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

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta

Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.

Evaluation of potential anti-RNA-dependent RNA polymerase (RdRP) drugs against the newly emerged model of COVID-19 RdRP using computational methods

INTRODUCTION

Despite all the efforts to treat COVID-19, no particular cure has been found for this virus. Since developing antiviral drugs is a time-consuming process, the most effective approach is to evaluate the approved and under investigation drugs using in silico methods. Among the different targets within the virus structure, as a vital component in the life cycle of coronaviruses, RNA-dependent RNA polymerase (RdRP) can be a critical target for antiviral drugs. The impact of the existence of RNA in the enzyme structure on the binding affinity of anti-RdRP drugs has not been investigated so far.

METHODS

In this study, the potential anti-RdRP effects of a variety of drugs from two databases (Zinc database and DrugBank) were evaluated using molecular docking. For this purpose, the newly emerged model of COVID-19 (RdRP) post-translocated catalytic complex (PDB ID: 7BZF) that consists of RNA was chosen as the target.

RESULTS

The results indicated that idarubicin (IDR), a member of the anthracycline antibiotic family, and fenoterol (FNT), a known beta-2 adrenergic agonist drug, tightly bind to the target enzyme and could be used as potential anti-RdRP inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These outcomes revealed that due to the ligand-protein interactions, the presence of RNA in this structure could remarkably affect the binding affinity of inhibitor compounds.

CONCLUSION

In silico approaches, such as molecular docking, could effectively address the problem of finding appropriate treatment for COVID-19. Our results showed that IDR and FNT have a significant affinity to the RdRP of SARS-CoV-2; therefore, these drugs are remarkable inhibitors of coronaviruses.

In silico identification of potential inhibitors from Cinnamon against main protease and spike glycoprotein of SARS CoV-2

Cinnamon has been utilized to remedy a lot of afflictions of humans. Literary works illustrate that it possesses numerous biological activities. Our research study is intended to recognize the phyto-derived antiviral substances from Cinnamon against COVID-19 main protease enzyme and to understand the in silico molecular basis of its activity. In the present study, 48 isolates compounds from Cinnamon retrieved from the PubMed database, are subjected to docking analysis. Docking study was performed using Autodock vina and PyRx software. Afterwards, admetSAR, as well as DruLiTo servers, were used to investigate drug-likeness prophecy. Our study shows that the nine phytochemicals of Cinnamon are very likely against the main protease enzyme of COVID-19. Further MD simulations could identify Tenufolin (TEN) and Pavetannin C1 (PAV) as hit compounds. Utilizing contemporary strategies, these phyto-compounds from a natural origin might establish a reliable medication or support lead identification. Identified hit compounds can be further taken for in vitro and in vivo studies to examine their effectiveness versus COVID-19.

Construction, expression and functional analysis of anti-clenbuterol codon-optimized scFv recombinant antibody

The construction, expression and functional analysis of codon-optimized single-chain variable fragment (coscFv) against clenbuterol (CBL) prepared from the Escherichia coli system is described. First, the ionic concentration for coscFv expression was optimized through single-factor experiments. Then, the extraction conditions of inclusion bodies were optimized, and coscFv was affinity-purified. Finally, the functional analysis of coscFv was elucidated by indirect competitive enzyme-linked immunosorbent assay (icELISA) and molecular docking. After optimizing the ionic concentration, the yield of coscFv increased from 21.69% to 23.26%. The molecular weight of coscFv was determined to be approximately 27 kDa according to the SDS-PAGE and Western blot assay. The percentage of coscFv was as high as 43.9% after the inclusion bodies were extracted, washed, and dissolved. Functional analysis indicated that the coscFv recognized CBL, and the 50% inhibition average concentration of CBL (IC₅₀) was 4.22 ± 0.01 (n = 3) ng/mL. The binding site between coscFv and CBL consisted of Asp33H, Met34H, Ser50H, Arg52H, Tyr57H, Leu59H, Asp99H, and Tyr93L. Our study confirms that coscFv can bind with CBL through the key amino acid residues and can be used to sensitively detect CBL.

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Complex carbohydrates are ubiquitous in nature, and together with proteins and nucleic acids they comprise the building blocks of life. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions.

