Novel structural features of CDK inhibition revealed by an ab initio computational method combined with dynamic simulations

Cyclin-Dependent Kinase 2 Inhibitors in Cancer Therapy: An Update

Cyclin-dependent kinase 2 (CDK2) drives the progression of cells into the S- and M-phases of the cell cycle. CDK2 activity is largely dispensable for normal development, but it is critically associated with tumor growth in multiple cancer types. Although the role of CDK2 in tumorigenesis has been controversial, emerging evidence proposes that selective CDK2 inhibition may provide a therapeutic benefit against certain tumors, and it continues to appeal as a strategy to exploit in anticancer drug development. Several small-molecule CDK2 inhibitors have progressed to the clinical trials. However, a CDK2-selective inhibitor is yet to be discovered. Here, we discuss the latest understandings of the role of CDK2 in normal and cancer cells, review the core pharmacophores used to target CDK2, and outline strategies for the rational design of CDK2 inhibitors. We attempt to provide an outlook on how CDK2-selective inhibitors may open new avenues for cancer therapy.

GPU Accelerated Quantum Virtual Screening: Application for the Natural Inhibitors of New Dehli Metalloprotein (NDM-1)

Quantum mechanical approaches for the massive computation on large biological system such as virtual screening in drug design and development have presented a challenge to computational chemists for many years. In this study, we demonstrated that by taking advantage of rapid growth of GPU-based hardware and software (i.e., teraChem), it is feasible to perform virtual screening of a refined chemical library at quantum mechanical level in order to identify the lead compounds with improved accuracy, especially for the drug targets such as metalloproteins in which significant charge transfer and polarization occur amongst the metal ions and their coordinated amino acids. Our calculations predicted four nature compounds (i.e., Curcumin, Catechin, menthol, and Ferulic acid) as the suitable inhibitors for antibiotics resistance against New Delhi Metallo-β-lactamase-1 (NDM-1). Molecular orbitals (MOs) of the QM region of metal ions and their coordinated residues indicate that the bridged hydroxide ion delocalized the electron over the Zn-OH-Zn group at HOMO, different from MOs when the OH^- is not presented in NDM-1. This indicates that the bridged hydroxide ion plays an important role in the design of antibiotics and other inhibitors targeting the metalloproteins.

Simulated and experimental force spectroscopy of lysozyme on silica

The force spectra of proteins detaching from oxide surfaces measured by atomic force microscopy (AFM) often present complex patterns of peaks, which are difficult to correlate with individual bond-breaking events at the atomic scale. In this work we rationalize experimental AFM force spectra of hen-egg-white lysozyme detaching from silica by means of all-atom steered molecular dynamics (SMD) simulations. In particular, we demonstrate that the native tertiary structure of lysozyme is preserved if, and only if, its four intramolecular disulfide bridges are intact. Otherwise, the protein pulled off the surface undergoes severe unfolding, which is well captured by SMD simulations in explicit solvent. Implicit solvent simulations, on the contrary, wrongly predict protein unfolding even in the presence of S-S bridges, due to the lack of additional structural stabilization provided by the water's hydrogen-bond network within and surrounding the protein. On the basis of our combined experimental and theoretical findings, we infer that the rugged force spectra characteristic of lysozyme/silica interfaces are not due to the successive breaking of internal disulfide bonds leading to partial unfolding events. Rather, they reflect the detachment of several molecules bound to the same AFM tip, each anchored to the surface via multiple hydrogen and ionic bonds.

Recent advances on CDK inhibitors: An insight by means of in silico methods

The cyclin dependent kinases (CDKs) are a small family of serine/threonine protein kinases that can act as a potential therapeutic target in several proliferative diseases, including cancer. This short review is a survey on the more recent research progresses in the field achieved by using in silico methods. All the "armamentarium" available to the medicinal chemists (docking protocols and molecular dynamics, fragment-based, de novo design, virtual screening, and QSAR) has been employed to the discovery of new, potent, and selective inhibitors of cyclin dependent kinases. The results cited herein can be useful to understand the nature of the inhibitor-target interactions, and furnish an insight on the structural/molecular requirements necessary to achieve the required selectivity against cyclin dependent kinases over other types of kinases.

Inhibitor Selectivity for Cyclin-Dependent Kinase 7: A Structural, Thermodynamic, and Modelling Study

Deregulation of the cell cycle by mechanisms that lead to elevated activities of cyclin-dependent kinases (CDK) is a feature of many human diseases, cancer in particular. We identified small-molecule inhibitors that selectively inhibit CDK7, the kinase that phosphorylates cell-cycle CDKs to promote their activities. To investigate the selectivity of these inhibitors we used a combination of structural, biophysical, and modelling approaches. We determined the crystal structures of the CDK7-selective compounds ICEC0942 and ICEC0943 bound to CDK2, and used these to build models of inhibitor binding to CDK7. Molecular dynamics (MD) simulations of inhibitors bound to CDK2 and CDK7 generated possible models of inhibitor binding. To experimentally validate these models, we gathered isothermal titration calorimetry (ITC) binding data for recombinant wild-type and binding site mutants of CDK7 and CDK2. We identified specific residues of CDK7, notably Asp155, that are involved in determining inhibitor selectivity. Our MD simulations also show that the flexibility of the G-rich and activation loops of CDK7 is likely an important determinant of inhibitor specificity similar to CDK2.

