Summary: Protein quality assessment is a long-standing problem in
bioinformatics. For more than a decade we have developed state-of-art
predictors by carefully selecting and optimising inputs to a machine learning
method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and
0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a
protein. Here, we show that a substantial improvement can be obtained using
exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector
machine by a deep neural network. This improves the Pearson correlation to 0.90
(0.85 using ProQ2 input features).
Availability: ProQ3D is freely available both as a webserver and a
stand-alone program at this http URL

使用深度神经网络替换支持向量机可以显著地提高蛋白质质量评估的准确性。

使用深度学习改进模型质量评估的 ProQ3D

ProQ3D: Improved model quality assessments using Deep Learning

Protein quality assessment (QA) has played an important role in protein
structure prediction. We developed a novel single-model quality assessment
method - Qprob. Qprob calculates the absolute error for each protein feature
value against the true quality scores (i.e. GDT-TS scores) of protein
structural models, and uses them to estimate its probability density
distribution for quality assessment. Qprob has been blindly tested on the 11th
Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as
MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one
of the top single-model QA methods. In addition, Qprob makes contributions to
our protein tertiary structure predictor MULTICOM, which is officially ranked
3rd out of 143 predictors. The good performance shows that Qprob is good at
assessing the quality of models of hard targets. These results demonstrate that
this new probability density distribution based method is effective for protein
single-model quality assessment and is useful for protein structure prediction.
The webserver and software packages of Qprob are available at:
this http URL

本文提出一种新型单一模型蛋白质质量评估方法 - Qprob，它计算蛋白质结构模型真实质量得分（GDT-TS 得分）与每个蛋白质特征值之间的绝对误差，并使用它们来估计其质量评估的概率密度分布。Qprob 在 11th Critical Assessment of Techniques for Protein Structure Prediction（CASP11）上进行了盲测试，并成功排名为最好的单一模型质量评估方法之一。该方法在预测难模型的蛋白质三级结构和质量评估方面表现出色，并为多个研究领域做出了贡献。