Computational Chemistry Tools in Glycobiology: Modelling of Carbohydrate–Protein Interactions

Molecular modelling provides a major impact in the field of glycosciences, helping in the characterisation of the molecular basis of the recognition between lectins from pathogens and human glycoconjugates, and in the design of glycocompounds with anti-infectious properties. The conformational properties of oligosaccharides are complex, and therefore, the simulation of these properties is a challenging task. Indeed, the development of suitable force fields is required for the proper simulation of important problems in glycobiology, such as the interatomic interactions responsible for oligosaccharide and glycoprotein dynamics, including O-linkages in oligo- and polysaccharides, and N- and O-linkages in glycoproteins. The computational description of representative examples is discussed, herein, related to biologically active oligosaccharides and their interaction with lectins and other proteins, and the new routes open for the design of glycocompounds with promising biological activities.

Understanding the molecular differential recognition of muramyl peptide ligands by LRR domains of human NOD receptors

Human nucleotide-binding oligomerization domain proteins, hNOD1 and hNOD2, are host intracellular receptors with C-terminal leucine-rich repeat (LRR) domains, which recognize specific bacterial peptidoglycan (PG) fragments as their ligands. The specificity of this recognition is dependent on the third amino acid of the stem peptide of the PG ligand, which is usually meso-diaminopimelic acid (mesoDAP) or l-lysine (l-Lys). Since the LRR domains of hNOD receptors had been experimentally shown to confer the PG ligand-sensing specificity, we developed three-dimensional structures of hNOD1-LRR and the hNOD2-LRR to understand the mechanism of differential recognition of muramyl peptide ligands by hNOD receptors. The hNOD1-LRR and hNOD2-LRR receptor models exhibited right-handed curved solenoid shape. The hot-spot residues experimentally proved to be critical for ligand recognition were located in the concavity of the NOD-LRR and formed the recognition site. Our molecular docking analyses and molecular electrostatic potential mapping studies explain the activation of hNOD-LRRs, in response to effective molecular interactions of PG ligands at the recognition site; and conversely, the inability of certain PG ligands to activate hNOD-LRRs, by deviations from the recognition site. Based on molecular docking studies using PG ligands, we propose few residues — G825, D826 and N850 in hNOD1-LRR and L904, G905, W931, L932 and S933 in hNOD2-LRR, evolutionarily conserved across different host species, which may play a major role in ligand recognition. Thus, our integrated experimental and computational approach elucidates the molecular basis underlying the differential recognition of PG ligands by hNOD receptors.

Molecular Simulations of Carbohydrates with a Fucose-Binding Burkholderia ambifaria Lectin Suggest Modulation by Surface Residues Outside the Fucose-Binding Pocket

Burkholderia ambifaria is an opportunistic respiratory pathogen belonging to the Burkholderia cepacia complex, a collection of species responsible for the rapidly fatal cepacia syndrome in cystic fibrosis patients. A fucose-binding lectin identified in the B. ambifaria genome, BambL, is able to adhere to lung tissue, and may play a role in respiratory infection. X-ray crystallography has revealed the bound complex structures for four fucosylated human blood group epitopes (blood group B, H type 1, H type 2, and Le^x determinants). The present study employed computational approaches, including docking and molecular dynamics (MD), to extend the structural analysis of BambL-oligosaccharide complexes to include four additional blood group saccharides (A, Le^a, Le^b, and Le^y) and a library of blood-group-related carbohydrates. Carbohydrate recognition is dominated by interactions with fucose via a hydrogen-bonding network involving Arg15, Glu26, Ala38, and Trp79 and a stacking interaction with Trp74. Additional hydrogen bonds to non-fucose residues are formed with Asp30, Tyr35, Thr36, and Trp74. BambL recognition is dominated by interactions with fucose, but also features interactions with other parts of the ligands that may modulate specificity or affinity. The detailed computational characterization of the BambL carbohydrate-binding site provides guidelines for the future design of lectin inhibitors.