Effect of electrostatic polarization and bridging water on CDK2–ligand binding affinities calculated using a highly efficient interaction entropy method

A new highly efficient interaction entropy (IE) method combined with the polarized protein-specific charge (PPC) force field is employed to investigate the interaction mechanism of CDK2–ligand binding and the effect of the bridging water.

Applications of large-scale density functional theory in biology

Density functional theory (DFT) has become a routine tool for the computation of electronic structure in the physics, materials and chemistry fields. Yet the application of traditional DFT to problems in the biological sciences is hindered, to a large extent, by the unfavourable scaling of the computational effort with system size. Here, we review some of the major software and functionality advances that enable insightful electronic structure calculations to be performed on systems comprising many thousands of atoms. We describe some of the early applications of large-scale DFT to the computation of the electronic properties and structure of biomolecules, as well as to paradigmatic problems in enzymology, metalloproteins, photosynthesis and computer-aided drug design. With this review, we hope to demonstrate that first principles modelling of biological structure-function relationships are approaching a reality.

Insight into the interactions between novel isoquinolin-1,3-dione derivatives and cyclin-dependent kinase 4 combining QSAR and molecular docking

Several small-molecule CDK inhibitors have been identified, but none have been approved for clinical use in the past few years. A new series of 4-[(3-hydroxybenzylamino)-methylene]-4H-isoquinoline-1,3-diones were reported as highly potent and selective CDK4 inhibitors. In order to find more potent CDK4 inhibitors, the interactions between these novel isoquinoline-1,3-diones and cyclin-dependent kinase 4 was explored via in silico methodologies such as 3D-QSAR and docking on eighty-one compounds displaying potent selective activities against cyclin-dependent kinase 4. Internal and external cross-validation techniques were investigated as well as region focusing, bootstraping and leave-group-out. A training set of 66 compounds gave the satisfactory CoMFA model (q² = 0.695, r² = 0.947) and CoMSIA model (q² = 0.641, r² = 0.933). The remaining 15 compounds as a test set also gave good external predictive abilities with r²_pred values of 0.875 and 0.769 for CoMFA and CoMSIA, respectively. The 3D-QSAR models generated here predicted that all five parameters are important for activity toward CDK4. Surflex-dock results, coincident with CoMFA/CoMSIA contour maps, gave the path for binding mode exploration between the inhibitors and CDK4 protein. Based on the QSAR and docking models, twenty new potent molecules have been designed and predicted better than the most active compound 12 in the literatures. The QSAR, docking and interactions analysis expand the structure-activity relationships of constrained isoquinoline-1,3-diones and contribute towards the development of more active CDK4 subtype-selective inhibitors.

QM and QM/MM simulations of proteins

Molecular dynamics simulations of biomolecules have matured into powerful tools of structural biology. In addition to the commonly used empirical force field potentials, quantum mechanical descriptions are gaining popularity for structure optimization and dynamic simulations of peptides and proteins. In this chapter, we introduce methodological developments such as the QM/MM framework and linear-scaling QM that make efficient calculations on large biomolecules possible. We identify the most common scenarios in which quantum descriptions of peptides and proteins are employed, such as structural refinement, force field development, treatment of unusual residues, and predicting spectroscopic and exited state properties. The benefits and shortcomings of QM potentials, in comparison to classical force fields, are discussed, with special emphasis on the sampling problems of protein conformational space. Finally, recent examples of QM/MM calculations in light-sensitive membrane proteins illustrate typical applications of the reviewed methods.

RANK-ORDERING THE BINDING AFFINITY FOR FKBP12 AND H1N1 NEURAMINIDASE INHIBITORS IN THE COMBINATION OF A PROTEIN MODEL WITH DENSITY FUNCTIONAL THEORY

The quantum mechanical interaction energies between FKBP12 as well as H1N1 neuraminidase and their inhibitors were directly calculated with an efficient density functional theory by mimicking the whole protein with a protein model composed of the amino acids surrounding the ligands. It was found that the calculated quantum mechanical interaction energies correlate well with the experimental binding free energies with the correlation coefficients of 0.88, 0.86, and the standard deviation of 0.93 and 1.00 kcal/mol, respectively. To compare with force field approach, the binding free energies with the correlation coefficient R = 0.80 and 0.47 were estimated by AutoDock 4.0 programs. It was indicated that the quantum interaction energy shows a better performance in rank-ordering the binding affinity between FKBP12 and H1N1 neuraminidase inhibitors than those of AutoDock 4.0 program. In combination protein model with density functional theory, the estimated quantum interaction energy could be a good predictor or scoring function in structure-based computer-aided drug design. Finally, five new FKBP12 inhibitors were designed based on calculated quantum mechanical interaction energy. In particular, the theoretical K i value of one compound is as low as 0.05 nM, nearly 8-fold more active than FK506.

