Inicio  /  Applied Sciences  /  Vol: 12 Par: 7 (2022)  /  Artículo
ARTÍCULO
TITULO

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Ümmü Gülsüm Söylemez    
Malik Yousef    
Zülal Kesmen    
Mine Erdem Büyükkiraz and Burcu Bakir-Gungor    

Resumen

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

 Artículos similares

       
 
Naseer Muhammad Khan, Liqiang Ma, Muhammad Zaka Emad, Tariq Feroze, Qiangqiang Gao, Saad S. Alarifi, Li Sun, Sajjad Hussain and Hui Wang    
The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock b... ver más
Revista: Water

 
Yucheng Yang, Guohua Xu, Yongjie Shi and Zhiyuan Hu    
This study develops a hybrid solver with reversed overset assembly technology (ROAT), a viscous vortex particle method (VVPM), and a CFD program based on the URNS method, in order to study the aerodynamic and acoustic characteristics of coaxial rigid rot... ver más
Revista: Aerospace

 
Sipho G. Thango, Georgios A. Drosopoulos, Siphesihle M. Motsa and Georgios E. Stavroulakis    
A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex d... ver más
Revista: Infrastructures

 
Caosen Xu, Jingyuan Li, Bing Feng and Baoli Lu    
Financial time-series prediction has been an important topic in deep learning, and the prediction of financial time series is of great importance to investors, commercial banks and regulators. This paper proposes a model based on multiplexed attention me... ver más
Revista: Applied Sciences

 
Ayushi Chahal, Preeti Gulia, Nasib Singh Gill and Ishaani Priyadarshini    
IoT devices collect time-series traffic data, which is stochastic and complex in nature. Traffic flow prediction is a thorny task using this kind of data. A smart traffic congestion prediction system is a need of sustainable and economical smart cities. ... ver más
Revista: Information