A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Developing a comprehensive solution aimed to disrupt LARS1/RagD protein-protein interaction

Aminoacyl-tRNA synthetases are crucial enzymes involved in protein synthesis and various cellular physiological reactions. Aside from their standard role in linking amino acids to the corresponding tRNAs, they also impact protein homeostasis by controlling the level of soluble amino acids within the cell. For instance, leucyl-tRNA synthetase (LARS1) acts as a leucine sensor for the mammalian target of rapamycin complex 1 (mTORC1), and may also function as a probable GTPase-activating protein (GAP) for the RagD subunit of the heteromeric activator of mTORC1. In turn, mTORC1 regulates cellular processes, such as protein synthesis, autophagy, and cell growth, and is implicated in various human diseases including cancer, obesity, diabetes, and neurodegeneration. Hence, inhibitors of mTORC1 or a deregulated mTORC1 pathway may offer potential cancer therapies. In this study, we investigated the structural requirements for preventing the sensing and signal transmission from LARS to mTORC1. Building upon recent studies on mTORC1 regulation activation by leucine, we lay the foundation for the development of chemotherapeutic agents against mTORC1 that can overcome resistance to rapamycin. Using a combination of in-silico approaches to develop and validate an alternative interaction model, discussing its benefits and advancements. Finally, we identified a set of compounds ready for testing to prevent LARS1/RagD protein-protein interactions. We establish a basis for creating chemotherapeutic drugs targeting mTORC1, which can conquer resistance to rapamycin. We utilize in-silico methods to generate and confirm an alternative interaction model, outlining its advantages and improvements, and pinpoint a group of novel substances that can prevent LARS1/RagD interactions.Communicated by Ramaswamy H. Sarma.

Virtual screening of ultra-large chemical libraries identifies cell-permeable small-molecule inhibitors of a "non-druggable" target, STAT3 N-terminal domain

STAT3 N-terminal domain is a promising molecular target for cancer treatment and modulation of immune responses. However, STAT3 is localized in the cytoplasm, mitochondria, and nuclei, and thus, is inaccessible to therapeutic antibodies. Its N-terminal domain lacks deep pockets on the surface and represents a typical "non-druggable" protein. In order to successfully identify potent and selective inhibitors of the domain, we have used virtual screening of billion structure-sized virtual libraries of make-on-demand screening samples. The results suggest that the expansion of accessible chemical space by cutting-edge ultra-large virtual compound databases can lead to successful development of small molecule drugs for hard-to-target intracellular proteins.

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Identification of a Novel Allosteric Site at the M5 Muscarinic Acetylcholine Receptor

The M₅ muscarinic acetylcholine receptor (mAChR) has emerged as an exciting therapeutic target for the treatment of addiction and behavioral disorders. This has been in part due to promising preclinical studies with the M₅ mAChR selective negative allosteric modulator (NAM), ML375. The binding site of ML375 remains unknown, however, making it difficult to develop improved M₅ mAChR selective modulators. To determine the possible location of the ML375 binding site, we used radioligand binding and functional assays to show that ML375 does not interact with the well-characterized "common" mAChR allosteric site located in the receptor's extracellular vestibule, nor a previously proposed second allosteric site recognized by the modulator, amiodarone. Molecular docking was used to predict potential allosteric sites within the transmembrane (TM) domain of the M₅ mAChR. These predicted sites were assessed using M₅-M₂ mAChR receptor chimeras and further targeted with site-directed mutagenesis, which enabled the identification of a putative binding site for ML375 at the interface of TMs 2-4. Collectively, these results identify a third allosteric site at the M₅ mAChR and highlight the ability of allosteric modulators to selectively target highly conserved proteins.

