Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks

Cellular senescence is a barrier to tumorigenesis in normal cells and tumour cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. 147 virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase (SA-β-gal) assays. Among the found hits a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced SA-β-gal activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1 and CDC25C. Additionally, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long term treatments. Preliminary structure-activity and structure clustering analyses are reported and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells

Computational Studies of Difference in Binding Modes of Peptide and Non-Peptide Inhibitors to MDM2/MDMX Based on Molecular Dynamics Simulations

Inhibition of p53-MDM2/MDMX interaction is considered to be a promising strategy for anticancer drug design to activate wild-type p53 in tumors. We carry out molecular dynamics (MD) simulations to study the binding mechanisms of peptide and non-peptide inhibitors to MDM2/MDMX. The rank of binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) method agrees with one of the experimental values. The results suggest that van der Waals energy drives two kinds of inhibitors to MDM2/MDMX. We also find that the peptide inhibitors can produce more interaction contacts with MDM2/MDMX than the non-peptide inhibitors. Binding mode predictions based on the inhibitor-residue interactions show that the π–π, CH–π and CH–CH interactions dominated by shape complimentarity, govern the binding of the inhibitors in the hydrophobic cleft of MDM2/MDMX. Our studies confirm the residue Tyr99 in MDMX can generate a steric clash with the inhibitors due to energy and structure. This finding may theoretically provide help to develop potent dual-specific or MDMX inhibitors

Predicting and Testing Helix-Mimetic Inhibitors of the p53-Mdm2 Interaction

Aberrant protein-protein interactions (PPIs) are found in many disease states. Consequently, there is a need for PPI inhibitors for use as research tools and pharmaceutical lead compounds. Computational methods could greatly assist with the search for new PPIs. Oligobenzamides are novel PPI inhibitors which can theoretically be produced to display any sequence of side chains. Understanding the nature of oligobenzamide binding is important for identification of the most efficient strategy of predicting oligobenzamide inhibitors. The prediction of oligobenzamide affinities using thermodynamic integration and implicit solvent methods is described. Affinities of oligobenzamides for Mdm2 predicted using implicit solvent methods bore a moderate correlation with measured affinities. Examination of MM-PBSA results using analysis of variance revealed that it is not necessary to run simulations with every member of a large combinatorial library in order to predict their relative affinities because within a particular binding site, the degree of interaction between the side chains is small. However, it could be useful to separate molecules based on their predicted binding pose because oligobenzamides can bind to Mdm2 in many different ways, depending on the choice of side chains. This insight will be valuable for future attempts to predict oligobenzamide affinities. The 1H-15N HSQC NMR spectrum peaks of 15N-labelled Mdm2 L33E were assigned to facilitate the future validation of binding poses. An oligoamide was shown using NMR to bind in the correct place. However, NMR testing revealed that oligobenzamides can aggregate in aqueous solution despite being soluble. A novel FRET-based method was also developed which can be used to test potential inhibitors with a low solubility and high absorbance during their development. It was adapted for a microwell plate to facilitate future high throughput screening and an assay involving Cherry-labelled Mdm2 was tested which could be developed into an in vivo assay in the future

