A fluorescence polarization based screening assay for identification of small molecule inhibitors of the PICK1 PDZ domain

PDZ (PSD-95/Discs-large/ZO-1 homology) domains represent putative targets in several diseases including cancer, stroke, addiction and neuropathic pain. Here we describe the application of a simple and fast screening assay based on fluorescence polarization (FP) to identify inhibitors of the PDZ domain in PICK1 (protein interacting with C kinase 1). We screened 43,380 compounds for their ability to inhibit binding of an Oregon Green labeled C-terminal dopamine transporter peptide (OrG-DAT C13) to purified PICK1 in solution. The assay was highly reliable with excellent screening assay parameters (Z'≈0.7 and Z≈0.6). Out of ~200 compounds that reduced FP to less than 80% of the control wells, six compounds were further characterized. The apparent affinities of the compounds were determined in FP competition binding experiments and ranged from ~5.0 μM to ~193 μM. Binding to the PICK1 PDZ domain was confirmed for five of the compounds (CSC-03, CSC-04, CSC-43, FSC-231 and FSC-240) in a non-fluorescence based assay by their ability to inhibit pull-down of PICK1 by a C-terminal DAT GST fusion protein. CSC-03 displayed the highest apparent affinity (5.0 μM) in the FP assay, and was according to fluorescence resonance energy transfer (FRET) experiments capable of inhibiting the interaction between the C-terminus of the GluR2 subunit of the AMPA-type glutamate receptor and PICK1 in live cells. Additional experiments suggested that CSC-03 most likely is an irreversible inhibitor but with specificity for PICK1 since it did not bind three different PDZ domains of PSD-95. Summarized, our data suggest that FP based screening assays might be a widely applicable tool in the search for small molecule inhibitors of PDZ domain interactions.     

A flexible docking procedure for the exploration of peptide binding selectivity to known structures and homology models of PDZ domains

PDZ domains are important scaffolding modules that typically bind to the C-termini of their interaction partners. Several structures of such complexes have been solved, revealing a conserved binding site in the PDZ domain and an extended conformation of the bound peptide. A compendium of information regarding PDZ complexes demonstrates that dissimilar C-terminal peptides bind to the same PDZ domain, and different PDZ domains can bind the same peptides. A detailed understanding of the PDZ-peptide recognition is needed to elucidate this complexity. To this end, we have designed a family of docking protocols for PDZ domains (termed PDZ-DocScheme) that is based on simulated annealing molecular dynamics and rotamer optimization, and is applicable to the docking of long peptides (20-40 rotatable bonds) to both known PDZ structures and to the more complicated problem of homology models of these domains. The resulting protocol reproduces the structures of PDZ complexes with peptides 4-8 amino acids long within 1-2 A from the experimental structure when the docking is performed to the original structure. If the structure of the target PDZ domain is an apo structure or a homology model, the docking protocol yields structures within 3 A in 9 out of 12 test cases. The automated docking procedure PDZ-DocScheme can serve in the generation of a structural context for validation of PDZ domain specificity from mutagenesis and ligand binding data.     

Virtual Screening and Molecular Dynamics Simulation Study of Influenza Polymerase PB2 Inhibitors

Influenza A virus is the main cause of worldwide epidemics and annual influenza outbreaks in humans. In this study, a virtual screen was performed to identify compounds that interact with the PB2 cap-binding domain (CBD) of influenza A polymerase. A virtual screening workflow based on Glide docking was used to screen an internal database containing 8417 molecules, and then the output compounds were selected based on solubility, absorbance, and structural fingerprints. Of the 16 compounds selected for biological evaluation, six compounds were identified that rescued cells from H1N1 virus-mediated death at non-cytotoxic concentrations, with EC50 values ranging from 2.5–55.43 μM, and that could bind to the PB2 CBD of H1N1, with Kd values ranging from 0.081–1.53 μM. Molecular dynamics (MD) simulations of the docking complexes of our active compounds revealed that each compound had its own binding characteristics that differed from those of VX-787. Our active compounds have novel structures and unique binding modes with PB2 proteins, and are suitable to serve as lead compounds for the development of PB2 inhibitors. An analysis of the MD simulation also helped us to identify the dominant amino acid residues that play a key role in binding the ligand to PB2, suggesting that we should focus on increasing and enhancing the interaction between inhibitors and these major amino acids during lead compound optimization to obtain more active PB2 inhibitors.     

