Deepcld: An Efficient Sequence-based Predictor of Intrinsically Disordered Proteins
Document Type
Article
Publication Date
2022
Digital Object Identifier (DOI)
https://doi.org/10.1109/TCBB.2021.3124273
Abstract
Intrinsic disorder is common in proteins, plays important roles in protein functionality, and is commonly associated with various human diseases. To have an accurate tool for the annotation of intrinsic disorder in proteins, this paper proposes a novel algorithm, DeepCLD, for sequence-based prediction of intrinsically disordered proteins. This algorithm uses amino acid position specific scoring matrix (PSSM) to capture the intrinsic variability characteristic of sequence patterns, ResNet to preserve feature space structure, and bidirectional CudnnLSTM as recurrent layer to further improve the efficiency. Futhermore, DeepCLD also utilized the attention mechanism to solve the problem of gradient disappearing in deep network. Comparative analyses show that DeepCLD has faster training speed and higher prediction accuracy than comparable methods.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
IEEE/ACM Transactions on Computational Biology and Bioinformatics, v. 19, issue 6, p. 3154-3159
Scholar Commons Citation
Fang, Min; He, Yufeng; Du, Zhihua; and Uversky, Vladimir N., "Deepcld: An Efficient Sequence-based Predictor of Intrinsically Disordered Proteins" (2022). Molecular Medicine Faculty Publications. 1046.
https://digitalcommons.usf.edu/mme_facpub/1046
