AlphaFold2 protein structure prediction: Implications for drug discovery

被引:34
|
作者
Borkakoti, Neera [1 ]
Thornton, Janet M. [1 ]
机构
[1] European Bioinformat Inst, Wellcome Trust Genome Campus, Cambridge CB10 1SD, England
关键词
DATA-DRIVEN;
D O I
10.1016/j.sbi.2022.102526
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
The drug discovery process involves designing compounds to selectively interact with their targets. The majority of thera-peutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.
引用
收藏
页数:7
相关论文
共 50 条
  • [11] Ins and outs of AlphaFold2 transmembrane protein structure predictions
    Hegedus, Tamas
    Geisler, Markus
    Lukacs, Gergely Laszlo
    Farkas, Bianka
    CELLULAR AND MOLECULAR LIFE SCIENCES, 2022, 79 (01)
  • [12] Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass
    Manalastas-Cantos, Karen
    Adoni, Kish R.
    Pfeifer, Matthias
    Martend, Birgit
    Gruenewald, Kay
    Thalassinos, Konstantinos
    Topf, Maya
    MOLECULAR & CELLULAR PROTEOMICS, 2024, 23 (03)
  • [13] Ins and outs of AlphaFold2 transmembrane protein structure predictions
    Tamás Hegedűs
    Markus Geisler
    Gergely László Lukács
    Bianka Farkas
    Cellular and Molecular Life Sciences, 2022, 79
  • [14] Outer membrane β-barrel structure prediction through the lens of AlphaFold2
    Topitsch, Annika
    Schwede, Torsten
    Pereira, Joana
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2024, 92 (01) : 3 - 14
  • [15] Assessing structure and dynamics of AlphaFold2 prediction of GeoCas9
    Arantes, Pablo R.
    Nierzwicki, Lukasz
    Belato, Helen
    D'Ordine, Alexandra M.
    Jogl, Gerwald
    Lisi, George
    Palermo, Giulia
    BIOPHYSICAL JOURNAL, 2022, 121 (03) : 45 - 45
  • [16] AlphaFold2 structures guide prospective ligand discovery
    Lyu, Jiankun
    Kapolka, Nicholas
    Gumpper, Ryan
    Alon, Assaf
    Wang, Liang
    Jain, Manish K.
    Barros-Alvarez, Ximena
    Sakamoto, Kensuke
    Kim, Yoojoong
    DiBerto, Jeffrey
    Kim, Kuglae
    Glenn, Isabella S.
    Tummino, Tia A.
    Huang, Sijie
    Irwin, John J.
    Tarkhanova, Olga O.
    Moroz, Yurii
    Skiniotis, Georgios
    Kruse, Andrew C.
    Shoichet, Brian K.
    Roth, Bryan L.
    SCIENCE, 2024, 384 (6702) : 1316 - +
  • [17] Author Correction: Improved prediction of protein-protein interactions using AlphaFold2
    Patrick Bryant
    Gabriele Pozzati
    Arne Elofsson
    Nature Communications, 13
  • [18] Protein Loop Modeling Using AlphaFold2
    Wang, Junlin
    Wang, Wenbo
    Shang, Yi
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2023, 20 (05) : 3306 - 3313
  • [19] Prediction of protein mononucleotide binding sites using AlphaFold2 and machine learning
    Yamaguchi, Shohei
    Nakashima, Haruka
    Moriwaki, Yoshitaka
    Terada, Tohru
    Shimizu, Kentaro
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2022, 100
  • [20] Grain Protein Function Prediction Based on CNN and Residual Attention Mechanism with AlphaFold2 Structure Data
    Liu, Jing
    Zhang, Xinping
    Huang, Kai
    Wei, Yuqi
    Guan, Xiao
    APPLIED SCIENCES-BASEL, 2025, 15 (04):