The Hormel Institute, University of Minnesota, Austin, Minnesota, United States of America.
PLoS One. 2012;7(5):e38261. doi: 10.1371/journal.pone.0038261. Epub 2012 May 31.
Plant-based polyphenols (i.e., phytochemicals) have been used as treatments for human ailments for centuries. The mechanisms of action of these plant-derived compounds are now a major area of investigation. Thousands of phytochemicals have been isolated, and a large number of them have shown protective activities or effects in different disease models. Using conventional approaches to select the best single or group of best chemicals for studying the effectiveness in treating or preventing disease is extremely challenging. We have developed and used computational-based methodologies that provide efficient and inexpensive tools to gain further understanding of the anticancer and therapeutic effects exerted by phytochemicals. Computational methods involving virtual screening, shape and pharmacophore analysis and molecular docking have been used to select chemicals that target a particular protein or enzyme and to determine potential protein targets for well-characterized as well as for novel phytochemicals.
植物来源的多酚(即植物化学物质)已被用于治疗人类疾病已有几个世纪。这些植物衍生化合物的作用机制现在是一个主要的研究领域。已经分离出数千种植物化学物质,其中许多在不同的疾病模型中显示出保护活性或效果。使用传统方法选择最佳单一化合物或最佳化合物组来研究治疗或预防疾病的效果极具挑战性。我们已经开发并使用了基于计算的方法,这些方法提供了高效且廉价的工具,可进一步了解植物化学物质的抗癌和治疗效果。涉及虚拟筛选、形状和药效团分析以及分子对接的计算方法已被用于选择针对特定蛋白质或酶的化学物质,并确定经过充分表征的以及新型植物化学物质的潜在蛋白质靶标。