Brewery: deep learning and deeper profiles for the prediction of 1D protein structure annotations

被引:9
作者
Torrisi, Mirko [1 ]
Pollastri, Gianluca [1 ]
机构
[1] Univ Coll Dublin, Sch Comp Sci, Dublin 4, Ireland
关键词
D O I
10.1093/bioinformatics/btaa204
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Protein structural annotations (PSAs) are essential abstractions to deal with the prediction of protein structures. Many increasingly sophisticated PSAs have been devised in the last few decades. However, the need for annotations that are easy to compute, process and predict has not diminished. This is especially true for protein structures that are hardest to predict, such as novel folds. Results: We propose Brewery, a suite of ab initio predictors of 1D PSAs. Brewery uses multiple sources of evolutionary information to achieve state-of-the-art predictions of secondary structure, structural motifs, relative solvent accessibility and contact density.
引用
收藏
页码:3897 / 3898
页数:2
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