Hierarchical docking of databases of multiple ligand conformations

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

Docking for Molecules That Bind in a Symmetric Stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of great current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures, the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural-network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study, SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Sampling and Scoring in Protein-Protein Docking

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction

Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional neural network (CNN) to effectively capture both local and global molecular information. Additionally, attention mechanisms can be incorporated to automatically learn and adjust the level of attention given to local and global information, thereby enhancing the performance of the model. To achieve this, we propose the PLAsformer approach, which encodes local and global information of molecules using 3DCNN and BiGRU with attention mechanism, respectively. This approach enhances the model's ability to encode comprehensive local and global molecular information. PLAsformer achieved a Pearson's correlation coefficient of 0.812 and a Root Mean Square Error (RMSE) of 1.284 when comparing experimental and predicted affinity on the PDBBind-2016 dataset. These results surpass the current state-of-the-art methods for binding affinity prediction. The high accuracy of PLAsformer's predictive performance, along with its excellent generalization ability, is clearly demonstrated by these findings.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Docking for molecules that bind in a symmetric stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of much current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed, and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Facilitating the drug repurposing with iC/E strategy: A practice on novel nNOS inhibitor discovery

Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol. In this study, we proposed an integrated computational/experimental (iC/E) strategy to facilitate the DRP within a framework of rational drug design, which was practiced on the identification of new neuronal nitric oxide synthase (nNOS) inhibitors from a structurally diverse, functionally distinct drug pool. We demonstrated that the iC/E strategy is very efficient and readily feasible, which confirmed that the phosphodiesterase inhibitor DB06237 showed a high inhibitory potency against nNOS synthase domain, while other two general drugs, i.e. DB02302 and DB08258, can also inhibit the synthase at nanomolar level. Structural bioinformatics analysis revealed diverse noncovalent interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of nNOS active site with these identified drugs, conferring both stability and specificity for the complex recognition and association.

Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A₁ and A_2A adenosine receptors (A₁R and A_2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A₁R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A₁R selective ligands were predicted. Of these, seven antagonized the A₁R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A₁R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening

In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.

Structure-Based Discovery of Negative Allosteric Modulators of the Metabotropic Glutamate Receptor 5

Recently determined structures of class C G protein-coupled receptors (GPCRs) revealed the location of allosteric binding sites and opened new opportunities for the discovery of novel modulators. In this work, molecular docking screens for allosteric modulators targeting the metabotropic glutamate receptor 5 (mGlu₅) were performed. The mGlu₅ receptor is activated by the main excitatory neurotransmitter of the nervous central system, L-glutamate, and mGlu₅ receptor activity can be allosterically modulated by negative or positive allosteric modulators. The mGlu₅ receptor is a promising target for the treatment of psychiatric and neurodegenerative diseases, and several allosteric modulators of this GPCR have been evaluated in clinical trials. Chemical libraries containing fragment- (1.6 million molecules) and lead-like (4.6 million molecules) compounds were docked to an allosteric binding site of mGlu₅ identified in X-ray crystal structures. Among the top-ranked compounds, 59 fragments and 59 lead-like compounds were selected for experimental evaluation. Of these, four fragment- and seven lead-like compounds were confirmed to bind to the allosteric site with affinities ranging from 0.43 to 8.6 μM, corresponding to a hit rate of 9%. The four compounds with the highest affinities were demonstrated to be negative allosteric modulators of mGlu₅ signaling in functional assays. The results demonstrate that virtual screens of fragment- and lead-like chemical libraries have complementary advantages and illustrate how access to high-resolution structures of GPCRs in complex with allosteric modulators can accelerate lead discovery.

Ligand Strain Energy in Large Library Docking

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing the ranks of strained high-scoring decoy molecules. In a 40-target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules and another based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on precalculated population distributions, this approach is rapid, taking less than 0.04 s to evaluate a conformation on a standard core, making it pragmatic for precalculating strain in even ultralarge libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

Identification of entacapone as a chemical inhibitor of FTO mediating metabolic regulation through FOXO1

