Biology subjects

Yoshida, M.

Publications and source records attributed to Yoshida, M..

75 records · Page 5Linked to original sources

bioRxiv2017

Chaperonin facilitates protein folding by avoiding polypeptide collapse

Chaperonins assist folding of many cellular proteins, including essential proteins for cell viability. However, it remains unclear how chaperonin-assisted folding is different from spontaneous folding. Chaperonin GroEL/GroES facilitates folding of denatured protein encapsulated in its central cage but the denatured protein often escapes from the cage to the outside during reaction. Here, we show evidence that the in-cage-folding and the escape occur diverging from the same intermediate complex in which polypeptide is tethered loosely to the cage and partly protrudes out of the cage. Furthermore, denatured proteins in the chaperonin cage start their folding from extended conformations but not from compact conformations as usually observed in spontaneous folding. We propose that the formation of tethered intermediate of polypeptide is necessary to prevent polypeptide collapse at the expense of polypeptide escape. The tethering of polypeptide would allow freely mobile portions of tethered polypeptide to fold segmentally. The folding acceleration and deceleration by chaperonin for various substrate proteins can be explained by considering the tethering.

biochemistry↗

bioRxiv2017

Large-scale interpretation of chemical-genetic interaction profiles using a genetic interaction network

Chemical-genetic interactions - observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes - contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

systems biology↗

bioRxiv2017

Functional Annotation of Chemical Libraries across Diverse Biological Processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compounds mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

systems biology↗