Biomedical subjects

Peter J Reilly

Publications and source records attributed to Peter J Reilly.

15 recordsLinked to original sources

PubMed2006

The fate of beta-D-mannopyranose after its formation by endoplasmic reticulum alpha-(1-->2)-mannosidase I catalysis.

The automated docking program AutoDock was used to dock all 38 characteristic beta-D-mannopyranose ring conformers into the active site of the yeast endoplasmic reticulum alpha-(1-->2)-mannosidase I, a Family 47 glycoside hydrolase that converts Man9GlcNAc2 to Man8GlcNAc2. The subject of this work is to establish the conformational pathway that allows the cleaved glycon product to leave the enzyme active site and eventually reach the ground-state conformation. Twelve of the 38 conformers optimally dock in the active site where the inhibitors 1-deoxymannonojirimycin and kifunensine are found in enzyme crystal structures. A further 23 optimally dock in a second site on the side of the active-site well, while three dock outside the active-site cavity. It appears, through analysis of the internal energies of different ring conformations, of intermolecular energies between the ligands and enzyme, and of forces exerted on the ligands by the enzyme, that beta-D-mannopyranose follows the path 3E-->1C4-->1H2-->B2,5 before being expelled by the enzyme. The highly conserved second site that strongly binds beta-D-mannopyranose-4C1 may exist to prevent competitive inhibition by the product, and is worthy of further investigation.

Binding Sites↗

PubMed2006

Comparing programs for rigid-body multiple structural superposition of proteins.

Different programs and methods were employed to superimpose protein structures, using members of four very different protein families as test subjects, and the results of these efforts were compared. Algorithms based on human identification of key amino acid residues on which to base the superpositions were nearly always more successful than programs that used automated techniques to identify key residues. Among those programs automatically identifying key residues, MASS could not superimpose all members of some families, but was very efficient with other families. MODELLER, MultiProt, and STAMP had varying levels of success. A genetic algorithm program written for this project did not improve superpositions when results from neighbor-joining and pseudostar algorithms were used as its starting cases, but it always improved superpositions obained by MODELLER and STAMP. A program entitled PyMSS is presented that includes three superposition algorithms featuring human interaction.

Amino Acid Sequence↗

PubMed2006

Docking studies on glycoside hydrolase Family 47 endoplasmic reticulum alpha-(1-->2)-mannosidase I to elucidate the pathway to the substrate transition state.

Alpha-(1-->2)-mannosidase I from the endoplasmic reticulum (ERManI), a Family 47 glycoside hydrolase, is a key enzyme in the N-glycan synthesis pathway. Catalytic-domain crystal structures of yeast and human ERMan1s have been determined, the former with a hydrolytic product and the latter without ligands, with the inhibitors 1-deoxymannojirimycin and kifunensine, and with a thiodisaccharide substrate analog. Both inhibitors were bound at the base of the funnel-shaped active site as the unusual 1C4 conformer, while the substrate analog glycon is a 3S1 conformer. In the current study, AutoDock was used to dock alpha-D-mannopyranosyl-(1-->2)-alpha-D-mannopyranose with its glycon in chair (1C4,4C1), half-chair (3H2,3H4,4H3), skew-boat (OS2,3S1,5S1), boat (2,5B,3,OB,B1,4,B2,5), and envelope (3E,4E,E3,E4) conformations into the yeast ERManI active site. Both docked energies and forces on docked ligand atoms were calculated to determine how the ligand distorts to the transition state. From these, we can conclude that (1) both 1C4 and OS2 can be the starting conformers; (2) the most likely binding pathway is 1C4-->3H2-->OS2-->3,OB-->3S1-->3E; (3) the transition state is likely to be close to a 3E conformation.

Binding Sites↗

PubMed2005

Force calculations in automated docking: enzyme-substrate interactions in Fusarium oxysporum Cel7B.

