Evaluation of docking programs for predicting binding of Golgi α-mannosidase II inhibitors: A comparison with crystallography

Pharmacophore Model-Based Virtual Screening Workflow for Discovery of Inhibitors Targeting Plasmodium falciparum Hsp90

Plasmodium falciparum causes the most lethal and widespread form of malaria. Eradication of malaria remains a priority due to the increasing number of cases of drug resistance. The heat shock protein 90 of P. falciparum (PfHsp90) is a validated drug target essential for parasite survival. Most PfHsp90 inhibitors bind at the ATP binding pocket found in its N-terminal domain, abolishing the chaperone's activities, which leads to parasite death. The challenge is that the NTD of PfHsp90 is highly conserved, and its disruption requires selective inhibitors that can act without causing off-target human Hsp90 activities. We endeavored to discover selective inhibitors of PfHsp90 using pharmacophore modeling, virtual screening protocols, induced fit docking (IFD), and cell-based and biochemical assays. The pharmacophore model (DHHRR), composed of one hydrogen bond donor, two hydrophobic groups, and two aromatic rings, was used to mine commercial databases for initial hits, which were rescored to 20 potential hits using IFD. Eight of these compounds displayed moderate to high activity toward P. falciparum NF54 (i.e., IC50s ranging from 6.0 to 0.14 μM) and averaged >10 in terms of selectivity indices toward CHO and HepG2 cells. Additionally, four compounds inhibited PfHsp90 with greater selectivity than a known inhibitor, harmine, and bound to PfHsp90 with weak to moderate affinity. Our findings support the use of a pharmacophore model to discover diverse chemical scaffolds such as FM2, FM6, F10, and F11 exhibiting anti-Plasmodium activities and serving as valuable new PfHsp90 inhibitors. Optimization of these hits may enable their development into potent leads for future antimalarial drugs.

1,4-Dideoxy-1,4-imino-D- and L-lyxitol-based inhibitors bind to Golgi α-mannosidase II in different protonation forms

The development of effective inhibitors of Golgi α-mannosidase II (GMII, E.C.3.2.1.114) with minimal off-target effects on phylogenetically-related lysosomal α-mannosidase (LMan, E.C.3.2.1.24) is a complex task due to the complicated structural and chemical properties of their active sites. The pKa values (and also protonation forms in some cases) of several ionizable amino acids, such as Asp, Glu, His or Arg of enzymes, can be changed upon the binding of the inhibitor. Moreover, GMII and LMan work under different pH conditions. The pKa calculations on large enzyme-inhibitor complexes and FMO-PIEDA energy decomposition analysis were performed on the structures of selected inhibitors obtained from docking and hybrid QM/MM calculations. Based on the calculations, the roles of the amino group incorporated in the ring of the imino-D-lyxitol inhibitors and some ionizable amino acids of Golgi-type (Asp270-Asp340-Asp341 of Drosophila melanogaster α-mannosidase dGMII) and lysosomal-type enzymes (His209-Asp267-Asp268 of Canavalia ensiformis α-mannosidase, JBMan) were explained in connection with the observed inhibitory properties. The pyrrolidine ring of the imino-D-lyxitols prefers at the active site of dGMII the neutral form while in JBMan the protonated form, whereas that of imino-L-lyxitols prefers the protonation form in both enzymes. The calculations indicate that the binding mechanism of inhibitors to the active-site of α-mannosidases is dependent on the inhibitor structure and could be used to design new selective inhibitors of GMII. A series of novel synthetic N-substituted imino-D-lyxitols were evaluated with four enzymes from the glycoside hydrolase GH38 family (two of Golgi-type, Drosophila melanogaster GMIIb and Caenorhabditis elegans AMAN-2, and two of lysosomal-type, Drosophila melanogaster LManII and Canavalia ensiformis JBMan, enzymes). The most potent structures [N-9-amidinononyl and N-2-(1-naphthyl)ethyl derivatives] inhibited GMIIb (Ki = 40 nM) and AMAN-2 (Ki = 150 nM) with a weak selectivity index (SI) toward Golgi-type enzymes of IC50(LManII)/IC50(GMIIb) = 35 or IC50(JBMan)/IC50(AMAN-2) = 86. On the other hand, weaker micromolar inhibitors, such as N-2-naphthylmethyl or 4-iodobenzyl derivatives [IC50(GMIIb) = 2.4 μM and IC50 (AMAN-2) = 7.6 μM], showed a significant SI in the range from 111 to 812.

Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores

Computational modeling of inhibitors for metalloenzymes in virtual drug development campaigns has proven challenging. To overcome this limitation, a technique for predicting the binding pose of metal-binding pharmacophores (MBPs) is presented. Using a combination of density functional theory (DFT) calculations and docking using a genetic algorithm, inhibitor binding was evaluated in silico and compared with inhibitor-enzyme cocrystal structures. The predicted binding poses were found to be consistent with the cocrystal structures. The computational strategy presented represents a useful tool for predicting metalloenzyme-MBP interactions.

Selective Golgi α-mannosidase II inhibitors: N-alkyl substituted pyrrolidines with a basic functional group

Enzymatic assays, molecular modeling and NMR studies of novel 1,4-dideoxy-1,4-imino-l-lyxitols provided new information on the GH38 family enzyme inhibitors and their selectivity.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Prediction of Contact Residues in Anti-HIV Neutralizing Antibody by Deep Learning

The monoclonal antibody 1C10 targets the V3 loop of HIV-1 and neutralizes a broad range of clade B viruses. However, the mode of interaction between 1C10 and the V3 loop remains unclear because crystallization of 1C10 and the V3 peptide was unsuccessful due to the flexible regions present in both 1C10 and the V3 peptide. In this study, we predicted the 1C10 amino acid residues that make contact with the V3 loop using a deep learning (DL)-based method. Inputs from ROSIE for docking simulation and FastContact, Naccess, and PDBtools, to approximate interactions were processed by Chainer for DL, and outputs were obtained as probabilities of contact residues. Using this DL algorithm, D95, D97, P100a, and D100b of CDRH3; D53, and D56 of CDRH2; and D61 of FR3 were highly ranked as contact residues of 1C10. Substitution of these residues with alanine significantly decreased the affinity of 1C10 to the V3 peptide. Moreover, the higher the rank of the residue, the more the binding activity diminished. This study demonstrates that the prediction of contact residues using a DL-based approach is a precise and useful tool for the analysis of antibody-antigen interactions.

Structure Based Multitargeted Molecular Docking Analysis of Selected Furanocoumarins against Breast Cancer

Breast cancer is one of the biggest global dilemmas and its current therapy is to target the hormone receptors by the use of partial agonists/antagonists. Potent drugs for breast cancer treatment are Tamoxifen, Trastuzumab, Paclitaxel, etc. which show adverse effects and resistance in patients. The aim of the study has been on certain phytochemicals which has potent actions on ERα, PR, EGFR and mTOR inhibition. The current study is performed by the use of molecular docking as protein-ligand interactions play a vital role in drug design. The 3D structures of ERα, PR, EGFR and mTOR were obtained from the protein data bank and docked with 23 3D PubChem structures of furanocoumarin compounds using FlexX. Drug-likeness property was checked by applying the Lipinski's rule of five on the furanocoumarins to evaluate anti-breast cancer activity. Antagonist and inhibition assay of ERα, EGFR and mTOR respectively has been performed using appropriate in-vitro techniques. The results confirm that Xanthotoxol has the best docking score for breast cancer followed by Bergapten, Angelicin, Psoralen and Isoimperatorin. Further, the in-vitro results also validate the molecular docking analysis. This study suggests that the selected furanocoumarins can be further investigated and evaluated for breast cancer treatment and management strategies.

Identification of a potential allosteric site of Golgi α-mannosidase II using computer-aided drug design

Golgi α-mannosidase II (GMII) is a glycoside hydrolase playing a crucial role in the N-glycosylation pathway. In various tumour cell lines, the distribution of N-linked sugars on the cell surface is modified and correlates with the progression of tumour metastasis. GMII therefore is a possible molecular target for anticancer agents. Here, we describe the identification of a non-competitive GMII inhibitor using computer-aided drug design methods including identification of a possible allosteric binding site, pharmacophore search and virtual screening.

