Anu Chakicherla at Lawrence Livermore National Laboratory is first author on a paper published in PLoS Computational Biology this week that's developed a computational method to predict protein-protein interactions based not on amino acid sequence, but structural similarity. Using a "domain-fusion algorithm [that] leverages quantitative structure information," the team was able to predict and then verify in vitro that SpaK and SpaR function as a sensor and a response regulator in a signal transduction system in the bacteria, Bacillus subtilis.
Also in PLoS Computational Biology this week, Georgia Institute of Technology's Michal Brylinski and Jeffrey Skolnick developed an algorithm that improves upon the high-throughput virtual screening process for ligand discovery. Called FINDSITE(LHM), the tool "employs structural information extracted from weakly related proteins to perform rapid ligand docking by homology modeling," they say in the abstract. It's more accurate and takes less processing time than currently used methods, they add, and "offers the possibility of rapid structure-based virtual screening at the proteome level."
Scientists at the University of North Carolina at Chapel Hill used a combination of array CGH and paired-end whole genome shotgun sequencing to perform a genome-wide screen for balanced structural polymorphisms in S. cerevisiae. The longest transposed segment, they say, is 13.5 kb and is near the telomere of the chromosome as well as contains several annotated genes. Their work was published in PLoS Genetics this week.
Finally, work led by NCBI's Scott William Roy investigated how genome-wide differences in the alternative 'splicing code' between two species may have affected the evolution of alternative splicing levels. Taking into account datasets of predicted exonic splicing regulators (ESRs) and their affect on exon inclusion levels in human and chimpanzee, they found no association between changes in predicted ESRs and changes in alternative splicing levels, they say. "These results underscore the difficulties of using current computational ESR prediction algorithms to identify truly functionally important motifs, and provide a cautionary tale for studies of the effect of SNPs on splicing in human disease," they write in the abstract in PLoS One.