Automated docking of alpha-(1-->4)- and alpha-(1-->6)-linked glucosyl trisaccharides and maltopentaose into the soybean beta-amylase active site

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.

Entropic-Based Separation of Diastereomers: Size-Exclusion Chromatography with Online Viscometry and Refractometry Detection for Analysis of Blends of Mannose and Galactose Methyl-α-pyranosides at “Ideal” Size-Exclusion Conditions

The separation of carbohydrate diastereomers by an ideal size-exclusion mechanism, i.e., in the absence of enthalpic contributions to the separation, can be considered one of the grand challenges in chromatography: Can a difference in the location of a single axial hydroxy group on a pyranose ring (e.g., the axial OH being located on carbon 2 versus on carbon 4 of the ring) sufficiently affect the solution conformational entropy of a monosaccharide in a manner which allows for members of a diastereomeric pair to be separated from each other by size-exclusion chromatography (SEC)? Previous attempts at answering this question, for aqueous solutions, have been thwarted by the mutarotation of sugars in water. Here, the matter is addressed by employing the non-mutarotating methyl-α-pyranosides of d-mannose and d-galactose. We show for the first time, using SEC columns, the entropically driven separation of members of this diastereomeric pair, at a resolution of 1.2–1.3 and with only a 0.4–1% change in solute distribution coefficient over a 25 °C range, thereby demonstrating the ideality of the separation. It is also shown how the newest generation of online viscometer allows for improved sensitivity, thereby extending the range of this so-called molar-mass-sensitive detector into the monomeric regime. Detector multidimensionality is showcased via the synergism of online viscometry and refractometry, which combine to measure the intrinsic viscosity and viscometric radius of the sugars continually across the elution profiles of each diastereomer, methyl-α-d-mannopyranoside and methyl-α-d-galactopyranoside.

Simultaneous enhancement of barley β-amylase thermostability and catalytic activity by R115 and T387 residue sites mutation

OBJECTIVE

To simultaneously increase the thermostability and catalytic activity of barley β-amylase.

METHODS

The amino acid sequences of various barley β-amylases with different enzyme properties were aligned, two amino acid residues R115 and T387 were identified to be important for barley β-amylase properties. R115C and T387V were then generated using site-directed and saturation mutagenesis.

RESULTS

R115C and T387V mutants increased the enzyme catalytic activity and thermostability, respectively. After combinational mutagenesis, the T₅₀ value and t_(1/2,60^o_C) value of R115C/T387V mutant reached 59.4 °C and 48.8 min, which were 3.6 °C higher and 29.5 min longer than those of wild-type. The k_cat/K_m value of mutant R115C/T387V were 59.82/s·mM, which were 54.7% higher than that of wild-type. The increased surface hydrophobicity and newly formed strong hydrogen bonds and salt bridges might be responsible for the enzyme thermostability improvement while the two additional hydrogen bonds formed in the active center may lead to the catalytic property enhancement.

CONCLUSIONS

The mutant R115C/T387V showed high catalytic activity and thermostability indicating great potential for application in industry.

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.

Role of water molecules in structure and energetics of Pseudomonas aeruginosa lectin I interacting with disaccharides

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

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.

Molecular docking

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand-protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. The setting up of the input structures for the docking is just as important as the docking itself, and analyzing the results of stochastic search methods can sometimes be unclear. This chapter discusses the background and theory of molecular docking software, and covers the usage of some of the most-cited docking software.

Automated docking to explore subsite binding by glycoside hydrolase family 6 cellobiohydrolases and endoglucanases

Cellooligosaccharides were computationally docked using AutoDock into the active sites of the glycoside hydrolase Family 6 enzymes Hypocrea jecorina (formerly Trichoderma reesei) cellobiohydrolase and Thermobifida fusca endoglucanase. Subsite -2 exerts the greatest intermolecular energy in binding beta-glucosyl residues, with energies progressively decreasing to either side. Cumulative forces imparting processivity exerted by these two enzymes are significantly less than by the equivalent glycoside hydrolase Family 7 enzymes studied previously. Putative subsites -4, -3, +3, and +4 exist in H. jecorina cellobiohydrolase, along with putative subsites -4, -3, and +3 in T. fusca endoglucanase, but they are less important than subsites -2, -1, +1, and +2. In general, binding adds 3-7 kcal/mol to ligand intramolecular energies because of twisting of scissile glycosidic bonds. Distortion of beta-glucosyl residues to the (2)S(O) conformation by binding in subsite -1 adds approximately 7 kcal/mol to substrate intramolecular energies.

