DeepCLD: An efficient sequence-based predictor of intrinsically disordered proteins

Fang, Min; He, Yufeng; Du, Zhihua; Vladimir, Uversky N.

doi:10.1109/tcbb.2021.3124273

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Fang et al [19] created DeepCLD, an algorithm for predicting intrinsically disordered proteins (IDPs) from sequence data. The method aims to provide an accurate and efficient tool for forecasting the behavior of certain types of proteins.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A.G,

2024

Preprint

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Alzheimer’s disease (AD) is a generic form of dementia causing memory loss and environmental responses. AD detection is pursued using the different protein structures and their intensity based on different physical behaviors. Using the time-series protein structures the detection and is eased through the proposed neural method for structural protein filling (NC-SSF). Structural differentiations are performed using the high and low intensity profiles observed. This analysis identifies the missing inputs and thereby the fillable sequences are identified. The protein biomarker determines the maximum filling requirement as per the changes observed. The neural network is trained using this sequence required under the low and high intensity variations. This process is recurrent until maximum false rate is confined through accuracy improvements. The AD progression detection is performed by estimating the intensity under different profile filling levels. The proposed method improves accuracy, sensitivity, and specificity by 8.74%, 10.29%, and 8.84% respectively. This method reduced the false rate and MMSE by 9.85% and 10.78% respectively.

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Section: Related Workmentioning

confidence: 99%

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A.G,

2024

Preprint

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“…The CAID’s results and subsequent follow-up studies reveal that modern disorder predictors, particularly those that rely on deep neural networks, produce accurate results ( 31 , 34 , 35 ). Example deep learning-based tools are flDPnn ( 36 ), SPOT-Disorder2 ( 37 ), RawMSA ( 38 ), AUCpreD ( 39 ), IDP-Seq2Seq ( 40 ), DeepIDP-2L ( 41 ) and DeepCLD ( 42 ).…”

Section: Introductionmentioning

confidence: 99%

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Basu

Gsponer

Kurgan

2023

Nucleic Acids Research

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Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/

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Resources for computational prediction of intrinsic disorder in proteins

Kurgan

2022

Methods

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DeepCLD: An efficient sequence-based predictor of intrinsically disordered proteins

Cited by 3 publications

References 48 publications

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Resources for computational prediction of intrinsic disorder in proteins

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