Molecular modeling and dynamics simulation of human cyclin-dependent kinase 3 complexed with inhibitors

Structural basis of CDK3 activation by cyclin E1 and inhibition by dinaciclib

Cell cycle transitions are controlled by multiple cell cycle regulators, especially CDKs. Several CDKs, including CDK1-4 and CDK6, promote cell cycle progression directly. Among them, CDK3 is critically important because it triggers the transitions of G0 to G1 and G1 to S phase through binding to cyclin C and cyclin E1, respectively. In contrast to its highly related homologs, the molecular basis of CDK3 activation remains elusive due to the lack of structural information of CDK3, particularly in cyclin bound form. Here we report the crystal structure of CDK3 in complex with cyclin E1 at 2.25 Å resolution. CDK3 resembles CDK2 in that both adopt a similar fold and bind cyclin E1 in a similar way. The structural discrepancy between CDK3 and CDK2 may reflect their substrate specificity. Profiling a panel of CDK inhibitors reveals that dinaciclib inhibits CDK3-cyclin E1 potently and specifically. The structure of CDK3-cyclin E1 bound to dinaciclib reveals the inhibitory mechanism. The structural and biochemical results uncover the mechanism of CDK3 activation by cyclin E1 and lays a foundation for structural-based drug design.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

BACKGROUND

The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities.

OBJECTIVE

Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures.

METHODS

We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models.

RESULTS

Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data.

CONCLUSION

Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Van der Waals Potential in Protein Complexes

Van der Waals forces are determinants of the formation of protein-ligand complexes. Physical models based on the Lennard-Jones potential can estimate van der Waals interactions with considerable accuracy and with a computational complexity that allows its application to molecular docking simulations and virtual screening of large databases of small organic molecules. Several empirical scoring functions used to evaluate protein-ligand interactions approximate van der Waals interactions with the Lennard-Jones potential. In this chapter, we present the main concepts necessary to understand van der Waals interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. We describe the Lennard-Jones potential and its application to calculate potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

Homology Modeling of Protein Targets with MODELLER

Homology modeling is a computational approach to generate three-dimensional structures of protein targets when experimental data about similar proteins are available. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance spectroscopy successfully solved the structures of nearly 150,000 macromolecules, there is still a gap in our structural knowledge. We can fulfill this gap with computational methodologies. Our goal in this chapter is to explain how to perform homology modeling of protein targets for drug development. We choose as a homology modeling tool the program MODELLER. To illustrate its use, we describe how to model the structure of human cyclin-dependent kinase 3 using MODELLER. We explain the modeling procedure of CDK3 apoenzyme and the structure of this enzyme in complex with roscovitine.

Hydrogen Bonds in Protein-Ligand Complexes

Fast and reliable evaluation of the hydrogen bond potential energy has a significant impact in the drug design and development since it allows the assessment of large databases of organic molecules in virtual screening projects focused on a protein of interest. Semi-empirical force fields implemented in molecular docking programs make it possible the evaluation of protein-ligand binding affinity where the hydrogen bond potential is a common term used in the calculation. In this chapter, we describe the concepts behind the programs used to predict hydrogen bond potential energy employing semi-empirical force fields as the ones available in the programs AMBER, AutoDock4, TreeDock, and ReplicOpter. We described here the 12-10 potential and applied it to evaluate the binding affinity for an ensemble of crystallographic structures for which experimental data about binding affinity are available.

Molecular dynamic simulation reveals damaging impact of RAC1 F28L mutation in the switch I region

Ras-related C3 botulinum toxin substrate 1 (RAC1) is a plasma membrane-associated small GTPase which cycles between the active GTP-bound and inactive GDP-bound states. There is wide range of evidences indicating its active participation in inducing cancer-associated phenotypes. RAC1 F28L mutation (RAC^F28L) is a fast recycling mutation which has been implicated in several cancer associated cases. In this work we have performed molecular docking and molecular dynamics simulation (~0.3 μs) to investigate the conformational changes occurring in the mutant protein. The RMSD, RMSF and NHbonds results strongly suggested that the loss of native conformation in the Switch I region in RAC1 mutant protein could be the reason behind its oncogenic transformation. The overall results suggested that the mutant protein attained compact conformation as compared to the native. The major impact of mutation was observed in the Switch I region which might be the crucial reason behind the loss of interaction between the guanine ring and F28 residue.

Molecular docking and dynamics simulations of A.niger RNase from Aspergillus niger ATCC26550: for potential prevention of human cancer

The aim of the present research was to study the anticancer effects of Aspergillus niger (A.niger) RNase. We found that RNase (A.niger RNase) significantly and dose dependently inhibited invasiveness of breast cancer cell line MDA MB 231 by 55 % (P<0.01) at 1 μM concentration. At a concentration of 2 μM, the anti invasive effect of the enzyme increased to 90 % (P<0.002). Keeping the aim to determine molecular level interactions (molecular simulations and protein docking) of human actin with A.niger RNase we extended our work in-vitro to in-silico studies. To gain better relaxation and accurate arrangement of atoms, refinement was done on the human actin and A.niger RNase by energy minimization (EM) and molecular dynamics (MD) simulations using 43A(2) force field of Gromacs96 implemented in the Gromacs 4.0.5 package, finally the interaction energies were calculated by protein-protein docking using the HEX. These in vitro and in-silico structural studies prove the effective inhibition of actin activity by A.niger RNase in neoplastic cells and thereby provide new insights for the development of novel anti cancer drugs.

