Charge optimization of the interface between protein kinases and their ligands

Examining the potential for electrostatic complementarity between a ligand and a receptor is a useful technique for rational drug design, and can demonstrate how a system prioritizes interactions when allowed to optimize its charge distribution. In this computational study, we implemented the previously developed, continuum solvent-based charge optimization theory with a simple, quadratic programming algorithm and the UHBD Poisson-Boltzmann solver. This method allows one to compute the best set of point charges for a ligand or ligand region based on the ligand and receptor shape, and the receptor partial charges, by optimizing the binding free energy obtained from a continuum-solvent model. We applied charge optimization to a fragment of the heat-stable protein kinase inhibitor (PKI) of protein kinase A (PKA), to three flavopiridol inhibitors of CDK2, and to cyclin A which interacts with CDK2 to regulate the cell cycle. We found that a combination of global (involving every charge) and local (involving only charges in a local region) optimization can give useful hints for designing better inhibitors. Although some parts of an inhibitor may already contribute significantly to binding, we found that they could still be the most important targets for modifications to obtain stronger binders. In studying the binding of flavopiridol inhibitors to CDK2, comparable binding affinity could be obtained regardless of whether the net charges of the inhibitors were constrained to -2, -1, 0, 1, or 2 during the optimization. This provides flexibility in inhibitor design when a certain net charge of the inhibitor is desired in addition to strong binding affinity. For the study of the PKA-PKI and CDK2-cyclin A interfaces, we identified residues whose charge distributions are already close to optimal and those whose charge distributions could be refined to further improve binding.     

Transferable scoring function based on semiempirical quantum mechanical PM6-DH2 method: CDK2 with 15 structurally diverse inhibitors

A semiempirical quantum mechanical PM6-DH2 method accurately covering the dispersion interaction and H-bonding was used to score fifteen structurally diverse CDK2 inhibitors. The geometries of all the complexes were taken from the X-ray structures and were reoptimised by the PM6-DH2 method in continuum water. The total scoring function was constructed as an estimate of the binding free energy, i.e., as a sum of the interaction enthalpy, interaction entropy and the corrections for the inhibitor desolvation and deformation energies. The applied scoring function contains a clear thermodynamical terms and does not involve any adjustable empirical parameter. The best correlations with the experimental inhibition constants (ln K _i) were found for bare interaction enthalpy (r ² = 0.87) and interaction enthalpy corrected for ligand desolvation and deformation energies (r ² = 0.77); when the entropic term was considered, however, the correlation becomes worse but still acceptable (r ² = 0.52). The resulting correlation based on the PM6-DH2 scoring function is better than previously published function based on various docking/scoring, SAR studies or advanced QM/MM approach, however, the robustness is limited by number of available experimental data used in the correlation. Since a very similar correlation between the experimental and theoretical results was found also for a different system of the HIV-1 protease, the suggested scoring function based on the PM6-DH2 method seems to be applicable in drug design, even if diverse protein–ligand complexes have to be ranked.     

Differential binding of inhibitors to active and inactive CDK2 provides insights for drug design

The cyclin-dependent kinases (CDKs) have been characterized in complex with a variety of inhibitors, but the majority of structures solved are in the inactive form. We have solved the structures of six inhibitors in both the monomeric CDK2 and binary CDK2/cyclinA complexes and demonstrate that significant differences in ligand binding occur depending on the activation state. The binding mode of two ligands in particular varies substantially in active and inactive CDK2. Furthermore, energetic analysis of CDK2/cyclin/inhibitors demonstrates that a good correlation exists between the in vitro potency and the calculated energies of interaction, but no such relationship exists for CDK2/inhibitor structures. These results confirm that monomeric CDK2 ligand complexes do not fully reflect active conformations, revealing significant implications for inhibitor design while also suggesting that the monomeric CDK2 conformation can be selectively inhibited.     

