背景基因翻译过程中可能发生的一种奇怪现象是,当核糖体沿着mRNA读取时,各种细胞和分子特性导致核糖体停滞在一个光滑的序列上,将核糖体移入另外两个交替阅读框之一。备用帧有不同的密码子,所以不同的氨基酸被添加到肽链上,但更重要的是,原来的终止密码子不再在框架内,所以核糖体可以绕过终止密码子,继续翻译过去的密码子。这产生了更长版本的蛋白质,原始框内氨基酸的融合,其次是所有的交替框架氨基酸。目前没有自动软件来预测这些程序化的核糖体移码(PRF)的发生。它们目前只能通过人工策展来识别。结果在这里我们提出了PRFect,一种创新的机器学习方法,用于检测和预测各种类型的编码基因中的PRFs。PRFect将先进的机器学习技术与多个复杂细胞属性的集成相结合,如二级结构,密码子使用,核糖体结合位点干扰,方向,和光滑的网站图案。计算和结合这些不同的属性提出了重大挑战,但是通过广泛的研究和开发,我们已经实现了用户友好的方法。PRFect的代码是免费提供的,开源,并且可以通过终端中的单个命令轻松安装。我们对不同生物的全面评估,包括细菌,古细菌,和噬菌体,展示了PRFect的强劲表现,实现高灵敏度,特异性,准确度超过90%。结论PRFect代表了PRF检测和预测领域的重大进步。为研究人员和科学家提供了一个强大的工具来解开编码基因中编程的核糖体移码的复杂性。
UNASSIGNED: One of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence, shifting the ribosome into one of the other two alternate reading frames. The alternate frame has different codons, so different amino acids are added to the peptide chain, but more importantly, the original stop codon is no longer in-frame, so the ribosome can bypass the stop codon and continue to translate the codons past it. This produces a longer version of the protein, a fusion of the original in-frame amino acids, followed by all the alternate frame amino acids. There is currently no automated software to predict the occurrence of these programmed
ribosomal frameshifts (PRF), and they are currently only identified by manual curation.
UNASSIGNED: Here we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage,
ribosomal binding site interference, direction, and slippery site motif. Calculating and incorporating these diverse properties posed significant challenges, but through extensive research and development, we have achieved a user-friendly approach. The code for PRFect is freely available, open-source, and can be easily installed via a single command in the terminal. Our comprehensive evaluations on diverse organisms, including bacteria, archaea, and phages, demonstrate PRFect\'s strong performance, achieving high sensitivity, specificity, and an accuracy exceeding 90%.
UNASSIGNED: PRFect represents a significant advancement in the field of PRF detection and prediction, offering a powerful tool for researchers and scientists to unravel the intricacies of programmed
ribosomal frameshifting in coding genes.