StemP：一种快速确定性的RNA和蛋白质折叠预测的Stem图方法
StemP: A fast and deterministic Stem-graph approach for RNA and protein folding prediction
摘要：
我们提出了一种新的确定性方法来预测RNA序列和蛋白质折叠。对结构预测来说，stem是否足够？主要思想是考虑给定序列中所有可能的茎形成。利用茎环的能量和茎的强度，我们探索了如何确定性地利用茎信息进行RNA序列和蛋白质折叠结构的预测。我们使用图形符号，其中所有可能的茎表示为顶点，共存为边。这个完整的Stem图呈现了所有可能的折叠结构，并且我们挑选子图，这些子图给出了用于折叠结构预测的最佳匹配能量。我们引入了一个Stem-Loop分数来增加结构信息并加快计算速度。所提出的方法可以处理二级结构预测以及蛋白质折叠与伪结。数值实验是用笔记本电脑完成的，结果只需要几分钟或几秒钟。这种方法的优点之一是算法的简单性和灵活性，它给出了确定性的答案。我们探索蛋白质数据库中的蛋白质序列，rRNA 5S序列和Gutell实验室的tRNA序列。各种实验和比较，包括验证所提出的方法。
英文摘要：
We propose a new deterministic methodology to predict RNA sequence and protein folding. Is stem enough for structure prediction? The main idea is to consider all possible stem formation in the given sequence. With the stem loop energy and the strength of stem, we explore how to deterministically utilize stem information for RNA sequence and protein folding structure prediction. We use graph notation, where all possible stems are represented as vertices, and co-existence as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for folding structure prediction. We introduce a Stem-Loop score to add structure information and to speed up the computation. The proposed method can handle secondary structure prediction as well as protein folding with pseudo knots. Numerical experiments are done using a laptop and results take only a few minutes or seconds. One of the strengths of this approach is in the simplicity and flexibility of the algorithm, and it gives deterministic answer. We explore protein sequences from Protein Data Bank, rRNA 5S sequences, and tRNA sequences from the Gutell Lab. Various experiments and comparisons are included to validate the propose method.
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