Integrative computational framework for pattern mining in big -omics data: linking synonymous codon usage to protein biogenesis

项目摘要 有效生产功能蛋白质可以说是细胞最重要的功能。核糖体 通过解码mRNA密码子来合成蛋白质，蛋白质的N末端部分甚至可以开始折叠， 合成仍在进行中遗传密码是简并的，这意味着大多数氨基酸可以被 由一个以上的密码子编码。因为同义密码子替换不会改变氨基酸 由于编码蛋白质的序列不同，它们在历史上被认为是“沉默的”。然而，现在我们知道， 一些同义取代可以破坏所述蛋白质的表达、折叠、靶向和/或功能， 编码的蛋白质，虽然精确的机制知之甚少。计算分析有 试图确定同义密码子的位置和编码的特征之间的联系， 蛋白质，但迄今为止已经产生了相互矛盾的结果，并且很少尝试进行实验测试 从这些计算研究中得出的预测。因此，我们目前缺乏系统的了解， 同义密码子使用与蛋白质生物合成之间的联系。建立这些联系 将广泛地改变我们对同义密码子替换的解释，包括单核苷酸 与人类疾病相关的SNP和全基因组中的同义替换 关联研究（GWAS）。建立这些连接还可以增加编码 序列设计是合理设计新基因产物（蛋白质）的一个组成部分。因此，我们旨在 设计一个创新的综合计算和实验策略，以确定连接 密码子使用模式和蛋白质生物合成之间的联系我们将广泛地寻找这种联系， 开发和应用几种新的新方法：（一）计算方法来跟踪，量化和 同源蛋白质中同义密码子使用模式的比对，（ii）密码子使用图谱的网络方法 蛋白质结构的所有水平，以及（iii）广泛和有针对性的实验的创新组合 测试改变密码子使用对蛋白质生物合成的重要性和具体影响的方法。 在整个项目中，将采用严格的统计方法来测试所确定的联系的有效性， 和细胞为基础的实验将被用来测试和完善假设所产生的计算 分析和发展新的假设，将反馈到计算分析。这个目标 一个项目是改变我们对同义密码子使用和蛋白质之间联系的理解 生物起源本项目的终点是为密码子开发一套通用原则 使用，包括用户友好的开源软件，使生物医学界能够分析基因的 对可能影响蛋白质生物发生同义密码子使用特征的兴趣。同时我们的 该方法将是可推广的，以允许公众搜索序列之间的其他联系， 和/或网络模式和蛋白质功能，以及其他领域中的类似连接。

PROJECT SUMMARY Efficient production of functional proteins is arguably the most important function of a cell. Ribosomes synthesize proteins by decoding mRNA codons, and N-terminal portions of proteins can begin to fold even while synthesis is still underway. The genetic code is degenerate, meaning that most amino acids can be encoded by more than one codon. Because synonymous codon substitutions do not alter the amino acid sequence of the encoded protein, they have historically been regarded as “silent”. However, it is now known that some synonymous substitutions can disrupt the expression, folding, targeting and/or function of the encoded protein, although the precise mechanisms are poorly understood. Computational analyses have attempted to identify connections between the locations of synonymous codons and features of the encoded protein, but to date have yielded conflicting results, and there have been few attempts to experimentally test predictions made from these computational studies. Hence we currently lack a systematic understanding of the connections between synonymous codon usage and protein biogenesis. Establishing these connections would broadly transform our interpretation of synonymous codon substitutions, including single-nucleotide polymorphisms (SNPs) associated with human disease and synonymous substitutions in genome-wide association studies (GWAS). Establishing these connections would also enable the addition of coding sequence design as an integral aspect of the rational design of novel gene products (proteins). Thus, we aim to design an innovative integrative computational and experimental strategy with which to identify connections between codon usage patterns and protein biogenesis. We will search broadly for such connections, developing and applying several novel new approaches: (i) computational approaches to track, quantify and align synonymous codon usage patterns in homologous proteins, (ii) network approaches to map codon usage onto all levels of protein structure, and (iii) an innovative combination of broad and targeted experimental approaches to test the importance and specific effects of altering codon usage on protein biogenesis. Throughout the project, rigorous statistical methods will be applied to test the validity of identified connections, and cell-based experiments will be used to both test and refine hypotheses resulting from the computational analyses and develop new hypotheses that will feed back into the computational analyses. The goal of this project is to transform our understanding of the connections between synonymous codon usage and protein biogenesis. The endpoint for this project period is the development of a set of general principles for codon usage, including user-friendly open-source software to enable the biomedical community to analyze genes of interest for synonymous codon usage features likely to affect protein biogenesis. At the same time, our methodology will be generalizable, to allow the public to search for additional connections between sequence and/or network patterns and protein function, as well as for similar connections in other domains.