LysM domains of Medicago truncatula NFP protein involved in Nod factor perception. Glycosylation state, molecular modeling and docking of chitooligosaccharides and Nod factors

The establishment of the symbiosis between legume plants and rhizobial bacteria depends on the production of rhizobial lipo-chitooligosaccharidic signals (the Nod factors) that are specifically recognized by roots of the host plant. In Medicago truncatula, specific recognition of Sinorhizobium meliloti and its Nod factors requires the NFP (Nod factor perception) gene, which encodes a putative serine/threonine receptor-like kinase (RLK). The extracellular region of this protein contains three tandem lysin motifs (LysMs), a short peptide domain that is implicated in peptidoglycan or chitin binding in various bacterial or eukaryotic proteins, respectively. We report here the homology modeling of the three LysM domains of M. truncatula NFP based on the structure of a LysM domain of the Escherichia coli membrane-bound lytic murein transglycosidase D (MltD). Expression of NFP in a homologous system (M. truncatula roots) revealed that the protein is highly N-glycosylated, probably with both high-mannose and complex glycans. Surface analysis and docking calculations performed on the models of the three domains were used to predict the most favored binding modes for chitooligosaccharides and Nod factors. A convergent model can be proposed where the sulfated, O-acetylated lipo-chitooligosaccharidic Nod factor of S. meliloti binds in similar orientation to the three LysM domains of M. truncatula NFP. N-glycosylation is not expected to interfere with Nod factor binding in this orientation.

Modeling protein recognition of carbohydrates

We have a limited understanding of the details of molecular recognition of carbohydrates by proteins, which is critical to a multitude of biological processes. Furthermore, carbohydrate-modifying proteins such as glycosyl hydrolases and phosphorylases are of growing importance as potential drug targets. Interactions between proteins and carbohydrates have complex thermodynamics, and in general the specific positioning of only a few hydroxyl groups determines their binding affinities. A thorough understanding of both carbohydrate and protein structures is thus essential to predict these interactions. An atomic-level view of carbohydrate recognition through structures of carbohydrate-active enzymes complexed with transition-state inhibitors reveals some of the distinctive molecular features unique to protein-carbohydrate complexes. However, the inherent flexibility of carbohydrates and their often water-mediated hydrogen bonding to proteins makes simulation of their complexes difficult. Nonetheless, recent developments such as the parameterization of specific force fields and docking scoring functions have greatly improved our ability to predict protein-carbohydrate interactions. We review protein-carbohydrate complexes having defined molecular requirements for specific carbohydrate recognition by proteins, providing an overview of the different computational techniques available to model them.

Visualizing complexes of phospholipids with Streptomyces phospholipase D by automated docking

The automated docking program AutoDock was used to dock nine phosphatidic acids (PAs), six phosphatidylcholines, five phosphatidylethanolamines, four phosphatidylglycerols, one phosphatidylinositol and two phosphatidylserines, which have two identical saturated fatty acid residues with an even numbers of carbon atoms, onto the active site of Streptomyces sp. PMF phospholipase D (PLD). Two PAs with one double bond on the fatty acid chain linked to the C2 of the glycerol residue were also docked. In general, binding energies become progressively more negative as fatty acid residues become longer. When these residues are of sufficient length, one is coiled against a hydrophobic cliff in a well that also holds the glycerol and phosphate residues and the head group, while the other generally is bound by a hydrophobic surface outside the well. Phosphatidylcholines have the only head group that is firmly bound by the active site, giving a possible structural explanation for the low selectivity of Streptomyces PLD for other phospholipid substrates.