In Silico and In Cell Hybrid Selection of Nonrapalog Ligands to Allosterically Inhibit the Kinase Activity of mTORC1

Cancer-specific metabolic alterations hyperactivate the kinase activity of the mammalian/mechanistic target of rapamycin (mTOR) for overcoming stressful environments. Rapalogs, which allosterically inhibit mTOR complex 1 (mTORC1), have been approved as anticancer agents. However, the immunosuppressive side effect of these compounds results in the promotion of tumor metastasis, thereby limiting their therapeutic efficacy. We first report a nonrapalog inhibitor, WRX606, identified by a hybrid strategy of in silico and in cell selections. Our studies showed that WRX606 formed a ternary complex with FK506-binding protein-12 (FKBP12) and FKBP-rapamycin-binding (FRB) domain of mTOR, resulting in the allosteric inhibition of mTORC1. WRX606 inhibited the phosphorylation of not only the ribosomal protein S6 kinase 1 (S6K1) but also eIF4E-binding protein-1 (4E-BP1). Hence, WRX606 efficiently suppressed tumor growth in mice without promotion of metastasis. These results suggest that WRX606 is a potent lead compound for developing anticancer drugs discovered by in silico and in cell methods.

Modelling of an autonomous Nav1.5 channel system as a part of in silico pharmacology study

A homology model of Nav1.5, based mainly on the crystal structures of Nav1.2/1.5 was built, optimized and successfully inserted into the membrane bilayer. We applied steered and free MD simulation protocols for the visualization of the mechanism of Nav1.5 activation. We constrained dihedrals of S4 trigger to introduce a structural tension with further rearrangement and movement of secondary structure elements. From these, we observed an intracellular gate opening and movement of the Lys1419 residue caused by a gradual displacement of the distal S6 α-helix with the extended S4 3-10 helix of voltage-sensing domains (VSD). A construction containing the Lys1419 residue in P-loop also changed its position due to the extension of this helix and subsequent induction of the pore-forming helixes motion. From this point, a double membrane system was generated, implying a free of ligand Nav1.5 protein and on the opposite side its copy containing a docked bupivacaine molecule inside the pore channel. The system can be used for the design of selective inhibitors against the Nav1.7 channel, instead of mixed effect on both channels.

Conjugation of biphenyl groups with poly(ethylene glycol) to enhance inhibitory effects on the PD-1/PD-L1 immune checkpoint interaction

Monoclonal antibodies have been developed as anticancer agents to block immune checkpoint pathways associated with programmed cell death 1 (PD-1) and its ligand PD-L1. However, the high cost of antibodies has encouraged researchers to develop other inhibitor types. Here, biphenyl compounds were conjugated with poly(ethylene glycol) (PEG) to enhance the activity of small molecular inhibitors. Immunoassay results revealed the decrease in the inhibition activity following conjugation with linear PEG, suggesting that the PEG moiety reduced the interaction between the biphenyl structure and PD-L1. However, the inhibitory effect on PD-1/PD-L1 interaction was further enhanced by using branched PEG conjugates. The increase in the number of conjugated biphenyl compounds resulted in increased inhibitory activity. The highest IC50 value was 0.33 μM, which was about 5 times higher than that observed for a non-conjugated monovalent compound. The inhibitory activity was more than 20 times the activity reported for the starting compound. Considering the increase in the inhibition activity, this multivalent strategy can be useful in the design of new immune checkpoint inhibitors.

Ab-initio binding of barnase–barstar with DelPhiForce steered Molecular Dynamics (DFMD) approach

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.

Preparation of Biphenyl-Conjugated Bromotyrosine for Inhibition of PD-1/PD-L1 Immune Checkpoint Interactions

Cancer immunotherapy has been revolutionized by the development of monoclonal antibodies (mAbs) that inhibit interactions between immune checkpoint molecules, such as programmed cell-death 1 (PD-1), and its ligand PD-L1. However, mAb-based drugs have some drawbacks, including poor tumor penetration and high production costs, which could potentially be overcome by small molecule drugs. BMS-8, one of the potent small molecule drugs, induces homodimerization of PD-L1, thereby inhibiting its binding to PD-1. Our assay system revealed that BMS-8 inhibited the PD-1/PD-L1 interaction with IC₅₀ of 7.2 μM. To improve the IC₅₀ value, we designed and synthesized a small molecule based on the molecular structure of BMS-8 by in silico simulation. As a result, we successfully prepared a biphenyl-conjugated bromotyrosine (X) with IC₅₀ of 1.5 μM, which was about five times improved from BMS-8. We further prepared amino acid conjugates of X (amino-X), to elucidate a correlation between the docking modes of the amino-Xs and IC₅₀ values. The results suggested that the displacement of amino-Xs from the BMS-8 in the pocket of PD-L1 homodimer correlated with IC₅₀ values. This observation provides us a further insight how to derivatize X for better inhibitory effect.

Negative allosteric modulators of the human calcium-sensing receptor bind to overlapping and distinct sites within the 7-transmembrane domain

BACKGROUND AND PURPOSE

Negative allosteric modulators (NAMs) that target the calcium-sensing receptor (CaS receptor) were originally developed for the treatment of osteoporosis by stimulating the release of endogenous parathyroid hormone, but failed in human clinical trials. Several chemically and structurally distinct NAM scaffolds have been described, but it is not known how these different scaffolds interact with the CaS receptor to inhibit receptor signalling in response to agonists.

EXPERIMENTAL APPROACH

In the present study, we used a mutagenesis approach combined with analytical pharmacology and computational modelling to probe the binding sites of four distinct NAM scaffolds.

KEY RESULTS

Although all four scaffolds bind to the 7-transmembrane and/or extracellular or intracellular loops, they occupy distinct regions, as previously shown for positive allosteric modulators of the CaS receptor. Furthermore, different NAM scaffolds mediate negative allosteric modulation via distinct amino acid networks.

CONCLUSION AND IMPLICATIONS

These findings aid our understanding of how different NAMs bind to and inhibit the CaS receptor. Elucidation of allosteric binding sites in the CaS receptor has implications for the discovery of novel allosteric modulators.

Assessing the Quality of in Silico Produced Biomolecules: The Discovery of a New Conformer

The computational procedures for predicting the 3D structure of aptamers interacting with different biological molecules have gained increasing attention in recent years. The information acquired through these methods represents a crucial input for research, especially when relevant crystallographic data are not available. A number of software programs able to perform macromolecular docking are currently accessible, leading to the prediction of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Nevertheless, the scoring protocols employed for ranking the candidate structures do not always produce satisfactory results, making difficult the identification of structures that are most likely to occur in nature. In this paper, we propose a novel procedure to improve the predictive performances of computational scoring protocols, using a maximum likelihood estimate based on topological and electrical properties of interacting biomolecules. The reliability of the new computational approach, enabling the ranking of aptamer-protein configurations produced by an open source docking program, has been assessed by its successful application to a set of antiangiopoietin aptamers, for which experimental data highlighting the sequence-dependent affinity toward the target protein are available. The procedure led to the identification of two main types of aptamer conformers involved in angiopoietin binding. Interestingly, one of these reproduces the arrangement of angiopoietin with its natural target, tyrosine kinase, while the other one is completely unexpected. The possible scenarios related to these results have been discussed. The methodology here described can be used to refine the outcomes of different computational procedures and can be applied to a wide range of biological molecules, thus representing a new tool for guiding the design of bioinspired sensors with enhanced selectivity.

In silico discovery of small molecules that inhibit RfaH recruitment to RNA polymerase

RfaH is required for virulence in several Gram-negative pathogens including Escherichia coli and Klebsiella pneumoniae. Through direct interactions with RNA polymerase (RNAP) and ribosome, RfaH activates the expression of capsule, cell wall and pilus biosynthesis operons by reducing transcription termination and activating translation. While E. coli RfaH has been extensively studied using structural and biochemical approaches, limited data are available for other RfaH homologs. Here we set out to identify small molecule inhibitors of E. coli and K. pneumoniae RfaHs. Results of biochemical and functional assays show that these proteins act similarly, with a notable difference between their interactions with the RNAP β subunit gate loop. We focused on high-affinity RfaH interactions with the RNAP β' subunit clamp helices as a shared target for inhibition. Among the top 10 leads identified by in silico docking using ZINC database, 3 ligands were able to inhibit E. coli RfaH recruitment in vitro. The most potent lead was active against both E. coli and K. pneumoniae RfaHs in vitro. Our results demonstrate the feasibility of identifying RfaH inhibitors using in silico docking and pave the way for rational design of antivirulence therapeutics against antibiotic-resistant pathogens.