In silico chemical library screening and experimental validation of a novel 9-aminoacridine based lead-inhibitor of human S-adenosylmethionine decarboxylase

In silico chemical library screening (virtual screening) was used to identify a novel lead compound capable of inhibiting S-adenosylmethionine decarboxylase (AdoMetDC). AdoMetDC is intimately involved in the biosynthesis of polyamines, which are essential for tumor progression and are elevated in numerous types of tumors. Therefore, inhibition of this enzyme provides an attractive target for the discovery of novel anticancer drugs. We performed virtual screening using a computer model derived from the X-ray crystal structure of human AdoMetDC and the National Cancer Institute's Diversity Set (1990 compounds). Our docking study suggested several compounds that could serve as drug candidates since their docking modes and scores revealed potential inhibitory activity toward AdoMetDC. Experimental testing of the top-scoring compounds indicated that one of these compounds (NSC 354961) possesses an IC50 in the low micromolar range. A search of the entire NCI compound collection for compounds similar to NSC 354961 yielded two additional compounds that exhibited activity in the experimental assay but with significantly diminished potency relative to NSC 354961. In this report, we disclose the activity of NSC 354961 against AdoMetDC and its probable binding mode based on computational modeling. We also discuss the importance of virtual screening in the context of enzymes that are not readily amenable to high-throughput assays, thereby demonstrating the efficacy of virtual screening, combined with selective experimental testing, in identifying new potential drug candidates.     

High Throughput and High Speed Organic Synthesis in Drug Discovery: An Integration of Chemistry and Technology

Drug discovery is a complicated process that involves multiple synthetic chemistry tasks. Among them, lead generation and optimization is the core business in the discovery research. During the stage of lead generation, a large library of many thousands individual compounds will be screened against a biological target to identify a set of hits that showed desirable activity. Once a hit has been identified, analog synthesis and development of SAR around this hit and establishment of relationsh…     

An improved method for the synthesis of cellulose membrane-bound peptides with free C termini is useful for PDZ domain binding studies

SPOT synthesis permits parallel synthesis and screening of thousands of cellulose membrane-bound peptides to study protein-protein interactions in a proteomic context. Recognition of C-terminal residues is one of the most common binding features of PDZ domains. Unfortunately, most solid support-bound peptide libraries lack a free C terminus due to C-terminal fixation on the solid support. To overcome this restriction, we developed a robust methodology based on our previous strategy for generating peptides with authentic C termini. To validate this improved method, we screened a human peptide library of 6223 C termini with the syntrophin PDZ domain. Furthermore, using the same library, new peptide ligands derived from membrane proteins and receptors were found for the ERBIN PDZ domain. Finally, we identified the protein kinase breakpoint cluster region, which is known as a negative regulator of cell proliferation and oncogenic transformation, as an ERBIN ligand.     

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.     

Can we use docking and scoring for hit-to-lead optimization?

Docking and scoring is currently one of the tools used for hit finding and hit-to-lead optimization when structural information about the target is known. Docking scores have been found useful for optimizing ligand binding to reproduce experimentally observed binding modes. The question is, can docking and scoring be used reliably for hit-to-lead optimization? To illustrate the challenges of scoring for hit-to-lead optimization, the relationship of docking scores with experimentally determined IC₅₀ values measured in-house were tested. The influences of the particular target, crystal structure, and the precision of the scoring function on the ability to differentiate between actives and inactives were analyzed by calculating the area under the curve of receiver operator characteristic curves for docking scores. It was found that for the test sets considered, MW and sometimes ClogP were as useful as GlideScores and no significant difference was observed between SP and XP scores for differentiating between actives and inactives. Interpretation by an expert is still required to successfully utilize docking and scoring in hit-to-lead optimization.     