Bacterial peptidoglycan with amidated meso-diaminopimelic acid evades NOD1 recognition: an insight into NOD1 structure–recognition

Nucleotide-binding oligomerization domain-containing protein 1 (NOD1) is an intracellular pattern recognition receptor that recognizes bacterial peptidoglycan (PG) containing meso-diaminopimelic acid (mesoDAP) and activates the innate immune system. Interestingly, a few pathogenic and commensal bacteria modify their PG stem peptide by amidation of mesoDAP (mesoDAPNH2). In the present study, NOD1 stimulation assays were performed using bacterial PG containing mesoDAP (PGDAP) and mesoDAPNH2 (PGDAPNH2) to understand the differences in their biomolecular recognition mechanism. PGDAP was effectively recognized, whereas PGDAPNH2 showed reduced recognition by the NOD1 receptor. Restimulation of the NOD1 receptor, which was initially stimulated with PGDAP using PGDAPNH2, did not show any further NOD1 activation levels than with PGDAP alone. But the NOD1 receptor initially stimulated with PGDAPNH2 responded effectively to restimulation with PGDAP. The biomolecular structure–recognition relationship of the ligand-sensing leucine-rich repeat (LRR) domain of human NOD1 (NOD1–LRR) with PGDAP and PGDAPNH2 was studied by different computational techniques to further understand the molecular basis of our experimental observations. The d-Glu–mesoDAP motif of GMTPDAP, which is the minimum essential motif for NOD1 activation, was found involved in specific interactions at the recognition site, but the interactions of the corresponding d-Glu–mesoDAP motif of PGDAPNH2 occur away from the recognition site of the NOD1 receptor. Hot-spot residues identified for effective PG recognition by NOD1–LRR include W820, G821, D826 and N850, which are evolutionarily conserved across different host species. These integrated results thus successfully provided the atomic level and biochemical insights on how PGs containing mesoDAPNH2 evade NOD1–LRR receptor recognition.

Residue-centric modeling and design of saccharide and glycoconjugate structures

The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench (.gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers. © 2016 Wiley Periodicals, Inc.

Computational analysis of interactions in structurally available protein-glycosaminoglycan complexes

Glycosaminoglycans represent a class of linear anionic periodic polysaccharides, which play a key role in a variety of biological processes in the extracellular matrix via interactions with their protein targets. Computationally, glycosaminoglycans are very challenging due to their high flexibility, periodicity and electrostatics-driven nature of the interactions with their protein counterparts. In this work, we carry out a detailed computational characterization of the interactions in protein-glycosaminoglycan complexes from the Protein Data Bank (PDB), which are split into two subsets accounting for their intrinsic nature: non-enzymatic-protein-glycosaminoglycan and enzyme-glycosaminoglycan complexes. We apply molecular dynamics to analyze the differences in these two subsets in terms of flexibility, retainment of the native interactions in the simulations, free energy components of binding and contributions of protein residue types to glycosaminoglycan binding. Furthermore, we systematically demonstrate that protein electrostatic potential calculations, previously found to be successful for glycosaminoglycan binding sites prediction for individual systems, are in general very useful for proposing protein surface regions as putative glycosaminoglycan binding sites, which can be further used for local docking calculations with these particular polysaccharides. Finally, the performance of six different docking programs (Autodock 3, Autodock Vina, MOE, eHiTS, FlexX and Glide), some of which proved to perform well for particular protein-glycosaminoglycan complexes in previous work, is evaluated on the complete protein-glycosaminoglycan data set from the PDB. This work contributes to widen our knowledge of protein-glycosaminoglycan molecular recognition and could be useful to steer a choice of the strategies to be applied in theoretical studies of these systems.

Carbohydrate-protein interactions: molecular modeling insights

The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses.

Antibody recognition of carbohydrate epitopes†

Carbohydrate antigens are valuable as components of vaccines for bacterial infectious agents and human immunodeficiency virus (HIV), and for generating immunotherapeutics against cancer. The crystal structures of anti-carbohydrate antibodies in complex with antigen reveal the key features of antigen recognition and provide information that can guide the design of vaccines, particularly synthetic ones. This review summarizes structural features of anti-carbohydrate antibodies to over 20 antigens, based on six categories of glyco-antigen: (i) the glycan shield of HIV glycoproteins; (ii) tumor epitopes; (iii) glycolipids and blood group A antigen; (iv) internal epitopes of bacterial lipopolysaccharides; (v) terminal epitopes on polysaccharides and oligosaccharides, including a group of antibodies to Kdo-containing Chlamydia epitopes; and (vi) linear homopolysaccharides.

