Structure-based design of a potent purine-based cyclin-dependent kinase inhibitor

Targeting cyclin-dependent kinases: From pocket specificity to drug selectivity

The development of selective modulators of cyclin-dependent kinases (CDKs), a kinase family with numerous members and functional variations, is a significant preclinical challenge. Recent advancements in crystallography have revealed subtle differences in the highly conserved CDK pockets. Exploiting these differences has proven to be an effective strategy for achieving excellent drug selectivity. While previous reports briefly discussed the structural features that lead to selectivity in individual CDK members, attaining inhibitor selectivity requires consideration of not only the specific structures of the target CDK but also the features of off-target members. In this review, we summarize the structure-activity relationships (SARs) that influence selectivity in CDK drug development and analyze the pocket features that lead to selectivity using molecular-protein binding models. In addition, in recent years, novel CDK modulators have been developed, providing more avenues for achieving selectivity. These cases were also included. We hope that these efforts will assist in the development of novel CDK drugs.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

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.

User-centric design of a 3D search interface for protein-ligand complexes

In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine's new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine's 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server ( https://proteins.plus ).

An Assessment of Dispersion-Corrected DFT Methods for Modeling Nonbonded Interactions in Protein Kinase Inhibitor Complexes

Accurate modeling of nonbonded interactions between protein kinases and their small molecule inhibitors is essential for structure-based drug design. Quantum chemical methods such as density functional theory (DFT) hold significant promise for quantifying the strengths of these key protein-ligand interactions. However, the accuracy of DFT methods can vary substantially depending on the choice of exchange-correlation functionals and associated basis sets. In this study, a comprehensive benchmarking of nine widely used DFT methods was carried out to identify an optimal approach for quantitative modeling of nonbonded interactions, balancing both accuracy and computational efficiency. From a database of 2139 kinase-inhibitor crystal structures, a diverse library of 49 nonbonded interaction motifs was extracted, encompassing CH-π, π-π stacking, cation-π, hydrogen bonding, and salt bridge interactions. The strengths of nonbonded interaction energies for all 49 motifs were calculated at the advanced CCSD(T)/CBS level of theory, which serve as references for a systematic benchmarking of BLYP, TPSS, B97, ωB97X, B3LYP, M062X, PW6B95, B2PLYP, and PWPB95 functionals with D3BJ dispersion correction alongside def2-SVP, def2-TZVP, and def2-QZVP basis sets. The RI, RIJK, and RIJCOSX approximations were used for selected functionals. It was found that the B3LYP/def2-TZVP and RIJK RI-B2PLYP/def2-QZVP methods delivered the best combination of accuracy and computational efficiency, making them well-suited for efficient modeling of nonbonded interactions responsible for molecular recognition of protein kinase inhibitors in their targets.

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.

The Cyclin-dependent kinase 1: more than a cell cycle regulator

The Cyclin-dependent kinase 1, as a serine/threonine protein kinase, is more than a cell cycle regulator as it was originally identified. During the last decade, it has been shown to carry out versatile functions during the last decade. From cell cycle control to gene expression regulation and apoptosis, CDK1 is intimately involved in many cellular events that are vital for cell survival. Here, we provide a comprehensive catalogue of the CDK1 upstream regulators and substrates, describing how this kinase is implicated in the control of key 'cell cycle-unrelated' biological processes. Finally, we describe how deregulation of CDK1 expression and activation has been closely associated with cancer progression and drug resistance.

PoseEdit: enhanced ligand binding mode communication by interactive 2D diagrams

In this article, we present PoseEdit, a new, interactive frontend of the popular pose visualization tool PoseView. PoseEdit automatically produces high-quality 2D diagrams of intermolecular interactions in 3D binding sites calculated from ligands in complex with protein, DNA, and RNA. The PoseView diagrams have been improved in several aspects, most notably in their interactivity. Thanks to the easy-to-use 2D editor of PoseEdit, the diagrams are extensively editable and extendible by the user, can be merged with other diagrams, and even be created from scratch. A large variety of graphical objects in the diagram can be moved, rotated, selected and highlighted, mirrored, removed, or even newly added. Furthermore, PoseEdit enables a synchronized 2D-3D view of macromolecule-ligand complexes simplifying the analysis of structural features and interactions. The representation of individual diagram objects regarding their visualized chemical properties, like stereochemistry, and general graphical styles, like the color of interactions, can additionally be edited. The primary objective of PoseEdit is to support scientists with an enhanced way to communicate ligand binding mode information through graphical 2D representations optimized with the scientist's input in accordance with objective criteria and individual needs. PoseEdit is freely available on the ProteinsPlus web server ( https://proteins.plus ).

