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Definition of the binding specificity of the T7 bacteriophage primase by analysis of a protein binding microarray using a thermodynamic model

机译:通过使用热力学模型分析蛋白质结合微阵列来定义 T7 噬菌体引物酶的结合特异性

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摘要

Protein binding microarrays (PBM), SELEX, RNAcompete and chromatin-immunoprecipitation have been intensively used to determine the specificity of nucleic acid binding proteins. While the specificity of proteins with pronounced sequence specificity is straightforward, the determination of the sequence specificity of proteins of modest sequence specificity is more difficult. In this work, an explorative data analysis workflow for nucleic acid binding data was developed that can be used by scientists that want to analyse their binding data. The workflow is based on a regressor realized in scikit-learn, the major machine learning module for the scripting language Python. The regressor is built on a thermodynamic model of nucleic acid binding and describes the sequence specificity with base- and position-specific energies. The regressor was used to determine the binding specificity of the T7 primase. For this, we reanalysed the binding data of the T7 primase obtained with a custom PBM. The binding specificity of the T7 primase agrees with the priming specificity (5′-GTC) and the template (5′-GGGTC) for the preferentially synthesized tetraribonucleotide primer (5′-pppACCC) but is more relaxed. The dominant contribution of two positions in the motif can be explained by the involvement of the initiating and elongating nucleotides for template binding.
机译:蛋白质结合微阵列 (PBM)、SELEX、RNAcompete 和染色质免疫沉淀已被大量用于确定核酸结合蛋白的特异性。虽然具有显著序列特异性的蛋白质的特异性很简单,但确定具有中等序列特异性的蛋白质的序列特异性则更为困难。在这项工作中,开发了一种核酸结合数据的探索性数据分析工作流程,可供想要分析其结合数据的科学家使用。该工作流基于在 scikit-learn(脚本语言 Python 的主要机器学习模块)中实现的回归器。回归器建立在核酸结合的热力学模型之上,并通过碱基和位置特异性能量描述序列特异性。回归因子用于确定 T7 引物酶的结合特异性。为此,我们重新分析了使用定制 PBM 获得的 T7 引物酶的结合数据。T7 引物酶的结合特异性与优先合成的四核糖核苷酸引物 (5'-pppACCC) 的引物特异性 (5'-GTC) 和模板 (5'-GGGTC) 一致,但更宽松。基序中两个位置的主要贡献可以通过模板结合的起始核苷酸和延伸核苷酸的参与来解释。

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