Recent studies have established the involvement of the fat mass and obesity-associated gene (FTO) in metabolic disorders such as obesity and diabetes. However, the precise molecular mechanism by which FTO regulates metabolism remains unknown. Here, we used a structure-based virtual screening of U.S. Food and Drug Administration-approved drugs to identify entacapone as a potential FTO inhibitor. Using structural and biochemical studies, we showed that entacapone directly bound to FTO and inhibited FTO activity in vitro. Furthermore, entacapone administration reduced body weight and lowered fasting blood glucose concentrations in diet-induced obese mice. We identified the transcription factor forkhead box protein O1 (FOXO1) mRNA as a direct substrate of FTO, and demonstrated that entacapone elicited its effects on gluconeogenesis in the liver and thermogenesis in adipose tissues in mice by acting on an FTO-FOXO1 regulatory axis.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Mechanism of Action of VP1-001 in cryAB(R120G)-Associated and Age-Related Cataracts

Purpose

We previously identified an oxysterol, VP1-001 (also known as compound 29), that partially restores the transparency of lenses with cataracts. To understand the mechanism of VP1-001, we tested the ability of its enantiomer, ent-VP1-001, to bind and stabilize αB-crystallin (cryAB) in vitro and to produce a similar therapeutic effect in cryAB(R120G) mutant and aged wild-type mice with cataracts. VP1-001 and ent-VP1-001 have identical physicochemical properties. These experiments are designed to critically evaluate whether stereoselective binding to cryAB is required for activity.

Methods

We compared the binding of VP1-001 and ent-VP1-001 to cryAB using in silico docking, differential scanning fluorimetry (DSF), and microscale thermophoresis (MST). Compounds were delivered by six topical administrations to mouse eyes over 2 weeks, and the effects on cataracts and lens refractive measures in vivo were examined. Additionally, lens epithelial and fiber cell morphologies were assessed via transmission electron microscopy.

Results

Docking studies suggested greater binding of VP1-001 into a deep groove in the cryAB dimer compared with ent-VP1-001. Consistent with this prediction, DSF and MST experiments showed that VP1-001 bound cryAB, whereas ent-VP1-001 did not. Accordingly, topical treatment of lenses with ent-VP1-001 had no effect, whereas VP1-001 produced a statistically significant improvement in lens clarity and favorable changes in lens morphology.

Conclusions

The ability of VP1-001 to bind native cryAB dimers is important for its ability to reverse lens opacity in mouse models of cataracts.

Rational discovery of antimetastatic agents targeting the intrinsically disordered region of MBD2

Although intrinsically disordered protein regions (IDPRs) are commonly engaged in promiscuous protein-protein interactions (PPIs), using them as drug targets is challenging due to their extreme structural flexibility. We report a rational discovery of inhibitors targeting an IDPR of MBD2 that undergoes disorder-to-order transition upon PPI and is critical for the regulation of the Mi-2/NuRD chromatin remodeling complex (CRC). Computational biology was essential for identifying target site, searching for promising leads, and assessing their binding feasibility and off-target probability. Molecular action of selected leads inhibiting the targeted PPI of MBD2 was validated in vitro and in cell, followed by confirming their inhibitory effects on the epithelial-mesenchymal transition of various cancer cells. Identified lead compounds appeared to potently inhibit cancer metastasis in a murine xenograft tumor model. These results constitute a pioneering example of rationally discovered IDPR-targeting agents and suggest Mi-2/NuRD CRC and/or MBD2 as a promising target for treating cancer metastasis.

Discovery of dual-activity small-molecule ligands of Pseudomonas aeruginosa LpxA and LpxD using SPR and X-ray crystallography

The lipid A biosynthesis pathway is essential in Pseudomonas aeruginosa. LpxA and LpxD are the first and third enzymes in this pathway respectively, and are regarded as promising antibiotic targets. The unique structural similarities between these two enzymes make them suitable targets for dual-binding inhibitors, a characteristic that would decrease the likelihood of mutational resistance and increase cell-based activity. We report the discovery of multiple small molecule ligands that bind to P. aeruginosa LpxA and LpxD, including dual-binding ligands. Binding poses were determined for select compounds by X-ray crystallography. The new structures reveal a previously uncharacterized magnesium ion residing at the core of the LpxD trimer. In addition, ligand binding in the LpxD active site resulted in conformational changes in the distal C-terminal helix-bundle, which forms extensive contacts with acyl carrier protein (ACP) during catalysis. These ligand-dependent conformational changes suggest a potential allosteric influence of reaction intermediates on ACP binding, and vice versa. Taken together, the novel small molecule ligands and their crystal structures provide new chemical scaffolds for ligand discovery targeting lipid A biosynthesis, while revealing structural features of interest for future investigation of LpxD function.

