Pawłowski Piotr H, Zielenkiewicz Piotr
Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warszawa, Poland.
Laboratory of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, Faculty of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland.
Life (Basel). 2024 Oct 18;14(10):1328. doi: 10.3390/life14101328.
This study showed that the predictor in logistic regression can be applied to estimating the Gibbs free energy of tRNAs' recognition of and binding to their aminoacyl-tRNA synthetases. Then, 24 linear logistic regression models predicting different classes of tRNAs loaded with a corresponding amino acid were trained in a machine learning classification method, reducing the misclassification error to zero. The models were based on minimal subsets of Boolean explanatory variables describing the favorite presence of nucleotides or nucleosides localized in the different parts of the tRNA. In 90% of cases, they agree with the components of the consensus strand in a class of tRNAs loaded by a given amino acid. According to the proposed theoretical model, the values of the free energy for the entry of the recognition state in the process of tRNA charging were obtained, and the inputs from identity nucleotides and the tRNA strand backbone were distinguished. Almost all the resulting models indicated leading anticodon tandems defining the first and second positions of the anticodon (positions 35 and 36 of the tRNA strand) and the small sets (up to six positions) of the other nucleotides as the natural identity nucleotides most influential in the free energy balance. The magnitude of their input to this energy depends on the position in the strand, favoring positions -1, 35, and 36. The role of position 34 is relatively smaller. These identity attributes may not always be fully arranged in a real single adaptor molecule but were comprehensively present in a given tRNA class. A detailed analysis of the resulting models showed that the absolute value of the energy of binding the tandem 35-36 decreases with the number of identity positions, as well as with the decreasing number of possible hydrogen bonds. On the other hand, in these conditions, the absolute value of the energy of binding of other identity nucleotides increases. All the models indicate that the nucleotide-independent energy of the repulsion tRNA backbone decreases with the number of identity nucleotides. It was also shown that the total free energy change in entering the recognition state increases with the amino acid mass, making this process less spontaneous, which may have an evolutionary reference.
本研究表明，逻辑回归中的预测因子可用于估计转运RNA（tRNA）识别并结合其氨酰-tRNA合成酶的吉布斯自由能。然后，采用机器学习分类方法训练了24个预测不同类别负载相应氨基酸的tRNA的线性逻辑回归模型，将误分类误差降至零。这些模型基于布尔解释变量的最小子集，这些变量描述了位于tRNA不同部位的核苷酸或核苷的有利存在情况。在90%的情况下，它们与给定氨基酸负载的一类tRNA中共识链的组成部分一致。根据提出的理论模型，获得了tRNA充电过程中识别状态进入的自由能值，并区分了来自特征核苷酸和tRNA链主链的输入。几乎所有生成模型都表明，主导反密码子串联决定了反密码子的第一和第二位置（tRNA链的第35和36位），以及其他核苷酸的小集合（最多六个位置）是对自由能平衡最有影响的天然特征核苷酸。它们对该能量的输入大小取决于链中的位置，有利于-1、35和36位。34位的作用相对较小。这些特征属性在真实的单个适配子分子中可能并不总是完全排列，但在给定的tRNA类别中全面存在。对生成模型的详细分析表明，35 - 36串联结合能的绝对值随着特征位置数量的增加以及可能氢键数量的减少而降低。另一方面，在这些条件下，其他特征核苷酸结合能的绝对值增加。所有模型都表明，tRNA主链的非核苷酸排斥能随着特征核苷酸数量的增加而降低。还表明，进入识别状态时的总自由能变化随着氨基酸质量的增加而增加，使得这个过程的自发性降低，这可能具有进化参考意义。