Understanding the Structure-Function Relationship of Lysozyme Resistance in Staphylococcus aureus by Peptidoglycan O-Acetylation Using Molecular Docking, Dynamics, and Lysis Assay

Lysozyme is an important component of the host innate defense system. It cleaves the β-1,4 glycosidic bonds between N-acetylmuramic acid and N-acetylglucosamine of bacterial peptidoglycan and induce bacterial lysis. Staphylococcus aureus (S. aureus), an opportunistic commensal pathogen, is highly resistant to lysozyme, because of the O-acetylation of peptidoglycan by O-acetyl transferase (oatA). To understand the structure-function relationship of lysozyme resistance in S. aureus by peptidoglycan O-acetylation, we adapted an integrated approach to (i) understand the effect of lysozyme on the growth of S. aureus parental and the oatA mutant strain, (ii) study the lysozyme induced lysis of exponentially grown and stationary phase of both the S. aureus parental and oatA mutant strain, (iii) investigate the dynamic interaction mechanism between normal (de-O-acetylated) and O-acetylated peptidoglycan substrate in complex with lysozyme using molecular docking and molecular dynamics simulations, and (iv) quantify lysozyme resistance of S. aureus parental and the oatA mutant in different human biological fluids. The results indicated for the first time that the active site cleft of lysozyme binding with O-acetylated peptidoglycan in S. aureus was sterically hindered and the structural stability was higher for the lysozyme in complex with normal peptidoglycan. This could have conferred reduced survival of the S. aureus oatA mutant in different human biological fluids. Consistent with this computational analysis, the experimental data confirmed decrease in the growth, lysozyme induced lysis, and lysozyme resistance, due to peptidoglycan O-acetylation in S. aureus.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r(2) of 0.71 and root-mean-squared-error of 1.25 kcal/mol (N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

Recent advances in employing molecular modelling to determine the specificity of glycan-binding proteins

Impressive improvements in docking performance can be achieved by applying energy bonuses to poses in which glycan hydroxyl groups occupy positions otherwise preferred by bound waters. In addition, inclusion of glycosidic conformational energies allows unlikely glycan conformations to be appropriately penalized. A method for predicting the binding specificity of glycan-binding proteins has been developed, which is based on grafting glycan branches onto a minimal binding determinant in the binding site. Grafting can be used either to screen virtual libraries of glycans, such as the known glycome, or to identify docked poses of minimal binding determinants that are consistent with specificity data. The reviewed advances allow accurate modelling of carbohydrate-protein 3D co-complexes, but challenges remain in ranking the affinity of congeners.

Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand-specific conformational energies. Here, we develop a set of Carbohydrate Intrinsic (CHI) energy functions that quantify the conformational properties of oligosaccharides, based on the values of their glycosidic torsion angles. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide-protein complexes in the protein data bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anticarbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X-ray structure refinement programs.

Deciphering the glycan preference of bacterial lectins by glycan array and molecular docking with validation by microcalorimetry and crystallography

Recent advances in glycobiology revealed the essential role of lectins for deciphering the glycocode by specific recognition of carbohydrates. Integrated multiscale approaches are needed for characterizing lectin specificity: combining on one hand high-throughput analysis by glycan array experiments and systematic molecular docking of oligosaccharide libraries and on the other hand detailed analysis of the lectin/oligosaccharide interaction by x-ray crystallography, microcalorimetry and free energy calculations. The lectins LecB from Pseudomonas aeruginosa and BambL from Burkholderia ambifaria are part of the virulence factors used by the pathogenic bacteria to invade the targeted host. These two lectins are not related but both recognize fucosylated oligosaccharides such as the histo-blood group oligosaccharides of the ABH(O) and Lewis epitopes. The specificities were characterized using semi-quantitative data from glycan array and analyzed by molecular docking with the Glide software. Reliable prediction of protein/oligosaccharide structures could be obtained as validated by existing crystal structures of complexes. Additionally, the crystal structure of BambL/Lewis x was determined at 1.6 Å resolution, which confirms that Lewis x has to adopt a high-energy conformation so as to bind to this lectin. Free energies of binding were calculated using a procedure combining the Glide docking protocol followed by free energy rescoring with the Prime/Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method. The calculated data were in reasonable agreement with experimental free energies of binding obtained by titration microcalorimetry. The established predictive protocol is proposed to rationalize large sets of data such as glycan arrays and to help in lead discovery projects based on such high throughput technology.

