Biomedical subjects

Cai-Zhuang Wang

Publications and source records attributed to Cai-Zhuang Wang.

4 recordsLinked to original sources

PubMed2006

Codability criterion for picking proteinlike structures from random three-dimensional configurations.

We show that the dominant eigenvectors of real protein structural contact matrices are highly correlated with their amino acid sequences. These results suggests that an ab initio sequence-independent profile exists for every protein structure and that this profile is highly effective in differentiating the ordering of amino acids in natural protein sequences from random sequences. This profile provides a structural code and is a key for understanding the unique behavior of protein structures. Using a lattice model, we show that there are special codable structures highly separated from random structures in the dominant eigenvector space of their structural contact matrices. As an example, we show our results provide a good explanation to the "designable principle" of protein structures.

Amino Acid Sequence↗

PubMed2006

Magic structures of h-passivated 110 silicon nanowires.

We report a genetic algorithm approach combined with ab initio calculations to determine the structure of hydrogenated 110 Si nanowires. As the number of atoms per length increases, we find that the cross section of the nanowire evolves from chains of six-atom rings to fused pairs of such chains to hexagons bounded by {001} and {111} facets. Our calculations predict that hexagonal wires become stable starting at about 1.2 nm diameter, which is consistent with recent experimental reports of nanowires with diameters of about 3 nm.

Hydrogen↗

PubMed2005

Fast method for estimating the energy distribution of globular states of proteins.

By an enumeration study, we show that the energy distributions of a lattice protein sequence on all possible compact lattice configurations can be approximated by the energy distribution of shuffled sequences on a given lattice structure. We also show that the random energy model (REM) gives a good analytical approximation for the energy distribution of shuffled sequences on lattice structures. For real proteins, when a gapped threading method is used, REM calculations systematically underestimate the mean value of the energy distributions. We found that this discrepancy can be roughly compensated by a linear correction obtained from empirical fits. This result can be used to greatly reduce the computational effort in protein threading calculations.

Algorithms↗

PubMed2004

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods.

BACKGROUND: Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. RESULTS: In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. CONCLUSIONS: We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.

Algorithms↗