DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins

Hosen, Md. Faruk; Mahmud, Sakib; Ahmed, Kawsar; Chen, Wenyu; Moni, Mohammad Ali; Deng, Hong−Wen; Shoombuatong, Watshara; Hasan, Md. Mehedi

doi:10.1016/j.compbiomed.2022.105433

Cited by 9 publications

(2 citation statements)

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“…Building stable, dependable classifiers with competitive performance requires efficient feature extraction ( Xie et al 2021 ). To thoroughly study the typical and particular patterns of bARTT proteins, we extracted 15 widely used predefined features, including three major groups: a sequence-based features group [AAC ( Anfinsen 1972 ), DPC ( Zou et al 2013 ), and TPC ( Chou 2000 , Hosen et al 2022 )], a physicochemical property-based features group [CTD ( Cao et al 2013 ), QSO ( Chou 2000 ), PAAC ( Chou 2001 ), APAAC ( Chou 2001 ), MBauto ( Lin and Pan 2001 ), Moranauto ( Horne 1988 ), and Gearyauto ( Sokal and Thomson 2006 )] and an evolutionary information-based features group [PSSM-composition ( Zou et al 2013 ), S-FPSSM ( Zahiri et al 2013 ), DPC-PSSM ( Liu et al 2010 ), Pse-PSSM ( Chou and Shen 2007 ), and RPSSM ( Chen et al 2023 )]. Sequence-based features describe the frequencies or compositions of sequence elements, whereas physicochemical property-based features represent the statistical information about the physicochemical properties of the amino acids in protein sequences.…”

Section: Methodsmentioning

confidence: 99%

A deep learning method to predict bacterial ADP-ribosyltransferase toxins

Zheng,

Zhou,

Chen

et al. 2024

Bioinformatics

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Motivation ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew’s correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes. Availability and implementation ARTNet is publicly accessible at http://www.mgc.ac.cn/ARTNet/. The source code of ARTNet is freely available at https://github.com/zhengdd0422/ARTNet/.

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Section: Methodsmentioning

confidence: 99%

A deep learning method to predict bacterial ADP-ribosyltransferase toxins

Zheng,

Zhou,

Chen

et al. 2024

Bioinformatics

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“…p ξ k,i means the occurrence frequency of the amino acid i of the protein sequence k in the subcellular position ξ. The amphiphilic pseudo amino acid composition (APAAC) was originally proposed by Chou (2005) to reflect the sequence-order effects by using the hydrophobicity and hydrophilicity of the constituent amino acids in a protein (Hosen et al, 2022;Qian et al, 2022). By using APAAC, a protein sample can be represented as follows:…”

Section: Feature Encodingmentioning

confidence: 99%

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

Deng

et al. 2023

Front. Genet.

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Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied.Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location.Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

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