Virtual screening for potential inhibitors of Mcl-1 conformations sampled by normal modes, molecular dynamics, and nuclear magnetic resonance

Polyoxometalates bind multiple targets involved in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by brain aggregates of amyloid-β (Aβ) plaques and Tau tangles. Despite extensive research, effective therapy for AD remains elusive. Polyoxometalates (POMs), a class of inorganic compounds with diverse chemical structures and properties, are emerging as potential candidates for AD treatment due to their ability to target key molecular players implicated in disease pathogenesis, such as Aβ, acetylcholinesterase (AChE) and butyryl acetylcholinesterase (BChE). Here, we use molecular docking to predict the binding pose and affinities of POMs to 10 top targets associated with AD. First, we validate our method by replicating experimentally known binding of POMs to Aβ (ΔG = - 9.67 kcal/mol), AChE (ΔG = - 9.39 kcal/mol) and BChE (ΔG = - 10.86 kcal/mol). Then, using this method, we show that POM can also bind β-secretase 1 (BACE1, ΔG = - 10.14 kcal/mol), presenilin 1 (PSEN1, ΔG = - 10.65 kcal/mol), presenilin 2 (PSEN2, ΔG = - 7.94 kcal/mol), Amyloid Precursor Protein (APP, ΔG = - 7.26 kcal/mol), Apolipoprotein E (APOE4, ΔG = - 10.05 kcal/mol), Microtubule-Associated Protein Tau (MAPT, ΔG = - 5.28 kcal/mol) depending on phosphorylation, and α-synuclein (SNCA, ΔG = - 7.64 kcal/mol). Through such binding, POMs offer the potential to mitigate APP cleavage, Aβ oligomer neurotoxicity, Aβ aggregation, thereby attenuating disease progression. Overall, our molecular docking study represents a powerful tool in the discovery of POM-based therapeutics for AD, facilitating the development of novel treatments for AD.

Improving drug discovery through parallelism

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

Exploring actinomycetes natural products to identify potential multi-target inhibitors against Leishmania donovani

Visceral leishmaniasis (VL) is a neglected tropical disease that mainly affects the poor population of the Indian, African, and South American subcontinent. The increasing resistance to antimonial and miltefosine and frequent toxicity of amphotericin B drives an urgent need to develop an anti-leishmanial drug with excellent efficacy and safety profile. In this study, three sequential docking protocols (HTVS, SP, and XP) were performed to screen the secondary metabolites (n = 6519) from the actinomycetes source against five key proteins involved in the metabolic pathway of Leishmania donovani. Those proteins were adenine phosphoribosyltransferase (PDB ID: 1QB7), trypanothione reductase (PDB ID: 2JK6), N-myristoyl transferase (PDB ID: 2WUU), pteridine reductase (PDB ID: 2XOX), and MAP kinase (PDB ID: 4QNY). Although the binding energy of top ligands was predicted using the MM-GBSA module of the Schrödinger suite. SP and XP docking mode resulted in 55 multi-targeted ligands against L donovani. MM-GBSA analysis selected the top 18 ligands with good-binding affinity and the binding-free energy for four proteins, as mentioned earlier, when compared with the miltefosine, paromomycin, and a reference ligand selected for each target. Finally, molecular dynamics simulation, post-MD-binding-free energy (MM-PBSA), and principal component analysis (PCA) proposed three best ligands (Adenosine pentaphosphate, Atetra P, and GDP-4-keto-6-deoxymannose) qualifying the above screening parameters and confirmed as a potential drug candidate to fight against Leishmania donovani parasites.

Virtual Screening for FDA-Approved Drugs That Selectively Inhibit Arginase Type 1 and 2

Arginases are often overexpressed in human diseases, and they are an important target for developing anti-aging and antineoplastic drugs. Arginase type 1 (ARG1) is a cytosolic enzyme, and arginase type 2 (ARG2) is a mitochondrial one. In this study, a dataset containing 2115-FDA-approved drug molecules is virtually screened for potential arginase binding using molecular docking against several ARG1 and ARG2 structures. The potential arginase ligands are classified into three categories: (1) Non-selective, (2) ARG1 selective, and (3) ARG2 selective. The evaluated potential arginase ligands are then compared with their clinical use. Remarkably, half of the top 30 potential drugs are used clinically to lower blood pressure and treat cancer, infection, kidney disease, and Parkinson’s disease thus partially validating our virtual screen. Most notable are the antihypertensive drugs candesartan, irbesartan, indapamide, and amiloride, the antiemetic rolapitant, the anti-angina ivabradine, and the antidiabetic metformin which have minimal side effects. The partial validation also favors the idea that the other half of the top 30 potential drugs could be used in therapeutic settings. The three categories greatly expand the selectivity of arginase inhibition.

