Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy

Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure-Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC₅₀ of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.

Developing an effective polarizable bond method for small molecules with application to optimized molecular docking

Electrostatic interaction plays an essential role in protein-ligand binding. Due to the polarization effect, electrostatic interactions are largely impacted by their local environments. However, traditional force fields use fixed point charge-charge interactions to describe electrostatic interactions but is unable to include the polarization effect. The lack of the polarization effect in the force field representation can result in substantial error in biomolecular studies, such as molecular dynamics and molecular docking. Docking programs usually employ traditional force fields to estimate the binding energy between a ligand and a protein for pose selection or scoring. The intermolecular interaction energy mainly consists of van der Waals and electrostatic interaction in the force field representation. In the current study, we developed an Effective Polarizable Bond (EPB) method for small organic molecules and applied this EPB method to optimize protein-ligand docking in computational tests for a variety of protein-ligand systems. We tested the method on a set of 38 cocrystallized structures taken from the Protein Data Bank (PDB) and found that the maximum error was reduced from 7.98 Å to 2.03 Å when using EPB Dock, providing strong evidence that the use of EPB charges is important. We found that our optimized docking approach with EPB charges could improve the docking performance, sometimes dramatically, and the maximum error was reduced from 12.88 Å to 1.57 Å in Optimized Docking (in the case of 1fqx). The average RMSD decreased from 2.83 Å to 1.85 Å. Further investigations showed that the use of the EBP method could enhance intermolecular hydrogen bonding, which is a major contributing factor to improved docking performance. Developed tools for the calculation of the polarized ligand charge from a protein-ligand complex structure with the EPB method are freely available on GitHub (https://github.com/Xundrug/EPB).

Discovery of potential targets of Triptolide through inverse docking in ovarian cancer cells

Triptolide (TPL) is proposed as an effective anticancer agent known for its anti-proliferation of a variety of cancer cells including ovarian cancer cells. Although some studies have been conducted, the mechanism by which TPL acts on ovarian cancer remains to be clearly described. Herein, systematic work based on bioinformatics was carried out to discover the potential targets of TPL in SKOV-3 cells. TPL induces the early apoptosis of SKOV-3 cells in a dose- and time-dependent manner with an IC₅₀ = 40 ± 0.89 nM when cells are incubated for 48 h. Moreover, 20 nM TPL significantly promotes early apoptosis at a rate of 40.73%. Using a self-designed inverse molecular docking protocol, we fish the top 19 probable targets of TPL from the target library, which was built on 2,250 proteins extracted from the Protein Data Bank. The 2D-DIGE assay reveals that the expression of eight genes is affected by TPL. The results of western blotting and qRT-PCR assay suggest that 40 nM of TPL up-regulates the level of Annexin A5 (6.34 ± 0.07 fold) and ATP syn thase (4.08 ± 0.08 fold) and down-regulates the level of β-Tubulin (0.11 ± 0.12 fold) and HSP90 (0.21 ± 0.09 fold). More details of TPL affecting on Annexin A5 signaling pathway will be discovered in the future. Our results define some potential targets of TPL, with the hope that this agent could be used as therapy for the preclinical treatment of ovarian cancer.

An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs. activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs. Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of K_i from 10² to 10³ nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

Hierarchical virtual screening of the dual MMP-2/HDAC-6 inhibitors from natural products based on pharmacophore models and molecular docking

The dual-target inhibitors tend to improve the response rate in treating tumors, comparing with the single-target inhibitors. Matrix metalloproteinase-2 (MMP-2) and histone deacetylase-6 (HDAC-6) are attractive targets for cancer therapy. In this study, the hierarchical virtual screening of dual MMP-2/HDAC-6 inhibitors from natural products is investigated. The pharmacophore model of MMP-2 inhibitors is built based on ligands, but the pharmacophore model of HDAC-6 inhibitors is built based on the experimental crystal structures of multiple receptor-ligand complexes. The reliability of these two pharmacophore models is validated subsequently. The hierarchical virtual screening, combining these two different pharmacophore models of MMP-2 and HDAC-6 inhibitors with molecular docking, is carried out to identify the dual MMP-2/HDAC-6 inhibitors from a database of natural products. The four potential dual MMP-2/HDAC-6 inhibitors of natural products, STOCK1 N-46177, STOCK1 N-52245, STOCK1 N-55477, and STOCK1 N-69706, are found. The studies of binding modes show that the screened four natural products can simultaneously well bind with the MMP-2 and HDAC-6 active sites by different kinds of interactions, to inhibit the MMP-2 and HDAC-6 activities. In addition, the ADMET properties of screened four natural products are assessed. These found dual MMP-2/HDAC-6 inhibitors of natural products could serve as the lead compounds for designing the new dual MMP-2/HDAC-6 inhibitors having higher biological activities by carrying out structural modifications and optimizations in the future studies.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Virtual screening and biological evaluation of biofilm inhibitors on dual targets in quorum sensing system

AIM

Resistance to conventional antibiotics has spurred interest in exploring new antimicrobial strategies. Suppressing quorum sensing within biofilm is a promising antimicrobial strategy. LasR in quorum sensing system of the Gram-negative bacteria, Pseudomonas aeruginosa, directly enhances virulence and antibiotic resistance, with QscR as its indirect suppressor, so targeting both of them can synergistically take the effect.

