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MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins
1Department of Biotechnology & Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India.
Abstract:
The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pneumonia etc. is becoming more difficult. Hence, it is crucial to understand the underlying mechanism of drug resistance mechanism and devising novel solution to address this problem. Multidrug And Toxin Extrusion (MATE) proteins, first characterized as bacterial drug transporters, are present in almost all species. It plays a very important function in the secretion of cationic drugs across the cell membrane. In this work, we propose SVM based method for prediction of MATE proteins. The data set employed for training consists of 189 non-redundant protein sequences, that are further classified as positive (63 sequences) set comprising of sequences from MATE family, and negative (126 sequences) set having protein sequences from other transporters families proteins and random protein sequences taken from NCBI while in the test set, there are 120 protein sequences in all (8 in positive and 112 in negative set). The model was derived using Position Specific Scoring Matrix (PSSM) composition and achieved an overall accuracy 92.06%. The five-fold cross validation was used to optimize SVM parameter and select the best model. The prediction algorithm presented here is implemented as a freely available web server MATEPred, which will assist in rapid identification of MATE proteins.
Insights
Drug resistance is a major public health threat. Researchers developed a new method using Support Vector Machines (SVM) to accurately predict Multidrug And Toxin Extrusion (MATE) proteins, aiding in the fight against infectious diseases.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology
- Microbiology
Background:
- Antimicrobial resistance poses a significant global health challenge, complicating the treatment of bacterial infections.
- Multidrug And Toxin Extrusion (MATE) proteins are crucial transporters involved in the efflux of various compounds, including drugs, across cell membranes.
- Understanding MATE protein function is vital for developing strategies to combat drug resistance.
Purpose of the Study:
- To develop a computational method for the accurate prediction of Multidrug And Toxin Extrusion (MATE) proteins.
- To identify novel MATE proteins that could be targets for antimicrobial drug development.
Main Methods:
- A Support Vector Machine (SVM) based classification model was developed.
- Protein sequences were represented using Position Specific Scoring Matrix (PSSM) composition.
- A dataset of 189 non-redundant protein sequences was used for training (63 MATE, 126 non-MATE).
- A test set of 120 protein sequences was used for evaluation.
- Five-fold cross-validation was employed for parameter optimization and model selection.
Main Results:
- The SVM model achieved a high prediction accuracy of 92.06%.
- The developed prediction algorithm, MATEPred, demonstrated robust performance in identifying MATE proteins.
- The model effectively distinguished MATE proteins from other transporter families and random sequences.
Conclusions:
- The proposed SVM-based method provides an accurate and efficient approach for MATE protein prediction.
- The freely available web server MATEPred can significantly accelerate the identification of MATE proteins.
- This tool will aid researchers in understanding drug resistance mechanisms and developing novel therapeutic strategies.
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