SUPPLEMENTING THE PBSA APPROACH WITH QUANTUM MECHANICS TO STUDY THE BINDING BETWEEN CDK2 AND N2-SUBSTITUTED O6-CYCLOHEXYLMETHOXYGUANINE INHIBITORS

Because classical Poisson–Boltzman Surface Area (PBSA) model does not allow re-polarization of charges and does not account for charge transfer when a ligand binds to a protein, we have examined a hybrid approach in which we describe the protein–ligand interface by quantum mechanics and the rest of the system with the classical PBSA model. We found this approach to rank order the binding of five N2 -substituted O6 -cyclohexylmethoxyguanine inhibitors to CDK2 (cyclin-dependent kinase 2) properly. The calculated binding free energy correlated well with experimental Log(IC50) with a correlation coefficient of 0.94. A regression fit between experimental Log(IC50) and calculated binding free energy yielded a root-mean-square error of 0.48 when Log(IC50) spanned a range over three units. In addition, we observed charge transfer between the ligand and the protein at the interface — an effect not accounted for by the classical PBSA model. We also found that the direct interactions between the protein and the ligands provided the dominant factor to distinguish the binding affinity of the five ligands studied here. This hybrid approach can better prioritize derivatives of lead compounds for synthesis and biological evaluation.

Physics-based scoring of protein–ligand interactions: explicit polarizability, quantum mechanics and free energies

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein–ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein–ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein–ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein–ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries

Inhibition of Eimeria tenella CDK-related kinase 2: From target identification to lead compounds

Apicomplexan parasites encompass several human- and animal-pathogenic protozoans such as Plasmodium falciparum, Toxoplasma gondii, and Eimeria tenella. E. tenella causes coccidiosis, a disease that afflicts chickens, leading to tremendous economic losses to the global poultry industry. The considerable increase in drug resistance makes it necessary to develop new therapeutic strategies against this parasite. Cyclin-dependent kinases (CDKs) are key molecules in cell-cycle regulation and are therefore prominent target proteins in parasitic diseases. Bioinformatics analysis revealed four potential CDK-like proteins, of which one-E. tenella CDK-related kinase 2 (EtCRK2)-has already been characterized by gene cloning and expression.1 By using the CDK-specific inhibitor flavopiridol in EtCRK2 enzyme assays and schizont maturation assays (SMA), we could chemically validate CDK-like proteins as potential drug targets. An X-ray crystal structure of human CDK2 (HsCDK2) served as a template to build protein models of EtCRK2 by comparative homology modeling. Structural differences in the ATP binding site between EtCRK2 and HsCDK2, as well as chicken CDK3, were addressed for the optimization of selective ATP-competitive inhibitors. Virtual screening and "wet-bench" high-throughput screening campaigns on large compound libraries resulted in an initial set of hit compounds. These compounds were further analyzed and characterized, leading to a set of four promising lead compounds for development as EtCRK2 inhibitors.

Computational study of the interactions between guanine derivatives and cyclin-dependent kinase 2 (CDK2) by CoMFA and QM/MM

Comparative molecular field analysis (CoMFA) and QM/MM hybrid calculations were performed on 9H-purine derivatives as CDK2 inhibitors. CoMFA was carried out to describe the activities of 78 analogues. The models were applied to a training set including 64 compounds. The best CoMFA model included steric and electrostatic fields, had a good Q(2) value of 0.845, and adequately predicted the compounds contained in the test set. Furthermore, plots of the steric CoMFA field allowed conclusions to be drawn for the choice of suitable inhibitors. In addition, the dynamical behavior of compounds with 4-(aminosulfonyl)phenyl, 4-[(methylamino)sulfonyl]phenyl, 4-[(dimethylamino)sulfonyl]phenyl, and [3-methoxy-4-(aminosulfonyl)]phenyl groups at position 2 of the 9H-purine scaffold inside the CDK2 active site were analyzed by QM/MM calculations. The interactions of these compounds with residues Lys89, Asp86, and Ile10 were characterized.

Calculations for millions of atoms with density functional theory: linear scaling shows its potential

An overview of the CONQUEST linear scaling density functional theory (DFT) code is given, focusing particularly on the scaling behaviour on modern high-performance computing platforms. We demonstrate that essentially perfect linear scaling and weak parallel scaling (with fixed number of atoms per processor core) can be achieved, and that DFT calculations on millions of atoms are now possible.

