DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model
Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The identification of QSP is vital for understanding these biological processes. While existing clinical and lab-base...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024011332 |
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| author | Md. Ashikur Rahman Md. Mamun Ali Kawsar Ahmed Imran Mahmud Francis M. Bui Li Chen Santosh Kumar Mohammad Ali Moni |
| author_facet | Md. Ashikur Rahman Md. Mamun Ali Kawsar Ahmed Imran Mahmud Francis M. Bui Li Chen Santosh Kumar Mohammad Ali Moni |
| author_sort | Md. Ashikur Rahman |
| collection | DOAJ |
| description | Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The identification of QSP is vital for understanding these biological processes. While existing clinical and lab-based methods are available, they can be costly and time-consuming. This study introduces DeepQSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. The DeepQSP model was evaluated using a dataset of 440 peptide sequences, achieving impressive performance metrics: 0.9697 accuracy, 0.9655 sensitivity, 0.9730 specificity, and a Matthews correlation coefficient (MCC) of 0.9385. The LSA combined with PAAC improves peptide sequence representation, while the CNN effectively captures complex patterns, leading to accurate QSP identification. These quantified results demonstrate the effectiveness of the DeepQSP method, offering a powerful tool for advancing the study of microbial interaction and quorum sensing. The enhanced identification of QSPs is critical for microbiology and bioengineering, aiding in the understanding of cell-to-cell communication in microorganisms. |
| format | Article |
| id | doaj-art-c27abfebbc8e4896ab6319ef7ee984af |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-c27abfebbc8e4896ab6319ef7ee984af2025-08-20T01:58:31ZengElsevierResults in Engineering2590-12302024-12-012410287810.1016/j.rineng.2024.102878DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network ModelMd. Ashikur Rahman0Md. Mamun Ali1Kawsar Ahmed2Imran Mahmud3Francis M. Bui4Li Chen5Santosh Kumar6Mohammad Ali Moni7Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka-1216, BangladeshDepartment of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka-1216, Bangladesh; Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, CanadaDepartment of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada; Bio-photomatiχ, Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka-1216, Bangladesh; Corresponding author at: Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7H 3W4, Canada.Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka-1216, BangladeshDepartment of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, CanadaDepartment of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, CanadaDepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522302, IndiaAI & Digital Health Technology, Artificial Intelligence & Cyber Future Institute, Charles Stuart University, Bathurst, NSW 2795, Australia; AI & Digital Health Technology, Rural Health Research Institute, Charles Stuart University, Orange, NSW 2800, AustraliaQuorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The identification of QSP is vital for understanding these biological processes. While existing clinical and lab-based methods are available, they can be costly and time-consuming. This study introduces DeepQSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. The DeepQSP model was evaluated using a dataset of 440 peptide sequences, achieving impressive performance metrics: 0.9697 accuracy, 0.9655 sensitivity, 0.9730 specificity, and a Matthews correlation coefficient (MCC) of 0.9385. The LSA combined with PAAC improves peptide sequence representation, while the CNN effectively captures complex patterns, leading to accurate QSP identification. These quantified results demonstrate the effectiveness of the DeepQSP method, offering a powerful tool for advancing the study of microbial interaction and quorum sensing. The enhanced identification of QSPs is critical for microbiology and bioengineering, aiding in the understanding of cell-to-cell communication in microorganisms.http://www.sciencedirect.com/science/article/pii/S2590123024011332Quorum sensing peptideConvolutional neural networkLatent semantic analysisMicroorganismsBioengineering |
| spellingShingle | Md. Ashikur Rahman Md. Mamun Ali Kawsar Ahmed Imran Mahmud Francis M. Bui Li Chen Santosh Kumar Mohammad Ali Moni DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model Results in Engineering Quorum sensing peptide Convolutional neural network Latent semantic analysis Microorganisms Bioengineering |
| title | DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model |
| title_full | DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model |
| title_fullStr | DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model |
| title_full_unstemmed | DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model |
| title_short | DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model |
| title_sort | deepqsp identification of quorum sensing peptides through neural network model |
| topic | Quorum sensing peptide Convolutional neural network Latent semantic analysis Microorganisms Bioengineering |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024011332 |
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