Identification of cylin-dependent kinase 1 inhibitors of a new chemical type by structure-based design and database searching

We have selected cyclin-dependent kinase 1 (CDK1), an enzyme participating in the regulation of the cell cycle, as a target in our efforts to discover new antitumor agents. By exploiting available structural information, we designed an ATP-site directed ligand scaffold that allowed us to identify 4-(3-methyl-1,4-dioxo-1,4-dihydro-naphthalen-2-ylamino)-benzenesulfonamide as a new potent inhibitor of CDK1 in a subsequent database search. The synthesis and testing of some analogues confirmed the interest of this class of compounds as novel CDK1 inhibitors.     

Structural determinants of CDK4 inhibition and design of selective ATP competitive inhibitors

A number of selective inhibitors of the CDK4/cyclin D1 complex have been reported recently. Due to the absence of an experimental CDK4 structure, the ligand and protein determinants contributing to CDK4 selectivity are poorly understood at present. Here, we report the use of computational methods to elucidate the characteristics of selectivity and to derive the structural basis for specific, high-affinity binding of inhibitors to the CDK4 active site. From these data, the hypothesis emerged that appropriate incorporation of an ionizable function into a CDK2 inhibitor results in more favorable binding to CDK4. This knowledge was applied to the design of compounds in the otherwise CDK2-selective 2-anilino-4-(thiazol-5-yl)pyrimidine pharmacophore that are potent and highly selective ATP antagonists of CDK4/cyclin D1. The findings of this study also have significant implications in the design of CDK4 mimic structures based on CDK2.     

Access to 2‐Arylindoles via Decarboxylative C−C Coupling in Aqueous Medium and to Heteroaryl Carboxylates under Base‐Free Conditions using Diaryliodonium Salts

Easily accessible heteroaromatic carboxylic acids and diaryliodonium salts were successfully employed to construct valuable 2‐arylindoles and heteroaryl carboxylates in a regioselective fashion. C2‐arylated indoles were produced using a Pd‐catalyzed decarboxylative strategy in water without any base, oxidant, or ligand. Heteroaryl carboxylates were prepared under metal and base‐free conditions. This protocol was successfully utilized to synthesize Paullone, a cyclin‐dependent kinase (CDK) inhibitor.     

A Bayesian statistical approach of improving knowledge‐based scoring functions for protein–ligand interactions

Knowledge‐based scoring functions are widely used for assessing putative complexes in protein–ligand and protein–protein docking and for structure prediction. Even with large training sets, knowledge‐based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge‐based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge‐based scoring function with an alternative, force‐field‐based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein–ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge‐based scoring functions and can also be applied to protein–protein docking and protein structure prediction. © 2014 Wiley Periodicals, Inc.     

Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA

In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM‐PBSA and MM‐GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein‐ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy‐minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein‐ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM‐PBSA or MM‐GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Inhibition of cyclin-dependent kinases, GSK-3beta and CK1 by hymenialdisine, a marine sponge constituent

BACKGROUND: Over 2000 protein kinases regulate cellular functions. Screening for inhibitors of some of these kinases has already yielded some potent and selective compounds with promising potential for the treatment of human diseases. RESULTS: The marine sponge constituent hymenialdisine is a potent inhibitor of cyclin-dependent kinases, glycogen synthase kinase-3beta and casein kinase 1. Hymenialdisine competes with ATP for binding to these kinases. A CDK2-hymenialdisine complex crystal structure shows that three hydrogen bonds link hymenialdisine to the Glu81 and Leu83 residues of CDK2, as observed with other inhibitors. Hymenialdisine inhibits CDK5/p35 in vivo as demonstrated by the lack of phosphorylation/down-regulation of Pak1 kinase in E18 rat cortical neurons, and also inhibits GSK-3 in vivo as shown by the inhibition of MAP-1B phosphorylation. Hymenialdisine also blocks the in vivo phosphorylation of the microtubule-binding protein tau at sites that are hyperphosphorylated by GSK-3 and CDK5/p35 in Alzheimer's disease (cross-reacting with Alzheimer's-specific AT100 antibodies). CONCLUSIONS: The natural product hymenialdisine is a new kinase inhibitor with promising potential applications for treating neurodegenerative disorders.     