N-Benzyl Substitution of Polyhydroxypyrrolidines: The Way to Selective Inhibitors of Golgi α-Mannosidase II

Inhibition of the biosynthesis of complex N-glycans in the Golgi apparatus influences progress of tumor growth and metastasis. Golgi α-mannosidase II (GMII) has become a therapeutic target for drugs with anticancer activities. One critical task for successful application of GMII drugs in medical treatments is to decrease their unwanted co-inhibition of lysosomal α-mannosidase (LMan), a weakness of all known potent GMII inhibitors. A series of novel N-substituted polyhydroxypyrrolidines was synthesized and tested with modeled GH38 α-mannosidases from Drosophila melanogaster (GMIIb and LManII). The most potent structures inhibited GMIIb (K_i =50-76 μm, as determined by enzyme assays) with a significant selectivity index of IC₅₀ (LManII)/IC₅₀ (GMIIb) >100. These compounds also showed inhibitory activities in in vitro assays with cancer cell lines (leukemia, IC₅₀ =92-200 μm) and low cytotoxic activities in normal fibroblast cell lines (IC₅₀ >200 μm). In addition, they did not show any significant inhibitory activity toward GH47 Aspergillus saitoiα1,2-mannosidase. An appropriate stereo configuration of hydroxymethyl and benzyl functional groups on the pyrrolidine ring of the inhibitor may lead to an inhibitor with the required selectivity for the active site of a target α-mannosidase.

Medicinal Chemistry Projects Requiring Imaginative Structure-Based Drug Design Methods

Computational methods for docking small molecules to proteins are prominent in drug discovery. There are hundreds, if not thousands, of documented examples-and several pertinent cases within our research program. Fifteen years ago, our first docking-guided drug design project yielded nanomolar metalloproteinase inhibitors and illustrated the potential of structure-based drug design. Subsequent applications of docking programs to the design of integrin antagonists, BACE-1 inhibitors, and aminoglycosides binding to bacterial RNA demonstrated that available docking programs needed significant improvement. At that time, docking programs primarily considered flexible ligands and rigid proteins. We demonstrated that accounting for protein flexibility, employing displaceable water molecules, and using ligand-based pharmacophores improved the docking accuracy of existing methods-enabling the design of bioactive molecules. The success prompted the development of our own program, Fitted, implementing all of these aspects. The primary motivation has always been to respond to the needs of drug design studies; the majority of the concepts behind the evolution of Fitted are rooted in medicinal chemistry projects and collaborations. Several examples follow: (1) Searching for HDAC inhibitors led us to develop methods considering drug-zinc coordination and its effect on the pKa of surrounding residues. (2) Targeting covalent prolyl oligopeptidase (POP) inhibitors prompted an update to Fitted to identify reactive groups and form bonds with a given residue (e.g., a catalytic residue) when the geometry allows it. Fitted-the first fully automated covalent docking program-was successfully applied to the discovery of four new classes of covalent POP inhibitors. As a result, efficient stereoselective syntheses of a few screening hits were prioritized rather than synthesizing large chemical libraries-yielding nanomolar inhibitors. (3) In order to study the metabolism of POP inhibitors by cytochrome P450 enzymes (CYPs)-for toxicology studies-the program Impacts was derived from Fitted and helped us to reveal a complex metabolism with unforeseen stereocenter isomerizations. These efforts, combined with those of other docking software developers, have strengthened our understanding of the complex drug-protein binding process while providing the medicinal chemistry community with useful tools that have led to drug discoveries. In this Account, we describe our contributions over the past 15 years-within their historical context-to the design of drug candidates, including BACE-1 inhibitors, POP covalent inhibitors, G-quadruplex binders, and aminoglycosides binding to nucleic acids. We also remark the necessary developments of docking programs, specifically Fitted, that enabled structure-based design to flourish and yielded multiple fruitful, rational medicinal chemistry campaigns.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q(2)=0.78-0.86 when cross-validation procedures were carried out; and R(2)=0.81-0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8-1.1kcalmol(-1). Also, 76-80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments.

