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.     

Novel strategy for three-dimensional fragment-based lead discovery

Fragment-based drug design (FBDD) is considered a promising approach in lead discovery. However, for a practical application of this approach, problems remain to be solved. Hence, a novel practical strategy for three-dimensional lead discovery is presented in this work. Diverse fragments with spatial positions and orientations retained in separately adjacent regions were generated by deconstructing well-aligned known inhibitors in the same target active site. These three-dimensional fragments retained their original binding modes in the process of new molecule construction by fragment linking and merging. Root-mean-square deviation (rmsd) values were used to evaluate the conformational changes of the component fragments in the final compounds and to identify the potential leads as the main criteria. Furthermore, the successful validation of our strategy is presented on the basis of two relevant tumor targets (CDK2 and c-Met), demonstrating the potential of our strategy to facilitate lead discovery against some drug targets.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

Ligand‐Induced Conformational Changes in HSP90 Monitored Time Resolved and Label Free—Towards a Conformational Activity Screening for Drug Discovery

Investigation of protein–ligand interactions is crucial during early drug‐discovery processes. ATR‐FTIR spectroscopy can detect label‐free protein–ligand interactions with high spatiotemporal resolution. Here we immobilized, as an example, the heat shock protein HSP90 on an ATR crystal. This protein is an important molecular target for drugs against several diseases including cancer. With our novel approach we investigated a ligand‐induced secondary structural change. Two specific binding modes of 19 drug‐like compounds were analyzed. Different binding modes can lead to different efficacy and specificity of different drugs. In addition, the k_obs values of ligand dissociation were obtained. The results were validated by X‐ray crystallography for the structural change and by SPR experiments for the dissociation kinetics, but our method yields all data in a single and simple experiment.     

Systems biology and systems chemistry: new directions for drug discovery

Improvements in drug design have historically been centered around structure-based optimization of molecule specificity for a targeted protein, in an effort to reduce unintentional binding to other proteins and off-target effects. Although the "one-to-one" drug design strategy has been successful in impairing function of targets associated with a number of diseases, recent reports of drug promiscuity, which are a potential source of adverse reactions in patients, make a case to refine the drug design strategy such that it includes an awareness of multiple interactions from both ligand and protein perspectives. Polypharmacology and chemical biology studies are amassing interaction data at rapid rates, and the integration of such data into an interpretable model requires zooming our perspective out from the single ligand-target level to the larger network-wide level. We review some of the recent developments in systems-level research for drug design and discovery, and discuss the directions that some drug design efforts are heading toward.     

De novo ligand design with explicit water molecules: an application to bacterial neuraminidase

Most computer-aided drug design methods ignore the presence of crystallographically-determined water molecules in the binding site of a target protein. In this paper, our de novo ligand design methods are applied to the X-ray crystal structure of bacterial neuraminidase in the presence of some selected water molecules. We have found that, for this particular protein, the complete removal of all bound water molecules leads to difficulties in generating any potential ligands if the unsatisfied hydrogen-bonding sitepoints left by removing these water molecules are to be satisfied by a ligand. As more of the crystallographically determined water molecules are allowed in the binding site, it becomes much easier to generate ligands in larger numbers and with wider chemical diversity. This example shows that, in some cases, bound water molecules can be more accessible for hydrogen bonding to an incoming ligand than the actual protein binding sitepoints associated with them. From the point of view of de novo ligand design, water molecules can thus act as versatile amphiprotic hydrogen-bonding sitepoints and reduce the conformational constraints of a particular binding site.     

A Hexasaccharide Containing Rare 2‐O‐Sulfate‐Glucuronic Acid Residues Selectively Activates Heparin Cofactor II

Glycosaminoglycan (GAG) sequences that selectively target heparin cofactor II (HCII), a key serpin present in human plasma, remain unknown. Using a computational strategy on a library of 46 656 heparan sulfate hexasaccharides we identified a rare sequence consisting of consecutive glucuronic acid 2‐O ‐sulfate residues as selectively targeting HCII. This and four other unique hexasaccharides were chemically synthesized. The designed sequence was found to activate HCII ca. 250‐fold, while leaving aside antithrombin, a closely related serpin, essentially unactivated. This group of rare designed hexasaccharides will help understand HCII function. More importantly, our results show for the first time that rigorous use of computational techniques can lead to discovery of unique GAG sequences that can selectively target GAG‐binding protein(s), which may lead to chemical biology or drug discovery tools.     

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ?=?0.614), performed slightly better than our ligand-based methods (ρ?=?0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.     

Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched?