Discovery of a novel allosteric inhibitor scaffold for polyadenosine-diphosphate-ribose polymerase 14 (PARP14) macrodomain 2

The polyadenosine-diphosphate-ribose polymerase 14 (PARP14) has been implicated in DNA damage response pathways for homologous recombination. PARP14 contains three (ADP ribose binding) macrodomains (MD) whose exact contribution to overall PARP14 function in pathology remains unclear. A medium throughput screen led to the identification of N-(2(-9H-carbazol-1-yl)phenyl)acetamide (GeA-69, 1) as a novel allosteric PARP14 MD2 (second MD of PARP14) inhibitor. We herein report medicinal chemistry around this novel chemotype to afford a sub-micromolar PARP14 MD2 inhibitor. This chemical series provides a novel starting point for further development of PARP14 chemical probes.

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.

Identification of mangiferin as a potential Glucokinase activator by structure-based virtual ligand screening

The natural product mangiferin (compound 7) has been identified as a potential glucokinase activator by structure-based virtual ligand screening. It was proved by enzyme activation experiment and cell-based assays in vitro, with potency in micromolar range. Meanwhile, this compound showed good antihyperglycemic activity in db/db mice without obvious side effects such as excessive hypoglycaemia.

Interolog interfaces in protein-protein docking

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Inhibition by stabilization: targeting the Plasmodium falciparum aldolase-TRAP complex

BACKGROUND

Emerging resistance of the malaria parasite Plasmodium to current therapies underscores the critical importance of exploring novel strategies for disease eradication. Plasmodium species are obligate intracellular protozoan parasites. They rely on an unusual form of substrate-dependent motility for their migration on and across host-cell membranes and for host cell invasion. This peculiar motility mechanism is driven by the 'glideosome', an actin-myosin associated, macromolecular complex anchored to the inner membrane complex of the parasite. Myosin A, actin, aldolase, and thrombospondin-related anonymous protein (TRAP) constitute the molecular core of the glideosome in the sporozoite, the mosquito stage that brings the infection into mammals.

METHODS

Virtual library screening of a large compound library against the PfAldolase-TRAP complex was used to identify candidate compounds that stabilize and prevent the disassembly of the glideosome. The mechanism of these compounds was confirmed by biochemical, biophysical and parasitological methods.

RESULTS

A novel inhibitory effect on the parasite was achieved by stabilizing a protein-protein interaction within the glideosome components. Compound 24 disrupts the gliding and invasive capabilities of Plasmodium parasites in in vitro parasite assays. A high-resolution, ternary X-ray crystal structure of PfAldolase-TRAP in complex with compound 24 confirms the mode of interaction and serves as a platform for future ligand optimization.

CONCLUSION

This proof-of-concept study presents a novel approach to anti-malarial drug discovery and design. By strengthening a protein-protein interaction within the parasite, an avenue towards inhibiting a previously "undruggable" target is revealed and the motility motor responsible for successful invasion of host cells is rendered inactive. This study provides new insights into the malaria parasite cell invasion machinery and convincingly demonstrates that liver cell invasion is dramatically reduced by 95 % in the presence of the small molecule stabilizer compound 24.

Structure-based predictions of activity cliffs

In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.

Principal Component Analysis reveals correlation of cavities evolution and functional motions in proteins

Protein conformation has been recognized as the key feature determining biological function, as it determines the position of the essential groups specifically interacting with substrates. Hence, the shape of the cavities or grooves at the protein surface appears to drive those functions. However, only a few studies describe the geometrical evolution of protein cavities during molecular dynamics simulations (MD), usually with a crude representation. To unveil the dynamics of cavity geometry evolution, we developed an approach combining cavity detection and Principal Component Analysis (PCA). This approach was applied to four systems subjected to MD (lysozyme, sperm whale myoglobin, Dengue envelope protein and EF-CaM complex). PCA on cavities allows us to perform efficient analysis and classification of the geometry diversity explored by a cavity. Additionally, it reveals correlations between the evolutions of the cavities and structures, and can even suggest how to modify the protein conformation to induce a given cavity geometry. It also helps to perform fast and consensual clustering of conformations according to cavity geometry. Finally, using this approach, we show that both carbon monoxide (CO) location and transfer among the different xenon sites of myoglobin are correlated with few cavity evolution modes of high amplitude. This correlation illustrates the link between ligand diffusion and the dynamic network of internal cavities.