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations

Human Serum Albumin (HSA) has been suggested to be an alternate biomarker to the existing Hemoglobin-A1c (HbA1c) marker for glycemic monitoring. Development and usage of HSA as an alternate biomarker requires the identification of glycation sites, or equivalently, glucose-binding pockets. In this work, we combine molecular dynamics simulations of HSA and the state-of-art machine learning method Support Vector Machine (SVM) to predict glucose-binding pockets in HSA. SVM uses the three dimensional arrangement of atoms and their chemical properties to predict glucose-binding ability of a pocket. Feature selection reveals that the arrangement of atoms and their chemical properties within the first 4Å from the centroid of the pocket play an important role in the binding of glucose. With a 10-fold cross validation accuracy of 84 percent, our SVM model reveals seven new potential glucose-binding sites in HSA of which two are exposed only during the dynamics of HSA. The predictions are further corroborated using docking studies. These findings can complement studies directed towards the development of HSA as an alternate biomarker for glycemic monitoring.

Charting a Path to Success in Virtual Screening

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

Simple Indolizidine and Quinolizidine Alkaloids

This review of simple indolizidine and quinolizidine alkaloids (i.e., those in which the parent bicyclic systems are in general not embedded in polycyclic arrays) is an update of the previous coverage in Volume 55 of this series (2001). The present survey covers the literature from mid-1999 to the end of 2013; and in addition to aspects of the isolation, characterization, and biological activity of the alkaloids, much emphasis is placed on their total synthesis. A brief introduction to the topic is followed by an overview of relevant alkaloids from fungal and microbial sources, among them slaframine, cyclizidine, Steptomyces metabolites, and the pantocins. The important iminosugar alkaloids lentiginosine, steviamine, swainsonine, castanospermine, and related hydroxyindolizidines are dealt with in the subsequent section. The fourth and fifth sections cover metabolites from terrestrial plants. Pertinent plant alkaloids bearing alkyl, functionalized alkyl or alkenyl substituents include dendroprimine, anibamine, simple alkaloids belonging to the genera Prosopis, Elaeocarpus, Lycopodium, and Poranthera, and bicyclic alkaloids of the lupin family. Plant alkaloids bearing aryl or heteroaryl substituents include ipalbidine and analogs, secophenanthroindolizidine and secophenanthroquinolizidine alkaloids (among them septicine, julandine, and analogs), ficuseptine, lasubines, and other simple quinolizidines of the Lythraceae, the simple furyl-substituted Nuphar alkaloids, and a mixed quinolizidine-quinazoline alkaloid. The penultimate section of the review deals with the sizable group of simple indolizidine and quinolizidine alkaloids isolated from, or detected in, ants, mites, and terrestrial amphibians, and includes an overview of the "dietary hypothesis" for the origin of the amphibian metabolites. The final section surveys relevant alkaloids from marine sources, and includes clathryimines and analogs, stellettamides, the clavepictines and pictamine, and bis(quinolizidine) alkaloids.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

Mutations in four glycosyl hydrolases reveal a highly coordinated pathway for rhodopsin biosynthesis and N-glycan trimming in Drosophila melanogaster

As newly synthesized glycoproteins move through the secretory pathway, the asparagine-linked glycan (N-glycan) undergoes extensive modifications involving the sequential removal and addition of sugar residues. These modifications are critical for the proper assembly, quality control and transport of glycoproteins during biosynthesis. The importance of N-glycosylation is illustrated by a growing list of diseases that result from defects in the biosynthesis and processing of N-linked glycans. The major rhodopsin in Drosophila melanogaster photoreceptors, Rh1, is highly unique among glycoproteins, as the N-glycan appears to be completely removed during Rh1 biosynthesis and maturation. However, much of the deglycosylation pathway for Rh1 remains unknown. To elucidate the key steps in Rh1 deglycosylation in vivo, we characterized mutant alleles of four Drosophila glycosyl hydrolases, namely α-mannosidase-II (α-Man-II), α-mannosidase-IIb (α-Man-IIb), a β-N-acetylglucosaminidase called fused lobes (Fdl), and hexosaminidase 1 (Hexo1). We have demonstrated that these four enzymes play essential and unique roles in a highly coordinated pathway for oligosaccharide trimming during Rh1 biosynthesis. Our results reveal that α-Man-II and α-Man-IIb are not isozymes like their mammalian counterparts, but rather function at distinct stages in Rh1 maturation. Also of significance, our results indicate that Hexo1 has a biosynthetic role in N-glycan processing during Rh1 maturation. This is unexpected given that in humans, the hexosaminidases are typically lysosomal enzymes involved in N-glycan catabolism with no known roles in protein biosynthesis. Here, we present a genetic dissection of glycoprotein processing in Drosophila and unveil key steps in N-glycan trimming during Rh1 biosynthesis. Taken together, our results provide fundamental advances towards understanding the complex and highly regulated pathway of N-glycosylation in vivo and reveal novel insights into the functions of glycosyl hydrolases in the secretory pathway.

