AlphaFold illuminates half of the dark human proteins

被引:53
作者
Binder, Jessica L. [1 ]
Berendzen, Joel [1 ,10 ]
Stevens, Amy O. [2 ]
He, Yi [1 ,2 ]
Wang, Jian [3 ]
Dokholyan, Nikolay, V [3 ,4 ,5 ]
Oprea, Tudor, I [1 ,6 ,7 ,8 ,9 ]
机构
[1] Univ New Mexico, Dept Internal Med, Translat Informat Div, Albuquerque, NM 87131 USA
[2] Univ New Mexico, Dept Chem & Chem Biol, Albuquerque, NM 87131 USA
[3] Penn State Univ, Dept Pharmacol, Dept Biochem & Mol Biol, Coll Med, Hershey, PA 17033 USA
[4] Penn State Univ, Dept Chem, University Pk, PA 16802 USA
[5] Penn State Univ, Dept Biomed Engn, University Pk, PA 16802 USA
[6] UNM Comprehens Canc Ctr, Albuquerque, NM 87102 USA
[7] Univ Gothenburg, Dept Rheumatol & Inflammat Res, Inst Med, Sahlgrenska Acad, Gothenburg, Sweden
[8] Univ Copenhagen, Novo Nordisk Fdn Ctr Prot Res, Fac Hlth & Med Sci, Copenhagen, Denmark
[9] Roivant Discovery Sci, Boston, MA 02210 USA
[10] GenerisBio LLC, Santa Fe, NM 87507 USA
基金
美国国家科学基金会;
关键词
Artificial intelligence; Protein folding; Virtual screening; Drug discovery; Understudied proteins; Model evaluation; INTRINSICALLY UNSTRUCTURED PROTEINS; DISORDERED PROTEINS; DOMAINS; PREDICTION; DISEASES; BIOLOGY;
D O I
10.1016/j.sbi.2022.102372
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.
引用
收藏
页数:8
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