Specific empirical free energy function for automated docking of carbohydrates to proteins

We present an automated docking protocol specifically optimized to predict the structure and affinity of a protein-carbohydrate complex. A scoring function was developed based on a training set of 30 protein-carbohydrate complexes of known structure and affinity. Combinations of several models for hydrogen bonding, torsional entropy loss, and solvation were tested for their ability to fit the training set data, and the best model was used with AutoDock. The electrostatic empirical coefficient is larger than in a previously obtained model using a training set comprised of various types of protein-ligand complexes, indicating that electrostatic interactions play a more important role in determining the affinity between a carbohydrate and a protein. The differences in the relative weighting of the empirical coefficients in the model yields predicted free energies for the training set with a standard error of 1.403 kcal/mol. The new scoring function was tested on 17 Aspergillus niger glucoamylase inhibitors for which binding energies had been determined experimentally. Free energies of complex formation were predicted with a residual standard error of 1.101 kcal/mol. The new scoring function therefore provides a robust method for predicting free energies of formation and optimal conformations of carbohydrate-protein complexes.

Anomeric configuration, glycosidic linkage, and the solution conformational entropy of O-linked disaccharides

Oligosaccharides perform a large number of biological roles, as dictated by their chemical structure and spatial arrangement. While conformational entropies are usually determined in vacuo by computer modeling, molecular recognition processes normally take place in solution. Here I show results of experiments using size-exclusion chromatography (SEC), an entropically driven solution technique. These clearly differentiate the individual contributions of the alpha and beta anomeric configurations and of the (1 --> 4) and (1 --> 6) glycosidic linkages to the solution conformational entropy of O-linked disaccharides. I also distinguish between the members of the epimeric disaccharide pair isomaltose-melibiose and trace the difference to that between their constituent monosaccharides, alpha-glucose and alpha-galactose.

Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations

BACKGROUND

The accurate prediction of enzyme-substrate interaction energies is one of the major challenges in computational biology. This study describes the improvement of protein-ligand binding energy prediction by incorporating protein flexibility through the use of molecular dynamics (MD) simulations.

RESULTS

Docking experiments were undertaken using the program AutoDock for twenty-five HIV-1 protease-inhibitor complexes determined by x-ray crystallography. Protein-rigid docking without any dynamics produced a low correlation of 0.38 between the experimental and calculated binding energies. Correlations improved significantly for all time scales of MD simulations of the receptor-ligand complex. The highest correlation coefficient of 0.87 between the experimental and calculated energies was obtained after 0.1 picoseconds of dynamics simulation.

CONCLUSION

Our results indicate that relaxation of protein complexes by MD simulation is useful and necessary to obtain binding energies that are representative of the experimentally determined values.

Understanding protein structure-function relationships in Family 47 alpha-1,2-mannosidases through computational docking of ligands

Family 47 alpha-1,2-mannosidases are crucial enzymes involved in N-glycan maturation in the endoplasmic reticula and Golgi apparati of eukaryotic cells. High-resolution crystal structures of the human and yeast endoplasmic reticulum alpha-1,2-mannosidases have been recently determined, the former complexed with the inhibitors 1-deoxymannojirimycin and kifunensine, both of which bind in its active site in the unusual 1C4 conformation. However, unambiguous identification of the catalytic proton donor and nucleophile involved in glycoside bond hydrolysis was not possible from this structural information. In this work, alpha-D-galactose, alpha-D-glucose, and alpha-D-mannose were computationally docked in the active site in the energetically stable 4C1 conformation as well as in the 1C4 conformation to compare their interaction energetics. From these docked structures, a model for substrate and conformer selectivity based on the dimensions of the active site was proposed. Alpha-D-galactopyranosyl-(1-->2)-alpha-D-mannopyranose, alpha-D-glucopyranosyl-(1-->2)-alpha-D-mannopyranose, and alpha-D-mannopyranosyl-(1-->2)-alpha-D-mannopyranose were also docked into the active site with their nonreducing-end residues in the 1C4 and E4 (representing the transition state) conformations. Based on the docked structure of alpha-D-mannopyranosyl-E4-(1-->2)-alpha-D-mannopyranose, the catalytic acid and base are Glu132 and Glu435, respectively.