Docking ligands into flexible and solvated macromolecules. 6. Development and application to the docking of HDACs and other zinc metalloenzymes inhibitors

Metalloenzymes are ubiquitous proteins which feature one or more metal ions either directly involved in the enzymatic activity and/or structural properties (i.e., zinc fingers). Several members of this class take advantage of the Lewis acidic properties of zinc ions to carry out their various catalytic transformations including isomerization or amide cleavage. These enzymes have been validated as drug targets for a number of diseases including cancer; however, despite their pharmaceutical relevance and the availability of crystal structures, structure-based drug design methods have been poorly and indirectly parametrized for these classes of enzymes. More specifically, the metal coordination component and proton transfers of the process of drugs binding to metalloenzymes have been inadequately modeled by current docking programs, if at all. In addition, several known issues, such as coordination geometry, atomic charge variability, and a potential proton transfer from small molecules to a neighboring basic residue, have often been ignored. We report herein the development of specific functions and parameters to account for zinc-drug coordination focusing on the above-listed phenomena and their impact on docking to zinc metalloenzymes. These atom-type-dependent but atomic charge-independent functions implemented into Fitted 3.1 enable the simulation of drug binding to metalloenzymes, considering an acid-base reaction with a neighboring residue when necessary with good accuracy.

Extra precision docking, free energy calculation and molecular dynamics simulation studies of CDK2 inhibitors

Molecular docking, free energy calculation and molecular dynamics (MD) simulation studies have been performed, to explore the putative binding modes of 3,5-diaminoindazoles, imidazo(1,2-b)pyridazines and triazolo(1,5-a) pyridazines series of Cyclin-dependent kinase (CDK2) inhibitors. To evaluate the effectiveness of docking protocol in flexible docking, we have selected crystallographic bound compound to validate our docking procedure as evident from root mean square deviations (RMSDs). We found different binding sites namely catalytic, inhibitory phosphorylation, cyclin binding and CKS-binding site of the CDK2 contributing towards the binding of these compounds. Moreover, correlation between free energy of binding and biological activity yielded a statistically significant correlation coefficient. Finally, three representative protein-ligand complexes were subjected to molecular dynamics simulation to determine the stability of the predicted conformations. The low value of the RMSDs between the initial complex structure and the energy minimized final average complex structure suggests that the derived docked complexes are close to equilibrium. We suggest that the phenylacetyl type of substituents and cyclohexyl moiety make the favorable interactions with a number of residues in the active site, and show better inhibitory activity to improve the pharmacokinetic profile of compounds against CDK2. The structure-based drug design strategy described in this study will be highly useful for the development of new inhibitors with high potency and selectivity.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database

Docking is one of the most commonly used techniques in drug design. It is used for both identifying correct poses of a ligand in the binding site of a protein as well as for the estimation of the strength of protein-ligand interaction. Because millions of compounds must be screened, before a suitable target for biological testing can be identified, all calculations should be done in a reasonable time frame. Thus, all programs currently in use exploit empirically based algorithms, avoiding systematic search of the conformational space. Similarly, the scoring is done using simple equations, which makes it possible to speed up the entire process. Therefore, docking results have to be verified by subsequent in vitro studies. The purpose of our work was to evaluate seven popular docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) on the extensive dataset composed of 1300 protein-ligands complexes from PDBbind 2007 database, where experimentally measured binding affinity values were also available. We compared independently the ability of proper posing [according to Root mean square deviation (or Root mean square distance) of predicted conformations versus the corresponding native one] and scoring (by calculating the correlation between docking score and ligand binding strength). To our knowledge, it is the first large-scale docking evaluation that covers both aspects of docking programs, that is, predicting ligand conformation and calculating the strength of its binding. More than 1000 protein-ligand pairs cover a wide range of different protein families and inhibitor classes. Our results clearly showed that the ligand binding conformation could be identified in most cases by using the existing software, yet we still observed the lack of universal scoring function for all types of molecules and protein families.