In silico driven redesign of a clinically relevant antibody for the treatment of GD2 positive tumors

Ganglioside GD2 is a cell surface glycolipid that is highly expressed on cancer cells of neuroectodermal origin, including neuroblastoma, retinoblastoma, melanoma, sarcomas, brain tumors and small cell lung cancer. Monoclonal antibodies (MoAb) that target GD2 have shown clinical efficacy in the treatment of GD2 expressing tumors, and are expected to be the new standard of care for the treatment of pediatric neuroblastoma. In this study, the crystal structure of anti-GD2 murine MoAb 3F8 was solved to 1.65 Å resolution and used as a template for molecular docking simulations of its antigen, the penta-saccharide head group of GD2. Molecular docking revealed a binding motif composed of 12 key interacting amino acid side-chains, involving an extensive network of interactions involving main-chain and side-chain hydrogen bonding, two Pi-CH interactions, and an important charged interaction between Arg95 of the H3 loop with the penultimate sialic acid residue of GD2. Based on in silico scanning mutagenesis of the 12 interacting amino acids from the docked 3F8:GD2 model, a single point mutation (Heavy Chain: Gly54Ile) was engineered into a humanized 3F8 (hu3F8) MoAb and found to have a 6-9 fold enhancement in antibody-dependent cell-mediated cytotoxicity of neuroblastoma and melanoma cell lines. With enhanced tumor-killing properties, the re-engineered hu3F8 has the potential be a more effective antibody for the treatment of GD2-positive tumors.

Solvent structure improves docking prediction in lectin-carbohydrate complexes

Recognition and complex formation between proteins and carbohydrates is a key issue in many important biological processes. Determination of the three-dimensional structure of such complexes is thus most relevant, but particularly challenging because of their usually low binding affinity. In silico docking methods have a long-standing tradition in predicting protein-ligand complexes, and allow a potentially fast exploration of a number of possible protein-carbohydrate complex structures. However, determining which of these predicted complexes represents the correct structure is not always straightforward. In this work, we present a modification of the scoring function provided by AutoDock4, a widely used docking software, on the basis of analysis of the solvent structure adjacent to the protein surface, as derived from molecular dynamics simulations, that allows the definition and characterization of regions with higher water occupancy than the bulk solvent, called water sites. They mimic the interaction held between the carbohydrate -OH groups and the protein. We used this information for an improved docking method in relation to its capacity to correctly predict the protein-carbohydrate complexes for a number of tested proteins, whose ligands range in size from mono- to tetrasaccharide. Our results show that the presented method significantly improves the docking predictions. The resulting solvent-structure-biased docking protocol, therefore, appears as a powerful tool for the design and optimization of development of glycomimetic drugs, while providing new insights into protein-carbohydrate interactions. Moreover, the achieved improvement also underscores the relevance of the solvent structure to the protein carbohydrate recognition process.

Structural analysis of a protective epitope of the Francisella tularensis O-polysaccharide

Francisella tularensis (Ft), the Gram-negative facultative intracellular bacterium that causes tularemia, is considered a biothreat because of its high infectivity and the high mortality rate of respiratory disease. The Ft lipopolysaccharide (Ft LPS) is thought to be a main protective antigen in mice and humans, and we have previously demonstrated the protective effect of the Ft LPS-specific monoclonal antibody Ab52 in a mouse model of respiratory tularemia. Immunochemical characterization has shown that the epitope recognized by Ab52 is contained within two internal repeat units of the O-polysaccharide [O-antigen (OAg)] of Ft LPS. To further localize the Ab52 epitope and understand the molecular interactions between the antibody and the saccharide, we determined the X-ray crystal structure of the Fab fragment of Ab52 and derived an antibody-antigen complex using molecular docking. The docked complex, refined through energy minimization, reveals an antigen binding site in the shape of a large canyon with a central pocket that accommodates a V-shaped epitope consisting of six sugar residues, α-D-GalpNAcAN(1→4)-α-D-GalpNAcAN(1→3)-β-D-QuipNAc(1→2)-β-D-Quip4NFm(1→4)-α-D-GalpNAcAN(1→4)-α-D-GalpNAcAN. These results inform the development of vaccines and immunotherapeutic/immunoprophylactic antibodies against Ft by suggesting a desired topology for binding of the antibody to internal epitopes of Ft LPS. This is the first report of an X-ray crystal structure of a monoclonal antibody that targets a protective Ft B cell epitope.

