SIP: A computational prediction of S-Adenosyl methionine (SAM) interacting proteins and their interaction sites through primary structures. (June 2022)

• Record Type: Journal Article
• Authors: Abbasi, Wajid Arshad; Ajaz, Syeda Adin; Arshad, Kinza; Liaqat, Sidra; Andleeb, Saiqa; Bibi, Maryum; Abbas, Syed Ali
• Is Part Of: Computational biology and chemistry. Volume 98(2022)
• Publication Date: 2022-06
• Subjects: Ligand protein interactions -- Sequence features -- Machine learning -- Predictive models -- S-Adenosyl methionine; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107662
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: S-Adenosyl methionine (SAM), a universal methyl group donor, plays a vital role in biosynthesis and acts as an inhibitor to many enzymes. Due to protein interaction-dependent biological role, SAM has become a favorite target in various therapeutical and clinical studies such as treating cancer, Alzheimer's, epilepsy, and neurological disorders. Therefore, the identification of the SAM interacting proteins and their interaction sites is a biologically significant problem. However, wet-lab techniques, though accurate, to identify SAM interactions and interaction sites are tedious and costly. Therefore, efficient and accurate computational methods for this purpose are vital to the design and assist such wet-lab experiments. In this study, we present machine learning-based models to predict SAM interacting proteins and their interaction sites by using only primary structures of proteins. Here we modeled SAM interaction prediction through whole protein sequence features along with different classifiers. Whereas, we modeled SAM interaction site prediction through overlapping sequence windows and ranking with multiple instance learning that allows handling imprecisely annotated SAM interaction sites. Through a series of simulation studies along with biological significant evaluation, we showed that our proposed models give a state-of-the-art performance for both SAM interaction and interaction site prediction. Through data mining in this study, we have also identified various characteristics of amino acid sub-sequences and their relative position to effectively locate interaction sites in a SAM interacting protein. Python code for training and evaluating our proposed models together with a webserver implementation as SIP (Sam Interaction Predictor) is available at the URL: https://sites.google.com/view/wajidarshad/software . Graphical Abstract: ga1 Highlights: A machine learning-based model to predict SAM interacting proteins and their interaction sites has been presented using only primary structures. SAM interaction prediction has been modeled through features extracted from the whole sequence of a protein along with different classifiers. Whereas, the SAM interaction site prediction problem has been modeled through overlapping sequence windows and ranking with Multiple Instance Learning (MIL). Series of simulation studies along with biological significant evaluation show state-of-the-art performance for both SAM interaction and interaction site prediction. A python based open-source implementation of our proposed solution is available publically.