AREA: Optimizing gene expression with mRNA free energy modeling and algorithms

区域：利用 mRNA 自由能建模和算法优化基因表达

• 批准号: 8689532
• 负责人: DANIEL PAUL AALBERTS
• 金额: $ 25.53万
• 依托单位: WILLIAMS COLLEGE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8689532
• 关键词: Adenine; Algorithm Design; Algorithms; Amino Acid Sequence; Amino Acids; Base Composition; Binding; Biology; Biotechnology; Code; Codon Nucleotides; Collaborations; Communities; Data; Data Set; Dependence; Engineering; Free Energy; Gene Expression; Gene Expression Regulation; Genes; Goals; Guanine; Learning; Link; Literature; Maps; Messenger RNA; Metric; MicroRNAs; Mining; Modeling; Mutate; Mutation; Nucleotides; Peptide Sequence Determination; Pilot Projects; Process; Production; Protein Overexpression; Proteins; RNA Folding; RNA Sequences; RNA Splicing; Recombinants; Research; Ribosomes; Role; Small Interfering RNA; Small RNA; Solubility; Structure; Synthetic Genes; System; Translations; Vaccine Production; Variant; base; computer program; cost; data mining; design; disorder risk; drug discovery; drug production; energy density; expression vector; human disease; improved; model design; novel; polypeptide; protein expression; protein structure; prototype; public health relevance; research study; structural biology; structural genomics; tool

DESCRIPTION (provided by applicant): Protein overexpression is desirable for many biotechnology applications ranging from vaccine production to drug discovery. Based on gene expression data of about 20,000 genes with common expression vectors from collaboration with the Northeast Structural Genomics Consortium, we observe that the free energy of the first ~50 coding nucleotides is strongly predictive of the expression level of polypeptides. Many mRNA sequences encode exactly the same protein sequence because multiple codons may map to the same amino acid. The recent emergence of experimental datasets of expression levels for genes has created an opportunity to maximize protein expression through modeling and algorithm design. We propose to develop algorithms that will enable biologists to evaluate whether native mRNA sequences are likely to express highly and to build synonymous mRNA sequences designed to optimize gene expression. Having relatively little mRNA secondary structure at the start of the coding region is of particular importance as that is where the ribosome assembles. Extensive mRNA secondary structure later in the gene also appears to be deleterious. In translation, splicing, and small interfering RNA gene regulation mechanisms, a region of messenger RNA must be unfolded to allow binding of the ribosome, splice factors, or microRNAs. Understanding the unfolding free energy costs offers opportunities to understand the biology of and to algorithmically engineer changes in gene expression.

描述（由申请人提供）：蛋白质过表达对于从疫苗生产到药物发现的许多生物技术应用是理想的。基于与Northeast Structural Genomics Consortium合作的约20，000个基因的基因表达数据，我们观察到前50个编码核苷酸的自由能强烈预测多肽的表达水平。许多mRNA序列编码完全相同的蛋白质序列，因为多个密码子可能映射到相同的氨基酸。最近出现的基因表达水平的实验数据集，创造了一个机会，通过建模和算法设计，最大限度地提高蛋白质的表达。我们建议开发算法，使生物学家能够评估天然mRNA序列是否可能高度表达，并构建旨在优化基因表达的同义mRNA序列。在编码区的起始处具有相对较少的mRNA二级结构是特别重要的，因为这是核糖体组装的地方。在基因后期广泛的mRNA二级结构似乎也是有害的。在翻译、剪接和小干扰RNA基因调节机制中，信使RNA的一个区域必须解折叠以允许核糖体、剪接因子或微小RNA的结合。理解自由能量成本的展开提供了理解基因表达的生物学和算法工程改变的机会。