Modeling G protein-coupled receptors in complex with biased agonists

The biological response to the activation of G protein-coupled receptors (GPCRs) typically originates from the simultaneous modulation of various signaling pathways that lead to distinct biological consequences. Hence, 'biased agonists' (i.e., compounds that selectively activate one of the pathways while blocking the others) are highly sought-after molecules to provide fine-tuned pharmacological interventions. This review describes strategies that can be deployed to model the conformation of GPCRs in complex with ligands endowed with specific signaling profiles useful for the generation of hypotheses on the structural requirements for the activation of different signaling pathways or for rational computer-aided ligand discovery campaigns. In particular, it focuses on strategies potentially applicable to model the global or local conformational states of GPCRs stabilized by specific ligands.

Exploiting conformational dynamics in drug discovery: design of C-terminal inhibitors of Hsp90 with improved activities

The interaction that occurs between molecules is a dynamic process that impacts both structural and conformational properties of the ligand and the ligand binding site. Herein, we investigate the dynamic cross-talk between a protein and the ligand as a source for new opportunities in ligand design. Analysis of the formation/disappearance of protein pockets produced in response to a first-generation inhibitor assisted in the identification of functional groups that could be introduced onto scaffolds to facilitate optimal binding, which allowed for increased binding with previously uncharacterized regions. MD simulations were used to elucidate primary changes that occur in the Hsp90 C-terminal binding pocket in the presence of first-generation ligands. This data was then used to design ligands that adapt to these receptor conformations, which provides access to an energy landscape that is not visible in a static model. The newly synthesized compounds demonstrated antiproliferative activity at ∼150 nM concentration. The method identified herein may be used to design chemical probes that provide additional information on structural variations of Hsp90 C-terminal binding site.

Neurotoxicology of bis(n)-tacrines on Blattella germanica and Drosophila melanogaster acetylcholinesterase

A series of bis(n)-tacrines were used as pharmacological probes of the acetylcholinesterase (AChE) catalytic and peripheral sites of Blattella germanica and Drosophila melanogaster, which express AChE-1 and AChE-2 isoforms, respectively. In general, the potency of bis(n)-tacrines was greater in D. melanogaster AChE (DmAChE) than in B. germanica AChE (BgAChE). The change in potency with tether length was high in DmAChE and low in BgAChE, associated with 90-fold and 5.2-fold maximal potency gain, respectively, compared to the tacrine monomer. The optimal tether length for Blattella was 8 carbons and for Drosophila was 10 carbons. The two species differed by only about twofold in their sensitivity to tacrine monomer, indicating that differential potency occurred among dimeric bis(n)-tacrines due to structural differences in the peripheral site. Multiple sequence alignment and in silico homology modeling suggest that aromatic residues of DmAChE confer higher affinity binding, and the lack of same at the BgAChE peripheral site may account, at least in part, to the greater overall sensitivity of DmAChE to bis(n)-tacrines, as reflected by in vitro assay data. Topical and injection assays in cockroaches found minimal toxicity of bis(n)-tacrines. Electrophysiological studies on D. melanogaster central nervous system showed that dimeric tacrines do not readily cross the blood brain barrier, explaining the observed nonlethality to insects. Although the bis(n)-tacrines were not good insecticide candidates, the information obtained in this study should aid in the design of selective bivalent ligands targeting insect, pests, and disease vectors.

Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening

The Filamenting temperature-sensitive mutant Z (FtsZ), an essential GTPase in bacterial cell division, is highly conserved among Gram-positive and Gram-negative bacteria and thus considered an attractive target to treat antibiotic-resistant bacterial infections. In this study, a new class of FtsZ inhibitors bearing the pyrimidine-quinuclidine scaffold was identified from structure-based virtual screening of natural product libraries. Iterative rounds of in silico studies and biological evaluation established the preliminary structure-activity relationships of the new compounds. Potent FtsZ inhibitors with low micromolar IC₅₀ and antibacterial activity against S. aureus and E. coli were found. These findings support the use of virtual screening and structure-based design for the rational development of new antibacterial agents with innovative mechanisms of action.

Toward quantitative estimates of binding affinities for protein-ligand systems involving large inhibitor compounds: a steered molecular dynamics simulation route

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein-ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein-ligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine-based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein-ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis-Menten mechanism with a competitive inhibitor.

What has virtual screening ever done for drug discovery?

BACKGROUND

Although virtual screening is now widely applied as a hit-finding methodology within drug discovery programmes, there are relatively few reports of its contributing to compounds on the market or in the clinic.

OBJECTIVE

To assess the impact of virtual screening on drug discovery.

METHOD

Such cases as can be found in the public domain at the current time are reviewed. Additionally, some of the current challenges in structure- and ligand-based virtual screening are discussed.

CONCLUSION

It is concluded that virtual screening has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. In terms of praxis, there is 'no free lunch' in virtual screening and as many methods as possible should be applied to maximise the likelihood of success.

Drug repositioning by structure-based virtual screening

Approved drugs have favourable or validated pharmacokinetic properties and toxicological profiles, and the repositioning of existing drugs for new indications can potentially avoid expensive costs associated with early-stage testing of the hit compounds. In recent years, technological advances in virtual screening methodologies have allowed medicinal chemists to rapidly screen drug libraries for therapeutic activity against new biomolecular targets in a cost-effective manner. This review article outlines the basic principles and recent advances in structure-based virtual screening and highlights the powerful synergy of in silico techniques in drug repositioning as demonstrated in several recent reports.

GalaxyDock: protein-ligand docking with flexible protein side-chains

An important issue in developing protein-ligand docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain docking. This method was tested on 3 sets of protein-ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible docking methods tested on the same sets, increasing the binding conformation prediction accuracy by 10%-60% compared to rigid-receptor docking. This encouraging result suggests that this powerful global energy optimization method may be further extended to incorporate larger magnitudes of receptor flexibility in the future. The program is available at http://galaxy.seoklab.org/softwares/galaxydock.html .

ALiBERO: evolving a team of complementary pocket conformations rather than a single leader

Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.

Protein flexibility in docking and surface mapping

Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein-ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80-95%. This review examines the current toolbox for flexible protein-ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.

Ligand-guided receptor optimization

Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.

Potential and limitations of ensemble docking

A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each ensemble size up to 500,000 combinations of protein structures were generated, and, for each ensemble, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein ensembles of size two or greater, respectively. We identified several key factors affecting ensemble docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an ensemble. Due to these factors, the prospective selection of optimum ensembles is a challenging task, shown by a reassessment of published ensemble selection protocols.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.

Systematic exploitation of multiple receptor conformations for virtual ligand screening

The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.

Improved ligand-protein binding affinity predictions using multiple binding modes

Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.

Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments

Docking into multiple receptor conformations ("ensemble docking") has been proposed, and employed, in the hope that it may account for receptor flexibility in virtual screening and thus provide higher enrichments than docking into single rigid receptor structures. The statistical analyses presented in this paper provide quantitative evidence that in some cases docking into a crystallographically derived conformational ensemble does indeed yield better enrichment than docking into any of the individual members of the ensemble. However, these "successful" ensembles account for only a minority of those examined and it would not have been possible to prospectively predict their identity using only protein structural information. A more frequently observed outcome is that the ensemble enrichment is higher than the mean of the enrichments provided by its individual members. An additional and promising finding is that, if a set of known active compounds is available, an approach based on induced-fit docking appears to be a reliable way to construct ensembles which provide relatively high enrichments.