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced μex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.

Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations

In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R² = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.

2-Anilino-4-(1-methyl-1H-pyrazol-4-yl)pyrimidine-derived CDK2 inhibitors as anticancer agents: Design, synthesis & evaluation

Deregulation of cyclin-dependent kinase 2 (CDK2) and its activating partners, cyclins A and E, is associated with the pathogenesis of a myriad of human cancers and with resistance to anticancer drugs including CDK4/6 inhibitors. Thus, CDK2 has become an attractive target for the development of new anticancer therapies and for the amelioration of the resistance to CDK4/6 inhibitors. Bioisosteric replacement of the thiazole moiety of CDKI-73, a clinically trialled CDK inhibitor, by a pyrazole group afforded 9 and 19 that displayed potent CDK2-cyclin E inhibition (K_i = 0.023 and 0.001 μM, respectively) with submicromolar antiproliferative activity against a panel of cancer cell lines (GI₅₀ = 0.025-0.780 μM). Mechanistic studies on 19 with HCT-116 colorectal cancer cells revealed that the compound reduced the phosphorylation of retinoblastoma at Ser807/811, arrested the cells at the G2/M phase, and induced apoptosis. These results highlight the potential of the 2-anilino-4-(1-methyl-1H-pyrazol-4-yl)pyrimidine series in developing potent and selective CDK2 inhibitors to combat cancer.

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.

Applications of covalent chemistry in targeted protein degradation

Proteolysis-targeting chimeras (PROTACs) and targeted covalent inhibitors (TCIs) are currently two exciting strategies in the fields of chemical biology and drug discovery. Extensive research in these two fields has been conducted, and significant progress in these fields has resulted in many clinical candidates, some of which have been approved by FDA. Recently, a novel concept termed covalent PROTACs that combine these two strategies has emerged and gained an increasing interest in the past several years. Herein, we briefly review and highlight the mechanism and advantages of TCIs and PROTACs, respectively, and the recent development of covalent PROTACs using irreversible and reversible covalent chemistry.

Assessing the effect of forcefield parameter sets on the accuracy of relative binding free energy calculations

Software for accurate prediction of protein-ligand binding affinity can be a key enabling tool for small molecule drug discovery. Free energy perturbation (FEP) is a computational technique that can be used to compute binding affinity differences between molecules in a congeneric series. It has shown promise in reliably generating accurate predictions and is now widely used in the pharmaceutical industry. However, the high computational cost and use of commercial software, together with the technical challenges to setup, run, and analyze the simulations, limits the usage of FEP. Here, we use an automated FEP workflow which uses the open-source OpenMM package. To enable effective application of FEP, we compared the performance of different water models, partial charge assignments, and AMBER protein forcefields in eight benchmark test cases previously assembled for FEP validation studies.

Design and development of novel 1,2,3-triazole chalcone derivatives as potential anti-osteosarcoma agents via inhibition of PI3K/Akt/mTOR signalling pathway

