Jianlin Cheng, of the University of Missouri, in Columbia, who was one of the first to apply deep learning in this way, says such programs should be able to spot correlations between three, four or more amino acids, and thus need fewer related proteins to predict structures. Jinbo Xu, of the Toyota Technological Institute in Chicago, claims to have achieved this already. He and his colleagues published their method in PLOS Computational Biology, in January, and it is now being tested.
哥伦比亚的密苏里州的大学的程建林最先把深度学习应用到这个方面。他说,这个程序能够找到三个、四个或者更多氨基酸之间的相互关性。并且需要更少的相关的蛋白质分子来预测其结构。芝加哥丰田技术研究所的徐金波声称现在已经达到这种技术水平。他和他同事在一月份将这一方法发表在《PLOS计算生物学》上,现处于测试阶段。
If the deep-learning approach to protein folding lives up to its promise, the number of known protein structures should multiply rapidly. More importantly, so should the number that belong to human proteins. That will be of immediate value to drugmakers. It will also help biologists understand better the fundamental workings of cells—and thus what, at a molecular level, it truly means to be alive.
对于蛋白质分子折叠,如果深度学习的方法达到了预期的效果,那么已知蛋白质结构的数目应该会迅速增加。更为重要的是,对人类蛋白质结构的了解也会增加。对于制药公司来说将会有即时的好处。这也将会帮助生物学家更好的理解细胞的基本功能。如此一来,意味着分子水平的研究真正开始了。