N2-substituted O6-cyclohexylmethylguanine derivatives: potent inhibitors of cyclin-dependent kinases 1 and 2

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

Identification of Potential Drug Targets of Calix[4]arene by Reverse Docking

Calixarenes are displaying great potential for the development of new drug delivery systems, diagnostic imaging, biosensing devices and inhibitors of biological processes. In particular, calixarene derivatives are able to interact with many different enzymes and function as inhibitors. By screening of the potential drug target database (PDTD) with a reverse docking procedure, we identify and discuss a selection of 100 proteins that interact strongly with calix[4]arene. We also discover that leucine (23.5 %), isoleucine (11.3 %), phenylalanines (11.3 %) and valine (9.5 %) are the most frequent binding residues followed by hydrophobic cysteines and methionines and aromatic histidines, tyrosines and tryptophanes. Top binders are peroxisome proliferator-activated receptors that already are targeted by commercial drugs, demonstrating the practical interest in calix[4]arene. Nuclear receptors, potassium channel, several carrier proteins, a variety of cancer-related proteins and viral proteins are prominent in the list. It is concluded that calix[4]arene, which is characterized by facile access, well-defined conformational characteristics, and ease of functionalization at both the lower and higher rims, could be a potential lead compound for the development of enzyme inhibitors and theranostic platforms.

Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

Accelerating and Automating the Free Energy Perturbation Absolute Binding Free Energy Calculation with the RED-E Function

The accurate prediction of the binding affinities between small molecules and biological macromolecules plays a fundamental role in structure-based drug design, which is still challenging. The free energy perturbation-based absolute binding free energy (FEP-ABFE) approach has shown potential in its reliability. To correctly calculate the energy related to the ligand being restrained by the receptor, additional restraints between the ligand and the receptor are needed. However, determining the restraint parameters for individual ligands empirically is too trivial to be automated, and usually gives rise to numerical instabilities, which set back the applications of FEP-ABFE. To address these issues, we derived the analytical expression for the probability distribution of energy differences, P(ΔU), during the process of restraint addition, which is called the RED-E (restraint energy distribution at equilibrium position) function. Simulations indicated that the RED-E function can accurately describe P(ΔU) when restraints are added at the equilibrium position. Based on the RED-E function, an automatic restraint selection method was proposed to select the best restraint. With this method, there is a high phase-space overlap between the free and restrained states, such that using a 2-λ perturbation can accurately calculate the free energy of the restraint addition, which is a nearly 6 times acceleration compared with current widely used 12-λ perturbation method. The RED-E function gives insight into the non-Gaussian behavior of the sampled P(ΔU) in certain FEP processes in an analytical way. The highly automated and accelerated restraint selection also makes it possible for the large-scale application of FEP-ABFE in real drug discovery practices.

Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy

We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.

ACES: Optimized Alchemically Enhanced Sampling

We present an alchemical enhanced sampling (ACES) method implemented in the GPU-accelerated AMBER free energy MD engine. The methods hinges on the creation of an "enhanced sampling state" by reducing or eliminating selected potential energy terms and interactions that lead to kinetic traps and conformational barriers while maintaining those terms that curtail the need to otherwise sample large volumes of phase space. For example, the enhanced sampling state might involve transforming regions of a ligand and/or protein side chain into a noninteracting "dummy state" with internal electrostatics and torsion angle terms turned off. The enhanced sampling state is connected to a real-state end point through a Hamiltonian replica exchange (HREMD) framework that is facilitated by newly developed alchemical transformation pathways and smoothstep softcore potentials. This creates a counterdiffusion of real and enhanced-sampling states along the HREMD network. The effect of a differential response of the environment to the real and enhanced-sampling states is minimized by leveraging the dual topology framework in AMBER to construct a counterbalancing HREMD network in the opposite alchemical direction with the same (or similar) real and enhanced sampling states at inverted end points. The method has been demonstrated in a series of test cases of increasing complexity where traditional MD, and in several cases alternative REST2-like enhanced sampling methods, are shown to fail. The hydration free energy for acetic acid was shown to be independent of the starting conformation, and the values for four additional edge case molecules from the FreeSolv database were shown to have a significantly closer agreement with experiment using ACES. The method was further able to handle different rotamer states in a Cdk2 ligand identified as fractionally occupied in crystal structures. Finally, ACES was applied to T4-lysozyme and demonstrated that the side chain distribution of V111χ1 could be reliably reproduced for the apo state, bound to p-xylene, and in p-xylene→ benzene transformations. In these cases, the ACES method is shown to be highly robust and superior to a REST2-like enhanced sampling implementation alone.