Osteosarcoma (OS) is an uncommon tumour that mainly affects bone in children and adolescents. The current treatment options of OS are of limited significance due to their immense side effects. In the present manuscript, we have developed a novel series of 1,2,3-triazole chalcone derivatives as potential agents against OS. The compounds were synthesized and evaluated for their PI3K and mTOR inhibitory activity using luminescent kinase assay, and Lance ultra assay, resp. The entire set of compounds showed significant to moderate inhibition of both kinases in the nanomolar range. The three most active compounds: 4e (N-(4-(3-(1-(4-bromophenyl)-1H-1,2,3-triazol-4-yl)acryloyl)phenyl)-4-nitrobenzamide), 4f (N-(4-(3-(1-(4-bromophenyl)-1H-1,2,3-triazol-4-yl)acryloyl)phenyl)-4-chlorobenzamide) and 4g (4-bromo-N-(4-(3-(1-(4-bromophenyl)-1H-1,2,3-triazol-4-yl)acryloyl)phenyl)benzamide), were evaluated for anticancer activity against human OS cancer cell line (MG-63), liver cancer cell line (HepG2), lung cancer cell line (A549) and cervical cancer (HeLa), using MTT assay. Among the tested series, compound 4e showed a better inhibitory profile than gedatolisib against PI3K and was approximately comparable to that of gedatolisib against mTOR. The most significant inhibitory activity was observed for compound 4e against all cell lines (MG-63, HepG2, A549 and HeLa), still somewhat lower to comparable to that of gedatolisib, but with the highest potency against MG-63 cells. Compound 4e was further tested for anti-cancer activity against other OS cells and showed to be equipo-tent to gedatolisib against U2OS and Saos-2 cells. Moreover, it was also found non-toxic to normal cells (BEAS-2B and MCF 10A). The effect of compound 4e was further determined on apoptosis of Saos-2 cells by Annexin-PI assay, where it significantly amplified the percentage of apoptotic cells. Novel 1,2,3-triazole chalcone derivatives are potential agents against OS.

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.

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.

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

Communicated by Ramaswamy H. Sarma.

High Affinity Binding of N2-Modified Guanine Derivatives Significantly Disrupts the Ligand Binding Pocket of the Guanine Riboswitch

Riboswitches are important model systems for the development of approaches to search for RNA-targeting therapeutics. A principal challenge in finding compounds that target riboswitches is that the effector ligand is typically almost completely encapsulated by the RNA, which severely limits the chemical space that can be explored. Efforts to find compounds that bind the guanine/adenine class of riboswitches with a high affinity have in part focused on purines modified at the C6 and C2 positions. These studies have revealed compounds that have low to sub-micromolar affinity and, in a few cases, have antimicrobial activity. To further understand how these compounds interact with the guanine riboswitch, we have performed an integrated structural and functional analysis of representative guanine derivatives with modifications at the C8, C6 and C2 positions. Our data indicate that while modifications of guanine at the C6 position are generally unfavorable, modifications at the C8 and C2 positions yield compounds that rival guanine with respect to binding affinity. Surprisingly, C2-modified guanines such as N2-acetylguanine completely disrupt a key Watson–Crick pairing interaction between the ligand and RNA. These compounds, which also modulate transcriptional termination as efficiently as guanine, open up a significant new chemical space of guanine modifications in the search for antimicrobial agents that target purine riboswitches.

Covalent Inhibition in Drug Discovery

Although covalent inhibitors have been used as therapeutics for more than a century, there has been general resistance in the pharmaceutical industry against their further development due to safety concerns. This inclination has recently been reverted after the development of a wide variety of covalent inhibitors to address human health conditions along with the US Food and Drug Administration (FDA) approval of several covalent therapeutics for use in humans. Along with this exciting resurrection of an old drug discovery concept, this review surveys enzymes that can be targeted by covalent inhibitors for the treatment of human diseases. We focus on protein kinases, RAS proteins, and a few other enzymes that have been studied extensively as targets for covalent inhibition, with the aim to address challenges in designing effective covalent drugs and to provide suggestions in the area that have yet to be explored.

Absolute Binding Free Energy Calculation and Design of a Subnanomolar Inhibitor of Phosphodiesterase-10

Accurate prediction of absolute protein-ligand binding free energy could considerably enhance the success rate of structure-based drug design but is extremely challenging and time-consuming. Free energy perturbation (FEP) has been proven reliable but is limited to prediction of relative binding free energies of similar ligands (with only minor structural differences) in binding with a same drug target in practical drug design applications. Herein, a Gaussian algorithm-enhanced FEP (GA-FEP) protocol has been developed to enhance the FEP simulation performance, enabling to efficiently carry out the FEP simulations on vanishing the whole ligand and, thus, predict the absolute binding free energies (ABFEs). Using the GA-FEP protocol, the FEP simulations for the ABFE calculation (denoted as GA-FEP/ABFE) can achieve a satisfactory accuracy for both structurally similar and diverse ligands in a dataset of more than 100 receptor-ligand systems. Further, our GA-FEP/ABFE-guided lead optimization against phosphodiesterase-10 led to the discovery of a subnanomolar inhibitor (IC₅₀ = 0.87 nM, ∼2000-fold improvement in potency) with cocrystal confirmation.

DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network

Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.

Recent development of CDK inhibitors: An overview of CDK/inhibitor co-crystal structures

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases that display crucial effects in regulation of cell cycle and transcription. While the excessive expression of CDKs is intimate related to the development of diseases including cancers, which provides opportunities for disease treatment. A large number of small molecules are explored targeting CDKs. CDK/inhibitor co-crystal structures play an important role during the exploration of inhibitors. So far nine kinds of CDK/inhibitor co-crystals have been determined, they account for the highest proportion among the Protein Data Bank (PDB) deposited crystal structures. Herein, we review main co-crystals of CDKs in complex with corresponding inhibitors reported in recent years, focusing our attention on the binding models and the pharmacological activities of inhibitors.

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.

Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase 1A (DYRK1A) Inhibitors as Potential Therapeutics

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a member of an evolutionarily conserved family of protein kinases that belongs to the CMGC group of kinases. DYRK1A, encoded by a gene located in the human chromosome 21q22.2 region, has attracted attention due to its association with both neuropathological phenotypes and cancer susceptibility in patients with Down syndrome (DS). Inhibition of DYRK1A attenuates cognitive dysfunctions in animal models for both DS and Alzheimer's disease (AD). Furthermore, DYRK1A has been studied as a potential cancer therapeutic target because of its role in the regulation of cell cycle progression by affecting both tumor suppressors and oncogenes. Consequently, selective synthetic inhibitors have been developed to determine the role of DYRK1A in various human diseases. Our perspective includes a comprehensive review of potent and selective DYRK1A inhibitors and their forthcoming therapeutic applications.

Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors

Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as potential CDK2 inhibitors, a systematic molecular modeling study was conducted on 35 purine derivatives as CDK2 inhibitors by combining three-dimensional quantitative SAR (3D-QSAR), virtual screening, molecular docking, and molecular dynamics (MD) simulations. The predictive CoMFA model (q² = 0.743, rpred2 = 0.991), the CoMSIA model (q² = 0.808, rpred2 = 0.990), and the Topomer CoMFA model (q² = 0.779, rpred2 = 0.962) were obtained. Contour maps revealed that the electrostatic, hydrophobic, hydrogen bond donor and steric fields played key roles in the QSAR models. Thirty-one novel candidate compounds with suitable predicted activity (predicted pIC₅₀ > 8) were designed by using the results of virtual screening. Molecular docking indicated that residues Asp86, Glu81, Leu83, Lys89, Lys33, and Gln131 formed hydrogen bonds with the ligand, which affected activity of the ligand. Based on the QSAR model prediction and molecular docking, two candidate compounds, I13 and I60 (predicted pIC₅₀ > 8, docking score > 10), with the most potential research value were further screened out. MD simulations of the corresponding complexes of these two candidate compounds further verified their stability. This study provided valuable information for the development of new potential CDK2 inhibitors.

Exploring Structure-Activity Relationships with Three-Dimensional Matched Molecular Pairs-A Review

A matched molecular pair (MMP) consists of two small molecules that differ by a few atoms only. The minor structural difference between the molecules allows a detailed analysis of changes in properties. Three-dimensional (3D) MMPs extend the concept of chemical similarity by spatial similarity. Conformations must be generated, and superimpositions have to be calculated. The additional complexity and uncertainty as well as the smaller amount of available experimental data substantially complicates the derivation of models. Nonetheless, there are some benefits that make the transition worthwhile. The 3D concept gives detailed insight into mechanisms behind several methods classically used by the 2D MMP approach. It can help to analyze disrupted series of structure-activity relationships or extend the 2D MMP concept with scaffold hopping. One of the most powerful features is the high confidence structure-activity relationship transfer between series of analogues. Several research groups have approached the problem from different directions. The models vary especially in the 3D similarity measure used and complexity of the applied descriptor selected or designed. Nonetheless, all approaches have increased the amount of information available by incorporating 3D structural information.

