基于多个同源氨基酸序列的蛋白质结构直接偶联分析的快速拟线性最大化方法
Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
摘要：
直接耦合分析是一组通过从数据中学习指数家族中的生成模型来获取蛋白质家族中共进化残基信息的方法。在实际大小的蛋白质家族中，这种学习只能近似完成，并且在推理精度和计算速度之间存在权衡。在这里，我们表明，早期推出的$l_2$正则化pseudolikestrandom最大化方法称为plmDCA可以修改为易于并行化，以及固有的速度更快，在一个单一的处理器上，在精度上的差异可以忽略不计。我们测试的新化身的方法对148个蛋白质家族的蛋白质家族数据库（PFAM），这类算法的最大的测试之一。
英文摘要：
Direct-Coupling Analysis is a group of methods to harvest information about coevolving residues in a protein family by learning a generative model in an exponential family from data. In protein families of realistic size, this learning can only be done approximately, and there is a trade-off between inference precision and computational speed. We here show that an earlier introduced $l_2$-regularized pseudolikelihood maximization method called plmDCA can be modified as to be easily parallelizable, as well as inherently faster on a single processor, at negligible difference in accuracy. We test the new incarnation of the method on 148 protein families from the Protein Families database (PFAM), one of the largest tests of this class of algorithms to date.
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