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

From in-silico screening to in-vitro evaluation: Enhancing the detection of Microcystins with engineered PP1 mutant variants

Cyanotoxins produced during harmful algal blooms (CyanoHABs) have become a worldwide issue of concern. Microcystins (MC) are the most ubiquitous group of cyanotoxins and have known carcinogenic and hepatotoxic effects. The protein phosphatase inhibition assays (PPIAs), based on the inhibition of Protein Phosphatase 1/2A (PP1/PP2A) by MC, are one of the most cost-effective options for detecting MC. In this work, we aimed to design in-silico and evaluate in-vitro mutant variants of the PP1 protein, in order to enhance their capabilities as a MC biosensor. To this end, we performed an in-silico active site-saturated mutagenesis screening, followed by stability and docking affinity calculation with the MCLR cyanotoxin. Candidates with improved both affinity and stability were further tested in a fully flexible active-site docking. The best-scored mutations (19) were individually analysed regarding their locations and interactions. Four of them (p.D197F; p.Q249Y; p.S129W; p.D220Q) were selected for in-vitro expression and evaluation. Mutant p.D197F, exhibited a significant increment in inhibition by MCLR with respect to the WT, while showing a non-significant difference in stability nor activity. This successful PP1 inhibition enhancement suggests the potential of the p.D197F variant for practical MC detection applications.

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.

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.

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.

Three-Dimensional Structures of Carbohydrates and Where to Find Them

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

Effect of Noncanonical Amino Acids on Protein-Carbohydrate Interactions: Structure, Dynamics, and Carbohydrate Affinity of a Lectin Engineered with Fluorinated Tryptophan Analogs

Protein–carbohydrate interactions play crucial roles in biology. Understanding and modifying these interactions is of major interest for fighting many diseases. We took a synthetic biology approach and incorporated noncanonical amino acids into a bacterial lectin to modulate its interactions with carbohydrates. We focused on tryptophan, which is prevalent in carbohydrate binding sites. The exchange of the tryptophan residues with analogs fluorinated at different positions resulted in three distinctly fluorinated variants of the lectin from Ralstonia solanacearum. We observed differences in stability and affinity toward fucosylated glycans and rationalized them by X-ray and modeling studies. While fluorination decreased the aromaticity of the indole ring and, therefore, the strength of carbohydrate–aromatic interactions, additional weak hydrogen bonds were formed between fluorine and the ligand hydroxyl groups. Our approach opens new possibilities to engineer carbohydrate receptors.

Assessing the Performance of MM/PBSA, MM/GBSA, and QM-MM/GBSA Approaches on Protein/Carbohydrate Complexes: Effect of Implicit Solvent Models, QM Methods, and Entropic Contributions

Rapid and accurate binding affinity prediction of protein-carbohydrate complexes is a major challenge in glycomimetics design. Among the existing computational techniques, end-point methods have received considerable interest because of their low computational cost. However, significant obstacles remain when such methods are applied to protein-glycan complexes. This article reports the performance of end-point free-energy calculation methods: molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), MM/generalized Born surface area (MM/GBSA), and quantum mechanics-MM/GBSA (QM-MM/GBSA) on monosaccharides bound to RSL lectin from Ralstonia solanacearum. A careful investigation of the molecular dynamics simulation length, van der Waals radii sets, GB models, QM Hamiltonians, and entropic compensation has been made, and the results are compared with the experimental binding free energies from isothermal titration calorimetry/surface plasmon resonance measurements. The binding free energies using implicit solvent methods are found to be sensitive to the simulation length, radii set, GB model, and QM Hamiltonian. A simulation length of 10 ns using the radii set mbondi provides the best agreement with the experimental values ( r² = 0.96) by MM/PBSA. The GB^HCT model is in accord with the experimental values in MM/GBSA ( r² = 0.91) or in combination with parameterized model number 6 (PM6) ( r² = 0.98) in QM-MM/GBSA. Out of 12 QM Hamiltonians tested, PM6, density functional theory-based tight binding (DFTB), and their variants proved to be more efficient than other semiempirical methods. These methods perform equally well in predicting both absolute and relative binding free energies.

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.

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.

Insights into the effects of glycosylation and the monosaccharide-binding activity of the plant lectin CrataBL

CrataBL is a glycoprotein isolated from Crataeva tapia bark, containing two N-glycosylation sites. It has been identified to present lectin activity with some specificity for binding glucose over galactose. However, to date, no information on the effects of glycosylation or CrataBL monosaccharide-binding sites and monosaccharide specificity has been obtained. Thus, molecular docking and molecular dynamics simulations were employed to characterize the glycosylated CrataBL conformation and dynamics in aqueous solutions, as well as the molecular basis for its binding specificity. The obtained results indicate both local and distant conformational stabilization effects of N-linked glycans over CrataBL protein moiety. Regarding its lectin activity, molecular docking calculations were performed in two possible binding sites, identified through sequence-based, structure-based and evolutionary information, using α- and β-anomeric states of the monosaccharides. The obtained poses were further refined through molecular dynamics simulations, suggesting that positively-charged amino acids dictate the binding preference for glucose over galactose in both sites. In addition, a possible preference for β-monosaccharides was proposed. Such data are expected to contribute to a better comprehension of the lectins monosaccharide-binding activities and carbohydrate-binding site structures.