Structure-based discovery of cyclin-dependent protein kinase inhibitors

The cell fate-determining roles played by members of the cyclin-dependent protein kinase (CDK) family explain why their dysregulation can promote proliferative diseases, and identify them as potential targets for drug discovery in oncology and beyond. After many years of research, the first efficacious CDK inhibitors have now been registered for clinical use in a defined segment of breast cancer. Research is underway to identify inhibitors with appropriate CDK-inhibitory profiles to recapitulate this success in other disease settings. Here, we review the structural data that illustrate the interactions and properties that confer upon inhibitors affinity and/or selectivity toward different CDK family members. We conclude that where CDK inhibitors display selectivity, that selectivity derives from exploiting active site sequence peculiarities and/or from the capacity of the target CDK(s) to access conformations compatible with optimizing inhibitor-target interactions.

Lysine-Targeting Covalent Inhibitors

Targeted covalent inhibitors have gained widespread attention in drug discovery as a validated method to circumvent acquired resistance in oncology. This strategy exploits small-molecule/protein crystal structures to design tightly binding ligands with appropriately positioned electrophilic warheads. Whilst most focus has been on targeting binding-site cysteine residues, targeting nucleophilic lysine residues can also represent a viable approach to irreversible inhibition. However, owing to the basicity of the ϵ-amino group in lysine, this strategy generates a number of specific challenges. Herein, we review the key principles for inhibitor design, give historical examples, and present recent developments that demonstrate the potential of lysine targeting for future drug discovery.

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.

Explicit treatment of active-site waters enhances quantum mechanical/implicit solvent scoring: Inhibition of CDK2 by new pyrazolo[1,5-a]pyrimidines

We present comprehensive testing of solvent representation in quantum mechanics (QM)-based scoring of protein-ligand affinities. To this aim, we prepared 21 new inhibitors of cyclin-dependent kinase 2 (CDK2) with the pyrazolo[1,5-a]pyrimidine core, whose activities spanned three orders of magnitude. The crystal structure of a potent inhibitor bound to the active CDK2/cyclin A complex revealed that the biphenyl substituent at position 5 of the pyrazolo[1,5-a]pyrimidine scaffold was located in a previously unexplored pocket and that six water molecules resided in the active site. Using molecular dynamics, protein-ligand interactions and active-site water H-bond networks as well as thermodynamics were probed. Thereafter, all the inhibitors were scored by the QM approach utilizing the COSMO implicit solvent model. Such a standard treatment failed to produce a correlation with the experiment (R² = 0.49). However, the addition of the active-site waters resulted in significant improvement (R² = 0.68). The activities of the compounds could thus be interpreted by taking into account their specific noncovalent interactions with CDK2 and the active-site waters. In summary, using a combination of several experimental and theoretical approaches we demonstrate that the inclusion of explicit solvent effects enhance QM/COSMO scoring to produce a reliable structure-activity relationship with physical insights. More generally, this approach is envisioned to contribute to increased accuracy of the computational design of novel inhibitors.

Exponential repulsion improves structural predictability of molecular docking

Molecular docking is a powerful tool for theoretical prediction of the preferred conformation and orientation of small molecules within protein active sites. The obtained poses can be used for estimation of binding energies, which indicate the inhibition effect of designed inhibitors, and therefore might be used for in silico drug design. However, the evaluation of ligand binding affinity critically depends on successful prediction of the native binding mode. Contemporary docking methods are often based on scoring functions derived from molecular mechanical potentials. In such potentials, nonbonded interactions are typically represented by electrostatic interactions between atom-centered partial charges and standard 6-12 Lennard-Jones potential. Here, we present implementation and testing of a scoring function based on more physically justified exponential repulsion instead of the standard Lennard-Jones potential. We found that this scoring function significantly improved prediction of the native binding modes in proteins bearing narrow active sites such as serine proteases and kinases. © 2016 Wiley Periodicals, Inc.

