Machine-learning software predicts behavior of bacteria

In a first for machine-learning algorithms, a new piece of software developed at Caltech can predict behavior of bacteria by reading the content of a gene. The breakthrough could have significant implications for our understanding of bacterial biochemistry and for the development of new medications. One thrust of modern pharmacology is focused on alleviating ailments by developing drugs that target specific proteins that reside in the membranes of our bodies' cells. These proteins, known as integral membrane proteins (IMP), act as receptors or "gates" that allow materials into and out of cells. Examples of IMPs are G-protein-coupled receptors, which relay information to a cell about its environment, and ion channels, which control the interior environment of a cell by acting as gatekeepers that selectively allow ions to pass in and out of the cell. IMPs are the targets of nearly 50 percent of all drugs on the market.