AMBER Drug Discovery Boost Tools: Automated Workflow for Production Free-Energy Simulation Setup and Analysis (ProFESSA)

We report an automated workflow for production free-energy simulation setup and analysis (ProFESSA) using the GPU-accelerated AMBER free-energy engine with enhanced sampling features and analysis tools, part of the AMBER Drug Discovery Boost package that has been integrated into the AMBER22 release. The workflow establishes a flexible, end-to-end pipeline for performing alchemical free-energy simulations that brings to bear technologies, including new enhanced sampling features and analysis tools, to practical drug discovery problems. ProFESSA provides the user with top-level control of large sets of free-energy calculations and offers access to the following key functionalities: (1) automated setup of file infrastructure; (2) enhanced conformational and alchemical sampling with the ACES method; and (3) network-wide free-energy analysis with the optional imposition of cycle closure and experimental constraints. The workflow is applied to perform absolute and relative solvation free-energy and relative ligand-protein binding free-energy calculations using different atom-mapping procedures. Results demonstrate that the workflow is internally consistent and highly robust. Further, the application of a new network-wide Lagrange multiplier constraint analysis that imposes key experimental constraints substantially improves binding free-energy predictions.

MM/PB(GB)SA benchmarks on soluble proteins and membrane proteins

Predicting protein-ligand binding free energy rapidly and accurately remains a challenging question in modern drug discovery. Molecular mechanics/Poisson-Boltzmann (Generalized Born) surface area (MM/PB(GB)SA) has emerged as an essential tool for accelerating cost-efficient binding free energy calculation. This study presents benchmarks with three membrane-bound protein systems and six soluble protein systems. Different parameters were sampled for different benchmarks to explore the highest accuracy. These include ligand charges, protein force fields, extra points, GB models, nonpolar optimization methods, internal dielectric constants and membrane dielectric constants. Comparisons of accuracy were made between MM/PB(GB)SA, docking and free energy perturbation (FEP). The results reveal a competitive performance between MM/PB(GB)SA and FEP. In summary, MM/PB(GB)SA is a powerful approach to predict ligand binding free energy rapidly and accurately. Parameters of MM/PB(GB)SA calculations, such as the GB models and membrane dielectric constants, need to be optimized for different systems. This method can be served as a powerful tool for drug design.

Applications and mechanisms of the cyclin-dependent kinase 4/6 inhibitor, PD-0332991, in solid tumors

Abnormal CDK4/6-Rb-E2F signal transduction is a common finding in tumors and is a driving factor for the excessive proliferation of various tumor cells. PD-0332991, a highly specific, small molecule inhibitor for CDK4 and 6, has been shown to inhibit tumor growth by abrogating the phosphorylating capacity of CDK4/6 and suppressing Rb phosphorylation. It has been promoted for the treatment of breast cancer and potentially for other tumor types such as liver cancers, lung cancers and sarcomas. Due to the risk of monotherapy resistance, PD-0332991 is commonly used in combination with other drugs. Such combination treatments have proved able to inhibit tumor proliferation more effectively, induce stronger senescence and apoptosis, and enhance the efficiency of immunotherapy. Therefore, tumor cells with senescence induced by PD-0332991 are now used as ideal screening tools of cytolytic drugs with more efficient and thorough anti-tumor properties. With more extensive understandings about the branching points between senescence and apoptosis, it is possible to refine the dosage of PD-0332991. Better characterization of resistant cells, of inhibitors and of adverse effects such as leukopenia are needed to overcome obstacles in the use of PD-0332991. In this review of PD-0332991 research, we hope to provide guidance of transitions from laboratory findings to clinical applications of PD-0332991 and to facilitate PD-0332991-based multi-inhibitor combination therapies for various tumors.

