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Data from "The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins"
WSU Dataset

UID: 107

Description
The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. In this study, researchers propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model.
Subject Domain
Keywords
Access Restrictions
Free to all
Access Instructions
Data can be downloaded directly from the article at the above link.
Associated Publications
Data Type
Genetic/Omic
Equipment Used
Multiple EM for Motif Elicitation (MEME)
Dataset Format(s)
PDF
Dataset Size
36 KB