Anthony D. Hill

Puckering Coordinates of Monocyclic Rings by Triangular Decomposition

We describe a new method of describing the pucker of an N-member monocyclic ring using N - 3 parameters. To accomplish this, three ring atoms define a reference plane, and the remainder of the ring is decomposed into triangular flaps. The angle of incidence for each flap upon the reference plane is then measured. The combination of these angles is characteristic of the rings pucker. This puckering coordinate system is compared to existing reduced parameter systems to describe rings using a cyclohexane molecule. We show that this method has the same descriptive power of previous systems while offering advantages in molecular simulations.

Computational Analysis of Glycoside Hydrolase Family 1 Specificities

Glycoside hydrolase family 1 consists of beta-glucosidases, beta-galactosidases, 6-phospho-beta-galactosidases, myrosinases, and other enzymes having similar primary and tertiary structures but diverse specificities. Among these enzymes, beta-glucosidases hydrolyze cellobiose to glucose, and therefore they are key players in any cellulose to glucose process. All family members attack beta-glycosidic bonds between a pyranosyl glycon and an aglycon, but most have little specificity for the aglycon or for the bond configuration. Furthermore, glycon specificity is not absolute. Sixteen family members (six beta-glucosidases, two cyanogenic beta-glucosidases, one 6-phospho-beta-galactosidase, two myrosinases, and five beta-glycosidases) have known tertiary structures. We have used automated docking to computationally bind disaccharides with allopyranosyl, galactopyranosyl, glucopyranosyl, mannopyranosyl, 6-phosphogalactopyranosyl, and 6-phosphoglucopyranosyl glycons, all linked by beta-(1,2), beta-(1,3), beta-(1,4), and beta-(1,6)-glycosidic bonds to beta-glucopyranoside aglycons, along with beta-(1,1-thio)-allopyranosyl, -galactopyranosyl, -glucopyranosyl, and -mannopyranosyl) beta-glucopyranosides, into all of these structures to investigate the structural determinants of their enzyme specificities. The following are the eight active-site residues: Glu191, Thr194, Phe205, Asn285, Arg336, Asn376, Trp378, and Trp465 (Zea mays beta-glucosidase numbering), that control a significant amount of glycon specificity.

A Gibbs free energy correlation for automated docking of carbohydrates

Thermodynamic information can be inferred from static atomic configurations. To model the thermodynamics of carbohydrate binding to proteins accurately, a large binding data set has been assembled from the literature. The data set contains information from 262 unique protein–carbohydrate crystal structures for which experimental binding information is known. Hydrogen atoms were added to the structures and training conformations were generated with the automated docking program AutoDock 3.06, resulting in a training set of 225,920 all‐atom conformations. In all, 288 formulations of the AutoDock 3.0 free energy model were trained against the data set, testing each of four alternate methods of computing the van der Waals, solvation, and hydrogen‐bonding energetic components. The van der Waals parameters from AutoDock 1 produced the lowest errors, and an entropic model derived from statistical mechanics produced the only models with five physically and statistically significant coefficients. Eight models predict the Gibbs free energy of binding with an error of less than 40% of the error of any similar models previously published.

Computational analyses of the conformational itinerary along the reaction pathway of GH94 cellobiose phosphorylase.

GH94 cellobiose phosphorylase (CBP) catalyzes the phosphorolysis of cellobiose into alpha-D-glucose 1-phosphate (G1P) and D-glucose with inversion of anomeric configuration. The complex crystal structure of CBP from Cellvibrio gilvus had previously been determined; glycerol, glucose, and phosphate are bound to subsites -1, +1, and the anion binding site, respectively. We performed computational analyses to elucidate the conformational itinerary along the reaction pathway of this enzyme. autodock was used to dock cellobiose with its glycon glucosyl residue in various conformations and with its aglycon glucosyl residue in the low-energy 4C1 conformer. An oxocarbenium ion-like glucose molecule mimicking the transition state was also docked. Based on the clustering analysis, docked energies, and comparison with the crystallographic ligands, we conclude that the reaction proceeds from 1S3 as the pre-transition state conformer (Michaelis complex) via E3 as the transition state candidate to 4C1 as the G1P product conformer. The predicted reaction pathway of the inverting phosphorylase is similar to that proposed for the first-half glycosylation reaction of retaining cellulases, but is different from those for inverting cellulases. NAMD was used to simulate molecular dynamics of the enzyme. The 1S3 pre-transition state conformer is highly stable compared with other conformers, and a conformational change from 4C1 to 1,4B was observed.

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. Proteins 2006.

Scoring Functions for AutoDock

Automated docking allows rapid screening of protein-ligand interactions. A scoring function composed of a force field and linear weights can be used to compute a binding energy from a docked atom configuration. For different force fields or types of molecules, it may be necessary to train a custom scoring function. This chapter describes the data and methods one must consider in developing a custom scoring function for use with AutoDock.