AutoDock is a small-molecule docking program that uses an energy function to score docked ligands. Here AutoDock's grid-based method for energy evaluation was exploited to evaluate the force exerted by Fusarium oxysporum Cel7B on the atoms of docked cellooligosaccharides and a thiooligosaccharide substrate analog. Coupled with the interaction energies evaluated for each docked ligand, these forces give insight into the dynamics of the ligand in the active site, and help to elucidate the relative importance of specific enzyme-substrate interactions in stabilizing the substrate transition-state conformation. The processive force on the docked substrate in the F. oxysporum Cel7B active site is less than half of that on the docked substrate in the Hypocrea jecorina Cel7A active site. Hydrogen bonding interactions of the enzyme with the C2 hydroxyl group of the glucosyl residue in subsite -2 and with the C3 hydroxyl group of the glucosyl residue in subsite +1 are the most significant in stabilizing the distorted14B transition-state conformation of the glucosyl residue in subsite -1. The force calculations also help to elucidate the mechanism that prevents the active site from fouling.

Cellulase↗

PubMed2005

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.

Acarbose↗

PubMed2005

Hypocrea jecorina (Trichoderma reesei) Cel7A as a molecular machine: A docking study.

Hypocrea jecorina (formerly Trichoderma reesei) Cel7A has a catalytic domain (CD) and a cellulose-binding domain (CBD) separated by a highly glycosylated linker. Very little is known of how the 2 domains interact to degrade crystalline cellulose. Based on the interaction energies and forces on cello-oligosaccharides computationally docked to the CD and CBD, we propose a molecular machine model, where the CBD wedges itself under a free chain end on the crystalline cellulose surface and feeds it to the CD active site tunnel. Enzyme-substrate interactions produce the forces required to pull cellulose chains from the surface and also to help the enzyme move on the cellulose chain for processive hydrolysis. The energy to generate these forces is ultimately derived from the chemical energy of glycosidic bond breakage.

Binding Sites↗

PubMed2005

Phylogenetic analysis of family 6 glycoside hydrolases.

Multiple sequence alignment separates members of glycoside hydrolase Family 6 into eight subfamilies: one of mainly actinobacterial endoglucanases (EGs), one of ascomycotal EGs, one of chytridiomycotal EGs and cellobiohydrolases (CBHs), one of actinobacterial and proteobacterial CBHs, one of chytridiomycotal CBHs, two of ascomycotal CBHs, and one of basidiomycotal CBHs. Each also has some proteins of unknown function. Multiple sequence alignment also extends to all of Family 6 the observation that lengths of loops that form the active-site tunnel in CBHs vary among subfamilies, and along with loop conformations, determine enzyme function.

Amino Acid Sequence↗

PubMed2004

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.

Binding Sites↗

PubMed2004

Arg343 in human surfactant protein D governs discrimination between glucose and N-acetylglucosamine ligands.

Surfactant protein D (SP-D), one of the members of the collectin family of C-type lectins, is an important component of pulmonary innate immunity. SP-D binds carbohydrates in a calcium-dependent manner, but the mechanisms governing its ligand recognition specificity are not well understood. SP-D binds glucose (Glc) stronger than N-acetylglucosamine (GlcNAc). Structural superimposition of hSP-D with mannose- binding protein C (MBP-C) complexed with GlcNAc reveals steric clashes between the ligand and the side chain of Arg343 in hSP-D. To test whether Arg343 contributes to Glc > GlcNAc recognition specificity, we constructed a computational model of Arg343-->Val (R343V) mutant hSP-D based on homology with MBP-C. Automated docking of alpha-Me-Glc and alpha-Me-GlcNAc into wild-type hSP-D and the R343V mutant of hSP-D suggests that Arg343 is critical in determining ligand-binding specificity by sterically prohibiting one binding orientation. To empirically test the docking predictions, an R343V mutant recombinant hSP-D was constructed. Inhibition analysis shows that the R343V mutant binds both Glc and GlcNAc with higher affinity than the wild-type protein and that the R343V mutant binds Glc and GlcNAc equally well. These data demonstrate that Arg343 is critical for hSP-D recognition specificity and plays a key role in defining ligand specificity differences between MBP and SP-D. Additionally, our results suggest that the number of binding orientations contributes to monosaccharide binding affinity.

Acetylglucosamine↗

PubMed2003

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.

Algorithms↗

PubMed2003

Crystal structure and evolution of a prokaryotic glucoamylase.

The first crystal structures of a two-domain, prokaryotic glucoamylase were determined to high resolution from the clostridial species Thermoanaerobacterium thermosaccharolyticum with and without acarbose. The N-terminal domain has 18 antiparallel strands arranged in beta-sheets of a super-beta-sandwich. The C-terminal domain is an (alpha/alpha)(6) barrel, lacking the peripheral subdomain of eukaryotic glucoamylases. Interdomain contacts are common to all prokaryotic Family GH15 proteins. Domains similar to those of prokaryotic glucoamylases in maltose phosphorylases (Family GH65) and glycoaminoglycan lyases (Family PL8) suggest evolution from a common ancestor. Eukaryotic glucoamylases may have evolved from prokaryotic glucoamylases by the substitution of the N-terminal domain with the peripheral subdomain and by the addition of a starch-binding domain.

Acarbose↗

PubMed2003

Starch-binding domain shuffling in Aspergillus niger glucoamylase.

Aspergillus niger glucoamylase (GA) consists mainly of two forms, GAI [from the N-terminus, catalytic domain + linker + starch-binding domain (SBD)] and GAII (catalytic domain + linker). These domains were shuffled to make RGAI (SBD + linker + catalytic domain), RGAIDeltaL (SBD + catalytic domain) and RGAII (linker + catalytic domain), with domains defined by function rather than by tertiary structure. In addition, Paenibacillus macerans cyclomaltodextrin glucanotransferase SBD replaced the closely related A.niger GA SBD to give GAE. Soluble starch hydrolysis rates decreased as RGAII approximately GAII approximately GAI > RGAIDeltaL approximately RGAI approximately GAE. Insoluble starch hydrolysis rates were GAI > RGAIDeltaL > RGAI >> GAE approximately RGAII > GAII, while insoluble starch-binding capacities were GAI > RGAI > RGAIDeltaL > RGAII > GAII > GAE. These results indicate that: (i) moving the SBD to the N-terminus or replacing the native SBD somewhat affects soluble starch hydrolysis; (ii) SBD location significantly affects insoluble starch binding and hydrolysis; (iii) insoluble starch hydrolysis is imperfectly correlated with its binding by the SBD; and (iv) placing the P.macerans cyclomaltodextrin glucanotransferase SBD at the end of a linker, instead of closely associated with the rest of the enzyme, severely reduces its ability to bind and hydrolyze insoluble starch.

Amino Acid Sequence↗

PubMed2002

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.

1-Deoxynojirimycin↗

PubMed2002

Fermentation of cottonseed and other feedstuffs in cattle rumen fluid.

Bovine rumen fluid was fermented anaerobically over 48 h with cottonseed, corn, alfalfa, or a mixture of these substrates in anaerobic mineral buffer. Samples taken at different incubation times were derivatized with n-butanol and subjected to gas chromatography and mass spectroscopy. No unusual fermentation end-products from the cottonseed substrate were detected. Cottonseed supported rumen fermentation at levels comparable to those of the other substrates. Major components were usually found in the decreasing order of acetate, propionate, butyrate, and valerate, although acetate and propionate concentrations decreased late in the alfalfa and mixed-feed fermentations, eventually allowing butyrate concentrations to exceed those of propionate. As expected, lactate was produced in high concentrations when corn was fermented. The minor components 2-methylpropionate, 2- and 3-methylbutyrate, phenylacetate, phenylpropionate, and caproate also accumulated, with their relative concentrations varying with the substrate. Succinate was produced in substantial amounts only when corn and alfalfa were fermented; it did not accumulate when cottonseed was the substrate. Samples containing cottonseed were derivatized and subjected to reversed-phase high-performance liquid chromatography, revealing that gossypol concentrations did not change during fermentation.

Acetates↗

PubMedDate not supplied

Problem-based learning biotechnology courses in chemical engineering.

We have developed a series of upper undergraduate/graduate lecture and laboratory courses on biotechnological topics to supplement existing biochemical engineering, bioseparations, and biomedical engineering lecture courses. The laboratory courses are based on problem-based learning techniques, featuring two- and three-person teams, journaling, and performance rubrics for guidance and assessment. Participants initially have found them to be difficult, since they had little experience with problem-based learning. To increase enrollment, we are combining the laboratory courses into 2-credit groupings and allowing students to substitute one of them for the second of our 2-credit chemical engineering unit operations laboratory courses.

Biotechnology↗