典型文献
Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation
文献摘要：
Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degrada-tion machinery.However,the susceptibility of proteins for targeting by TPD approaches,termed"degradability",is largely unknown.Here,we developed a machine learning model,model-free anal-ysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein tar-gets.MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision-recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to inde-pendent non-kinase proteins.We found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most predictive.By structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase degradability.Finally,we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development.
文献关键词：
中图分类号：
作者姓名：
Wubing Zhang;Shourya S.Roy Burman;Jiaye Chen;Katherine A.Donovan;Yang Cao;Chelsea Shu;Boning Zhang;Zexian Zeng;Shengqing Gu;Yi Zhang;Dian Li;Eric S.Fischer;Collin Tokheim;X.Shirley Liu
作者机构：
Department of Data Science,Dana-Farber Cancer Institute,Boston,MA 02215,USA;Department of Biostatistics,Harvard T.H.Chan School of Public Health,Boston,MA 02115,USA;Department of Cancer Biology,Dana-Farber Cancer Institute,Boston,MA 02215,USA;Department of Biological Chemistry and Molecular Pharmacology,Harvard Medical School,Boston,MA 02115,USA;Department of Biomedical Informatics,Harvard Medical School,Boston,MA 02115,USA;Center of Growth,Metabolism,and Aging,Key Laboratory of Bio-resource and Eco-environment,Ministry of Education,College of Life Sciences,Sichuan University,Chengdu 610064,China;Research Scholar Initiative,Graduate School of Arts and Sciences,Harvard University,Cambridge,MA 02138,USA
文献出处：
引用格式：
[1]Wubing Zhang;Shourya S.Roy Burman;Jiaye Chen;Katherine A.Donovan;Yang Cao;Chelsea Shu;Boning Zhang;Zexian Zeng;Shengqing Gu;Yi Zhang;Dian Li;Eric S.Fischer;Collin Tokheim;X.Shirley Liu-.Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation)[J].基因组蛋白质组与生物信息学报（英文版）,2022(05):882-898
A类：
Tractability,MAPD,AUPRC
B类：
Machine,Learning,Modeling,Protein,intrinsic,Features,Predicts,Targeted,Degradation,degradation,TPD,has,rapidly,emerged,therapeutic,modality,eliminate,previously,undruggable,proteins,by,repurposing,cell,endogenous,machinery,However,susceptibility,targeting,approaches,termed,degradability,largely,unknown,Here,developed,learning,free,anal,ysis,from,features,gets,shows,accurate,performance,predicting,kinases,that,degradable,compounds,area,under,precision,recall,curve,receiver,operating,characteristic,AUROC,likely,generalizable,inde,pendent,We,found,five,statistical,significance,achieve,optimal,ubiquitination,potential,being,most,predictive,By,structural,modeling,E2,accessible,sites,but,not,lysine,residues,particularly,associated,Finally,extended,predictions,entire,proteome,find,disease,causing,including,encoded,cancer,genes,may,tractable,development
AB值：
0.598846
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