Fast and accurate predicting of the binding affinities of large sets of diverse protein?ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein?ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K(i)/K(d) units from measured inhibition affinities and the highest Pearson's correlation coefficient of R(p)2 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high-quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge-based scoring functions in binding affinity prediction.     

Docking-undocking combination applied to the D3R Grand Challenge 2015

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.     

Small molecule inhibitors of the MDM2-p53 interaction discovered by ensemble-based receptor models

Five nonpeptide, small-molecule inhibitors of the human MDM2-p53 interaction are presented, and each inhibitor represents a new scaffold. The most potent compound exhibited a Ki of 110 +/- 30 nM. These compounds were identified using our multiple protein structure (MPS) method which incorporates protein flexibility into a receptor-based pharmacophore model that identifies appropriate hotspots of binding. Docking the inhibitors with an induced-fit docking protocol suggested that the inhibitors mimicked the three critical binding residues of p53 (Phe19, Trp23, and Leu26). Docking also predicted a new orientation of the scaffolds that more fully fills the binding cleft, enabling the inhibitors to take advantage of additional hydrogen-bonding possibilities not explored by other small molecule inhibitors. One inhibitor in particular was proposed to probe the hydrophobic core of the protein by taking advantage of the flexibility of the binding cleft floor. These results show that the MPS technique is a promising advance for structure-based drug discovery and that the method can truly explore broad chemical space efficiently in the quest to discover potent, small-molecule inhibitors of protein-protein interactions. Our MPS technique is one of very few ensemble-based techniques to be proven through experimental verification of the discovery of new inhibitors.     

Boron‐Based Drug Design

The use of the element boron, which is not generally observed in a living body, possesses a high potential for the discovery of new biological activity in pharmaceutical drug design. In this account, we describe our recent developments in boron‐based drug design, including boronic acid containing protein tyrosine kinase inhibitors, proteasome inhibitors, and tubulin polymerization inhibitors, and ortho‐carborane‐containing proteasome activators, hypoxia‐inducible factor 1 inhibitors, and topoisomerase inhibitors. Furthermore, we applied a closo‐dodecaborate as a water‐soluble moiety as well as a boron‐10 source for the design of boron carriers in boron neutron capture therapy, such as boronated porphyrins and boron lipids for a liposomal boron delivery system.     

High-throughput screening of protein binding by equilibrium dialysis combined with liquid chromatography and mass spectrometry

A new approach for screening plasma protein binding is presented. The method is based on equilibrium dialysis combined with rapid generic LC-MS bioanalysis by using a sample pooling approach enabling high-throughput screening of protein binding in the drug discovery phase. The method is evaluated by a comparison of measured unbound free fractions f(u) (%) between single and pooled compounds for a test set of structurally diverse compounds with a wide range of unbound fractions. Test compounds include 1 acidic and 10 basic drug standards along with 36 new chemical entities. A good correlation (R2>0.95) of f(u) (%) between the single and pooled compounds is found, suggesting that at least 10 compounds can be simultaneously measured with acceptable accuracy. A simplified drug-protein binding model is applied to calculate the f(u) (%) of drugs at various drug and protein concentrations and this is applied to elucidate the applicability of the sample pooling approach from a theoretical standpoint. Moreover, pH shifts in the plasma were observed after dialysis when using different types of buffers and the impact of that on the f(u) is illustrated in association with their physicochemical properties, in particular the ionization state of compounds by the profile of effective mobility as a function of pH. A new buffer is proposed being able to minimize the pH shift of plasma during the dialysis. In addition, the application of the proposed buffer does not necessarily require adjusting plasma pH before the dialysis and utilizing a CO2 incubator during the dialysis. The effect of the ionic strengths of different buffers on MS signals is investigated with regard to ion suppression. The sample pooling method not only significantly reduces the plasma volume required but also the number of bioanalysis samples as compared to the single compound measurements by a conventional approach. The new proposed approach is especially beneficial for measuring in vitro protein binding in matrices such as mouse plasma where plasma is available only in limited amounts. The current new development will facilitate the drug discovery process by more rapidly assessing the protein binding potential of drug candidates.     

