Browsing by Author "Soylemez, Ummu Gulsum"

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Citation - WoS: 9
Citation - Scopus: 12
AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring Approach
(MDPI, 2023) Soylemez, Ummu Gulsum; Yousef, Malik; Bakir-Gungor, Burcu
Due to the increasing resistance of bacteria to antibiotics, scientists began seeking new solutions against this problem. One of the most promising solutions in this field are antimicrobial peptides (AMP). To identify antimicrobial peptides, and to aid the design and production of novel antimicrobial peptides, there is a growing interest in the development of computational prediction approaches, in parallel with the studies performing wet-lab experiments. The computational approaches aim to understand what controls antimicrobial activity from the perspective of machine learning, and to uncover the biological properties that define antimicrobial activity. Throughout this study, we aim to develop a novel prediction approach that can identify peptides with high antimicrobial activity against selected target bacteria. Along this line, we propose a novel method called AMP-GSM (antimicrobial peptide-grouping-scoring-modeling). AMP-GSM includes three main components: grouping, scoring, and modeling. The grouping component creates sub-datasets via placing the physicochemical, linguistic, sequence, and structure-based features into different groups. The scoring component gives a score for each group according to their ability to distinguish whether it is an antimicrobial peptide or not. As the final part of our method, the model built using the top-ranked groups is evaluated (modeling component). The method was tested for three AMP prediction datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. When we used 10 features (which are members of the physicochemical group), we obtained the highest area under curve (AUC) value for both the Gram-negative (99%) and Gram-positive (98%) datasets. AMP-GSM investigates the most significant feature groups that improve AMP prediction. A number of physico-chemical features from the AMP-GSM's final selection demonstrate how important these variables are in terms of defining peptide characteristics and how they should be taken into account when creating models to predict peptide activity.
Citation - WoS: 4
Citation - Scopus: 4
Novel Antimicrobial Peptide Design Using Motif Match Score Representation
(IEEE Computer Soc, 2024) Soylemez, Ummu Gulsum; Yousef, Malik; Kesmen, Zulal; Bakir-Gungor, Burcu
Antimicrobial peptides (AMPs) have drawn the interest of the researchers since they offer an alternative to the traditional antibiotics in the fight against antibiotic resistance and they exhibit additional pharmaceutically significant properties. Recently, computational approaches attemp to reveal how antibacterial activity is determined from a machine learning perspective and they aim to search and find the biological cues or characteristics that control antimicrobial activity via incorporating motif match scores. This study is dedicated to the development of a machine learning framework aimed at devising novel antimicrobial peptide (AMP) sequences potentially effective against Gram-positive/Gram-negative bacteria. In order to design newly generated sequences classified as either AMP or non-AMP, various classification models were trained. These novel sequences underwent validation utilizing the "DBAASP: strain-specific antibacterial prediction based on machine learning approaches and data on AMP sequences" tool. The findings presented herein represent a significant stride in this computational research, streamlining the process of AMP creation or modification within wet lab environments.
Citation - WoS: 20
Citation - Scopus: 26
Prediction of Linear Cationic Antimicrobial Peptides Active Against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models
(MDPI, 2022) Soylemez, Ummu Gulsum; Yousef, Malik; Kesmen, Zulal; Buyukkiraz, Mine Erdem; Bakir-Gungor, Burcu
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.