Understanding the Book of Life: Bayesian Protein Secondary Structure Analysis and its Application to Protein Function Prediction

鉴定基因组序列中所有蛋白质编码基因和确定这些基因编码的蛋白质的细胞功能只能通过将强大的计算工具与各种实验方法相结合来完成。期望通过实验研究每一个基因和蛋白质是不现实的。然而，使用相对便宜和快速的计算方法，通常可以可靠地预测DNA序列中的蛋白质编码区域并预测编码蛋白质的功能。在这种情况下，研究者通过使用静态分析发现最重要的依赖关系来提高二级结构预测的准确性。此外，研究者还结合了β链中氢键相互作用产生的非局部相关性。这是通过在相互作用的β链片段中提取氢键统计数据，并通过实现将非局部氢键模式与局部物理化学相互作用相结合的隐马尔可夫模型（HMM）来实现的。

The identification of all protein coding genes in a genome sequence and the determination of the cellular functions of the proteins encoded by these genes can only be accomplished by combining powerful computational tools with a variety of experimental approaches. It is unrealistic to expect that every single gene and protein will ever be studied experimentally. However, using relatively cheap and fast computational approaches, it is usually possible to reliably predict the protein-coding regions in DNA sequences and to predict the functions of the encoded proteins.Within this context, the investigator improves the accuracy of secondary structure predictions by discovering the most significant dependencies using statical analysis. In addition, the investigator incorporates non-local correlations that arise from hydrogen bonding interactions in beta-strands. This is achieved by extracting hydrogen bonding statistics in interacting beta-strand segments and by implementing a hidden Markov model (HMM) that combines non-local hydrogen bonding patterns with local physiochemical interactions.