Analysis and prediction of myristoylation sites using the mRMR method, the IFS method and an extreme learning machine algorithm.

Analysis and prediction of myristoylation sites using the mRMR method, the IFS method and an extreme learning machine algorithm. Comb Chem High Throughput Screen. 2016 Dec 20; Authors: Wang S, Zhang YH, Huang G, Chen L, Cai YD Abstract Myristoylation is an important hydrophobic post-translational modificationthat is covalently bound tothe amino group of Gly residueson the N-terminus of proteins. The many diversefunctions of myristoylation on proteins,such as membrane targeting, signal pathway regulation and apoptosis,are largely due to the lipid modification,whereasabnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function o fmyristoylated sites and to correctly identify them in protein sequences, this study conducted a novel investigation of myristoylation sites. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy, incremental feature selection, and a machine learning algorithm (extreme learning machine method) were adopted to analyze these features. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also perfor...
Source: Combinatorial Chemistry and High Throughput Screening - Category: Chemistry Authors: Tags: Comb Chem High Throughput Screen Source Type: research