Talha Imtiaz Baig^1,*, Yaser Daanial Khan¹, Talha Mahboob Alam², Bharat Biswal³, Hanan Aljuaid⁴, Durdana Qaiser Gillani⁵

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 215-230, 2022, DOI:10.32604/cmc.2022.021849

Abstract Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that… More >

ALAA OMRAN ALMAGRABI¹, YASER DAANIAL KHAN², SHER AFZAL KHAN^3,*

BIOCELL, Vol.45, No.5, pp. 1287-1298, 2021, DOI:10.32604/biocell.2021.013770

Abstract Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspartate sites is critical, and its experimental identification can be expensive, time-consuming, and tedious. For this purpose, we propose iPhosD-PseAAC, a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues: (1) Benchmark dataset. (2) The feature extraction techniques such as pseudo amino acid composition (PseAAC), statistical… 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 >