Molecular docking of carbohydrate ligands to antibodies: structural validation against crystal structures

Cell surface glycoproteins play vital roles in cellular homeostasis and disease. Antibody recognition of glycosylation on different cells and pathogens is critically important for immune surveillance. Conversely, adverse immune reactions resulting from antibody-carbohydrate interactions have been implicated in the development of autoimmune diseases and impact areas such as xenotransplantation and cancer treatment. Understanding the nature of antibody-carbohydrate interactions and the method by which saccharides fit into antibody binding sites is important in understanding the recognition process. In silico techniques offer attractive alternatives to experimental methods (X-ray crystallography and NMR) for the study of antibody-carbohydrate complexes. In particular, molecular docking provides information about protein-ligand interactions in systems that are difficult to study with experimental techniques. Before molecular docking can be used to investigate antibody-carbohydrate complexes, validation of an appropriate docking method is required. In this study, four popular docking programs, Glide, AutoDock, GOLD, and FlexX, were assessed for their ability to accurately dock carbohydrates to antibodies. Comparison of top ranking poses with crystal structures highlighted the strengths and weaknesses of these programs. Rigid docking, in which the protein conformation remains static, and flexible docking, where both the protein and ligand are treated as flexible, were compared. This study has revealed that generally molecular docking of carbohydrates to antibodies has been performed best by Glide.

Human lysosomal alpha-mannosidases exhibit different inhibition and metal binding properties

Two structurally-related members of the lysosomal mannosidase family, the broad substrate specificity enzyme human lysosomal alpha-mannosidase (hLM, MAN2B1) and the human core alpha-1, 6-specific mannosidase (hEpman, MAN2B2) act in a complementary fashion on different glycosidic linkages, to effect glycan degradation in the lysosome. We have successfully expressed these enzymes in Drosophila S2 cells and functionally characterized them. hLM and hEpman were significantly inhibited by the class II alpha-mannosidase inhibitors, swainsonine and mannostatin A. We show that three pyrrolidine-based compounds designed for selective inhibition of Golgi alpha-mannosidase II (GMII) exhibited varying degrees of inhibition for hLM and hEpman. While these compounds inhibited hLM and GMII similarly, they inhibited hEpman to a lesser extent. Further, the two lysosomal alpha-mannosidases also show differential metal dependency properties. This has led us to propose a secondary metal binding site in hEpman. These results set the stage for the development of selective inhibitors to members of the GH38 family, and, henceforth, the further investigation of their physiological roles.

Structure and kinetic investigation of Streptococcus pyogenes family GH38 alpha-mannosidase

Background

The enzymatic hydrolysis of α−mannosides is catalyzed by glycoside hydrolases (GH), termed α−mannosidases. These enzymes are found in different GH sequence–based families. Considerable research has probed the role of higher eukaryotic “GH38” α−mannosides that play a key role in the modification and diversification of hybrid N-glycans; processes with strong cellular links to cancer and autoimmune disease. The most extensively studied of these enzymes is the Drosophila GH38 α−mannosidase II, which has been shown to be a retaining α−mannosidase that targets both α−1,3 and α−1,6 mannosyl linkages, an activity that enables the enzyme to process GlcNAc(Man)₅(GlcNAc)₂ hybrid N-glycans to GlcNAc(Man)₃(GlcNAc)₂. Far less well understood is the observation that many bacterial species, predominantly but not exclusively pathogens and symbionts, also possess putative GH38 α−mannosidases whose activity and specificity is unknown.

Methodology/Principal Findings

Here we show that the Streptococcus pyogenes (M1 GAS SF370) GH38 enzyme (Spy1604; hereafter SpGH38) is an α−mannosidase with specificity for α−1,3 mannosidic linkages. The 3D X-ray structure of SpGH38, obtained in native form at 1.9 Å resolution and in complex with the inhibitor swainsonine (K_i 18 µM) at 2.6 Å, reveals a canonical GH38 five-domain structure in which the catalytic “–1” subsite shows high similarity with the Drosophila enzyme, including the catalytic Zn²⁺ ion. In contrast, the “leaving group” subsites of SpGH38 display considerable differences to the higher eukaryotic GH38s; features that contribute to their apparent specificity.

