MetaMQAP: a meta-server for the quality assessment of protein models.
MetaMQAP: a meta-server for the quality assessment of protein models.
复制标题
DOI：
10.1186/1471-2105-9-403
复制
发表时间：
2008-09-29
影响因子：
3
通讯作者：
Bujnicki JM
中科院分区：
文献类型：
--
作者：
Pawlowski M;Gajda MJ;Matlak R;Bujnicki JM
蛋白质结构的计算模型通常是不准确的，并且与真实结构存在显著偏差。模型的效用取决于这些偏差的程度。已经开发了许多预测方法来区分全局不正确和近似正确的模型。然而，只有少数方法预测计算模型的不同部分的正确性。已经开发了几种模型质量评估程序（MQAP）来检测未精炼晶体学模型中的局部不准确性，但不知道它们是否对计算模型有用，这些模型通常表现出不同的和更严重的错误。在CASP-5和CASP-6实验的8251个模型上，通过计算这些方法的每个残基分数与模型与实验结构中C-α原子之间的局部偏差之间的斯皮尔曼等级相关系数，测试了8种MQAP识别模型中局部错误的能力：VERIFY 3D、PROSA、巴拉、ANOLEA、PROVE、DAE、REFINER、PROQRES。作为参考，我们计算了局部偏差和可以直接从模型中为每个残基计算的平凡特征之间的相关性值，即溶剂可及性、结构中的深度以及局部和非局部邻居的数量。我们发现，所有方法的MQAP和局部偏差返回的分数的绝对相关性都很差。此外，PROQRES和其他几个MQAP的得分与“琐碎”功能密切相关。因此，我们开发了MetaMQAP，一个基于多元回归模型的元预测器，它使用上述方法的分数，但其中平凡的参数被控制。MetaMQAP预测模型和未知真实结构之间的单个C-α原子的绝对偏差（以μ ngströms为单位）以及全局偏差（表示为均方根偏差和GDT_TS分数）。MetaMQAP预测的局部模型准确性显示出令人印象深刻的相关系数为0.7，与天然结构的真实偏差相比，所有组成主要MQAP评分都有显着改善。总体MetaMQAP评分与模型GDT_TS在0.89的水平上相关。最后，我们使用CASP 7服务器模型（不包括在MetaMQAP训练集中）作为测试数据，将我们的方法与CASP第7版中得分最高的MQAP进行了比较。在我们的基准测试中，MetaMQAP的表现仅优于PCONS 6和方法QA_556 -这些方法需要比较多个备选模型，并根据其与其他模型的相似性对每个模型进行评分。然而，MetaMQAP是能够评估单个模型的方法中最好的。我们将MetaMQAP实现为一个Web服务器，供所有学术用户免费使用，网址为
Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors. The ability to identify local errors in models was tested for eight MQAPs: VERIFY3D, PROSA, BALA, ANOLEA, PROVE, TUNE, REFINER, PROQRES on 8251 models from the CASP-5 and CASP-6 experiments, by calculating the Spearman's rank correlation coefficients between per-residue scores of these methods and local deviations between C-alpha atoms in the models vs. experimental structures. As a reference, we calculated the value of correlation between the local deviations and trivial features that can be calculated for each residue directly from the models, i.e. solvent accessibility, depth in the structure, and the number of local and non-local neighbours. We found that absolute correlations of scores returned by the MQAPs and local deviations were poor for all methods. In addition, scores of PROQRES and several other MQAPs strongly correlate with 'trivial' features. Therefore, we developed MetaMQAP, a meta-predictor based on a multivariate regression model, which uses scores of the above-mentioned methods, but in which trivial parameters are controlled. MetaMQAP predicts the absolute deviation (in Ångströms) of individual C-alpha atoms between the model and the unknown true structure as well as global deviations (expressed as root mean square deviation and GDT_TS scores). Local model accuracy predicted by MetaMQAP shows an impressive correlation coefficient of 0.7 with true deviations from native structures, a significant improvement over all constituent primary MQAP scores. The global MetaMQAP score is correlated with model GDT_TS on the level of 0.89. Finally, we compared our method with the MQAPs that scored best in the 7th edition of CASP, using CASP7 server models (not included in the MetaMQAP training set) as the test data. In our benchmark, MetaMQAP is outperformed only by PCONS6 and method QA_556 – methods that require comparison of multiple alternative models and score each of them depending on its similarity to other models. MetaMQAP is however the best among methods capable of evaluating just single models. We implemented the MetaMQAP as a web server available for free use by all academic users at the URL
登录
查看更多内容
影响因子：
6.8
作者：
Buchete, NV;Straub, JE;Thirumalai, D
通讯作者：
Thirumalai, D
影响因子：
5.6
作者：
Jones, DT
通讯作者：
Jones, DT
影响因子：
2.9
作者：
Cozzetto, Domenico;Kryshtafovych, Andriy;Tramontano, Anna
通讯作者：
Tramontano, Anna
影响因子：
2.9
作者：
Kaminska, Katarzyna H.;Baraniak, Urszula;Bujnicki, Janusz M.
通讯作者：
Bujnicki, Janusz M.
DOI：
10.1093/protein/gzg063
发表时间：
2003-07-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Albrecht, M;Tosatto, SCE;Valle, G
通讯作者：
Valle, G