Identification of bicyclic compounds that act as dual inhibitors of Bcl-2 and Mcl-1

Elevated expression of anti-apoptotic proteins, such as Bcl-2 and Mcl-1 contributes to poor prognosis and resistance to current treatment modalities in multiple cancers. Here, we report the design, synthesis and characterization of benzimidazole chalcone and flavonoid scaffold-derived bicyclic compounds targeting both Bcl-2 and Mcl-1 by optimizing the structural differences in the binding sites of both these proteins. Initial docking screen of Bcl-2 and Mcl-1 with pro-apoptotic protein Bim revealed possible hits with optimal binding energies. All the optimized bicyclic compounds were screened for their in vitro cytotoxic activity against two oral cancer cell lines (AW8507 and AW13516) which express high levels of Bcl-2 and Mcl-1. Compound 4d from the benzimidazole chalcone series and compound 6d from the flavonoid series exhibited significant cytotoxic activity (IC₅₀ 7.12 μM and 17.18 μM, respectively) against AW13516 cell line. Time Resolved-Fluorescence Resonance Energy Transfer (TR-FRET) analysis further demonstrated that compound 4d and compound 6d could effectively inhibit the Bcl-2 and Mcl-1 proteins by displacing their BH3 binding partners. Both compounds exhibited potent activation of canonical pathway of apoptosis evident from appearance of cleaved Caspase-3 and PARP. Further, treatment of oral cancer cells with the inhibitors induced dissociation of the BH3 only protein Bim from Mcl-1 and Bak from Bcl-2 but failed to release Bax from Bcl-xL thereby confirming the nature of compounds as BH3-mimetics selectively targeting Bcl-2 and Mcl-1. Our study thus identifies bicyclic compounds as promising candidates for anti-apoptotic Bcl-2/Mcl-1 dual inhibitors with a potential for further development.

Drug Repurposing: Deferasirox Inhibits the Anti-Apoptotic Activity of Mcl-1

Introduction

With the aim of repositioning commercially available drugs for the inhibition of the anti-apoptotic myeloid cell leukemia protein, Mcl-1, implied in various cancers, five molecules, highlighted from a published theoretical screening, were selected to experimentally validate their affinity toward Mcl-1.

Results

A detailed NMR study revealed that only two of the five tested drugs, Torsemide and Deferasirox, interacted with Mcl-1. NMR data analysis allowed the complete characterization of the binding mode of both drugs to Mcl-1, including the estimation of their affinity for Mcl-1. Biological assays evidenced that the biological activity of Torsemide was lower as compared to the Deferasirox, which was able to efficiently and selectively inhibit the anti-apoptotic activity of Mcl-1. Finally, docking and molecular dynamics led to a 3D model for the Deferasirox:Mcl-1 complex and revealed the positioning of the drug in the Mcl-1 P2/P3 pockets as well as almost all synthetic Mcl-1 inhibitors. Interestingly, contrary to known synthetic Mcl-1 inhibitors which interact through Arg263, Deferasirox, establishes a salt bridge with Lys234.

Conclusion

Deferasirox could be a potential candidate for drug repositioning as Mcl-1 inhibitor.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Discovery of small molecule inhibitors through pharmacophore modeling, molecular docking, molecular dynamics simulation and experimental validation against myeloid cell leukemia-1 (Mcl-1)

Myeloid cell leukemia-1 (Mcl-1) protein is a family of Bcl-2 (B cell lymphoma 2) rich proteases of the most common increase threshold for genetic aberrations observed in human cancer, including lung, breast, pancreatic, cervical, and ovarian cancers as well as leukemia and lymphoma. Mcl-1 is recognized as an attractive drug target in number of diseases, including cancer. In the present study we surveyed and collected queries compounds from PDB database of Mcl-1 protein and generated pharmacophore-based models adapted to screen the drug-like compounds from FDA approved database. The 206 best lead molecules from pharmacophore-screening were further evaluated by molecular docking, molecular dynamics simulation, MM-GBSA calculation, as well as experimental validation. Two hits, ZINC00601272 and ZINC00002166, showed the best docking scores, which showed a tendency to inhibit cell viability of HL60 and K562 leukemia cells with Mcl-1 expressions. Conclusively, the present study provides structural information of Mcl-1 inhibitors for next generations of cancer therapeutics through computational and experimental validation approach.Communicated by Ramaswamy H. Sarma.