Discovery of 5,7-Dihydro-6H-pyrrolo[2,3-d]pyrimidin-6-ones as Highly Selective CDK2 Inhibitors

A series of exceptionally selective CDK2 inhibitors are described. Starting from an HTS hit, we successfully scaffold hopped to a 5,7-dihydro-6H-pyrrolo[2,3-d]pyrimidin-6-one core structure, which imparted a promising initial selectivity within the CDK family. Extensive further SAR identified additional factors that drove selectivity to above 200× for CDKs 1/4/6/7/9. General kinome selectivity was also greatly improved. Finally, use of in vivo metabolite identification allowed us to pinpoint sulfonamide dealkylation as the primary metabolite, which was ameliorated through the deuterium effect.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

A revised synthesis of 6-alkoxy-2-aminopurines with late-stage convergence allowing for increased molecular complexity

This streamlined synthesis allows the alcohol at the 6-position of 6-alkoxy-2-arylaminopurines to be used only in moderate excess, rather than as solvent, opening up the possibility of accessing more complicated molecules.

Review of rationale and progress toward targeting cyclin-dependent kinase 2 (CDK2) for male contraception†

Cyclin-dependent kinase 2 (CDK2) is a member of the larger cell cycle regulating CDK family of kinases, activated by binding partner cyclins as its name suggests. Despite its canonical role in mitosis, CDK2 knockout mice are viable but sterile, suggesting compensatory mechanisms for loss of CDK2 in mitosis but not meiosis. Here, we review the literature surrounding the role of CDK2 in meiosis, particularly a cyclin-independent role in complex with another activator, Speedy 1 (SPY1). From this evidence, we suggest that CDK2 could be a viable nonhormonal male contraceptive target. Finally, we review the literature of pertinent CDK2 inhibitors from the preclinical to clinical stages, mostly developed to treat various cancers. To date, there is no potent yet selective CDK2 inhibitor that could be repurposed as a contraceptive without appreciable off-target toxicity. To achieve selectivity for CDK2 over closely related kinases, developing compounds that bind outside the conserved adenosine triphosphate-binding site may be necessary.

Structural and binding studies of cyclin-dependent kinase 2 with NU6140 inhibitor

Cyclin-dependent kinase 2 (CDK2) is an established target protein for therapeutic intervention in various diseases, including cancer. Reported inhibitors of CDK2 target the ATP-binding pocket to inhibit the kinase activity. Many small molecule CDK2 inhibitors have been discovered, and their crystal structure with CDK2 or CDK2-cyclin A complex has been published. NU6140 is a CDK2 inhibitor with moderate potency and selectivity. Herein, we report the cocrystal structure determination of NU6140 in complex with CDK2 and confirmation of the binding using various biophysical methods. Our data show that NU6140 binds to CDK2 with a Kd of 800 nM as determined by SPR and stabilizes the protein against thermal denaturation (ΔT_m -5°C). The cocrystal structure determined in our study shows that NU6140 binds in the ATP-binding pocket as expected for this class of compounds and interacts with Leu83 and Glu81 with regular hydrogen bonds and with Asp145 via water-mediated H-bond. Based on these data, we propose structural modifications of NU6140 to introduce new interactions with CDK2 that can improve its potency while retaining the selectivity.

Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.

A network pharmacology strategy to investigate the anti-osteoarthritis mechanism of main lignans components of Schisandrae Fructus

Osteoarthritis (OA) is a chronic age-related progressive joint disorder. Degradation of the cartilage extracellular matrix (ECM) is considered a hallmark of OA and may be a target for new therapeutic methods. Schisandrae Fructus (SF) has been shown to be effective in treating OA. The major active components of SF are lignans. However, the targets of SF and the pharmacological mechanisms underlying the effects of SF lignans in the treatment of OA have not been elucidated. Therefore, based on network pharmacology, this research predicted the treatment targets of six lignans in SF, constructed a protein-protein interaction network and identified 15 hub genes in the OA-target protein-protein interaction network. Through Gene Ontology function and pathway analyses, the gene functions of lignans in the treatment of OA were determined. Finally, the anti-OA effects of lignans and underlying mechanisms identified in the network pharmacology analysis were verified by molecular docking, real-time PCR and western blotting in vitro. The biological processes of the genes and proteins targeted by lignans in the treatment of OA included the immune response, inflammatory response, cell signal transduction and phospholipid metabolism. Moreover, 20 metabolic pathways were enriched. Network pharmacology, molecular docking and in vitro and in vivo experimental results revealed that SF, schisanhenol and gamma-schisandrin inhibited EGFR and MAPK14 gene expression by inhibiting SRC gene expression and activity and then decreased MMP 13 and collagen II protein and gene expression. This research provides a basis for further study of the anti-OA effects and mechanisms of SF, schisanhenol and gamma-schisandrin.

Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach

Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC₅₀ values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research.

Variational Method for Networkwide Analysis of Relative Ligand Binding Free Energies with Loop Closure and Experimental Constraints

We describe an efficient method for the simultaneous solution of all free energies within a relative binding free-energy (RBFE) network with cycle closure and experimental/reference constraint conditions using Bennett Acceptance Ratio (BAR) and Multistate BAR (MBAR) analysis. Rather than solving the BAR or MBAR equations for each transformation independently, the simultaneous solution of all transformations are obtained by performing a constrained minimization of a global objective function. The nonlinear optimization of the objective function is subjected to affine linear constraints that couple the free energies between the network edges. The constraints are used to enforce the closure of thermodynamic cycles within the RBFE network, and to enforce an additional set of linear constraint conditions demonstrated here to be subsets of (1 or 2) experimental values. We describe details of the practical implementation of the network BAR/MBAR procedure, including use of generalized coordinates in the minimization of the free-energy objective function, propagation of bootstrap errors from those coordinates, and performance and memory optimization. In some cases it is found that use of restraints in the optimization is more practical than use of generalized coordinates for enforcing constraint conditions. The fast BARnet and MBARnet methods are used to analyze the RBFEs of six prototypical protein-ligand systems, and it is shown that enforcement of cycle closure conditions reduces the error in the predictions only modestly, and further reduction in errors can be achieved when one or two experimental RBFEs are included in the optimization procedure. These methods have been implemented into FE-ToolKit, a new free-energy analysis toolkit. The BARnet/MBARnet framework presented here opens the door to new, more efficient and robust free-energy analysis with enhanced predictive capability for drug discovery applications.

TIES 20: Relative Binding Free Energy with a Flexible Superimposition Algorithm and Partial Ring Morphing

The TIES (Thermodynamic Integration with Enhanced Sampling) protocol is a formally exact alchemical approach in computational chemistry to the calculation of relative binding free energies. The validity of TIES relies on the correctness of matching atoms across compared pairs of ligands, laying the foundation for the transformation along an alchemical pathway. We implement a flexible topology superimposition algorithm which uses an exhaustive joint-traversal for computing the largest common component(s). The algorithm is employed to enable matching and morphing of partial rings in the TIES protocol along with a validation study using 55 transformations and five different proteins from our previous work. We find that TIES 20 with the RESP charge system, using the new superimposition algorithm, reproduces the previous results with mean unsigned error of 0.75 kcal/mol with respect to the experimental data. Enabling the morphing of partial rings decreases the size of the alchemical region in the dual-topology transformations resulting in a significant improvement in the prediction precision. We find that increasing the ensemble size from 5 to 20 replicas per λ window only has a minimal impact on the accuracy. However, the non-normal nature of the relative free energy distributions underscores the importance of ensemble simulation. We further compare the results with the AM1-BCC charge system and show that it improves agreement with the experimental data by slightly over 10%. This improvement is partly due to AM1-BCC affecting only the charges of the atoms local to the mutation, which translates to even fewer morphed atoms, consequently reducing issues with sampling and therefore ensemble averaging. TIES 20, in conjunction with the enablement of ring morphing, reduces the size of the alchemical region and significantly improves the precision of the predicted free energies.

How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations?

With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.

Molecular dynamics simulations reveal the determinants of cyclin-dependent kinase 2 inhibition by 5-nitrosopyrimidine derivatives

Communicated by Ramaswamy H. Sarma.

Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF

Accurate prediction of the absolute or relative protein-ligand binding affinity is one of the major tasks in computer-aided drug design projects, especially in the stage of lead optimization. In principle, the alchemical free energy (AFE) methods such as thermodynamic integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-analysis procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calculations on protein-ligand binding free energies. We have tested 134 protein-ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the commercial Schrodinger FEP+ program (WangJ. Am. Chem. Soc.2015, 137, 2695-2703). The achieved accuracy fits within the requirements for computations to generate effective guidance for experimental work in drug lead optimization, and the needed wall time is short enough for practical application. Our verified protocol provides a practical solution for routine AFE calculations in real drug design projects.