Discovery of Novel Pim-1 Kinase Inhibitors with a Flexible-Receptor Docking Protocol

A flexible-receptor docking protocol was designed for treating binding-site side-chain flexibility by integrating essential aspects of "Conformational Selection" and "Induced Fit" in a hierarchical fashion. Assessed in a diverse set of pharmaceutically relevant targets, this protocol showed improved performance in reproducing binding poses and ligand enrichment studies compared to rigid-receptor docking. Moreover, it has also exhibited encouraging efficiency in prospective ligand discovery for Pim-1 kinase, which led to novel Pim-1 inhibitors with single-digit nanomolar potencies.

Differential long-term regulation of TAS2R14 by structurally distinct agonists

Bitter taste receptor-14 (TAS2R14) is a GPCR also expressed on human airway smooth muscle cells, which signals to intracellular [Ca²⁺], resulting in relaxation of the airway, and is a novel target for bronchodilators. Here, we examine long-term, agonist-promoted down-regulation of TAS2R14 expression because tachyphylaxis would be an undesirable therapeutic characteristic. Five TAS2R structurally distinct full agonists were studied to ascertain biasing away from down-regulation. Agonist exposure for 18 h caused minimal desensitization by diphenhydramine (DPD) compared with ∼50% desensitization with all other agonists. Agonists evoked β-arrestin recruitment to TAS2R14, which was not seen with a phosphoacceptor-deficient mutant, TAS2R14-10A. All agonists except for DPD also caused subsequent TAS2R14 internalization and trafficking via early and late endosomes to down-regulation. TAS2R14-10A failed to undergo these events with any agonist. Molecular docking showed that DPD has specific interactions deep within a binding pocket that are not observed with the other agonists, which may lock the receptor in a conformation that does not internalize and therefore does not undergo down-regulation. Thus, TAS2R14 is subject to β-arrestin-mediated internalization and subsequent down-regulation with chronic exposure to most agonists. However, by manipulating the agonist structure, biasing toward G-protein coupling but away from long-term down-regulation can be achieved.-Woo, J. A., Castaño, M., Goss, A., Kim, D., Lewandowski, E. M., Chen, Y., Liggett, S. B. Differential long-term regulation of TAS2R14 by structurally distinct agonists.

Active-Site Druggability of Carbapenemases and Broad-Spectrum Inhibitor Discovery

Serine and metallo-carbapenemases are a serious health concern due to their capability to hydrolyze nearly all β-lactam antibiotics. However, the molecular basis for their unique broad-spectrum substrate profile is poorly understood, particularly for serine carbapenemases, such as KPC-2. Using substrates and newly identified small molecules, we compared the ligand binding properties of KPC-2 with the noncarbapenemase CTX-M-14, both of which are Class A β-lactamases with highly similar active sites. Notably, compared to CTX-M-14, KPC-2 was more potently inhibited by hydrolyzed β-lactam products (product inhibition), as well as by a series of novel tetrazole-based inhibitors selected from molecular docking against CTX-M-14. Together with complex crystal structures, these data suggest that the KPC-2 active site has an enhanced ability to form favorable interactions with substrates and small molecule ligands due to its increased hydrophobicity and flexibility. Such properties are even more pronounced in metallo-carbapenemases, such as NDM-1, which was also inhibited by some of the novel tetrazole compounds, including one displaying comparable low μM affinities against both KPC-2 and NDM-1. Our results suggest that carbapenemase activity confers an evolutionary advantage on producers via a broad β-lactam substrate scope but also a mechanistic Achilles' heel that can be exploited for new inhibitor discovery. The complex structures demonstrate, for the first time, how noncovalent inhibitors can be engineered to simultaneously target both serine and metallo-carbapenemases. Despite the relatively modest activity of the current compounds, these studies also demonstrate that hydrolyzed products and tetrazole-based chemotypes can provide valuable starting points for broad-spectrum inhibitor discovery against carbapenemases.

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field.