QSAR models for predicting the similarity in binding profiles for pairs of protein kinases and the variation of models between experimental data sets

We propose a direct QSAR methodology to predict how similar the inhibitor-binding profiles of two protein kinases are likely to be, based on the properties of the residues surrounding the ATP-binding site. We produce a random forest model for each of five data sets (one in-house, four from the literature) where multiple compounds are tested on many kinases. Each model is self-consistent by cross-validation, and all models point to only a few residues in the active site controlling the binding profiles. While all models include the "gatekeeper" as one of the important residues, consistent with previous literature, some models suggest other residues as being more important. We apply each model to predict the similarity in binding profile to all pairs in a set of 411 kinases from the human genome and get very different predictions from each model. This turns out not to be an issue with model-building but with the fact that the experimental data sets disagree about which kinases are similar to which others. It is possible to build a model combining all the data from the five data sets that is reasonably self-consistent but not surprisingly, given the disagreement between data sets, less self-consistent than the individual models.

Delayed administration of a potent cyclin dependent kinase and glycogen synthase kinase 3 beta inhibitor produces long-term neuroprotection in a hypoxia-ischemia model of brain injury

We compared the neuroprotective efficacy of a potent and CNS-penetrant cyclin dependent kinase (CDK) and glycogen synthase kinase 3 beta (GSK3beta) inhibitor (Compound 1) in juvenile (postnatal day 21; P21) and adult C57Bl/6 mice (postnatal day 60; P60) using a model of hypoxic-ischemic brain injury (HI). Neuronal cell counts and density measures from brain sections stained with Cresyl Violet revealed that exposure of P21 mice to 60 min of HI resulted in extensive damage to the ipsilateral cornu ammonis 1 (CA1) region of the hippocampus (40% cell loss) and striatum (30% cell loss) 7 days later. Exposure of P60 mice to 40 min of HI produced a similar pattern of cell loss. Intraperitoneal administration of Compound 1 (3 mg/kg) 1, 5 and 9 h after 60 min of HI did not reduce brain injury in P21 mice relative to vehicle controls. By contrast, in P60 mice, this treatment significantly decreased cell loss in the ipsilateral hippocampus (10% cell loss) and striatum (15% loss) relative to vehicle controls. Terminal uridine deoxynucleotidyl transferase (TUNNEL) positive cell counts and infarct volume were also substantially reduced in P60 mice treated with Compound 1. A motor coordination test performed twice weekly until 5 weeks post-HI confirmed that Compound 1 produced long lasting functional recovery. Our results indicate that Compound 1 produced long lasting neuroprotective effects in adult but not juvenile mice suggesting that inhibition of the CDKs and GSK3beta plays a distinct neuroprotective role in the juvenile and adult brain.

Recent progress in linear-scaling density functional calculations with plane waves and pseudopotentials: the ONETEP code

The ONETEP program employs the single-particle density matrix reformulation of Kohn-Sham density functional theory to achieve computational cost and memory requirements which increase only linearly with the number of atoms. As the code employs a plane wave basis set (in the form of periodic sinc functions) and pseudopotentials it is able to achieve levels of accuracy and systematic improvability comparable to those of conventional cubic-scaling plane wave approaches. The code has been developed with the aim of running efficiently on a variety of parallel architectures ranging from commodity clusters with tens of processors to large national facilities with thousands of processors. Recent and ongoing studies which we are performing with ONETEP involve problems ranging from materials to biomolecules to nanostructures.

In silico three-dimensional pharmacophores for aiding the discovery of the Pfmrk (Plasmodium cyclin-dependent protein kinases) specific inhibitors for the therapeutic treatment of malaria

The resurgence of malaria and lack of effective antimalarial drugs affect millions of people worldwide every year, causing several million deaths. With the emergence of structure-based drug design methodologies, a major thrust in drug discovery efforts has shifted towards targeting specific proteins in parasites that are involved in their metabolic pathways. Although cyclin-dependent kinases (CDKs), due to their direct role in cell cycle regulations, have been targeted for the development of cancer therapeutics, CDKs for Plasmodium falciparum have only been recently identified to be attractive for the discovery of antimalarials. One of the plasmodium CDK targets is Pfmrk. Being a putative homolog of Cdk7 and, thus, having the possibility of dual functions, both in cell cycle control and gene expression within the parasite, pfrmk has become an interesting antimalarial chemotherapeutic target. This review discusses how in silico methodologies, without the knowledge of the X-ray crystallographic structure of Pfmrk, particularly based on the development of pharmacophores on known inhibitors can aid the discovery and design of Pfmrk-specific inhibitors through virtual screening of compound databases and provides insights into the understanding of the mechanism of binding in the active site of this enzyme.