Calculations of the absolute binding free energies for Ralstonia solanacearum lectins bound with methyl-α-l-fucoside at molecular mechanical and quantum mechanical/molecular mechanical levels

In this work, both a molecular mechanical (MM) method and a hybrid quantum mechanical/molecular mechanical (QM/MM) method have been applied in the study of the binding affinities of methyl-α-l-fucoside to Ralstonia solanacearum lectins.

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.

Anomer-Specific Recognition and Dynamics in a Fucose-Binding Lectin

Sugar binding by a cell surface ∼29 kDa lectin (RSL) from the bacterium Ralstonia solanacearum was characterized by NMR spectroscopy. The complexes formed with four monosaccharides and four fucosides were studied. Complete resonance assignments and backbone dynamics were determined for RSL in the sugar-free form and when bound to l-fucose or d-mannose. RSL was found to interact with both the α- and the β-anomer of l-fucose and the "fucose like" sugars d-arabinose and l-galactose. Peak splitting was observed for some resonances of the binding site residues. The assignment of the split signals to the α- or β-anomer was confirmed by comparison with the spectra of RSL bound to methyl-α-l-fucoside or methyl-β-l-fucoside. The backbone dynamics of RSL were sensitive to the presence of ligand, with the protein adopting a more compact structure upon binding to l-fucose. Taking advantage of tryptophan residues in the binding sites, we show that the indole resonance is an excellent reporter on ligand binding. Each sugar resulted in a distinct signature of chemical shift perturbations, suggesting that tryptophan signals are a sufficient probe of sugar binding.

Interolog interfaces in protein-protein docking

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

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.

Evaluation of Selected Classical Force Fields for Alchemical Binding Free Energy Calculations of Protein-Carbohydrate Complexes

Protein-carbohydrate recognition is crucial in many vital biological processes including host-pathogen recognition, cell-signaling, and catalysis. Accordingly, computational prediction of protein-carbohydrate binding free energies is of enormous interest for drug design. However, the accuracy of current force fields (FFs) for predicting binding free energies of protein-carbohydrate complexes is not well understood owing to technical challenges such as the highly polar nature of the complexes, anomerization, and conformational flexibility of carbohydrates. The present study evaluated the performance of alchemical predictions of binding free energies with the GAFF1.7/AM1-BCC and GLYCAM06j force fields for modeling protein-carbohydrate complexes. Mean unsigned errors of 1.1 ± 0.06 (GLYCAM06j) and 2.6 ± 0.08 (GAFF1.7/AM1-BCC) kcal·mol(-1) are achieved for a large data set of monosaccharide ligands for Ralstonia solanacearum lectin (RSL). The level of accuracy provided by GLYCAM06j is sufficient to discriminate potent, moderate, and weak binders, a goal that has been difficult to achieve through other scoring approaches. Accordingly, the protocols presented here could find useful applications in carbohydrate-based drug and vaccine developments.

The Interaction of Heparin Tetrasaccharides with Chemokine CCL5 Is Modulated by Sulfation Pattern and pH

Interactions between chemokines such as CCL5 and glycosaminoglycans (GAGs) are essential for creating haptotactic gradients to guide the migration of leukocytes into inflammatory sites, and the GAGs that interact with CCL5 with the highest affinity are heparan sulfates/heparin. The interaction between CCL5 and its receptor on monocytes, CCR1, is mediated through residues Arg-17 and -47 in CCL5, which overlap with the GAG-binding (44)RKNR(47) "BBXB" motifs. Here we report that heparin and tetrasaccharide fragments of heparin are able to inhibit CCL5-CCR1 binding, with IC50 values showing strong dependence on the pattern and extent of sulfation. Modeling of the CCL5-tetrasaccharide complexes suggested that interactions between specific sulfate and carboxylate groups of heparin and residues Arg-17 and -47 of the protein are essential for strong inhibition; tetrasaccharides lacking the specific sulfation pattern were found to preferentially bind CCL5 in positions less favorable for inhibition of the interaction with CCR1. Simulations of a 12-mer heparin fragment bound to CCL5 indicated that the oligosaccharide preferred to interact simultaneously with both (44)RKNR(47) motifs in the CCL5 homodimer and engaged residues Arg-47 and -17 from both chains. Direct engagement of these residues by the longer heparin oligosaccharide provides a rationalization for its effectiveness as an inhibitor of CCL5-CCR1 interaction. In this mode, histidine (His-23) may contribute to CCL5-GAG interactions when the pH drops just below neutral, as occurs during inflammation. Additionally, an examination of the contribution of pH to modulating CCL5-heparin interactions suggested a need for careful interpretation of experimental results when experiments are performed under non-physiological conditions.

