Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia.
BMC Bioinformatics. 2010 Apr 7;11:172. doi: 10.1186/1471-2105-11-172.
Ever since the ground-breaking work of Anfinsen et al. in which a denatured protein was found to refold to its native state, it has been frequently stated by the protein fold prediction community that all the information required for protein folding lies in the amino acid sequence. Recent in vitro experiments and in silico computational studies, however, have shown that cotranslation may affect the folding pathway of some proteins, especially those of ancient folds. In this paper aspects of cotranslational folding have been incorporated into a protein structure prediction algorithm by adapting the Rosetta program to fold proteins as the nascent chain elongates. This makes it possible to conduct a pairwise comparison of folding accuracy, by comparing folds created sequentially from each end of the protein.
A single main result emerged: in 94% of proteins analyzed, following the sense of translation, from N-terminus to C-terminus, produced better predictions than following the reverse sense of translation, from the C-terminus to N-terminus. Two secondary results emerged. First, this superiority of N-terminus to C-terminus folding was more marked for proteins showing stronger evidence of cotranslation and second, an algorithm following the sense of translation produced predictions comparable to, and occasionally better than, Rosetta.
There is a directionality effect in protein fold prediction. At present, prediction methods appear to be too noisy to take advantage of this effect; as techniques refine, it may be possible to draw benefit from a sequential approach to protein fold prediction.
自从 Anfinsen 等人的开创性工作发现变性蛋白可以重新折叠成天然状态以来,蛋白质折叠预测界频繁声称,蛋白质折叠所需的所有信息都存在于氨基酸序列中。然而,最近的体外实验和计算机模拟研究表明,共翻译可能会影响某些蛋白质的折叠途径,尤其是那些古老折叠的蛋白质。在本文中,通过调整 Rosetta 程序使蛋白质在新生链延伸时折叠,将共翻译折叠的各个方面纳入蛋白质结构预测算法中。这使得可以通过比较从蛋白质两端顺序创建的折叠来进行折叠准确性的成对比较。
出现了一个单一的主要结果:在分析的 94%的蛋白质中,从 N 端到 C 端的翻译方向比从 C 端到 N 端的翻译方向产生更好的预测结果。出现了两个次要结果。首先,对于共翻译证据较强的蛋白质,N 端到 C 端折叠的这种优势更为明显;其次,遵循翻译方向的算法产生的预测结果与 Rosetta 相当,有时甚至更好。
蛋白质折叠预测存在方向性效应。目前,预测方法似乎过于嘈杂,无法利用这种效应;随着技术的完善,可能可以从蛋白质折叠预测的顺序方法中受益。