DPI_CDF: druggable protein identifier using cascade deep forest

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 145 - 18
• Main Authors: Arif, Muhammad, Fang, Ge, Ghulam, Ali, Musleh, Saleh, Alam, Tanvir
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05.04.2024; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Amino Acid Sequence; Analysis; Availability; Bioinformatics; Biological Evolution; Cascade deep forest; Computational Biology - methods; Datasets; Deep learning; Drug discovery; Druggable proteins; Drugs; Machine learning; Methods; Neural networks; NMR; Nuclear magnetic resonance; Peptides; Physicochemical features; Physiochemistry; Position-Specific Scoring Matrices; Protein sequencing; Proteins; PSSM; Qualitative analysis; Sequencing; Software; Support vector machines; Technology application; Therapeutic targets; Training; Qatar; PSSM; Cascade deep forest; Bioinformatics; Druggable proteins; Physicochemical features
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05744-3

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. 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. 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. The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .