Biomedical subjects

Chun Hua Li

Publications and source records attributed to Chun Hua Li.

5 recordsLinked to original sources

PubMed2006

Analysis of domain movements in glutamine-binding protein with simple models.

In this work, the mechanism of domain movements of glutamine-binding protein (GlnBP), especially the influence of the ligand on GlnBP dynamic behavior is investigated with the aid of a Gaussian network model (GNM) and an anisotropy elastic network model. The results show that the "open-closed" transition mainly appears as the large movement of the small domain, especially the top region including two alpha-helices and two beta-strands. The slowest mode of each three forms of GlnBP--ligand-free open, ligand-bound closed, and ligand-free closed GlnBP--shows that the open-closed motion of the two domains has a common hinge axis centered on Lys-87 and Gln-183. Accompanying the conformational transition, the residues within both large and small domains move in a highly coupled way. The peaks of the fast modes correspond to residues that were thought, in the GNM, to be important for the stability of the protein, and these residues may be involved in the interactions with the membrane-bound components. With the contacts between the large domain and the small domain increasing, the ability of the "open-closed" motion is decreased. All the results agree well with those of molecular dynamics simulations, and it is thought that the open-closed conformation transition is the nature of the topology structure of GlnBP. Also, the influence of the ligand on GlnBP is studied with a modified GNM method. The results obtained show that the ligand does not influence the closed-to-open transition tendency.

Carrier Proteins↗

PubMed2005

Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition.

An efficient biologically enhanced sampling geometric docking method is presented based on the FTDock algorithm to predict the protein-protein binding modes. The active site data from different sources, such as biochemical and biophysical experiments or theoretical analyses of sequence data, can be incorporated in the rotation-translation scan. When discretizing a protein onto a 3-dimensional (3D) grid, a zero value is given to grid points outside a sphere centered on the geometric center of specified residues. In this way, docking solutions are biased toward modes where the interface region is inside the sphere. We also adopt a multiconformational superposition scheme to represent backbone flexibility in the proteins. When these procedures were applied to the targets of CAPRI, a larger number of hits and smaller ligand root-mean-square deviations (RMSDs) were obtained at the conformational search stage in all cases, and especially Target 19. With Target 18, only 1 near-native structure was retained by the biologically enhanced sampling geometric docking method, but this number increased to 53 and the least ligand RMSD decreased from 8.1 A to 2.9 A after performing multiconformational superposition. These results were obtained after the CAPRI prediction deadlines.

Algorithms↗

PubMed2003

A soft docking algorithm for predicting the structure of antibody-antigen complexes.

An efficient soft docking algorithm is described for predicting the mode of binding between an antibody and its antigen based on the three-dimensional structures of the molecules. The basic tools are the "simplified protein" model and the docking algorithm of Wodak and Janin. The side-chain flexibility of Arg, Lys, Asp, Glu, and Met residues on the protein surface is taken into account. A combined filtering technique is used to select candidate binding modes. After energy minimization, we calculate a scoring function, which includes electrostatic and desolvation energy terms. This procedure was applied to targets 04, 05, and 06 of CAPRI, which are complexes of three different camelid antibody VHH variable domains with pig alpha-amylase. For target 06, two native-like structures with a root-mean-square deviation < 4.0 A relative to the X-ray structure were found within the five top ranking structures. For targets 04 and 05, our procedure produced models where more than half of the antigen residues forming the epitope were correctly predicted, albeit with a wrong VHH domain orientation. Thus, our soft docking algorithm is a promising tool for predicting antibody-antigen recognition.

Algorithms↗

PubMed2003

A protein-protein docking algorithm dependent on the type of complexes.

An efficient 'soft docking' algorithm is described to assist the prediction of protein-protein association using three-dimensional structures of molecules. The basic tools are the 'simplified protein' model and the docking algorithm of Wodak and Janin. The side chain flexibility of Arg, Lys, Asp, Glu and Met residues at the protein surface is taken into account. The complex type-dependent filtering technique on the basis of the geometric matching, hydrophobicity and electrostatic complementarity is used to select candidate binding modes. Subsequently, we calculate a scoring function which includes electrostatic and desolvation energy terms. In the 44 complexes tested including enzyme-inhibitor, antibody-antigen and other complexes, native-like structures were all found, of which 30 were ranked in the top 20. Thus, our soft docking algorithm has the potential to predict protein-protein recognition.

Algorithms↗

PubMed2002

A fast empirical approach to binding free energy calculations based on protein interface information.

Three useful variables from the interfaces of 20 protein-protein complexes were investigated. These variables are the side-chain accessible number (N(b)), the number of hydrophilic pairs (N(pair)) and buried a polar solvent accessible surface areas (DeltaDeltaASA(apol)). An empirical model based on the three variables was developed to describe the free energy of protein associations. As the results show, the side-chain accessible numbers characterize the loss of side-chain conformational entropy of protein interactions and the effective empirical function presented here has great capability for estimating the binding free energy. It was found that the variables of interface information capture most of the significant features of protein-protein association. Also, we applied the model based on the variables as a rescoring function to docking simulations and found that it has the potential to distinguish the 'true' binding mode. It is clear that the simple and empirical scale developed here is an attractive target function for calculating binding free energy for various biological processes to rational protein design.

Algorithms↗