HybAVPnet: a novel hybrid network architecture for antiviral peptides identification

Ge, Ruiquan; Xia, Yixiao; Jiang, Minchao; Jia, Gangyong; Jing, Xiaoyang; Li, Ye; Cai, Yunpeng

doi:10.1101/2022.06.10.495721

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…In order to further explain the contribution of engineered features, we used two dimension scatters plot t-SNE, 49 as shown in Figure 4 A–F. The red dots denote the Non-AntiMARSA, and green dots denote the Anti-MRSA peptide samples.…”

Section: Resultsmentioning

confidence: 99%

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Arif,

Fang,

Fida

et al. 2024

ACS Omega

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Methicillin-resistant Staphylococcus aureus (MRSA) is a growing concern for human lives worldwide. Anti-MRSA peptides act as potential antibiotic agents and play significant role to combat MRSA infection. Traditional laboratory-based methods for annotating Anti-MRSA peptides are although precise but quite challenging, costly, and time-consuming. Therefore, computational methods capable of identifying Anti-MRSA peptides accelerate the drug designing process for treating bacterial infections. In this study, we developed a novel sequence-based predictor "iM-RSAPred" for screening Anti-MRSA peptides by incorporating energy estimation and physiochemical and sequential information. We successfully resolved the skewed imbalance phenomena by using synthetic minority oversampling technique plus Tomek link (SMOTETomek) algorithm. Furthermore, the Shapley additive explanation method was leveraged to analyze the impact of topranked features in the prediction task. We evaluated multiple machine learning algorithms, i.e., CatBoost, Cascade Deep Forest, Kernel and Tree Boosting, support vector machine, and HistGBoost classifiers by 10-fold cross-validation and independent testing. The proposed iMRSAPred method significantly improved the overall performance in terms of accuracy and Matthew's correlation coefficient (MCC) by 5.45 and 0.083%, respectively, on the training data set. On the independent data set, iMRSAPred improved accuracy and MCC by 3.98 and 0.055%, respectively. We believe that the proposed method would be useful in large-scale Anti-MRSA peptide prediction and provide insights into other bioactive peptides.

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Section: Resultsmentioning

confidence: 99%

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Arif,

Fang,

Fida

et al. 2024

ACS Omega

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Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model

Akbar,

Raza,

Zou

2024

BMC Bioinformatics

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Background Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. Methods In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. Results The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. Conclusion Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

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DPI_CDF: druggable protein identifier using cascade deep forest

Arif,

Fang,

Ghulam

et al. 2024

BMC Bioinformatics

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Background Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor’s performance is still not satisfactory. Methods In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. Results The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew’s-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. Availability The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF.

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HybAVPnet: a novel hybrid network architecture for antiviral peptides identification

Cited by 3 publications

References 37 publications

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model

DPI_CDF: druggable protein identifier using cascade deep forest

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