Insights into the binding specificity of wild type and mutated wheat germ agglutinin towards Neu5Acα(2-3)Gal: a study by in silico mutations and molecular dynamics simulations

Wheat germ agglutinin (WGA) is a plant lectin, which specifically recognizes the sugars NeuNAc and GlcNAc. Mutated WGA with enhanced binding specificity can be used as biomarkers for cancer. In silico mutations are performed at the active site of WGA to enhance the binding specificity towards sialylglycans, and molecular dynamics simulations of 20 ns are carried out for wild type and mutated WGAs (WGA1, WGA2, and WGA3) in complex with sialylgalactose to examine the change in binding specificity. MD simulations reveal the change in binding specificity of wild type and mutated WGAs towards sialylgalactose and bound conformational flexibility of sialylgalactose. The mutated polar amino acid residues Asn114 (S114N), Lys118 (G118K), and Arg118 (G118R) make direct and water mediated hydrogen bonds and hydrophobic interactions with sialylgalactose. An analysis of possible hydrogen bonds, hydrophobic interactions, total pair wise interaction energy between active site residues and sialylgalactose and MM-PBSA free energy calculation reveals the plausible binding modes and the role of water in stabilizing different binding modes. An interesting observation is that the binding specificity of mutated WGAs (cyborg lectin) towards sialylgalactose is found to be higher in double point mutation (WGA3). One of the substituted residues Arg118 plays a crucial role in sugar binding. Based on the interactions and energy calculations, it is concluded that the order of binding specificity of WGAs towards sialylgalactose is WGA3 > WGA1 > WGA2 > WGA. On comparing with the wild type, double point mutated WGA (WGA3) exhibits increased specificity towards sialylgalactose, and thus, it can be effectively used in targeted drug delivery and as biological cell marker in cancer therapeutics.

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.

Protein engineering study of β-mannosidase to set up a potential chemically efficient biocatalyst

This study is focused on the analysis and mutagenesis of β-mannosidase from Bacteroides thetaiotaomicron with the aim of broadening its substrate specificity to 2-acetamido-2-deoxy-β-d-mannopyranosyl (β-ManNAc) derivatives. Various conformations ((4)C1, (4)H5 and (1)S5) of native and modified ligands were docked to the binding site of the protein to determine the most suitable conformation of sugars for further hydrolysis. Key amino acid residues were mutated in silico focusing on stabilizing the acetamido group of β-ManNAc as well as forming the oxazoline intermediate needed for hydrolysis. The results of large set of 5 ns molecular dynamic simulations showed that the majority of the active site residues are involved in substrate interaction and do not exhibit a higher flexibility except for Asn178. Mutations of Asn178 to alanine and Asp199 to serine could lead to a stabilization of the acetamido group in the binding site. So far, in vitro mutagenesis and the screen of a large variety of biological sources were unable to extend β-mannosidase's activity to include β-ManNAc derivatives.

Reduction of lectin valency drastically changes glycolipid dynamics in membranes but not surface avidity

Multivalency is proposed to play a role in the strong avidity of lectins for glycosylated cell surfaces and also in their ability to affect membrane dynamics by clustering glycosphingolipids. Lectins with modified valency were designed from the β-propeller fold of Ralstonia solanacearum lectin (RSL) that presents six fucose binding sites. After identification of key amino acids by molecular dynamics calculations, two mutants with reduced valency were produced. Isothermal titration calorimetry confirmed the loss of three high affinity binding sites for both mutants. Crystal structures indicated that residual low affinity binding occurred in W76A but not in R17A. The trivalent R17A mutant presented unchanged avidity toward fucosylated surfaces, when compared to hexavalent RSL. However, R17A is not able anymore to induce formation of membrane invaginations on giant unilamellar vesicules, indicating the crucial role of number of binding sites for clustering of glycolipids. In the human lung epithelial cell line H1299, wt-RSL is internalized within seconds whereas the kinetics of R17A uptake is largely delayed. Neolectins with tailored valency are promising tools to study membrane dynamics.

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 characterization of a novel β-1,3-glucanase gene from Bacillus amyloliquefaciens--a bacterial endophyte of Hevea brasiliensis antagonistic to Phytophthora meadii

We report the molecular characterization of β-1,3-glucanase-producing Bacillus amyloliquefaciens-an endophyte of Hevea brasiliensis antagonistic to Phytophthora meadii. After cloning and sequencing, the β-1,3-glucanase gene was found to be 747 bp in length. A homology model of the β-1,3-glucanase protein was built from the amino acid sequence obtained upon translation of the gene. The target β-1,3-glucanase protein and the template protein, endo β-1,3-1,4-glucanase protein (PDB ID: 3o5s), were found to share 94% sequence identity and to have similar secondary and tertiary structures. In the modeled structure, three residues in the active site region of the template-Asn52, Ile157 and Val158-were substituted with Asp, Leu and Ala, respectively. Computer-aided docking studies of the substrate disaccharide (β-1, 3-glucan) with the target as well as with the template proteins showed that the two protein-substrate complexes were stabilized by three hydrogen bonds and by many van der Waals interactions. Although the binding energies and the number of hydrogen bonds were the same in both complexes, the orientations of the substrate in the active sites of the two proteins were different. These variations might be due to the change in the three amino acids in the active site region of the two proteins. The difference in substrate orientation in the active site could also affect the catalytic potential of the β-1,3 glucanase enzyme.

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.