Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

ABSTRACT: As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6954445 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature