基于全原子知识的 RNA 结构预测和评估的势能函数。

All-atom knowledge-based potential for RNA structure prediction and assessment.

机构信息

Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Principe Felipe, 46012 Valencia, Spain.

出版信息

Bioinformatics. 2011 Apr 15;27(8):1086-93. doi: 10.1093/bioinformatics/btr093. Epub 2011 Feb 23.

DOI:10.1093/bioinformatics/btr093

PMID:21349865

Abstract

MOTIVATION

Over the recent years, the vision that RNA simply serves as information transfer molecule has dramatically changed. The study of the sequence/structure/function relationships in RNA is becoming more important. As a direct consequence, the total number of experimentally solved RNA structures has dramatically increased and new computer tools for predicting RNA structure from sequence are rapidly emerging. Therefore, new and accurate methods for assessing the accuracy of RNA structure models are clearly needed.

RESULTS

Here, we introduce an all-atom knowledge-based potential for the assessment of RNA three-dimensional (3D) structures. We have benchmarked our new potential, called Ribonucleic Acids Statistical Potential (RASP), with two different decoy datasets composed of near-native RNA structures. In one of the benchmark sets, RASP was able to rank the closest model to the X-ray structure as the best and within the top 10 models for ∼93 and ∼95% of decoys, respectively. The average correlation coefficient between model accuracy, calculated as the root mean square deviation and global distance test-total score (GDT-TS) measures of C3' atoms, and the RASP score was 0.85 and 0.89, respectively. Based on a recently released benchmark dataset that contains hundreds of 3D models for 32 RNA motifs with non-canonical base pairs, RASP scoring function compared favorably to ROSETTA FARFAR force field in the selection of accurate models. Finally, using the self-splicing group I intron and the stem-loop IIIc from hepatitis C virus internal ribosome entry site as test cases, we show that RASP is able to discriminate between known structure-destabilizing mutations and compensatory mutations.

AVAILABILITY

RASP can be readily applied to assess all-atom or coarse-grained RNA structures and thus should be of interest to both developers and end-users of RNA structure prediction methods. The computer software and knowledge-based potentials are freely available at http://melolab.org/supmat.html.

CONTACT

fmelo@bio.puc.cl; mmarti@cipf.es

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

近年来，RNA 仅仅作为信息传递分子的观点发生了巨大变化。研究 RNA 的序列/结构/功能关系变得越来越重要。因此，实验解决的 RNA 结构的总数急剧增加，并且从序列预测 RNA 结构的新计算机工具也在迅速出现。因此，显然需要新的和准确的方法来评估 RNA 结构模型的准确性。

结果

在这里，我们引入了一种用于评估 RNA 三维（3D）结构的全原子基于知识的势能。我们已经使用由近天然 RNA 结构组成的两个不同的诱饵数据集对我们的新势能，称为核糖核酸统计势能（RASP）进行了基准测试。在基准测试集中的一个中，RASP 能够将最接近 X 射线结构的模型排名为最佳，并且在约 93%和 95%的诱饵中，模型分别在 10 个最佳模型内。模型准确性的平均相关系数，计算为均方根偏差和全局距离测试-总得分（GDT-TS）C3'原子的度量，与 RASP 分数分别为 0.85 和 0.89。基于最近发布的包含 32 个 RNA 基序中非规范碱基对的数百个 3D 模型的基准数据集，RASP 评分函数在选择准确模型方面与 ROSETTA FARFAR 力场相比具有优势。最后，使用自我剪接的 I 组内含子和丙型肝炎病毒内部核糖体进入位点的茎环 IIIc 作为测试用例，我们表明 RASP 能够区分已知的结构不稳定突变和补偿突变。