METHODOLOGY/RESULTS

An in silico protocol combining pharmacophores with molecular docking was applied. Pharmacophores of QscR agonists and LasR antagonists were prepared for preliminary screening, followed by counter-screen using a pharmacophore model of LasR agonists and molecular docking of LasR. Four compounds with novel scaffolds were confirmed as potential biofilm inhibitors with preliminary experimental data.

CONCLUSION

Novel biofilm inhibitors can be found with the method.

Protein-Ligand Empirical Interaction Components for Virtual Screening

A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products

Matrix metalloproteinase-9 (MMP-9) is an attractive target for cancer therapy. In this study, the pharmacophore model of MMP-9 inhibitors is built based on the experimental binding structures of multiple receptor-ligand complexes. It is found that the pharmacophore model consists of six chemical features, including two hydrogen bond acceptors, one hydrogen bond donor, one ring aromatic regions, and two hydrophobic (HY) features. Among them, the two HY features are especially important because they can enter the S1' pocket of MMP-9 which determines the selectivity of MMP-9 inhibitors. The reliability of pharmacophore model is validated based on the two different decoy sets and relevant experimental data. The virtual screening, combining pharmacophore model with molecular docking, is performed to identify the selective MMP-9 inhibitors from a database of natural products. The four novel MMP-9 inhibitors of natural products, NP-000686, NP-001752, NP-014331, and NP-015905, are found; one of them, NP-000686, is used to perform the experiment of in vitro bioassay inhibiting MMP-9, and the IC₅₀ value was estimated to be only 13.4 µM, showing the strongly inhibitory activity of NP-000686 against MMP-9, which suggests that our screening results should be reliable. The binding modes of screened inhibitors with MMP-9 active sites were discussed. In addition, the ADMET properties and physicochemical properties of screened four compounds were assessed. The found MMP-9 inhibitors of natural products could serve as the lead compounds for designing the new MMP-9 inhibitors by carrying out structural modifications in the future.

Discovery of novel inhibitors disrupting HIF-1α/von Hippel-Lindau interaction through shape-based screening and cascade docking

Major research efforts have been devoted to the discovery and development of new chemical entities that could inhibit the protein–protein interaction between HIF-1α and the von Hippel–Lindau protein (pVHL), which serves as the substrate recognition subunit of an E3 ligase and is regarded as a crucial drug target in cancer, chronic anemia, and ischemia. Currently there is only one class of compounds available to interdict the HIF-1α/pVHL interaction, urging the need to discover chemical inhibitors with more diversified structures. We report here a strategy combining shape-based virtual screening and cascade docking to identify new chemical scaffolds for the designing of novel inhibitors. Based on this strategy, nine active hits have been identified and the most active hit, 9 (ZINC13466751), showed comparable activity to pVHL with an IC50 of 2.0 ± 0.14 µM, showing the great potential of utilizing these compounds for further optimization and serving as drug candidates for the inhibition of HIF-1α/von Hippel–Lindau interaction.

Medicinal Chemistry Strategies to Disrupt the p53-MDM2/MDMX Interaction

The growth inhibitory activity of p53 tumor suppressor is tightly regulated by interaction with two negative regulatory proteins, murine double minute 2 (MDM2) and X (MDMX), which are overexpressed in about half of all human tumors. The elucidation of crystallographic structures of MDM2/MDMX complexes with p53 has been pivotal for the identification of several classes of inhibitors of the p53-MDM2/MDMX interaction. The present review provides in silico strategies and screening approaches used in drug discovery as well as an overview of the most relevant classes of small-molecule inhibitors of the p53-MDM2/MDMX interaction, their progress in pipeline, and highlights particularities of each class of inhibitors. Most of the progress made with high-throughput screening has led to the development of inhibitors belonging to the cis-imidazoline, piperidinone, and spiro-oxindole series. However, novel potent and selective classes of inhibitors of the p53-MDM2 interaction with promising antitumor activity are emerging. Even with the discovery of the 3D structure of complex p53-MDMX, only two small molecules were reported as selective p53-MDMX antagonists, WK298 and SJ-172550. Dual inhibition of the p53-MDM2/MDMX interaction has shown to be an alternative approach since it results in full activation of the p53-dependent pathway. The knowledge of structural requirements crucial to the development of small-molecule inhibitors of the p53-MDMs interactions has enabled the identification of novel antitumor agents with improved in vivo efficacy.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Rational design of protein–protein interaction inhibitors

Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.