QligFEP: an automated workflow for small molecule free energy calculations in Q

The process of ligand binding to a biological target can be represented as the equilibrium between the relevant solvated and bound states of the ligand. This which is the basis of structure-based, rigorous methods such as the estimation of relative binding affinities by free energy perturbation (FEP). Despite the growing capacity of computing power and the development of more accurate force fields, a high throughput application of FEP is currently hampered due to the need, in the current schemes, of an expert user definition of the "alchemical" transformations between molecules in the series explored. Here, we present QligFEP, a solution to this problem using an automated workflow for FEP calculations based on a dual topology approach. In this scheme, the starting poses of each of the two ligands, for which the relative affinity is to be calculated, are explicitly present in the MD simulations associated with the (dual topology) FEP transformation, making the perturbation pathway between the two ligands univocal. We show that this generalized method can be applied to accurately estimate solvation free energies for amino acid sidechain mimics, as well as the binding affinity shifts due to the chemical changes typical of lead optimization processes. This is illustrated in a number of protein systems extracted from other FEP studies in the literature: inhibitors of CDK2 kinase and a series of A2A adenosine G protein-coupled receptor antagonists, where the results obtained with QligFEP are in excellent agreement with experimental data. In addition, our protocol allows for scaffold hopping perturbations to identify the binding affinities between different core scaffolds, which we illustrate with a series of Chk1 kinase inhibitors. QligFEP is implemented in the open-source MD package Q, and works with the most common family of force fields: OPLS, CHARMM and AMBER.

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.

How Selective Are Pharmacological Inhibitors of Cell-Cycle-Regulating Cyclin-Dependent Kinases?

Cyclin-dependent kinases (CDKs) are an important and emerging class of drug targets for which many small-molecule inhibitors have been developed. However, there is often insufficient data available on the selectivity of CDK inhibitors (CDKi) to attribute the effects on the presumed target CDK to these inhibitors. Here, we highlight discrepancies between the kinase selectivity of CDKi and the phenotype exhibited; we evaluated 31 CDKi (claimed to target CDK1-4) for activity toward CDKs 1, 2, 4, 5, 7, 9 and for effects on the cell cycle. Our results suggest that most CDKi should be reclassified as pan-selective and should not be used as a tool. In addition, some compounds did not even inhibit CDKs as their primary cellular targets; for example, NU6140 showed potent inhibition of Aurora kinases. We also established an online database of commercially available CDKi for critical evaluation of their utility as molecular probes. Our results should help researchers select the most relevant chemical tools for their specific applications.

Combined venetoclax and alvocidib in acute myeloid leukemia

More effective treatment options for elderly acute myeloid leukemia (AML) patients are needed as only 25-50% of patients respond to standard-of-care therapies, response duration is typically short, and disease progression is inevitable even with some novel therapies and ongoing clinical trials. Anti-apoptotic BCL-2 family inhibitors, such as venetoclax, are promising therapies for AML. Nonetheless, resistance is emerging. We demonstrate that venetoclax combined with cyclin-dependent kinase (CDK) inhibitor alvocidib is potently synergistic in venetoclax-sensitive and -resistant AML models in vitro, ex vivo and in vivo. Alvocidib decreased MCL-1, and/or increased pro-apoptotic proteins such as BIM or NOXA, often synergistically with venetoclax. Over-expression of BCL-XL diminished synergy, while knock-down of BIM almost entirely abrogated synergy, demonstrating that the synergistic interaction between alvocidib and venetoclax is primarily dependent on intrinsic apoptosis. CDK9 inhibition predominantly mediated venetoclax sensitization, while CDK4/6 inhibition with palbociclib did not potentiate venetoclax activity. Combined, venetoclax and alvocidib modulate the balance of BCL-2 family proteins through complementary, yet variable mechanisms favoring apoptosis, highlighting this combination as a promising therapy for AML or high-risk MDS with the capacity to overcome intrinsic apoptosis mechanisms of resistance. These results support clinical testing of combined venetoclax and alvocidib for the treatment of AML and advanced MDS.

