Biomedical subjects

Cun Xin Wang

Publications and source records attributed to Cun Xin Wang.

4 recordsLinked to original sources

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

Protein molecular dynamics with electrostatic force entirely determined by a single Poisson-Boltzmann calculation.

The electrostatic force including the intramolecular Coulombic interactions and the electrostatic contribution of solvation effect were entirely calculated by using the finite difference Poisson-Boltzmann method (FDPB), which was incorporated into the GROMOS96 force field to complete a new finite difference stochastic dynamics procedure (FDSD). Simulations were performed on an insulin dimer. Different relative dielectric constants were successively assigned to the protein interior; a value of 17 was selected as optimal for our system. The simulation data were analyzed and compared with those obtained from 500-ps molecular dynamics (MD) simulation with explicit water and a 500-ps conventional stochastic dynamics (SD) simulation without the mean solvent force. The results indicate that the FDSD method with GROMOS96 force field is suitable to study the dynamics and structure of proteins in solution if used with the optimal protein dielectric constant.

Computational Biology↗

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↗