DNA 擬態蛋白質是較鮮為人知的控制因子，與 DNA 有一些相似之處。 他們通過使用帶負電荷的氨基酸，即天冬氨酸（ASP 或 D）和谷氨酸（GLU 或 E）來模擬DNA 磷酸鹽主鏈的負電荷分佈。 已知的 DNA 擬態蛋白質通過干預 DNA 與效應蛋白的結合來控制各種細胞機制，例如轉錄、DNA 修復和基因調控。 除了具有重要的功能外，DNA 模擬蛋白還具有用於生物技術應用的潛力。 例如，來自單增李斯特菌原噬菌體的 DNA 擬態蛋白質 AcrIIA4 可以通過控制 CRISPR-Cas9 的活性來提高基因編輯的準確性。 因此，DNA 擬態蛋白質值得做進一步的研究。 然而，由於其獨特的氨基酸序列和結構特徵，大多數 DNA 擬態蛋白質無法使用傳統的生物信息學方法進行識別。 因此，我們開發了一種新的蛋白質指紋，稱為相對距離蛋白質指紋 (RD-PFP)，可用於分析蛋白質表面氨基酸的分佈。 我們通過使用機器學習和DNA 擬態蛋白質的特徵（即它們的與 DNA 磷酸鹽骨架相似的負電荷分佈）優化了我們的 RD-PFP，以更準確地預測蛋白質序列的 DNA 模擬。 我們的開創性研究有助於開發基於機器學習的生物信息學方法來篩選 DNA 擬態蛋白質。

DNA mimic proteins are relatively obscure control factors that share some similarities with DNA. They emulate the negative charge distribution of the phosphate backbone of DNA by using negatively charged amino acids, namely, aspartic acids (ASP or D) and glutamic acids (GLU or E). Known DNA mimic proteins control various cellular mechanisms, such as transcription, DNA repair, and gene regulation, by intervening in the binding of DNA to effector proteins. In addition to being functionally important, DNA mimic proteins have potential for use in biotechnological applications. For example, the DNA mimic protein AcrIIA4 from Listeria monocytogenes prophages can improve the accuracy of gene editing by
controlling the activity of CRISPR-Cas9. Therefore, DNA mimic proteins warrant further research. However, most DNA mimic proteins cannot be identified using traditional bioinformatics methods owing to their unique amino acid sequences and structural features. Accordingly, we developed a new protein fingerprint, called relative distance protein fingerprint (RDPFP), that can be used to analyze the distribution of amino acids on a protein surface. We optimized our RD-PFP by using machine learning and the characteristic feature of DNA mimic proteins (namely, their DNA-like negative charge distribution) to more accurately predict DNA mimicry from protein sequences. Our pioneering study contributes to the development of machine learning--based bioinformatics methods for screening DNA mimic proteins.

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