Chemically modified peptides targeting the PDZ domain of GIPC as a therapeutic approach for cancer

GIPC (GAIP-interacting protein, C terminus) represents a new target class for the discovery of chemotherapeutics. While many of the current generation of anticancer agents function by directly binding to intracellular kinases or cell surface receptors, the disruption of cytosolic protein-protein interactions mediated by non-enzymatic domains is an underdeveloped avenue for inhibiting cancer growth. One such example is the PDZ domain of GIPC. Previously we developed a molecular probe, the cell-permeable octapeptide CR1023 (N-myristoyl-PSQSSSEA), which diminished proliferation of pancreatic cancer cells. We have expanded upon that discovery using a chemical modification approach and here report a series of cell-permeable, side chain-modified lipopeptides that target the GIPC PDZ domain in vitro and in vivo. These peptides exhibit significant activity against pancreatic and breast cancers, both in cellular and animal models. CR1166 (N-myristoyl-PSQSK(εN-4-bromobenzoyl)SK(εN-4-bromobenzoyl)A), bearing two halogenated aromatic units on alternate side chains, was found to be the most active compound, with pronounced down-regulation of EGFR/1GF-1R expression. We hypothesize that these organic acid-modified residues extend the productive reach of the peptide beyond the canonical binding pocket, which defines the limit of accessibility for the native proteinogenic sequences that the PDZ domain has evolved to recognize. Cell permeability is achieved with N-terminal lipidation using myristate, rather than a larger CPP (cell-penetrating peptide) sequence. This, in conjunction with optimization of targeting through side chain modification, has yielded an approach that will allow the discovery and development of next-generation cellular probes for GIPC PDZ as well as for other PDZ domains.

Structural Bioinformatics Approach of Cyclin-Dependent Kinases 1 and 3 Complexed with Inhibitors

The cyclin-dependent kinases or CDKs participate in the regulation of both the cell progression cycle and the RNA polymerase-II transcription cycle. In several human tumours deregulation of CDK-related mechanisms have been detected, e.g., overexpression of cyclins or deletion of genes encoding for CKIs. Regarding these observations, CDKs came up to be interesting targets for elaboration of novel antitumour drugs. Based on the importance of the CDKs, this research aimed to describe, to characterize and to compare the molecular models of CDK1 and CDK3. Since the structures of human CDK1 and CDK3 are unavailable in the Protein Data Bank -PDB, homology models were created based on the CDK2 as the template, once they share a substantial identity. The structural studies of the CDK1 and CDK3 biding sites were conducted by molecular docking with 15 different CDK inhibitors previously identified to CDK2. This study allowed the understanding of the structure of the complexes between CDK1/ CDK3 with inhibitors. The knowledge of their structural features mainly the biding sites might be useful to discovery and rationalization of drug design process.

Pharmacological targeting of CDK9 in cardiac hypertrophy

Cardiac hypertrophy allows the heart to adapt to workload, but persistent or unphysiological stimulus can result in pump failure. Cardiac hypertrophy is characterized by an increase in the size of differentiated cardiac myocytes. At the molecular level, growth of cells is linked to intensive transcription and translation. Several cyclin-dependent kinases (CDKs) have been identified as principal regulators of transcription, and among these CDK9 is directly associated with cardiac hypertrophy. CDK9 phosphorylates the C-terminal domain of RNA polymerase II and thus stimulates the elongation phase of transcription. Chronic activation of CDK9 causes not only cardiac myocyte enlargement but also confers predisposition to heart failure. Due to the long interest of molecular oncologists and medicinal chemists in CDKs as potential targets of anticancer drugs, a portfolio of small-molecule inhibitors of CDK9 is available. Recent determination of CDK9's crystal structure now allows the development of selective inhibitors and their further optimization in terms of biochemical potency and selectivity. CDK9 may therefore constitute a novel target for drugs against cardiac hypertrophy.

ATP and its N⁶-substituted analogues: parameterization, molecular dynamics simulation and conformational analysis

In this work we used a combination of classical molecular dynamics and simulated annealing techniques to shed more light on the conformational flexibility of 12 adenosine triphosphate (ATP) analogues in a water environment. We present simulations in AMBER force field for ATP and 12 published analogues [Shah et al. (1997) Proc Natl Acad Sci USA 94: 3565–3570]. The calculations were carried out using the generalized Born (GB) solvation model in the presence of the cation Mg²⁺. The ion was placed at a close distance (2 Å) from the charged oxygen atoms of the beta and gamma phosphate groups of the −3 negatively charged ATP analogue molecules. Analysis of the results revealed the distribution of inter-proton distances H8–H1′ and H8–H2′ versus the torsion angle ψ (C4–N9-C1′–O4′) for all conformations of ATP analogues. There are two gaps in the distribution of torsion angle ψ values: the first is between −30 and 30 degrees and is described by cis-conformation; and the second is between 90 and 175 degrees, which mostly covers a region of anti conformation. Our results compare favorably with results obtained in experimental assays [Jiang and Mao (2002) Polyhedron 21:435–438].

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