Tracing Binding Modes in Hit‐to‐Lead Optimization: Chameleon‐Like Poses of Aspartic Protease Inhibitors

Successful lead optimization in structure‐based drug discovery depends on the correct deduction and interpretation of the underlying structure–activity relationships (SAR) to facilitate efficient decision‐making on the next candidates to be synthesized. Consequently, the question arises, how frequently a binding mode (re)‐validation is required, to ensure not to be misled by invalid assumptions on the binding geometry. We present an example in which minor chemical modifications within one inhibitor series lead to surprisingly different binding modes. X‐ray structure determination of eight inhibitors derived from one core scaffold resulted in four different binding modes in the aspartic protease endothiapepsin, a well‐established surrogate for e.g. renin and β‐secretase. In addition, we suggest an empirical metrics that might serve as an indicator during lead optimization to qualify compounds as candidates for structural revalidation.     

Structure activity relationships of quinoxalin-2-one derivatives as platelet-derived growth factor-beta receptor (PDGFbeta R) inhibitors, derived from molecular modeling

We previously reported a quinoxalin-2-one compound (Compound 1) that had inhibitory activity equivalent to existing platelet-derived growth factor-beta receptor (PDGFbeta R) inhibitors. Lead optimization of Compound 1 to increase its activity and selectivity, using structural information regarding PDGFbeta R-ligand interactions, is urgently needed. Here we present models of the PDGFbeta R kinase domain complexed with quinoxalin-2-one derivatives. The models were constructed using comparative modeling, molecular dynamics (MD) and ligand docking. In particular, conformations derived from MD, and ligand binding site information presented by alpha-spheres in the pre-docking processing, allowed us to identify optimal protein structures for docking of target ligands. By carrying out molecular modeling and MD of PDGFbeta R in its inactive state, we obtained two structural models having good Compound 1 binding potentials. In order to distinguish the optimal candidate, we evaluated the structural activity relationships (SAR) between the ligand-binding free energies and inhibitory activity values (IC50 values) for available quinoxalin-2-one derivatives. Consequently, a final model with a high SAR was identified. This model included a molecular interaction between the hydrophobic pocket behind the ATP binding site and the substitution region of the quinoxalin-2-one derivatives. These findings should prove useful in lead optimization of quinoxalin-2-one derivatives as PDGFb R inhibitors.     

Predicting binding energies of CDK6 inhibitors in the hit-to-lead process

The main challenge for the ??hit-to-lead?? stage in the drug discovery process relies on the accuracy of existing docking methods. In fact, accuracy of docking methods depends not only on the scoring function used to rank the poses but also on the ability of the docking method to reproduce the experimental binding mode. At this purpose, the performance of different approximations to properly dock and score compounds with known activity in a narrow range of IC₅₀ values was analyzed. A set of five ATP-competitive CDK6 inhibitors and three receptor conformations for CDK6 were considered for analysis, and three methodologies were used and analyzed in order to include different degrees of receptor flexibility. Thus, a completely rigid receptor is considered when using Glide, while the so-called Induced Fit Docking Protocol accounts for receptor sidechain rearrangements. Finally, force field calculations were also performed in order to consider a completely flexible receptor.     

Discovering potent inhibitors against c-Met kinase: molecular design, organic synthesis and bioassay

The receptor tyrosine kinase c-Met is an attractive target for therapeutic treatment of cancers nowadays. The discovery of small molecule inhibitors is of special interest in the blockade of the c-Met kinase pathway. Here, we initiated our study from compound 1a, a novel inhibitor against c-Met kinase. A substructure similarity search against the SPECS database and chemical synthesis methods were performed to obtain a series of pyrazolidine-3,5-dione derivatives. Through the enzyme-based assay against c-Met kinase, 4 compounds (1c, 1e, 1m and 1o) showed potential inhibitory activity, with IC(50) values mostly less than 10 μM. Based on the structure-activity relationship (SAR) and binding mode analysis, a focused combinatorial library was designed by the LD1.0 program. Taking into account ADMET properties and synthesis accessibility, seven candidate compounds (5a-g) were successfully synthesized. The activity of the most potent compounds 5b (IC(50) = 0.46 μM) was 20 fold higher than that of the lead 1a. Taken together, our findings identified the pyrazolidine-3,5-dione derivatives as potent inhibitors against c-Met kinase and demonstrated the efficiency of the strategy in the development of small molecules against c-Met kinase.     

