Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.
Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.
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DOI：
10.1186/1472-6807-14-13
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发表时间：
2014-04-15
影响因子：
--
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Cao R;Wang Z;Cheng J
蛋白质模型质量评估是生成和使用蛋白质结构模型的重要组成部分。在第十次蛋白质结构预测技术关键评估 (CASP10) 期间，我们开发并测试了四种自动化方法（MULTICOM-REFINE、MULTICOM-CLUSTER、MULTICOM-NOVEL 和 MULTICOM-CONSTRUCT），可预测蛋白质结构模型的局部和全局质量。 MULTICOM-REFINE 是一种聚类方法，它使用模型之间的平均成对结构相似性来衡量全局质量，并使用模型与几个排名靠前的模型之间的平均欧几里德距离来衡量局部质量。 MULTICOM-CLUSTER 和 MULTICOM-NOVEL 是两种基于支持向量机的新方法，用于预测单个蛋白质模型的局部和全局质量。 MULTICOM-CONSTRUCT 是一种新的加权成对模型比较（聚类）方法，它使用池中模型之间的加权平均相似度来衡量全局模型质量。我们的实验表明，当池中大部分模型质量良好时，成对模型评估方法效果更好，而当池中只有一小部分模型质量合理时，单模型质量评估方法在某些硬目标上表现更好。由于从大量低质量模型中挖掘出一些好的模型是蛋白质结构预测的主要挑战，因此单一模型质量评估方法似乎将为蛋白质结构建模做出重要贡献。另一个有趣的发现是，单模型质量评估分数可以通过共识成对模型比较方法对模型进行加权，以提高其准确性。
Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.
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