注释
计算机科学
螺旋线圈
条件随机场
深度学习
人工智能
鉴定(生物学)
模式识别(心理学)
试验装置
自然语言处理
机器学习
生物
生物化学
植物
作者
Giovanni Madeo,Castrense Savojardo,Matteo Manfredi,Pier Luigi Martelli,Rita Casadio
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2023-08-01
卷期号:39 (8)
被引量:3
标识
DOI:10.1093/bioinformatics/btad495
摘要
Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state.In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states.CoCoNat web server is available at https://coconat.biocomp.unibo.it. Standalone version is available on GitHub at https://github.com/BolognaBiocomp/coconat.
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