In silico mutagenesis and docking study of Ralstonia solanacearum RSL lectin: performance of docking software to predict saccharide binding

In this study, in silico mutagenesis and docking in Ralstonia solanacearum lectin (RSL) were carried out, and the ability of several docking software programs to calculate binding affinity was evaluated. In silico mutation of six amino acid residues (Agr17, Glu28, Gly39, Ala40, Trp76, and Trp81) was done, and a total of 114 in silico mutants of RSL were docked with Me-α-L-fucoside. Our results show that polar residues Arg17 and Glu28, as well as nonpolar amino acids Trp76 and Trp81, are crucial for binding. Gly39 may also influence ligand binding because any mutations at this position lead to a change in the binding pocket shape. The Ala40 residue was found to be the most interesting residue for mutagenesis and can affect the selectivity and/or affinity. In general, the docking software used performs better for high affinity binders and fails to place the binding affinities in the correct order.

Unlocking the binding and reaction mechanism of hydroxyurea substrates as biological nitric oxide donors

Hydroxyurea is the only FDA approved treatment of sickle cell disease. It is believed that the primary mechanism of action is associated with the pharmacological elevation of nitric oxide in the blood; however, the exact details of this are still unclear. In the current work, we investigate the atomic level details of this process using a combination of flexible-ligand/flexible-receptor virtual screening coupled with energetic analysis that decomposes interaction energies. Utilizing these methods, we were able to elucidate the previously unknown substrate binding modes of a series of hydroxyurea analogs to hemoglobin and the concomitant structural changes of the enzyme. We identify a backbone carbonyl that forms a hydrogen bond with bound substrates. Our results are consistent with kinetic and electron paramagnetic resonance (EPR) measurements of hydroxyurea-hemoglobin reactions, and a full mechanism is proposed that offers new insights into possibly improving substrate binding and/or reactivity.

Antibody recognition of cancer-related gangliosides and their mimics investigated using in silico site mapping

Modified gangliosides may be overexpressed in certain types of cancer, thus, they are considered a valuable target in cancer immunotherapy. Structural knowledge of their interaction with antibodies is currently limited, due to the large size and high flexibility of these ligands. In this study, we apply our previously developed site mapping technique to investigate the recognition of cancer-related gangliosides by anti-ganglioside antibodies. The results reveal a potential ganglioside-binding motif in the four antibodies studied, suggesting the possibility of structural convergence in the anti-ganglioside immune response. The structural basis of the recognition of ganglioside-mimetic peptides is also investigated using site mapping and compared to ganglioside recognition. The peptides are shown to act as structural mimics of gangliosides by interacting with many of the same binding site residues as the cognate carbohydrate epitopes. These studies provide important clues as to the structural basis of immunological mimicry of carbohydrates.

Induced fit or conformational selection? The role of the semi-closed state in the maltose binding protein

A full characterization of the thermodynamic forces underlying ligand-associated conformational changes in proteins is essential for understanding and manipulating diverse biological processes, including transport, signaling, and enzymatic activity. Recent experiments on the maltose binding protein (MBP) have provided valuable data about the different conformational states implicated in the ligand recognition process; however, a complete picture of the accessible pathways and the associated changes in free energy remains elusive. Here we describe results from advanced accelerated molecular dynamics (aMD) simulations, coupled with adaptively biased force (ABF) and thermodynamic integration (TI) free energy methods. The combination of approaches allows us to track the ligand recognition process on the microsecond time scale and provides a detailed characterization of the protein’s dynamic and the relative energy of stable states. We find that an induced-fit (IF) mechanism is most likely and that a mechanism involving both a conformational selection (CS) step and an IF step is also possible. The complete recognition process is best viewed as a “Pac Man” type action where the ligand is initially localized to one domain and naturally occurring hinge-bending vibrations in the protein are able to assist the recognition process by increasing the chances of a favorable encounter with side chains on the other domain, leading to a population shift. This interpretation is consistent with experiments and provides new insight into the complex recognition mechanism. The methods employed here are able to describe IF and CS effects and provide formally rigorous means of computing free energy changes. As such, they are superior to conventional MD and flexible docking alone and hold great promise for future development and applications to drug discovery.

Integrating structure-based and ligand-based approaches for computational drug design

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

Peptide inhibitors of xenoreactive antibodies mimic the interaction profile of the native carbohydrate antigens

Carbohydrate-antibody interactions mediate many cellular processes and immune responses. Carbohydrates expressed on the surface of cells serve as recognition elements for particular cell types, for example, in the ABO(H) blood group system. Antibodies that recognize host-incompatible ABO(H) system antigens exist in the bloodstream of all individuals (except AB individuals), preventing blood transfusion and organ transplantation between incompatible donors and recipients. A similar barrier exists for cross-species transplantation (xenotransplantation), in particular for pig-to-human transplantation. All humans express antibodies against the major carbohydrate xenoantigen, Galalpha (1,3)Gal (alphaGal), preventing successful xenotransplantation. Although antibody binding sites are precisely organized so as to selectively bind a specific antigen, many antibodies recognize molecules other than their native antigen. A range of peptides have been identified that can mimic carbohydrates and inhibit anti-alphaGal antibodies. However, the structural basis of how the peptides achieved this was not known. Previously, we developed an in silico method which we used to investigate carbohydrate recognition by a panel of anti-alphaGal antibodies. The method involves molecular docking of carbohydrates to antibodies and uses the docked carbohydrate poses to generate maps of th antibody binding sites in terms of prevalent hydrogen bonding and van der Waals interactions. We have applied this method to investigate peptide recognition by the anti-alphaGal antibodies. It was found that the site maps of the peptides and the carbohydrates were similar, indicating that the peptides interact with the same residues as those involved in carbohydrate recognition. This study demonstrates the potential for "design by mapping" of anti-carbohydrate antibody inhibitors.

A computational approach for exploring carbohydrate recognition by lectins in innate immunity

Recognition of pathogen-associated carbohydrates by a broad range of carbohydrate-binding proteins is central to both adaptive and innate immunity. A large functionally diverse group of mammalian carbohydrate-binding proteins are lectins, which often display calcium-dependent carbohydrate interactions mediated by one or more carbohydrate recognition domains. We report here the application of molecular docking and site mapping to study carbohydrate recognition by several lectins involved in innate immunity or in modulating adaptive immune responses. It was found that molecular docking programs can identify the correct carbohydrate-binding mode, but often have difficulty in ranking it as the best pose. This is largely attributed to the broad and shallow nature of lectin binding sites, and the high flexibility of carbohydrates. Site mapping is very effective at identifying lectin residues involved in carbohydrate recognition, especially with cases that were found to be particularly difficult to characterize via molecular docking. This study highlights the need for alternative strategies to examine carbohydrate–lectin interactions, and specifically demonstrates the potential for mapping methods to extract additional and relevant information from the ensembles of binding poses generated by molecular docking.

Dynamic aspects of antibody:oligosaccharide complexes characterized by molecular dynamics simulations and saturation transfer difference nuclear magnetic resonance

Carbohydrates are likely to maintain significant conformational flexibility in antibody (Ab):carbohydrate complexes. As demonstrated herein for the protective monoclonal Ab (mAb) F22-4 recognizing the Shigella flexneri 2a O-antigen (O-Ag) and numerous synthetic oligosaccharide fragments thereof, the combination of molecular dynamics simulations and nuclear magnetic resonance saturation transfer difference experiments, supported by physicochemical analysis, allows us to determine the binding epitope and its various contributions to affinity without using any modified oligosaccharides. Moreover, the methods used provide insights into ligand flexibility in the complex, thus enabling a better understanding of the Ab affinities observed for a representative set of synthetic O-Ag fragments. Additionally, these complementary pieces of information give evidence to the ability of the studied mAb to recognize internal as well as terminal epitopes of its cognate polysaccharide antigen. Hence, we show that an appropriate combination of computational and experimental methods provides a basis to explore carbohydrate functional mimicry and receptor binding. The strategy may facilitate the design of either ligands or carbohydrate recognition domains, according to needed improvements of the natural carbohydrate:receptor properties.

Docking glycosaminoglycans to proteins: analysis of solvent inclusion

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein-protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.