Compressed images for affinity prediction (CIFAP): a study on predicting binding affinities for checkpoint kinase 1 protein inhibitors

Analyses of known protein–ligand interactions play an important role in designing novel and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have proven useful in the design of novel drugs, which utilize intelligent techniques to predict the outcome of unknown protein–ligand interactions by learning from the physical and geometrical properties of known protein–ligand interactions. The aim of this study is to work through a specific example of a novel computational method, namely compressed images for affinity prediction (CIFAP), in which binding affinities for structurally related ligands in complexes with human checkpoint kinase 1 (CHK1) are predicted. The CIFAP algorithm presented here relates published pIC ₅₀ values of 57 compounds, derived from a thienopyridine pharmacophore, in complexes with CHK1 to their two‐dimensional (2D) electrostatic potential images compressed in orthogonal dimensions. Patterns obtained from the 2D images are then used as inputs in regression and learning algorithms such as support vector regression (SVR) and adaptive neuro‐fuzzy inference system (ANFIS) methods to validate the experimental pIC ₅₀ values. This study revealed that the 2D image pixels in the vicinity of bound ligand surfaces provide more relevant information to make correlations with the empirical pIC ₅₀ values. As compared with ANFIS, SVR gave rise to the lowest root mean square errors and the greatest correlations, suggesting that SVR could be a plausible choice of machine learning methods in predicting binding affinities by CIFAP. Copyright © 2013 John Wiley & Sons, Ltd.     

A Blocking Group Scan Using a Spherical Organometallic Complex Identifies an Unprecedented Binding Mode with Potent Activity In Vitro and In Vivo for the Opioid Peptide Dermorphin

Herein, the selective enforcement of one particular receptor‐ligand interaction between specific domains of the μ‐selective opioid peptide dermorphin and the μ opioid receptor is presented. For this, a blocking group scan is described which exploits the steric demand of a bis(quinolinylmethyl)amine rhenium(I) tricarbonyl complex conjugated to a number of different, strategically chosen positions of dermorphin. The prepared peptide conjugates lead to the discovery of two different binding modes: An expected N‐terminal binding mode corresponds to the established view of opioid peptide binding, whereas an unexpected C‐terminal binding mode is newly discovered. Surprisingly, both binding modes provide high affinity and agonistic activity at the μ opioid receptor in vitro. Furthermore, the unprecedented C‐terminal binding mode shows potent dose‐dependent antinociception in vivo. Finally, in silico docking studies support receptor activation by both dermorphin binding modes and suggest a biological relevance for dermorphin itself. Relevant ligand‐protein interactions are similar for both binding modes, which is in line with previous protein mutation studies.     

Assessment and acceleration of binding energy calculations for protein–ligand complexes by the fragment molecular orbital method

In the field of drug discovery, it is important to accurately predict the binding affinities between target proteins and drug applicant molecules. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics‐based force fields, although they cannot fully describe protein–ligand interactions. A noteworthy computational method in development involves large‐scale electronic structure calculations. Fragment molecular orbital (FMO) method, which is one of such large‐scale calculation techniques, is applied in this study for calculating the binding energies between proteins and ligands. By testing the effects of specific FMO calculation conditions (including fragmentation size, basis sets, electron correlation, exchange‐correlation functionals, and solvation effects) on the binding energies of the FK506‐binding protein and 10 ligand complex molecule, we have found that the standard FMO calculation condition, FMO2‐MP2/6‐31G(d), is suitable for evaluating the protein–ligand interactions. The correlation coefficient between the binding energies calculated with this FMO calculation condition and experimental values is determined to be R = 0.77. Based on these results, we also propose a practical scheme for predicting binding affinities by combining the FMO method with the quantitative structure–activity relationship (QSAR) model. The results of this combined method can be directly compared with experimental binding affinities. The FMO and QSAR combined scheme shows a higher correlation with experimental data (R = 0.91). Furthermore, we propose an acceleration scheme for the binding energy calculations using a multilayer FMO method focusing on the protein–ligand interaction distance. Our acceleration scheme, which uses FMO2‐HF/STO‐3G:MP2/6‐31G(d) at R_int = 7.0 Å, reduces computational costs, while maintaining accuracy in the evaluation of binding energy. © 2015 Wiley Periodicals, Inc.     

SEC–MS as an Approach to Isolate and Directly Identifying Small Molecular GPCR–Ligands from Complex Mixtures Without Labeling

G protein coupled receptors (GPCRs) belong to the most successful targets in drug discovery. However, the development of assays with an appropriately labeled high affinity reporter compound is laborious. In the present study an MS-based binding assay is described using the rat histamine receptor 2 (rH2) as a model GPCR system. Instead of using a purified receptor it is demonstrated that it is possible to use an unpurified receptor to extract active compounds from a solution or small mixture of compounds. By using SEC it is possible to separate the bound ligand from the unbound ligand. The major advantage of this approach is that there is no labeling of ligands required (direct monitoring based on the appropriate m/z values).     

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.