Scavenging of superoxide by a membrane-bound superoxide oxidase

Superoxide is a reactive oxygen species produced during aerobic metabolism in mitochondria and prokaryotes. It causes damage to lipids, proteins and DNA and is implicated in cancer, cardiovascular disease, neurodegenerative disorders and aging. As protection, cells express soluble superoxide dismutases, disproportionating superoxide to oxygen and hydrogen peroxide. Here, we describe a membrane-bound enzyme that directly oxidizes superoxide and funnels the sequestered electrons to ubiquinone in a diffusion-limited reaction. Experiments in proteoliposomes and inverted membranes show that the protein is capable of efficiently quenching superoxide generated at the membrane in vitro. The 2.0 Å crystal structure shows an integral membrane di-heme cytochrome b poised for electron transfer from the P-side and proton uptake from the N-side. This suggests that the reaction is electrogenic and contributes to the membrane potential while also conserving energy by reducing the quinone pool. Based on this enzymatic activity, we propose that the enzyme family be denoted superoxide oxidase (SOO).

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson's Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson's disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity ( K_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxydopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases.

Structure-based discovery of selective positive allosteric modulators of antagonists for the M2 muscarinic acetylcholine receptor

The orthosteric binding sites of the five muscarinic acetylcholine receptor (mAChR) subtypes are highly conserved, making the development of selective antagonists challenging. The allosteric sites of these receptors are more variable, allowing one to imagine allosteric modulators that confer subtype selectivity, which would reduce the major off-target effects of muscarinic antagonists. Accordingly, a large library docking campaign was prosecuted seeking unique positive allosteric modulators (PAMs) for antagonists, ultimately revealing a PAM that substantially potentiates antagonist binding leading to subtype selectivity at the M₂ mAChR. This study supports the feasibility of discovering PAMs that can convert an armamentarium of potent but nonselective G-protein–coupled receptor (GPCR) antagonist drugs into subtype-selective reagents.

Subtype-selective antagonists for muscarinic acetylcholine receptors (mAChRs) have long been elusive, owing to the highly conserved orthosteric binding site. However, allosteric sites of these receptors are less conserved, motivating the search for allosteric ligands that modulate agonists or antagonists to confer subtype selectivity. Accordingly, a 4.6 million-molecule library was docked against the structure of the prototypical M₂ mAChR, seeking molecules that specifically stabilized antagonist binding. This led us to identify a positive allosteric modulator (PAM) that potentiated the antagonist N-methyl scopolamine (NMS). Structure-based optimization led to compound ’628, which enhanced binding of NMS, and the drug scopolamine itself, with a cooperativity factor (α) of 5.5 and a K_B of 1.1 μM, while sparing the endogenous agonist acetylcholine. NMR spectral changes determined for methionine residues reflected changes in the allosteric network. Moreover, ’628 slowed the dissociation rate of NMS from the M₂ mAChR by 50-fold, an effect not observed at the other four mAChR subtypes. The specific PAM effect of ’628 on NMS antagonism was conserved in functional assays, including agonist stimulation of [³⁵S]GTPγS binding and ERK 1/2 phosphorylation. Importantly, the selective allostery between ’628 and NMS was retained in membranes from adult rat hypothalamus and in neonatal rat cardiomyocytes, supporting the physiological relevance of this PAM/antagonist approach. This study supports the feasibility of discovering PAMs that confer subtype selectivity to antagonists; molecules like ’628 can convert an armamentarium of potent but nonselective GPCR antagonist drugs into subtype-selective reagents, thus reducing their off-target effects.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

In Silico Identification of a Novel Hinge-Binding Scaffold for Kinase Inhibitor Discovery

To explore novel kinase hinge-binding scaffolds, we carried out structure-based virtual screening against p38α MAPK as a model system. With the assistance of developed kinase-specific structural filters, we identify a novel lead compound that selectively inhibits a panel of kinases with threonine as the gatekeeper residue, including BTK and LCK. These kinases play important roles in lymphocyte activation, which encouraged us to design novel kinase inhibitors as drug candidates for ameliorating inflammatory diseases and cancers. Therefore, we chemically modified our substituted triazole-class lead compound to improve the binding affinity and selectivity via a "minimal decoration" strategy, which resulted in potent and selective kinase inhibitors against LCK (18 nM) and BTK (8 nM). Subsequent crystallographic experiments validated our design. These rationally designed compounds exhibit potent on-target inhibition against BTK in B cells or LCK in T cells, respectively. Our work demonstrates that structure-based virtual screening can be applied to facilitate the development of novel chemical entities in crowded chemical space in the field of kinase inhibitor discovery.

Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space

Fragment-based lead discovery has emerged as a leading drug development strategy for novel therapeutic targets. Although fragment-based drug discovery benefits immensely from access to atomic-resolution information, structure-based virtual screening has rarely been used to drive fragment discovery and optimization. Here, molecular docking of 0.3 million fragments to a crystal structure of cancer target MTH1 was performed. Twenty-two predicted fragment ligands, for which analogs could be acquired commercially, were experimentally evaluated. Five fragments inhibited MTH1 with IC₅₀ values ranging from 6 to 79 μM. Structure-based optimization guided by predicted binding modes and analogs from commercial chemical libraries yielded nanomolar inhibitors. Subsequently solved crystal structures confirmed binding modes predicted by docking for three scaffolds. Structure-guided exploration of commercial chemical space using molecular docking gives access to fragment libraries that are several orders of magnitude larger than those screened experimentally and can enable efficient optimization of hits to potent leads.

A drug pocket at the lipid bilayer-potassium channel interface

Many pharmaceutical drugs against neurological and cardiovascular disorders exert their therapeutic effects by binding to specific sites on voltage-gated ion channels of neurons or cardiomyocytes. To date, all molecules targeting known ion channel sites bind to protein pockets that are mainly surrounded by water. We describe a lipid-protein drug-binding pocket of a potassium channel. We synthesized and electrophysiologically tested 125 derivatives, analogs, and related compounds to dehydroabietic acid. Functional data in combination with docking and molecular dynamics simulations mapped a binding site for small-molecule compounds at the interface between the lipid bilayer and the transmembrane segments S3 and S4 of the voltage-sensor domain. This fundamentally new binding site for small-molecule compounds paves the way for the design of new types of drugs against diseases caused by altered excitability.

An APE1 inhibitor reveals critical roles of the redox function of APE1 in KSHV replication and pathogenic phenotypes

APE1 is a multifunctional protein with a DNA base excision repair function in its C-terminal domain and a redox activity in its N-terminal domain. The redox function of APE1 converts certain transcription factors from inactive oxidized to active reduced forms. Given that among the APE1-regulated transcription factors many are critical for KSHV replication and pathogenesis, we investigated whether inhibition of APE1 redox function blocks KSHV replication and Kaposi’s sarcoma (KS) phenotypes. With an shRNA-mediated silencing approach and a known APE-1 redox inhibitor, we demonstrated that APE1 redox function is indeed required for KSHV replication as well as KSHV-induced angiogenesis, validating APE1 as a therapeutic target for KSHV-associated diseases. A ligand-based virtual screening yielded a small molecular compound, C10, which is proven to bind to APE1. C10 exhibits low cytotoxicity but efficiently inhibits KSHV lytic replication (EC₅₀ of 0.16 μM and selective index of 165) and KSHV-mediated pathogenic phenotypes including cytokine production, angiogenesis and cell invasion, demonstrating its potential to become an effective drug for treatment of KS.

As a major AIDS-associated malignancy, Kaposi’s sarcoma (KS) is caused by Kaposi’s sarcoma-associated herpesvirus (KSHV). Currently there is no definitive cure for KS. In this study, we identified a cellular protein, namely APE1, as an effective therapeutic target for blocking KSHV replication and inhibiting the development of KS phenotypes. We showed that the redox function of APE1 is absolutely required for KSHV replication, virally induced cytokine secretion and angiogenesis. Blockade of APE1 expression or inhibition of APE1 redox activity led to inhibition of KSHV replication and reduction of cytokine release and angiogenesis. Furthermore, we identified a novel small molecular compound, C10, which exhibited specific inhibitory activity on APE1 redox function and was demonstrated to efficiently inhibit KSHV replication and paracrine-mediated KS phenotypes such as angiogenesis and cell invasion. As a potent inhibitor of APE1 redox, C10 not only has value in development of a novel therapeutics for KS, but also may be used in therapies for other human diseases such as leukemia, pancreatic cancer and macular degeneration.

