNA delivery.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Structure-Based Design of Novel Benzimidazole Derivatives as Pin1 Inhibitors

Peptidyl-prolyl cis/trans isomerase Pin1 plays a key role in amplifying and translating multiple oncogenic signaling pathways during oncogenesis. The blockade of Pin1 provided a unique way of disrupting multiple oncogenic pathways and inducing apoptosis. Aiming to develop potent Pin1 inhibitors, a series of benzimidazole derivatives were designed and synthesized. Among the derivatives, compounds 6h and 13g showed the most potent Pin1 inhibitory activity with IC₅₀ values of 0.64 and 0.37 μM, respectively. In vitro antiproliferative assay demonstrated that compounds 6d, 6g, 6h, 6n, 6o and 7c exhibited moderate antiproliferative activity against human prostate cancer PC-3 cells. Taken together, these unique benzimidazole derivatives exhibited great potential to be further explored as potent Pin1 inhibitors with improved potency.

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.