Biomedical subjects

Xueguang Shao

Publications and source records attributed to Xueguang Shao.

4 recordsLinked to original sources

PubMed2003

Resolution of multicomponent NMR signals using wavelet compression and immune algorithm.

A novel algorithm for fast resolution of multicomponent overlapping nuclear magnetic resonance (NMR) spectrum using the wavelet compression and the immune algorithm (IA) is proposed. Taking advantage of the linear property of the wavelet transform (WT), the overlapping NMR spectrum (antigen) can be compressed by WT before it is input into the immune network, the antibodies are also compressed to the same scheme. After extraction of the compressed information of each component, the resolved NMR spectra can be restored by the WT reconstruction. It was proven that the result is almost the same with the result from an IA, but the calculation speed is much faster.

Algorithms↗

PubMed2002

A fast annealing evolutionary algorithm for global optimization.

By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimization of test functions and a comparison of the algorithm with other stochastic optimization methods, it is shown that the algorithm is a highly efficient optimization method. It was also applied in optimization of Lennard-Jones clusters and compared with other methods in this study. The results indicate that the algorithm is a good tool for the energy minimization problem.

Journal Article↗

PubMed2002

Protein-ligand recognition using spherical harmonic molecular surfaces: towards a fast and efficient filter for large virtual throughput screening.

Molecular surfaces are important because surface-shape complementarity is often a necessary condition in protein-ligand interactions and docking studies. We have previously described a fast and efficient method to obtain triangulated surface-meshes by topologically mapping ellipsoids on molecular surfaces. In this paper, we present an extension of our work to spherical harmonic surfaces in order to approximate molecular surfaces of both ligands and receptor-cavities and to easily check the surface-shape complementarity. The method consists of (1) finding lobes and holes on both ligand and cavity surfaces using contour maps of radius functions with spherical harmonic expansions, (2) superposing the surfaces around a given binding site by minimizing the distance between their respective expansion coefficients. This docking procedure capabilities was demonstrated by application to 35 protein-ligand complexes of known crystal structures. The method can also be easily and efficiently used as a filter to detect in a large conformational sampling the possible conformations presenting good complementarity with the receptor site, and being, therefore, good candidates for further more elaborate docking studies. This "virtual screening" was demonstrated on the platelet thrombin receptor.

HIV Protease↗

PubMedDate not supplied

Global optimization of Lennard-Jones clusters by a parallel fast annealing evolutionary algorithm.

A parallel fast annealing evolutionary algorithm (PFAEA) was presented and applied to optimize Lennard-Jones (LJ) clusters. All the lowest known minima up to LJ(116) with both icosahedral and nonicosahedral structure, including the truncated octahedron of LJ(38), central fcc tetrahedron of LJ(98), the Marks' decahedron of LJ(75)(-)(77), and LJ(102)(-)(104), were located successfully by the unbiased algorithm. PFAEA is a parallel version of fast annealing evolutionary algorithm (FAEA) that combines the aspect of population in genetic algorithm and annealing algorithm with a very fast annealing schedule. A master-slave paradigm is used to parallelize FAEA to improve the efficiency. The performance of PFAEA is studied, and the scaling of execution time with the cluster size is approximately cubic, which is important for larger scale energy minimization systems.

Journal Article↗