Fusion of Structure and Ligand Based Methods for Identification of Novel CDK2 Inhibitors

Cyclin dependent kinases play a central role in cell cycle regulation which makes them a promising target with multifarious therapeutic potential. CDK2 regulates various events of the eukaryotic cell division cycle, and the pharmacological evidence indicates that overexpression of CDK2 causes abnormal cell-cycle regulation, which is directly associated with hyperproliferation of cancer cells. Therefore, CDK2 is regarded as a potential target molecule for anticancer medication. Thus, to decline CDK2 activity by potential lead compounds has proved to be an effective treatment for cancer. The availability of a large number of X-ray crystal structures and known inhibitors of CDK2 provides a gateway to perform efficient computational studies on this target. With the aim to identify new chemical entities from commercial libraries, with increased inhibitory potency for CDK2, ligand and structure based computational drug designing approaches were applied. A druglike library of 50,000 compounds from ChemDiv and ChemBridge databases was screened against CDK2, and 110 compounds were identified using the parallel application of these models. On in vitro evaluation of 40 compounds, seven compounds were found to have more than 50% inhibition at 10 μM. MD studies of the hits revealed the stability of these inhibitors and pivotal role of Glu81 and Leu83 for binding with CDK2. The overall study resulted in the identification of four new chemical entities possessing CDK2 inhibitory activity.

Structure-guided design of purine-based probes for selective Nek2 inhibition

Nek2 (NIMA-related kinase 2) is a cell cycle-dependent serine/threonine protein kinase that regulates centrosome separation at the onset of mitosis. Overexpression of Nek2 is common in human cancers and suppression can restrict tumor cell growth and promote apoptosis. Nek2 inhibition with small molecules, therefore, offers the prospect of a new therapy for cancer. To achieve this goal, a better understanding of the requirements for selective-inhibition of Nek2 is required. 6-Alkoxypurines were identified as ATP-competitive inhibitors of Nek2 and CDK2. Comparison with CDK2-inhibitor structures indicated that judicious modification of the 6-alkoxy and 2-arylamino substituents could achieve discrimination between Nek2 and CDK2. In this study, a library of 6-cyclohexylmethoxy-2-arylaminopurines bearing carboxamide, sulfonamide and urea substituents on the 2-arylamino ring was synthesized. Few of these compounds were selective for Nek2 over CDK2, with the best result being obtained for 3-((6-(cyclohexylmethoxy)-9H-purin-2-yl)amino)-N,N-dimethylbenzamide (CDK2 IC50 = 7.0 μM; Nek2 IC50 = 0.62 μM) with >10-fold selectivity. Deletion of the 6-substituent abrogated activity against both Nek2 and CDK2. Nine compounds containing an (E)-dialkylaminovinyl substituent at C-6, all showed selectivity for Nek2, e.g. (E)-6-(2-(azepan-1-yl)vinyl)-N-phenyl-9H-purin-2-amine (CDK2 IC50 = 2.70 μM; Nek2 IC50 = 0.27 μM). Structural biology of selected compounds enabled a partial rationalization of the observed structure activity relationships and mechanism of Nek2 activation. This showed that carboxamide 11 is the first reported inhibitor of Nek2 in the DFG-in conformation.

Cyclin-Dependent Kinase (CDK) Inhibitors: Structure-Activity Relationships and Insights into the CDK-2 Selectivity of 6-Substituted 2-Arylaminopurines

Purines and related heterocycles substituted at C-2 with 4'-sulfamoylanilino and at C-6 with a variety of groups have been synthesized with the aim of achieving selectivity of binding to CDK2 over CDK1. 6-Substituents that favor competitive inhibition at the ATP binding site of CDK2 were identified and typically exhibited 10-80-fold greater inhibition of CDK2 compared to CDK1. Most impressive was 4-((6-([1,1'-biphenyl]-3-yl)-9H-purin-2-yl)amino) benzenesulfonamide (73) that exhibited high potency toward CDK2 (IC50 0.044 μM) but was ∼2000-fold less active toward CDK1 (IC50 86 μM). This compound is therefore a useful tool for studies of cell cycle regulation. Crystal structures of inhibitor-kinase complexes showed that the inhibitor stabilizes a glycine-rich loop conformation that shapes the ATP ribose binding pocket and that is preferred in CDK2 but has not been observed in CDK1. This aspect of the active site may be exploited for the design of inhibitors that distinguish between CDK1 and CDK2.

Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration

The accurate prediction of the binding affinities of ligands to proteins is a major goal in drug discovery and personalized medicine. The time taken to make such predictions is of similar importance to their accuracy, precision, and reliability. In the past few years, an ensemble based molecular dynamics approach has been proposed that provides a route to reliable predictions of free energies based on the molecular mechanics Poisson-Boltzmann surface area method which meets the requirements of speed, accuracy, precision, and reliability. Here, we describe an equivalent methodology based on thermodynamic integration to substantially improve the speed, accuracy, precision, and reliability of calculated relative binding free energies. We report the performance of the method when applied to a diverse set of protein targets and ligands. The results are in very good agreement with experimental data (90% of calculations agree to within 1 kcal/mol), while the method is reproducible by construction. Statistical uncertainties of the order of 0.5 kcal/mol or less are achieved. We present a systematic account of how the uncertainty in the predictions may be estimated.

Synthesis and Biological Evaluation of N(2) -Substituted 2,4-Diamino-6-cyclohexylmethoxy-5-nitrosopyrimidines and Related 5-Cyano-NNO-azoxy Derivatives as Cyclin-Dependent Kinase 2 (CDK2) Inhibitors

The potent and selective cyclin-dependent kinase 2 (CDK2) inhibitor NU6027 (6-cyclohexylmethoxy-5-nitroso-2,4-diaminopyrimidine) was used as the lead for the synthesis of a series of analogues in order to provide further insight into the structure-activity relationships for 2,4-diaminopyrimidine CDK2 inhibitors. Aliphatic amino substituents were introduced at position 2. The use of linear or less sterically hindered amines gave rise to compounds endowed with slightly better activity than the lead; on the other hand, the compounds were less active if a bulkier amino substituent was used. Substitution of the 5-nitroso group with a 5-cyano-NNO-azoxy moiety afforded a new class of inhibitors, the activity of which against CDK2 was found to be similar to that of the nitroso series. The most active nitroso compound was 8 b ((2S)-2-[(4-amino-6-cyclohexylmethoxy-5-nitrosopyrimidin-2-yl)amino]propan-1-ol; IC50 =0.16 μm), while in the 5-cyano-NNO-azoxy series the most active compound was 9 b (4-amino-5-[(Z)-cyano-NNO-azoxy]-2-{[(2S)-1-hydroxypropan-2-yl]amino}-6-cyclohexylmethoxypyrimidine; IC50 =0.30 μm). Taken together, these new analogues of NU6027 enhance our understanding of the structure-activity relationships for 2,4-diaminopyrimidine CDK2 inhibitors.

Synthesis of cycloalkyl substituted purine nucleosides via a metal-free radical route

An efficient route to synthesize cycloalkyl substituted purine nucleosides was developed. This metal-free C-H activation was accomplished by a tBuOOtBu initiated radical reaction. By adjusting the amount of tBuOOtBu and reaction time, the selective synthesis of C6-monocycloalkyl or C6,C8-dicycloalkyl substituted purine nucleosides could be realized. Furthermore, uracil and related nucleosides were also suitable substrates, giving the C5-cyclohexyl substituted uracil derivatives in good yields with excellent regioselectivities.

Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins

The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials & Methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results & discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.[Formula: see text]

Identification and Characterization of an Irreversible Inhibitor of CDK2

Irreversible inhibitors that modify cysteine or lysine residues within a protein kinase ATP binding site offer, through their distinctive mode of action, an alternative to ATP-competitive agents. 4-((6-(Cyclohexylmethoxy)-9H-purin-2-yl)amino)benzenesulfonamide (NU6102) is a potent and selective ATP-competitive inhibitor of CDK2 in which the sulfonamide moiety is positioned close to a pair of lysine residues. Guided by the CDK2/NU6102 structure, we designed 6-(cyclohexylmethoxy)-N-(4-(vinylsulfonyl)phenyl)-9H-purin-2-amine (NU6300), which binds covalently to CDK2 as shown by a co-complex crystal structure. Acute incubation with NU6300 produced a durable inhibition of Rb phosphorylation in SKUT-1B cells, consistent with it acting as an irreversible CDK2 inhibitor. NU6300 is the first covalent CDK2 inhibitor to be described, and illustrates the potential of vinyl sulfones for the design of more potent and selective compounds.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches.

Polyphony: superposition independent methods for ensemble-based drug discovery

Background

Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates.

