Biology subjects

Ho, C.

Publications and source records attributed to Ho, C..

2 recordsLinked to original sources

bioRxiv2017

A general method to predict the effect of single amino acid substitutions on enzyme catalytic activity

Over the past thirty years, site-directed mutagenesis has become established as one of the most powerful techniques to probe enzyme reaction mechanisms1-3. Substitutions of active site residues are most likely to yield significant perturbations in kinetic parameters, but there are many examples of profound changes in these values elicited by remote mutations4-6. Ortholog comparisons of extant sequences show that many mutations do not have profound influence on enzyme function. As the number of potential single natural amino acid substitutions that can be introduced in a protein of N amino acids in length by directed mutation is very large (19 * N), it would be useful to have a method to predict which amino acid substitutions are more likely to introduce significant changes in kinetic parameters in order to design meaningful probes into enzyme function. What is especially desirable is the identification of critical residues that do not contact the substrate directly, and may be remote from the active site.\n\nWe collected literature data reflecting the effects of 2,804 mutations on kinetic properties for 12 enzymes. These data along with characteristic predictors were used in a machine-learning scheme to train a classifier to predict the effect of mutation. Use of this algorithm allows one to predict with a 2.5-fold increase in precision, if a given mutation, made anywhere in the enzyme, will cause a decrease in kcat/Km value of [≥] 95%. The improved precision allows the experimentalist to reduce the number of mutations necessary to probe the enzyme reaction mechanism.

bioinformatics↗

bioRxiv2017

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Recent pharmacogenomic studies profiled large panels of cancer cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbation, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging this valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated pharmacogenomic datasets that are otherwise disparate and difficult to integrate.\n\nKey pointsO_LICuration of cell line and drug identifiers in the largest pharmacogenomic studies published to date\nC_LIO_LIUniform processing of drug sensitivity data to reduce heterogeneity across studies\nC_LIO_LIMultiple drug response summary metrics enabling visual comparison and integrative analysis\nC_LI

bioinformatics↗