Matching for Identification of Antibiotics

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Computational 'Match Game' Identifies Potential Antibiotics
Carnegie Mellon News    By Byron Spice

Carnegie Mellon University (CMU) computational biologists collaborated with researchers at seven other institutions to develop a software tool that identifies bioactive molecules and the microbial genes that generate them, for assessment as potential antibiotics. The team demonstrated that MetaMiner can detect such molecules at least 100 times faster than was possible with previous techniques. MetaMiner applies genome mining methodology, analyzing gene clusters to deduce molecules the genes produce. CMU's Hosein Mohimani and Liu Cao bypassed genome mining's high susceptibility to error by building an error-tolerant search engine that finds matches between databases of microbial DNA and databases that classify molecular products according to mass spectra. With MetaMiner, the researchers identified 31 known and seven previously unknown ribosomally synthesized and post-translationally modified peptides in about 14 days; Mohimani said obtaining those results manually likely would have taken decades. ... "