Sharaf J. Malebary¹, Yaser Daanial Khan^2,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2863-2881, 2021, DOI:10.32604/cmc.2021.015041

Abstract With the advancement in cellular biology, the use of antimicrobial peptides (AMPs) against many drug-resistant pathogens has increased. AMPs have a broad range of activity and can work as antibacterial, antifungal, antiviral, and sometimes even as anticancer peptides. The traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab experiments. Such experiments are both time-consuming and expensive. With the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such problems. This study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and non-AMPs. The proposed methodology uses machine… More >

Shaheena Khanum¹, Muhammad Adeel Ashraf², Asim Karim¹, Bilal Shoaib³, Muhammad Adnan Khan⁴, Rizwan Ali Naqvi⁵, Kamran Siddique⁶, Mohammed Alswaitti^6,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2165-2181, 2021, DOI:10.32604/cmc.2020.013646

Abstract Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide. It is a clinically important attribute to numerous age-related, metabolic, and chronic diseases such as diabetes, Alzheimer’s, renal failure, etc. Identification of a non-enzymatic reaction are quite challenging in research. Manual identification in labs is a very costly and time-consuming process. In this research, we developed an accurate, valid, and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites. Comprehensive techniques using position relative features are used for feature extraction. An algorithm named as a random forest with some preprocessing… More >