Ligand Discovery for a Peptide-Binding GPCR by Structure-Based Screening of Fragment- and Lead-Like Chemical Libraries

Peptide-recognizing G protein-coupled receptors (GPCRs) are promising therapeutic targets but often resist drug discovery efforts. Determination of crystal structures for peptide-binding GPCRs has provided opportunities to explore structure-based methods in lead development. Molecular docking screens of two chemical libraries, containing either fragment- or lead-like compounds, against a neurotensin receptor 1 crystal structure allowed for a comparison between different drug development strategies for peptide-binding GPCRs. A total of 2.3 million molecules were screened computationally, and 25 fragments and 27 leads that were top-ranked in each library were selected for experimental evaluation. Of these, eight fragments and five leads were confirmed as ligands by surface plasmon resonance. The hit rate for the fragment screen (32%) was thus higher than for the lead-like library (19%), but the affinities of the fragments were ∼100-fold lower. Both screens returned unique scaffolds and demonstrated that a crystal structure of a stabilized peptide-binding GPCR can guide the discovery of small-molecule agonists. The complementary advantages of exploring fragment- and lead-like chemical space suggest that these strategies should be applied synergistically in structure-based screens against challenging GPCR targets.

Structure-Based Screening of Uncharted Chemical Space for Atypical Adenosine Receptor Agonists

Small molecule screening libraries cover only a small fraction of the astronomical number of possible drug-like compounds, limiting the success of ligand discovery efforts. Computational screening of virtual libraries representing unexplored chemical space could potentially bridge this gap. Drug development for adenosine receptors (ARs) as targets for inflammation and cardiovascular diseases has been hampered by the paucity of agonist scaffolds. To identify novel AR agonists, a virtual library of synthetically tractable nucleosides with alternative bases was generated and structure-based virtual screening guided selection of compounds for synthesis. Pharmacological assays were carried out at three AR subtypes for 13 ribosides. Nine compounds displayed significant activity at the ARs, and several of these represented atypical agonist scaffolds. The discovered ligands also provided insights into receptor activation and revealed unknown interactions of endogenous and clinical compounds with the ARs. The results demonstrate that virtual compound databases provide access to bioactive matter from regions of chemical space that are sparsely populated in commercial libraries, an approach transferrable to numerous drug targets.

An in silico algorithm for identifying stabilizing pockets in proteins: test case, the Y220C mutant of the p53 tumor suppressor protein

The p53 tumor suppressor protein performs a critical role in stimulating apoptosis and cell cycle arrest in response to oncogenic stress. The function of p53 can be compromised by mutation, leading to increased risk of cancer; approximately 50% of cancers are associated with mutations in the p53 gene, the majority of which are in the core DNA-binding domain. The Y220C mutation of p53, for example, destabilizes the core domain by 4 kcal/mol, leading to rapid denaturation and aggregation. The associated loss of tumor suppressor functionality is associated with approximately 75 000 new cancer cases every year. Destabilized p53 mutants can be 'rescued' and their function restored; binding of a small molecule into a pocket on the surface of mutant p53 can stabilize its wild-type structure and restore its function. Here, we describe an in silico algorithm for identifying potential rescue pockets, including the algorithm's integration with the Dynameomics molecular dynamics data warehouse and the DIVE visual analytics engine. We discuss the results of the application of the method to the Y220C p53 mutant, entailing finding a putative rescue pocket through MD simulations followed by an in silico search for stabilizing ligands that dock into the putative rescue pocket. The top three compounds from this search were tested experimentally and one of them bound in the pocket, as shown by nuclear magnetic resonance, and weakly stabilized the mutant.

Structure-Based Identification of Novel Ligands Targeting Multiple Sites within a Chemokine-G-Protein-Coupled-Receptor Interface

CXCL12 is a human chemokine that recognizes the CXCR4 receptor and is involved in immune responses and metastatic cancer. Interactions between CXCL12 and CXCR4 are an important drug target but, like other elongated protein-protein interfaces, present challenges for small molecule ligand discovery due to the relatively shallow and featureless binding surfaces. Calculations using an NMR complex structure revealed a binding hot spot on CXCL12 that normally interacts with the I4/I6 residues from CXCR4. Virtual screening was performed against the NMR model, and subsequent testing has verified the specific binding of multiple docking hits to this site. Together with our previous results targeting two other binding pockets that recognize sulfotyrosine residues (sY12 and sY21) of CXCR4, including a new analog against the sY12 binding site reported herein, we demonstrate that protein-protein interfaces can often possess multiple sites for engineering specific small molecule ligands that provide lead compounds for subsequent optimization by fragment based approaches.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Structure-Based Discovery of Novel and Selective 5-Hydroxytryptamine 2B Receptor Antagonists for the Treatment of Irritable Bowel Syndrome

