Accurate and efficient structure-based computational mutagenesis for modeling fluorescence levels of Aequorea victoria green fluorescent protein mutants

作     者：Masso, Majid 

作者机构：George Mason Univ Sch Syst Biol Lab Struct Bioinformat 10900 Univ Blvd MS 5B3 Manassas VA 20110 USA 

出 版 物：《PROTEIN ENGINEERING DESIGN & SELECTION》 (蛋白质工程、设计与精选)

年 卷 期：2020年第33卷第33期

页      面：gzaa022页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 

主　　题：GFP machine learning prediction structure-function relationships 

摘      要：A computational mutagenesis technique was used to characterize the structural effects associated with over 46 000 single and multiple amino acid variants of Aequorea victoria green fluorescent protein (GFP), whose functional effects (fluorescence levels) were recently measured by experimental researchers. For each GFP mutant, the approach generated a single score reflecting the overall change in sequence-structure compatibility relative to native GFP, as well as a vector of environmental perturbation (EP) scores characterizing the impact at all GFP residue positions. A significant GFP structure-function relationship (P 0.0001) was elucidated by comparing the sequence-structure compatibility scores with the functional data. Next, the computed vectors for GFP mutants were used to train predictive models of fluorescence by implementing random forest (RF) classification and tree regression machine learning algorithms. Classification performance reached 0.93 for sensitivity, 0.91 for precision and 0.90 for balanced accuracy, and regression models led to Pearson s correlation as high as r = 0.83 between experimental and predicted GFP mutant fluorescence. An RF model trained on a subset of over 1000 experimental single residue GFP mutants with measured fluorescence was used for predicting the 3300 remaining unstudied single residue mutants, with results complementing known GFP biochemical and biophysical properties. In addition, models trained on the subset of experimental GFP mutants harboring multiple residue replacements successfully predicted fluorescence of the single residue GFP mutants. The models developed for this study were accurate and efficient, and their predictions outperformed those of several related state-of-the-art methods.