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.

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.

Physalis peruviana, Patlıcangiller (Solanaceae) familyasının Physalis cinsine ait Güney Amerika’nın tropikal bölgelerinde doğal yayılış gösteren egzotik bir türüdür. Sarı-turuncu renkte, hemen hemen yuvarlak, üzümsü ve etli yapıda olan P. peruviana meyveleri çeşitli rakımlara, topraklara ve iklim koşullarına uyum sağlayabilmektedir. Physalis cinsinin ülkemizde doğal olarak yetişen dört türü bulunmaktadır ve halk arasında güveyfeneri olarak bilinen cinsin kültürü yapılmakta olup, ekonomik öneme sahip olduğu bilinmektedir. C vitamini, fosfor potasyum ve fenolik bileşikler açısından zengin olan P. peruviana fonksiyonel bir ürün özelliği göstermektedir. Farmakolojik ve fitokimyasal çalışmalarda antibakteriyel, antikanserojen, antidiyabetik, antioksidan etkileri ortaya konulmuştur. Domatese oldukça yakın tat ve görünüşü olan P. peruviana meyvesinin taze tüketiminin yanı sıra meyve suyu, marmelat, reçel ve tatlılarda, soslarda ve glazelerde kullanımı yaygındır. Hem kendine has lezzeti, hem de sağlık üzerinde birçok etkisi sebebiyle bu meyvenin gelecekte daha fazla yaygınlaşması beklenmektedir. Bu çalışmada P. peruviana bitkisinin botanik ve fonksiyonel özelliklerine değinilerek sağlık üzerindeki potansiyel etkileri değerlendirilmiş ve alternatif kullanım alanları ile ilgili bilgiler derlenmiştir.