Structural basis for ligand recognition in a mushroom lectin: solvent structure as specificity predictor

Lectins are able to recognize specific carbohydrate structures through their carbohydrate recognition domain (CRD). The lectin from the mushroom Agaricus bisporus (ABL) has the remarkable ability of selectively recognizing the TF-antigen, composed of Galβ1-3GalNAc, Ser/Thr linked to proteins, specifically exposed in neoplastic tissues. Strikingly, the recently solved crystal structure of tetrameric ABL in the presence of TF-antigen and other carbohydrates showed that each monomer has two CRDs, each being able to bind specifically to different monosaccharides that differ only in the configuration of a single hydroxyl, like N-acetyl-d-galactosamine (GalNAc) and N-acetyl-d-glucosamine (GlcNAc). Understanding how lectin CRDs bind and discriminate mono and/or (poly)-saccharides is an important issue in glycobiology, with potential impact in the design of better and selective lectin inhibitors with potential therapeutic properties. In this work, and based on the unusual monosaccharide epimeric specificity of the ABL CRDs, we have performed molecular dynamics simulations of the natural (crystallographic) and inverted (changing GalNAc for GlcNAc and vice-versa) ABL-monosaccharide complexes in order to understand the selective ligand recognition properties of each CRD. We also performed a detailed analysis of the CRD local solvent structure, using previously developed methodology, and related it with the recognition mechanism. Our results provide a detailed picture of each ABL CRD specificity, allowing a better understanding of the carbohydrate selective recognition process in this particular lectin.

The modulation of adaptive immune responses by bacterial zwitterionic polysaccharides

The detection of pathogen-derived molecules as foreign particles by adaptive immune cells triggers T and B lymphocytes to mount protective cellular and humoral responses, respectively. Recent immunological advances elucidated that proteins and some lipids are the principle biological molecules that induce protective T cell responses during microbial infections. Polysaccharides are important components of microbial pathogens and many vaccines. However, research concerning the activation of the adaptive immune system by polysaccharides gained interest only recently. Traditionally, polysaccharides were considered to be T cell-independent antigens that did not directly activate T cells or induce protective immune responses. Here, we review several recent advances in “carbohydrate immunobiology”. A group of bacterial polysaccharides that are known as “zwitterionic polysaccharides (ZPSs)” were recently identified as potent immune modulators. The immunomodulatory effect of ZPSs required antigen processing and presentation by antigen presenting cells, the activation of CD4 T cells and subpopulations of CD8 T cells and the modulation of host cytokine responses. In this review, we also discuss the potential use of these unique immunomodulatory ZPSs in new vaccination strategies against chronic inflammatory conditions, autoimmunity, infectious diseases, allergies and asthmatic conditions.

Computational glycoscience: characterizing the spatial and temporal properties of glycans and glycan-protein complexes

Modern computational methods offer the tools to provide insight into the structural and dynamic properties of carbohydrate-protein complexes, beyond that provided by experimental structural biology. Dynamic properties such as the fluctuation of inter-molecular hydrogen bonds, the residency times of bound water molecules, side chain motions and ligand flexibility may be readily determined computationally. When taken with respect to the unliganded states, these calculations can also provide insight into the entropic and enthalpic changes in free energy associated with glycan binding. In addition, virtual ligand screening may be employed to predict the three dimensional (3D) structures of carbohydrate-protein complexes, given 3D structures for the components. In principle, the 3D structure of the protein may itself be derived by modeling, leading to the exciting--albeit high risk--realm of virtual structure prediction. This latter approach is appealing, given the difficulties associated with generating experimental 3D structures for some classes of glycan binding proteins; however, it is also the least robust. An unexpected outcome of the development of algorithms for modeling carbohydrate-protein interactions has been the discovery of errors in reported experimental 3D structures and a heightened awareness of the need for carbohydrate-specific computational tools for assisting in the refinement and curation of carbohydrate-containing crystal structures. Here we present a summary of the basic strategies associated with employing classical force field based modeling approaches to problems in glycoscience, with a focus on identifying typical pitfalls and limitations. This is not an exhaustive review of the current literature, but hopefully will provide a guide for the glycoscientist interested in modeling carbohydrates and carbohydrate-protein complexes, as well as the computational chemist contemplating such tasks.