Combined PI3K and CDK2 inhibition induces cell death and enhances in vivo antitumour activity in colorectal cancer

BACKGROUND

The phosphatidylinositol-3-kinase/mammalian target of rapamycin (PI3K/mTOR) pathway is commonly deregulated in human cancer, hence many PI3K and mTOR inhibitors have been developed and have now reached clinical trials. Similarly, CDKs have been investigated as cancer drug targets.

METHODS

We have synthesised and characterised a series of 6-aminopyrimidines identified from a kinase screen that inhibit PI3K and/or mTOR and/or CDK2. Kinase inhibition, tumour cell growth, cell cycle distribution, cytotoxicity and signalling experiments were undertaken in HCT116 and HT29 colorectal cancer cell lines, and in vivo HT29 efficacy studies.

RESULTS

2,6-Diaminopyrimidines with an O(4)-cyclohexylmethyl substituent and a C-5-nitroso or cyano group (1,2,5) induced cell cycle phase alterations and were growth inhibitory (GI50<20 μM). Compound 1, but not 2 or 5, potently inhibits CDK2 (IC50=0.1 nM) as well as PI3K, and was cytotoxic at growth inhibitory concentrations. Consistent with kinase inhibition data, compound 1 reduced phospho-Rb and phospho-rS6 at GI50 concentrations. Combination of NU6102 (CDK2 inhibitor) and pictilisib (GDC-0941; pan-PI3K inhibitor) resulted in synergistic growth inhibition, and enhanced cytotoxicity in HT29 cells in vitro and HT29 tumour growth inhibition in vivo.

CONCLUSIONS

These studies identified a novel series of mixed CDK2/PI3K inhibitors and demonstrate that dual targeting of CDK2 and PI3K can result in enhanced antitumour activity.

Cyclin A2 and CDK2 as Novel Targets of Aspirin and Salicylic Acid: A Potential Role in Cancer Prevention

Data emerging from the past 10 years have consolidated the rationale for investigating the use of aspirin as a chemopreventive agent; however, the mechanisms leading to its anticancer effects are still being elucidated. We hypothesized that aspirin's chemopreventive actions may involve cell-cycle regulation through modulation of the levels or activity of cyclin A2/cyclin-dependent kinase-2 (CDK2). In this study, HT-29 and other diverse panel of cancer cells were used to demonstrate that both aspirin and its primary metabolite, salicylic acid, decreased cyclin A2 (CCNA2) and CDK2 protein and mRNA levels. The downregulatory effect of either drugs on cyclin A2 levels was prevented by pretreatment with lactacystin, an inhibitor of proteasomes, suggesting the involvement of 26S proteasomes. In-vitro kinase assays showed that lysates from cells treated with salicylic acid had lower levels of CDK2 activity. Importantly, three independent experiments revealed that salicylic acid directly binds to CDK2. First, inclusion of salicylic acid in naïve cell lysates, or in recombinant CDK2 preparations, increased the ability of the anti-CDK2 antibody to immunoprecipitate CDK2, suggesting that salicylic acid may directly bind and alter its conformation. Second, in 8-anilino-1-naphthalene-sulfonate (ANS)-CDK2 fluorescence assays, preincubation of CDK2 with salicylic acid dose-dependently quenched the fluorescence due to ANS. Third, computational analysis using molecular docking studies identified Asp145 and Lys33 as the potential sites of salicylic acid interactions with CDK2. These results demonstrate that aspirin and salicylic acid downregulate cyclin A2/CDK2 proteins in multiple cancer cell lines, suggesting a novel target and mechanism of action in chemoprevention.

IMPLICATIONS

Biochemical and structural studies indicate that the antiproliferative actions of aspirin are mediated through cyclin A2/CDK2.

Design strategies, structure activity relationship and mechanistic insights for purines as kinase inhibitors

Kinases control a diverse set of cellular processes comprising of reversible phosphorylation of proteins. Protein kinases play a pivotal role in human tumor cell proliferation, migration and survival of neoplasia. In the recent past, purine based molecules have emerged as significantly potent kinase inhibitors. In view of their promising potential for the inhibition of kinases, this review article focuses on purines which have progressed as kinase inhibitors during the last five years. A detailed account of the design strategies employed for the synthesis of purine analogs exerting inhibitory effects on diverse kinases has been presented. Apart from presenting the design strategies, the article also highlights the structure activity relationship along with mechanistic insights revealed during the biological evaluation of the purine analogs for kinase inhibition. The interactions with the amino acid residues responsible for kinase inhibitory potential of purine based molecules have also been discussed. In this assemblage, purine based protein kinase inhibitors patented in the past have also been summarized in the tabular form. This compilation will be of great interest for the researchers working in the area of protein kinase inhibitors.

