By a News Reporter-Staff News Editor at Journal of Technology -- Investigators publish new report on Computational Biology. According to news reporting originating in Stanford, California, by VerticalNews journalists, research stated, "Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space."
The news reporters obtained a quote from the research from Stanford University, "However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors."
According to the news reporters, the research concluded: "FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening."
For more information on this research see: Knowledge-based fragment binding prediction. Plos Computational Biology, 2014;10(4):e1003589. (Public Library of Science - www.plos.org; Plos Computational Biology - www.ploscompbiol.org)
Our news correspondents report that additional information may be obtained by contacting G.W. Tang, Dept. of Bioengineering, Stanford University, Stanford, California, United States.
Keywords for this news article include: Stanford, California, United States, Computational Biology, North and Central America.
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