National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
BMC Bioinformatics. 2011 May 26;12:208. doi: 10.1186/1471-2105-12-208.
Development of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.
Based on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.
Although NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.
开发有效的评分函数是蛋白质结构建模成功的关键组成部分。此前，许多工作都致力于开发评分函数。尽管如此，开发一个既能达到高精度又能快速的有效评分函数仍然是一个巨大的挑战。
我们仅使用四个主链原子（N、Cα、C 和 O）对蛋白质结构进行粗粒度表示，开发了一种基于知识的评分函数，称为 NCACO-score，它整合了不同的结构信息，从序列中快速模拟蛋白质结构。在 Decoys'R'Us 数据集上的测试表明，NCACO-score 可以有效地从其假构象中识别出天然构象。此外，我们证明 NCACO-score 可以有效地指导蛋白质结构预测的片段组装，在从 CASP8 进行硬目标结构模型构建方面，它在准确性和速度方面都取得了良好的性能。
虽然 NCACO-score 是基于粗粒度模型开发的，但它能够以高精度区分天然构象和假构象。NCACO 是一种非常有效的结构建模评分函数。
