冲刺
马修斯相关系数
UniProt公司
计算生物学
判别式
人工智能
赖氨酸
计算机科学
化学
生物
氨基酸
生物化学
支持向量机
基因
软件工程
作者
Ghazaleh Taherzadeh,Yuedong Yang,Haodong Xu,Yu Xue,Alan Wee‐Chung Liew,Yaoqi Zhou
摘要
Malonylation is a recently discovered post‐translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT‐Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half‐sphere exposure provides additional improvement to the prediction performance. SPRINT‐Mal trained on mouse data yields robust performance for 10‐fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews’ Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT‐Mal achieved comparable performance when testing on H. sapiens proteins without species‐specific training but not in bacterium S. erythraea . This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT‐Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/ . © 2018 Wiley Periodicals, Inc.
科研通智能强力驱动
Strongly Powered by AbleSci AI