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iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary
Shahid Akbar1, Quan Zou2, Ali Raza3
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
Abstract:
Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections. The existing wet-laboratory-based medications are expensive, time-consuming, and may have adverse side effects on normal cells. In the last decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction scheme called iAFPs-Mv-BiTCN to predict antifungal peptides correctly. The training peptides are encoded using word embedding methods such as skip-gram and attention mechanism-based bidirectional encoder representation using transformer. Additionally, transform-based evolutionary features are generated using the Pseduo position-specific scoring matrix using discrete wavelet transform (PsePSSM-DWT). The fused vector of word embedding and evolutionary descriptors is formed to compensate for the limitations of single encoding methods. A Shapley Additive exPlanations (SHAP) based global interpolation approach is applied to reduce training costs by choosing the optimal feature set. The selected feature set is trained using a bi-directional temporal convolutional network (BiTCN). The proposed iAFPs-Mv-BiTCN model achieved a predictive accuracy of 98.15 % and an AUC of 0.99 using training samples. In the case of the independent samples, our model obtained an accuracy of 94.11 % and an AUC of 0.98. Our iAFPs-Mv-BiTCN model outperformed existing models with a ~4 % and ~5 % higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed iAFPs-Mv-BiTCN model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.
Insights
A new computational model, iAFPs-Mv-BiTCN, accurately predicts antifungal peptides, offering a faster and more cost-effective alternative to traditional drug development for fungal infections.
Area of Science:
- Computational biology and bioinformatics
- Drug discovery and development
- Mycology and infectious diseases
Background:
- Fungal infections pose a significant global health threat, exacerbated by factors like COVID-19.
- Current antifungal drug development is costly, time-consuming, and can cause adverse effects.
- Peptide therapeutics show promise due to their high specificity and targeted action.
Purpose of the Study:
- To develop a highly accurate computational model for predicting antifungal peptides.
- To overcome limitations of existing prediction methods by integrating diverse feature encoding techniques.
- To provide a valuable tool for accelerating the discovery of novel antifungal peptide therapeutics.
Main Methods:
- Utilized word embedding (skip-gram) and transformer-based bidirectional encoder representations for peptide encoding.
- Integrated transform-based evolutionary features using Pseudo Position-Specific Scoring Matrix with Discrete Wavelet Transform (PsePSSM-DWT).
- Employed Shapley Additive exPlanations (SHAP) for optimal feature selection and trained the model using a bi-directional temporal convolutional network (BiTCN).
Main Results:
- The iAFPs-Mv-BiTCN model achieved high predictive accuracy (98.15% on training data, 94.11% on independent data) and AUC (0.99 on training, 0.98 on independent).
- Demonstrated superior performance compared to existing models, with approximately 4-5% higher accuracy.
- The model's feature selection approach reduced training costs effectively.
Conclusions:
- The iAFPs-Mv-BiTCN model is a reliable and effective tool for predicting antifungal peptides.
- This computational approach can significantly aid pharmaceutical design and accelerate antifungal drug discovery.
- The study highlights the potential of integrated computational methods in addressing critical health challenges like fungal infections.