Effect of ligand volume correction on PMF scoring

Recently, a knowledge‐based scoring function has been introduced that estimates the protein‐binding affinity based on the 3D structure of a protein–ligand complex (J Med Chem 1999, 42, 791). A ligand volume correction factor has been proposed and applied to filter out intraligand interactions in this simplified potential approach. Here we evaluate the effect of the ligand volume correction on the predictive power of the PMF scoring function. It is found that the effect of the ligand volume correction is significant on the derived potentials and large on the overall score. However, the effect of the ligand correction on the predictive power of the scoring function appears to be smaller. For a test set containing serine proteases the predictive power of the PMF scoring function does not change with the introduction of the volume correction. For a test set of metalloprotease complexes, the predictive power of the PMF scoring function improves only slightly when the volume correction is applied. For five test sets comprising a total of 225 diverse protein ligand complexes taken from the Brookhaven Protein Data Bank it is found, however, that the introduction of the ligand volume correction consistently improves the correlation between the PMF scores and the measured binding affinities. The effect of the correction factor on docking/scoring experiments is also analyzed using a test set of 61 biphenyl inhibitor‐stromelysin complexes. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 418–425, 2001     

Combinatorial ligand design targeted at protein families

We describe a method to create ligands specific for a given protein family. The method is applied to generate ligand candidates for the cyclin-dependent kinase (CDK) family. The CDK family of proteins is involved in regulating the cell cycle by alternately activating and deactivating the cell's progression through the cycle. CDKs are activated by association with cyclin and are inhibited by complexation with small molecules. X-ray crystal structures are available for three of the thirteen known CDK family members: CDK2, CDK5 and CDK 6. In this work, we use novel computational approaches to design ligand candidates that are potentially inhibitory across the three CDK family members as well as more specific molecules which can potentially inhibit one or any combination of two of the three CDK family members. We define a new scoring term, SpecScore, to quantify the potential inhibitory power of the generated structures. According to a search of the World Drug Alerts, the highest scoring SpecScore molecule that is specific for the three CDK family members shows very similar chemical characteristics and functional groups to numerous molecules known to deactivate several members of the CDK family.     

Computer simulations of ligand–protein binding with ensembles of protein conformations: A Monte Carlo study of HIV‐1 protease binding energy landscapes

We present the results of molecular docking simulations with HIV‐1 protease for the sb203386 and skf107457 inhibitors by Monte Carlo simulated annealing. A simplified piecewise linear energy function, the standard AMBER force field, and the AMBER force field with solvation and a soft‐core smoothing component are employed in simulations with a single‐protein conformation to determine the relationship between docking simulations with a simple energy function and more realistic force fields. The temperature‐dependent binding free energy profiles of the inhibitors interacting with a single protein conformation provide a detailed picture of relative thermodynamic stability and a distribution of ligand binding modes in agreement with experimental crystallographic data. Using the simplified piecewise linear energy function, we also performed Monte Carlo docking simulations with an ensemble of protein conformations employing preferential biased sampling of low‐energy protein conformations, and the results are analyzed in connection with the free energy profiles. ©1999 John Wiley & Sons, Inc. Int J Quant Chem 72: 73–84, 1999     

A Combination of Spin Diffusion Methods for the Determination of Protein–Ligand Complex Structural Ensembles

Structure‐based drug design (SBDD) is a powerful and widely used approach to optimize affinity of drug candidates. With the recently introduced INPHARMA method, the binding mode of small molecules to their protein target can be characterized even if no spectroscopic information about the protein is known. Here, we show that the combination of the spin‐diffusion‐based NMR methods INPHARMA, trNOE, and STD results in an accurate scoring function for docking modes and therefore determination of protein–ligand complex structures. Applications are shown on the model system protein kinase A and the drug targets glycogen phosphorylase and soluble epoxide hydrolase (sEH). Multiplexing of several ligands improves the reliability of the scoring function further. The new score allows in the case of sEH detecting two binding modes of the ligand in its binding site, which was corroborated by X‐ray analysis.     