Identification of Potential HCV Inhibitors Based on the Interaction of Epigallocatechin-3-Gallate with Viral Envelope Proteins

Hepatitis C is affecting millions of people around the globe annually, which leads to death in very high numbers. After many years of research, hepatitis C virus (HCV) remains a serious threat to the human population and needs proper management. The in silico approach in the drug discovery process is an efficient method in identifying inhibitors for various diseases. In our study, the interaction between Epigallocatechin-3-gallate, a component of green tea, and envelope glycoprotein E2 of HCV is evaluated. Epigallocatechin-3-gallate is the most promising polyphenol approved through cell culture analysis that can inhibit the entry of HCV. Therefore, various in silico techniques have been employed to find out other potential inhibitors that can behave as EGCG. Thus, the homology modelling of E2 protein was performed. The potential lead molecules were predicted using ligand-based as well as structure-based virtual screening methods. The compounds obtained were then screened through PyRx. The drugs obtained were ranked based on their binding affinities. Furthermore, the docking of the topmost drugs was performed by AutoDock Vina, while its 2D interactions were plotted in LigPlot+. The lead compound mms02387687 (2-[[5-[(4-ethylphenoxy) methyl]-4-prop-2-enyl-1,2,4-triazol-3-yl] sulfanyl]-N-[3(trifluoromethyl) phenyl] acetamide) was ranked on top, and we believe it can serve as a drug against HCV in the future, owing to experimental validation.     

Synthesis and Anticoagulant Bioactivity Evaluation of 1, 2, 5-Trisubstituted Benzimidazole Fluorinated Derivatives

Some 1,2,5-trisubstituted benzimidazole fluorinated derivatives were designed and screened by molecular docking. Five compounds which obtained high scores were selected to synthesize. All the target products were char-acterized by ¹H NMR, ¹³C NMR and high resolution mass spectra(HRMS) and preliminarily screened for inhibitory activity against thrombin, among which three compounds(5a, 5c and 5e) were evaluated in vitro. The results showed that compounds 5a, 5c and 5e exhibited better anticoagulant activity than argatroban. Docking simulations demonstrated that these compounds may act as candidates for further studies on thrombin inhibitors.     

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of ‘make-on-demand’ chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.     

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.     

Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain–peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with Rosetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several Rosetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain–peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain–peptide interactions.     

A multilayer screening approach toward the discovery of novel Pf-DHFR inhibitors

A small yet diverse xanthone library was build and computationally docked against wild type Pf-DHFR by Molegro Virtual Docker (MolDock). For analysis of results an integrated approach based on re-ranking, scaling (based on heavy atom counts), pose clustering and visual inspection was implemented. Standard methods such as self-docking (for docking), EF analysis, average rank determinations (for size normalization), and cluster quality indices (for pose clustering) were used for validation of results. Three compounds X5, X113A and X164B displayed contact footprints similar to the known inhibitors with good scores. Finally, 16 compounds were extracted from ZINC data base by similarity based screening, docking score and drug/lead likeness. Out of these 16 compounds, 11 displayed very close contact footprints to experimentally known inhibitors, indicating there potential utility in further drug discovery efforts.     

In silico model for P-glycoprotein substrate prediction: insights from molecular dynamics and in vitro studies

P-glycoprotein (P-gp) is a plasma membrane efflux transporter belonging to ATP-binding cassette superfamily, responsible for multidrug resistance in tumor cells. Over-expression of P-gp in cancer cells limits the efficacy of many anticancer drugs. A clear understanding of P-gp substrate binding will be advantageous in early drug discovery process. However, substrate poly-specificity of P-gp is a limiting factor in rational drug design. In this investigation, we report a dynamic trans-membrane model of P-gp that accurately identified the substrate binding residues of known anticancer agents. The study included homology modeling of human P-gp based on the crystal structure of C. elegans P-gp, molecular docking, molecular dynamics analyses and binding free energy calculations. The model was further utilized to speculate substrate propensity of in-house anticancer compounds. The model demonstrated promising results with one anticancer compound (NSC745689). As per our observations, the molecule could be a potential lead for anticancer agents devoid of P-gp mediated multiple drug resistance. The in silico results were further validated experimentally using Caco-2 cell lines studies, where NSC745689 exhibited poor permeability (P _app 1.03 ± 0.16 × 10^?6 cm/s) and low efflux ratio of 0.26.