Dubchak I, Muchnik I, Kim S H
E. O. Lawrence Berkeley National Laboratory, University of California, Berkeley, USA.
Microb Comp Genomics. 1998;3(3):171-5. doi: 10.1089/omi.1.1998.3.171.
Analysis of DNA sequences of several microbial genomes has revealed that a large fraction of predicted coding regions has no known protein function. Information about the three-dimensional folds of these proteins may provide insight into their possible functions. To predict the folds for protein sequences with little or no homology to proteins of known function, we used computational neural networks trained on the database of proteins with known three-dimensional structures. Global descriptions of protein sequences based on physical and structural properties of the constituent amino acids were used as inputs for neural networks. Of the 131, 498, and 868 protein sequences of unknown function from Mycoplasma genitalium, Haemophilus influenzae, and Methanococcus jannaschii (Fleischmann et al. 1995), we have made high-confidence fold assignments for 4, 10, and 19 sequences, respectively.
对几个微生物基因组的DNA序列分析表明，很大一部分预测的编码区域没有已知的蛋白质功能。有关这些蛋白质三维折叠的信息可能有助于了解它们可能的功能。为了预测与已知功能蛋白质几乎没有或没有同源性的蛋白质序列的折叠情况，我们使用了在具有已知三维结构的蛋白质数据库上训练的计算神经网络。基于组成氨基酸的物理和结构特性对蛋白质序列进行的全局描述被用作神经网络的输入。对于来自生殖支原体、流感嗜血杆菌和詹氏甲烷球菌（Fleischmann等人，1995年）的131、498和868个未知功能的蛋白质序列，我们分别对4、10和19个序列进行了高可信度的折叠分配。