Computational studies of drug-binding kinetics

The drug-receptor binding kinetics are defined by the rate at which a given drug associates with and dissociates from its binding site on its macromolecular receptor. The lead optimization stage of drug discovery programs usually emphasizes optimizing the affinity (as described by the equilibrium dissociation constant, Kd) of a drug which depends on the strength of its binding to a specific target. Since affinity is optimized under equilibrium conditions, it does not always ensures higher potency in vivo. There has been a growing consensus that, in addition to Kd, kinetic parameters (kon and koff ) should be optimized to improve the chances of a good clinical outcome. However, current understanding of the physicochemical features that contribute to differences in binding kinetics is limited. Experimental methods that are used to determine kinetic parameters for drug binding and unbinding are often time consuming and labor-intensive. Therefore, robust, high-throughput in silico methods are needed to predict binding kinetic parameters and to explore the mechanistic determinants of drug-protein binding. As the experimental data on drug-binding kinetics is continuously growing and the number of crystallographic structures of ligand-receptor complexes is also increasing, methods to compute three dimensional (3D) Quantitative-Structure-Kinetics relationships (QSKRs) offer great potential for predicting kinetic rate constants for new compounds. COMparative BINding Energy(COMBINE) analysis is one example of such approach that was developed to derive target-specific scoring functions based on molecular mechanics calculations. It has been used extensively to predict properties such as binding affinity, target selectivity, and substrate specificity. In this thesis, I made the first application of COMBINE analysis to derive Quantitative Structure-Kinetics Relationships (QSKRs) for the dissociation rates. I obtained models for koff of inhibitors of HIV-1 protease and heat shock protein 90 (HSP90) with very good predictive power and identified the key ligand-receptor interactions that contribute to the variance in binding kinetics. With technological and methodological advances, the use of all-atom unbiased Molecular Dynamics (MD) simulations can allow sampling upto the millisecond timescale and investigation of the kinetic profile of drug binding and unbinding to a receptor. However, the residence times of drug-receptor complexes are usually longer than the timescales that are feasible to simulate using conventional molecular dynamics techniques. Enhanced sampling methods can allow faster sampling of protein and ligand dynamics, thereby resulting in application of MD techniques to study longer timescale processes. I have evaluated the application of Tau-Random Acceleration Molecular Dynamics (Tau-RAMD), an enhanced sampling method based on MD, to compute the relative residence times of a series of compounds binding to Haspin kinase. A good correlation (R2 = 0.86) was observed between the computed residence times and the experimental residence times of these compounds. I also performed interaction energy calculations, both at the quantum chemical level and at the molecular mechanics level, to explain the experimental observation that the residence times of kinase inhibitors can be prolonged by introducing halogen-aromatic pi interactions between halogen atoms of inhibitors and aromatic residues at the binding site of kinases. I determined different energetic contributions to this highly polar and directional halogen-bonding interaction by partitioning the total interaction energy calculated at the quantum-chemical level into its constituent energy components. It was observed that the major contribution to this interaction energy comes from the correlation energy which describes second-order intermolecular dispersion interactions and the correlation corrections to the Hartree-Fock energy. In addition, a protocol to determine diffusional kon rates of low molecular weight compounds from Brownian Dynamics (BD) simulations of protein-ligand association was established using SDA 7 software. The widely studied test case of benzamidine binding to trypsin was used to evaluate a set of parameters and a robust set of optimal parameters was determined that should be generally applicable for computing the diffusional association rate constants of a wide range of protein-ligand binding pairs. I validated this protocol on inhibitors of several targets with varying complexity such as Human Coagulation Factor Xa, Haspin kinase and N1 Neuraminidase, and the computed diffusional association rate constants were compared with the experiments. I contributed to the development of a toolbox of computational methods: KBbox (http://kbbox.h-its.org/toolbox/), which provides information about various computational methods to study molecular binding kinetics, and different computational tools that employ them. It was developed to guide researchers on the use of the different computational and simulation approaches available to compute the kinetic parameters of drug-protein binding

VIRTUAL SCREENING AND DISCOVERY OF LEAD COMPOUNDS AS POTENTIAL DNA METHYLTRANSFERASE 1 INHIBITORS AND ANTICANCER AGENTS

Epigenetic changes consist of DNA methylation, histone modification, micro RNA and genome imprinting. DNA methylation of the CpG islands is one of the main methods of epigenetic inactivation of genes and aberrant methylations at promoter regions of tumor suppressor genes can alter gene expression and play an important role in cancer development. DNA methyltransferase I (Dnmt1) is the enzyme responsible for maintaining methylation patterns during cell division and it is overexpressed in many cancers. Thus, Dnmt1 is a promising therapeutic target for development of novel anticancer agents and epigenetic modulators. We have developed two promising class of lead candidates, compounds 5-hydroxy-2-(4-hydroxyphenethyl)-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline-1-carboxamide 47, 2-(2-(1H-indol-3-yl)ethyl)-5-hydroxy-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline- 1-carboxamide 51 and 1-(4-isopropylphenyl)-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole 96, as potential leads compounds that can be optimized for pharmaceutical applications.