Here we employed structure-based ligand discovery techniques to explore a recently determined crystal structure of the 5-hydroxytryptamine 2B (5-HT2B) receptor. Ten compounds containing a novel chemical scaffold were identified; among them, seven molecules were active in cellular function assays with the most potent one exhibiting an IC50 value of 27.3 nM. We then systematically probed the binding characteristics of this scaffold by designing, synthesizing, and testing a series of structural modifications. The structure-activity relationship studies strongly support our predicted binding model. The binding profiling across a panel of 11 5-HT receptors indicated that these compounds are highly selective for the 5-HT2B receptor. Oral administration of compound 15 (30 mg/kg) produced significant attenuation of visceral hypersensitivity in a rat model of irritable bowel syndrome (IBS). We expect this novel scaffold will serve as the foundation for the development of 5-HT2B antagonists for the treatment of IBS.

The Recognition of Identical Ligands by Unrelated Proteins

The binding of drugs and reagents to off-targets is well-known. Whereas many off-targets are related to the primary target by sequence and fold, many ligands bind to unrelated pairs of proteins, and these are harder to anticipate. If the binding site in the off-target can be related to that of the primary target, this challenge resolves into aligning the two pockets. However, other cases are possible: the ligand might interact with entirely different residues and environments in the off-target, or wholly different ligand atoms may be implicated in the two complexes. To investigate these scenarios at atomic resolution, the structures of 59 ligands in 116 complexes (62 pairs in total), where the protein pairs were unrelated by fold but bound an identical ligand, were examined. In almost half of the pairs, the ligand interacted with unrelated residues in the two proteins (29 pairs), and in 14 of the pairs wholly different ligand moieties were implicated in each complex. Even in those 19 pairs of complexes that presented similar environments to the ligand, ligand superposition rarely resulted in the overlap of related residues. There appears to be no single pattern-matching "code" for identifying binding sites in unrelated proteins that bind identical ligands, though modeling suggests that there might be a limited number of different patterns that suffice to recognize different ligand functional groups.

Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models

Fragment-based lead discovery (FBLD) holds great promise for drug discovery, but applications to G protein-coupled receptors (GPCRs) have been limited by a lack of sensitive screening techniques and scarce structural information. If virtual screening against homology models of GPCRs could be used to identify fragment ligands, FBLD could be extended to numerous important drug targets and contribute to efficient lead generation. Access to models of multiple receptors may further enable the discovery of fragments that bind specifically to the desired target. To investigate these questions, we used molecular docking to screen >500 000 fragments against homology models of the A3 and A1 adenosine receptors (ARs) with the goal to discover A3AR-selective ligands. Twenty-one fragments with predicted A3AR-specific binding were evaluated in live-cell fluorescence-based assays; of eight verified ligands, six displayed A3/A1 selectivity, and three of these had high affinities ranging from 0.1 to 1.3 μM. Subsequently, structure-guided fragment-to-lead optimization led to the identification of a >100-fold-selective antagonist with nanomolar affinity from commercial libraries. These results highlight that molecular docking screening can guide fragment-based discovery of selective ligands even if the structures of both the target and antitarget receptors are unknown. The same approach can be readily extended to a large number of pharmaceutically important targets.

Discovery of 2-Acylaminothiophene-3-Carboxamides as Multitarget Inhibitors for BCR-ABL Kinase and Microtubules

The emergence of drug resistance of the BCR-ABL kinase inhibitor imatinib, especially toward the T315I gatekeeper mutation, poses a great challenge to targeted therapy in treating chronic myeloid leukemia (CML) patients. To discover novel inhibitors against drug-resistant CML bearing T315I mutation, we applied a physics-based hierarchical virtual screening approach to dock a large chemical library against ATP binding pockets of both wild-type (WT) and T315I mutant ABL kinases in a combinatorial fashion. This strategy automatically resulted in 87 compounds satisfying structural and energetic criteria of both WT and T315I mutant kinases. Among them, nine compounds, which share a common thiophene-based scaffold and adopt similar binding poses, were chosen for experimental testing and one of them was shown to have low micromolar inhibition activities against both WT and mutant ABL kinases. Structure-activity relationship analysis with a series of structural modifications based on 2-acylaminothiophene-3-carboxamide scaffold supports our predicted binding mode. Interestingly, the same chemical scaffold was also enriched in our previous virtual screening campaign against colchicine site of microtubules using the same computational protocol, which suggests our virtual screening strategy is capable of discovering small-molecule ligands targeting distinct protein binding sites without sharing any sequential and structural similarity. Furthermore, the multitarget inhibition activity of this class of compounds was assessed in cellular experiments. We expect that the 2-acylaminothiophene-3-carboxamide scaffold may serve as a promising starting point for developing multitarget inhibitors in cancer treatment by targeting both kinases and microtubules.

