Targeting the human cancer pathway protein interaction network by structural genomics.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN.

The effect of natural selection on the performance of maximum parsimony.

Background: Maximum parsimony is one of the most commonly used and extensively studied phylogeny reconstruction methods. While current evaluation methodologies such as computer simulations provide insight into how well maximum parsimony reconstructs phylogenies, they tell us little about how well maximum parsimony performs on taxa drawn from populations of organisms that evolved subject to natural selection in addition to the random factors of drift and mutation. It is clear that natural selection has a significant impact on Among Site Rate Variation (ASRV) and the rate of accepted substitutions; that is, accepted mutations do not occur with uniform probability along the genome and some substitutions are more likely to occur than other substitutions.

Using Avida to test the effects of natural selection on phylogenetic reconstruction methods.

Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms.

Automated data interpretation based on the concept of "negative signature mass" for mass-mapping disulfide structures of cystinyl proteins.

An efficient method for data processing and interpretation is needed to support and extend disulfide mass-mapping methodology based on partial reduction and cyanylation-induced cleavage to proteins containing more than four cystines. Here, the concept of "negative signature mass" is introduced as the novel feature of an algorithm designed to identify the disulfide structure of a cystinyl protein given an input of mass spectral data and an amino acid sequence. The "negative signature mass" process is different from the conventional approach in that it does not directly rule-in disulfide linkages, but rather eliminates linkages from a list of all possible theoretical linkages, with the goal of ruling out enough linkages so that only one disulfide structure can be constructed.