Cross-docking of inhibitors into CDK2 structures. 1

Predicting protein/ligand binding affinity is one of the most challenging computational chemistry tasks. Numerous methods have been developed to address this challenge, but they all have limitations. Failure to account for protein flexibility has been a shortcoming of many methods. In this cross-docking study the data set comprised 150 inhibitor complexes of the protein kinase CDK2. Gold and Glide performed well in terms of docking accuracy. The chance of cross-docking a ligand within a 2 A RMSD of its experimental pose was found to be 50%. Relative binding potency was not properly predicted from scoring functions, even though cross-docking of each inhibitor into each protein structure was performed and only scores of correctly docked ligands were considered. An accompanying paper (Voigt, J. H.; Elkin, C.; Madison, V. S. Duca, J. S. J. Chem. Inf. Model. 2008, 48, 669-678) covers cross-docking and docking accuracy from the perspective of using multiple protein structures.     

A CIEF‐LIF method for simultaneous analysis of multiple protein kinases and screening of inhibitors

Here, a CIEF‐LIF method for multiple protein kinase simultaneous analysis and inhibitors throughput screening with fast rate and low cost is presented. Comparing with CZE, CIEF‐LIF exhibited great focusing ability and high separation efficiency for substrate and phosphorylated peptides, and is applicable for multiple kinases simultaneous analysis regardless of their substrate peptides compositions and charge statuses. Thus, highly sensitive analysis for cyclic adenosine 3’, 5’‐monophosphate‐dependent protein kinase (PKA) and cyclin‐dependent kinase 1 (CDK1) was achieved in CIEF‐LIF analysis with detection sensitivity up to 1.25 mU/μL and 0.4 mU/μL, respectively, two magnitudes higher than that of CZE and comparable with that in nanomaterials or green fluorescent protein‐based kinase assay. Moreover, the inhibition effect of inhibitors on multiple kinases could be simultaneously readout in a single electrophoretic run, with half maximal inhibitory concentration of H‐89 for PKA and Ro‐3306 for CDK1 calculated as 37.0 and 35.9 nM, respectively, consistent with literatures reported. The CIEF‐LIF also exhibited strong anti‐interference ability in human breast cancer cell lysates analysis and simulators such as forskolin and 3‐isobutyl‐1‐methylxantine assessment. Therefore, CIEF‐LIF is desirable for future biological application and clinical diagnostics and drug discovery.     

A molecular dynamics investigation of CDK8/CycC and ligand binding: conformational flexibility and implication in drug discovery

Abnormal activity of cyclin-dependent kinase 8 (CDK8) along with its partner protein cyclin C (CycC) is a common feature of many diseases including colorectal cancer. Using molecular dynamics (MD) simulations, this study determined the dynamics of the CDK8-CycC system and we obtained detailed breakdowns of binding energy contributions for four type-I and five type-II CDK8 inhibitors. We revealed system motions and conformational changes that will affect ligand binding, confirmed the essentialness of CycC for inclusion in future computational studies, and provide guidance in development of CDK8 binders. We employed unbiased all-atom MD simulations for 500 ns on twelve CDK8-CycC systems, including apoproteins and protein–ligand complexes, then performed principal component analysis (PCA) and measured the RMSF of key regions to identify protein dynamics. Binding pocket volume analysis identified conformational changes that accompany ligand binding. Next, H-bond analysis, residue-wise interaction calculations, and MM/PBSA were performed to characterize protein–ligand interactions and find the binding energy. We discovered that CycC is vital for maintaining a proper conformation of CDK8 to facilitate ligand binding and that the system exhibits motion that should be carefully considered in future computational work. Surprisingly, we found that motion of the activation loop did not affect ligand binding. Type-I and type-II ligand binding is driven by van der Waals interactions, but electrostatic energy and entropic penalties affect type-II binding as well. Binding of both ligand types affects protein flexibility. Based on this we provide suggestions for development of tighter-binding CDK8 inhibitors and offer insight that can aid future computational studies.     

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure‐based computer‐aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand–protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state‐of‐the‐art docking programs. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.