Virtual Screening for UDP-Galactopyranose Mutase Ligands Identifies a New Class of Antimycobacterial Agents

Galactofuranose (Galf) is present in glycans critical for the virulence and viability of several pathogenic microbes, including Mycobacterium tuberculosis, yet the monosaccharide is absent from mammalian glycans. Uridine 5'-diphosphate-galactopyranose mutase (UGM) catalyzes the formation of UDP-Galf, which is required to produce Galf-containing glycoconjugates. Inhibitors of UGM have therefore been sought, both as antimicrobial leads and as tools to delineate the roles of Galf in cells. Obtaining cell permeable UGM probes by either design or high throughput screens has been difficult, as has elucidating how UGM binds small molecule, noncarbohydrate inhibitors. To address these issues, we employed structure-based virtual screening to uncover new inhibitor chemotypes, including a triazolothiadiazine series. These compounds are among the most potent antimycobacterial UGM inhibitors described. They also facilitated determination of a UGM-small molecule inhibitor structure, which can guide optimization. A comparison of results from the computational screen and a high-throughput fluorescence polarization (FP) screen indicated that the scaffold hits from the former had been evaluated in the FP screen but missed. By focusing on promising compounds, the virtual screen rescued false negatives, providing a blueprint for generating new UGM probes and therapeutic leads.

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery

Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

Molecular docking screening using agonist-bound GPCR structures: probing the A2A adenosine receptor

Crystal structures of G protein-coupled receptors (GPCRs) have recently revealed the molecular basis of ligand binding and activation, which has provided exciting opportunities for structure-based drug design. The A2A adenosine receptor (A2AAR) is a promising therapeutic target for cardiovascular diseases, but progress in this area is limited by the lack of novel agonist scaffolds. We carried out docking screens of 6.7 million commercially available molecules against active-like conformations of the A2AAR to investigate whether these structures could guide the discovery of agonists. Nine out of the 20 predicted agonists were confirmed to be A2AAR ligands, but none of these activated the ARs. The difficulties in discovering AR agonists using structure-based methods originated from limited atomic-level understanding of the activation mechanism and a chemical bias toward antagonists in the screened library. In particular, the composition of the screened library was found to strongly reduce the likelihood of identifying AR agonists, which reflected the high ligand complexity required for receptor activation. Extension of this analysis to other pharmaceutically relevant GPCRs suggested that library screening may not be suitable for targets requiring a complex receptor-ligand interaction network. Our results provide specific directions for the future development of novel A2AAR agonists and general strategies for structure-based drug discovery.

Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases

Enzyme function prediction remains an important open problem. Though structure-based modeling, such as metabolite docking, can identify substrates of some enzymes, it is ill-suited to reactions that progress through a covalent intermediate. Here we investigated the ability of covalent docking to identify substrates that pass through such a covalent intermediate, focusing particularly on the haloalkanoate dehalogenase superfamily. In retrospective assessments, covalent docking recapitulated substrate binding modes of known cocrystal structures and identified experimental substrates from a set of putative phosphorylated metabolites. In comparison, noncovalent docking of high-energy intermediates yielded nonproductive poses. In prospective predictions against seven enzymes, a substrate was identified for five. For one of those cases, a covalent docking prediction, confirmed by empirical screening, and combined with genomic context analysis, suggested the identity of the enzyme that catalyzes the orphan phosphatase reaction in the riboflavin biosynthetic pathway of Bacteroides.

Binding site druggability assessment in fragment-based drug design

Target druggability refers to the propensity that a particular target is amenable to bind high-affinity drug-like molecules. A robust yet accurate computational assessment of target druggability would greatly benefit the fields of chemical genomics and drug discovery. Here, we illustrate a structure-based computational protocol to quantitatively assess the target binding-site druggability via in silico screening a fragment-like compound library. In particular, we provide guidelines, suggestions, and critical thoughts on different aspects of this computational protocol, including: construction of fragment library, preparation of target structure, in silico fragment screening, and analysis of druggability.