Conclusions/Significance

Although the in vivo function of this streptococcal GH38 α−mannosidase remains unknown, it is shown to be an α−mannosidase active on N-glycans. SpGH38 lies on an operon that also contains the GH84 hexosaminidase (Spy1600) and an additional putative glycosidase. The activity of SpGH38, together with its genomic context, strongly hints at a function in the degradation of host N- or possibly O-glycans. The absence of any classical signal peptide further suggests that SpGH38 may be intracellular, perhaps functioning in the subsequent degradation of extracellular host glycans following their initial digestion by secreted glycosidases.

Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins?

In our previous report, we investigated the impact of protein flexibility and the presence of water molecules on the pose-prediction accuracy of major docking programs. To complete these investigations, we report herein a study of the impact of these two aspects on the accuracy of scoring functions. To this effect, we developed two sets of protein/ligand complexes made up of ligands cross-docked or cocrystallized with a large variety of proteins, featuring bridging water molecules and demonstrating protein flexibility. Efforts were made to reduce the correlation between the molecular weights of the selected ligands and their binding affinities, a major bias in some previously reported benchmark sets. Using these sets, 18 available scoring functions have been assessed for their accuracy to predict binding affinities and to rank-order compounds by their affinity to cocrystallized proteins. This study confirmed the good and similar accuracy of Xscore, GlideScore, DrugScore(CSD), GoldScore, PLP1, ChemScore, RankScore, and the eHiTS scoring function. Our next investigations demonstrated that most of the assessed scoring functions were much less accurate when the correct protein conformation was not provided. This study also revealed that considering the water molecules for scoring does not greatly affect the accuracy. Finally, this work sheds light on the high correlation between scoring functions and the poor increase in accuracy one can expect from consensus scoring.

Comparison of several molecular docking programs: pose prediction and virtual screening accuracy

Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.

The molecular basis of inhibition of Golgi alpha-mannosidase II by mannostatin A

Mannostatin A is a potent inhibitor of the mannose-trimming enzyme, Golgi alpha-mannosidase II (GMII), which acts late in the N-glycan processing pathway. Inhibition of this enzyme provides a route to blocking the transformation-associated changes in cancer cell surface oligosaccharide structures. Here, we report on the synthesis of new Mannostatin derivatives and analyze their binding in the active site of Drosophila GMII by X-ray crystallography. The results indicate that the interaction with the backbone carbonyl of Arg876 is crucial to the high potency of the inhibitor-an effect enhanced by the hydrophobic interaction between the thiomethyl group and an aromatic pocket vicinal to the cleavage site. The various structures indicate that differences in the hydration of protein-ligand complexes are also important determinants of plasticity as well as selectivity of inhibitor binding.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method.

Protein-protein interactions: analysis and prediction

Proteins represent the tools and appliances of the cell — they assemble into larger structural elements, catalyze the biochemical reactions of metabolism, transmit signals, move cargo across membrane boundaries and carry out many other tasks. For most of these functions proteins cannot act in isolation but require close cooperation with other proteins to accomplish their task. Often, this collaborative action implies physical interaction of the proteins involved. Accordingly, experimental detection, in silico prediction and computational analysis of protein-protein interactions (PPI) have attracted great attention in the quest for discovering functional links among proteins and deciphering the complex networks of the cell.

In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein–drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein–ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.

Early identification of the side effects of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, as unexpected side effects account for one-third of all drug failures in drug development and lead to drugs being withdrawn from the market. Compared with the experimental identification of off-target proteins that cause side effects, computational approaches not only save time and costs by providing a candidate list of potential off-targets, but also provide insight into understanding the molecular mechanisms of protein–drug interactions. In this paper we describe an integrated approach to identifying similar drug binding pockets across protein families that have different global shapes. In a case study, we elucidate a possible molecular mechanism for the observed side effects of selective estrogen receptor modulators (SERMs), which are widely used to treat and prevent breast cancer and other diseases. The prediction provides molecular insight into reducing the side effects of SERMs and is supported by clinical and biochemical observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals and to repurpose existing safe pharmaceuticals to treat different diseases. The process can be included in a drug discovery pipeline in an effort to optimize drug leads, reduce unwanted side effects, and accelerate development of new drugs.