Potent VEGFR-2 inhibitors for resistant breast cancer: a comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives

More people are being diagnosed with resistant breast cancer, increasing the urgency of developing new effective treatments. Several lines of evidence suggest that blocking the kinase activity of VEGFR-2 reduces angiogenesis and slows tumor growth. In this study, we developed novel VEGFR-2 inhibitors based on the triazolopyrazine template by using comparative molecular field analysis (CoMFA) and molecular similarity indices (CoMSIA) models for 3D-QSAR analysis of 23 triazolopyrazine-based compounds against breast cancer cell lines (MCF -7). Both CoMFA (Q2 = 0.575; R2 = 0.936, Rpred2 = 0.956) and CoMSIA/SE (Q2 = 0.575; R2 = 0.936, Rpred2 = 0.847) results demonstrate the robustness and stability of the constructed model. Six novel compounds with potent inhibitory activity were carefully designed, and screening of ADMET properties revealed their good oral bioavailability and ability to diffuse through various biological barriers. When compared with the most active molecule in the data set and with Foretinib (breast cancer drug), molecular docking revealed that the six designed compounds had strengthened affinity (−8.9 to −10 kcal/mol) to VEGFR-2. Molecular Dynamics Simulations and MMPBSA calculations were applied to the selected compound T01 with the highest predicted inhibitory activity, confirming its stability in the active pocket of VEGFR-2 over 100 ns. The present results provided the basis for the chemical synthesis of new compounds with improved inhibitory properties against the breast cancer cell line (MCF -7)

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery

A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein–Protein Interaction Interfaces

The binding affinity of a protein–protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein–protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein–protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein–protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein–protein interactions do not optimally mimic protein–ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials

IMPROVING RATIONAL DRUG DESIGN BY INCORPORATING NOVEL BIOPHYSICAL INSIGHT

Computer-aided drug design is a valuable and effective complement to conventional experimental drug discovery methods. In this thesis, we will discuss our contributions to advancing a number of outstanding challenges in computational drug discovery: understanding protein flexibility and dynamics, the role of water in small molecule binding and using and understanding large amounts of data. First, we describe the molecular steps involved in the induced-fit binding mechanism of p53 and MDM2. We use molecular dynamics simulations to understand the key chemistry responsible for the dynamic transition between the apo and holo structures of MDM2. This chemistry involves not only the indole side chain of the anchor residue of p53, Trp23, but surprisingly, the beta-carbon as well. We demonstrate that this chemistry plays a key role in opening the binding site by coordinating the position and orientation of MDM2 residues, Val93 and His96, through a previously undescribed transition state. We confirm these findings by observing that this chemistry is preserved in all available inhibitor-bound MDM2 co-crystal structures. Second, we discuss our advances in understanding water molecules in ligand binding sites by data mining the structural information of water molecules found in X-ray crystal structures. We examine a large set of paired bound and unbound proteins and compare the water molecules found in the binding site of the unbound structure to the functional groups on the ligand that displace them upon binding. We identify a number of generalized functional groups that are associated with characteristic clusters of water molecules. This information has been utilized in several successful and ongoing virtual screens. Third, we discuss software that we have developed that allows for very efficient exploration and selection of virtual screening results. Implemented as a PyMOL plugin, ClusterMols clusters compounds based on a user-defined level of chemical similarity. The software also provides advanced visualization tools and a number of controls for quickly navigating and selecting compounds of interest, as well as the ability to check online for available vendors. Finally, we present several published examples of modeling protein-lipid and protein-small molecules interactions for a number of important targets including ABL, c-Src and 5-LOX