Results

Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered.

Conclusions

New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].

Multisite phosphorylation of C-Nap1 releases it from Cep135 to trigger centrosome disjunction

During mitotic entry, centrosomes separate to establish the bipolar spindle. Delays in centrosome separation can perturb chromosome segregation and promote genetic instability. However, interphase centrosomes are physically tethered by a proteinaceous linker composed of C-Nap1 (also known as CEP250) and the filamentous protein rootletin. Linker disassembly occurs at the onset of mitosis in a process known as centrosome disjunction and is triggered by the Nek2-dependent phosphorylation of C-Nap1. However, the mechanistic consequences of C-Nap1 phosphorylation are unknown. Here, we demonstrate that Nek2 phosphorylates multiple residues within the C-terminal domain of C-Nap1 and, collectively, these phosphorylation events lead to loss of oligomerization and centrosome association. Mutations in non-phosphorylatable residues that make the domain more acidic are sufficient to release C-Nap1 from the centrosome, suggesting that it is an increase in overall negative charge that is required for this process. Importantly, phosphorylation of C-Nap1 also perturbs interaction with the core centriolar protein, Cep135, and interaction of endogenous C-Nap1 and Cep135 proteins is specifically lost in mitosis. We therefore propose that multisite phosphorylation of C-Nap1 by Nek2 perturbs both oligomerization and Cep135 interaction, and this precipitates centrosome disjunction at the onset of mitosis.

Synthesis and kinase inhibitory activity of new sulfonamide derivatives of pyrazolo[4,3-e][1,2,4]triazines

A new series of sulfonamide derivatives of pyrazolo[4,3-e][1,2,4]triazine has been synthesized and characterized. Their anticancer activity was tested in vitro against multiple human cancer cell lines and were found to have dose-dependent antiproliferative effects. Furthermore, some of the new compounds inhibited the Abl protein kinase with low micromolar IC50 values and exhibited selective activity against the Bcr-Abl positive K562 and BV173 cell lines, providing starting points for the further development of this new kinase inhibitor scaffold.

Parametrization of an Orbital-Based Linear-Scaling Quantum Force Field for Noncovalent Interactions

We parametrize a linear-scaling quantum mechanical force field called mDC for the accurate reproduction of nonbonded interactions. We provide a new benchmark database of accurate ab initio interactions between sulfur-containing molecules. A variety of nonbond databases are used to compare the new mDC method with other semiempirical, molecular mechanical, ab initio, and combined semiempirical quantum mechanical/molecular mechanical methods. It is shown that the molecular mechanical force field significantly and consistently reproduces the benchmark results with greater accuracy than the semiempirical models and our mDC model produces errors twice as small as the molecular mechanical force field. The comparisons between the methods are extended to the docking of drug candidates to the Cyclin-Dependent Kinase 2 protein receptor. We correlate the protein-ligand binding energies to their experimental inhibition constants and find that the mDC produces the best correlation. Condensed phase simulation of mDC water is performed and shown to produce O-O radial distribution functions similar to TIP4P-EW.

8-Substituted O(6)-cyclohexylmethylguanine CDK2 inhibitors: using structure-based inhibitor design to optimize an alternative binding mode

Evaluation of the effects of purine C-8 substitution within a series of CDK1/2-selective O(6)-cyclohexylmethylguanine derivatives revealed that potency decreases initially with increasing size of the alkyl substituent. Structural analysis showed that C-8 substitution is poorly tolerated, and to avoid unacceptable steric interactions, these compounds adopt novel binding modes. Thus, 2-amino-6-cyclohexylmethoxy-8-isopropyl-9H-purine adopts a "reverse" binding mode where the purine backbone has flipped 180°. This provided a novel lead chemotype from which we have designed more potent CDK2 inhibitors using, in the first instance, quantum mechanical energy calculations. Introduction of an ortho-tolyl or ortho-chlorophenyl group at the purine C-8 position restored the potency of these "reverse" binding mode inhibitors to that of the parent 2-amino-6-cyclohexylmethoxy-9H-purine. By contrast, the corresponding 8-(2-methyl-3-sulfamoylphenyl)-purine derivative exhibited submicromolar CDK2-inhibitory activity by virtue of engineered additional interactions with Asp86 and Lys89 in the reversed binding mode, as confirmed by X-ray crystallography.