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.

Recent Advances on Small-Molecule Survivin Inhibitors

Survivin, a member of the inhibitor of apoptosisproteins family, is highly expressed in most human neoplasms, but its expression is very low or undetectable in terminally differentiated normal tissues. Survivin has been shown to inhibit cancer cell apoptosis and promote cell proliferation. The overexpression of survivin closely correlates with tumor progression and drug resistance. Because of its key role in tumor formation and maintenance, survivin is considered as an ideal target for anticancer treatment. However, the development of small-molecule survivin inhibitors has been challenging due to the requirement to disrupt the protein-protein interactions. Currently only a limited number of survivin inhibitors have been developed in recent years, and most of these inhibitors reduce survivin levels by interacting with other biomolecules instead of directly interacting with survivin protein. Despite these challenges, developing potent and selective small-molecule survivin inhibitors will be important in both basic science to better understand survivin biology and in translational research to develop potentially more effective, broad-spectrum anticancer agents. In this review, the functions of survivin and its role in cancer are summarized. Recent developments, challenges, and future direction of small-molecule survivin inhibitors are also discussed in detail.

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.

Targeting cyclin-dependent kinases in human cancers: from small molecules to Peptide inhibitors

Cyclin-dependent kinases (CDK/Cyclins) form a family of heterodimeric kinases that play central roles in regulation of cell cycle progression, transcription and other major biological processes including neuronal differentiation and metabolism. Constitutive or deregulated hyperactivity of these kinases due to amplification, overexpression or mutation of cyclins or CDK, contributes to proliferation of cancer cells, and aberrant activity of these kinases has been reported in a wide variety of human cancers. These kinases therefore constitute biomarkers of proliferation and attractive pharmacological targets for development of anticancer therapeutics. The structural features of several of these kinases have been elucidated and their molecular mechanisms of regulation characterized in depth, providing clues for development of drugs and inhibitors to disrupt their function. However, like most other kinases, they constitute a challenging class of therapeutic targets due to their highly conserved structural features and ATP-binding pocket. Notwithstanding, several classes of inhibitors have been discovered from natural sources, and small molecule derivatives have been synthesized through rational, structure-guided approaches or identified in high throughput screens. The larger part of these inhibitors target ATP pockets, but a growing number of peptides targeting protein/protein interfaces are being proposed, and a small number of compounds targeting allosteric sites have been reported.

Assessment of the Potential of CDK2 Inhibitor NU6140 to Influence the Expression of Pluripotency Markers NANOG, OCT4, and SOX2 in 2102Ep and H9 Cells

As cyclin-dependent kinases (CDKs) regulate cell cycle progression and RNA transcription, CDKs are attractive targets for creating cancer cell treatments. In this study we investigated the effects of the small molecular agent NU6140 (inhibits CDK2 and cyclin A interaction) on human embryonic stem (hES) cells and embryonal carcinoma-derived (hEC) cells via the expression of transcription factors responsible for pluripotency. A multiparameter flow cytometric method was used to follow changes in the expression of NANOG, OCT4, and SOX2 together in single cells. Both hES and hEC cells responded to NU6140 treatment by induced apoptosis and a decreased expression of NANOG, OCT4, and SOX2 in surviving cells. A higher sensitivity to NU6140 application in hES than hEC cells was detected. NU6140 treatment arrested hES and hEC cells in the G2 phase and inhibited entry into the M phase as evidenced by no significant increase in histone 3 phosphorylation. When embryoid bodies (EBs) formed from NU6104 treated hES cells were compared to EBs from untreated hES cells differences in ectodermal, endodermal, and mesodermal lineages were found. The results of this study highlight the importance of CDK2 activity in maintaining pluripotency of hES and hEC cells and in differentiation of hES cells.