计算机科学
人工智能
航程(航空)
数据挖掘
序列(生物学)
特征(语言学)
深度学习
光学(聚焦)
机器学习
范围(计算机科学)
功能(生物学)
构造(python库)
模式识别(心理学)
哲学
复合材料
物理
材料科学
程序设计语言
光学
生物
进化生物学
遗传学
语言学
作者
Chenchen Zhao,Shunfang Wang
标识
DOI:10.1016/j.compbiomed.2023.107822
摘要
Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this paper, we propose an algorithm, DeepMSA+, to generate protein multiple sequence alignments (MSAs) and to construct feature representations based on co-evolutionary information and sequence information derived from MSAs. We also propose an improved deep learning model, AttCON, for training input features to predict protein contact map. The model incorporates an attention module, and by comparing different attention modules, we find a parameter-free attention module suitable for contact map prediction. Additionally, we use the Focal Loss function to better address the data imbalance issue in protein contact map. We also developed a weighted evaluation index (W score) for model evaluation, which takes into account a wide range of metrics. W score is comprehensive in its scope, with a particular focus on the precision of predictions for medium-range and long-range contacts. Experimental results show that AttCON achieves good precision results on datasets from CASP11 to CASP15. Compared to the state-of-the-art methods, it achieves an average improvement of over 5% in both medium-range and long-range predictions, and W score is improved by an average of 2 points.
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