한빛사논문
- 조회130
Gyuri Kim1,10, Sewon Lee2,10, Eli Levy Karin3,10, Hyunbin Kim1, Yoshitaka Moriwaki4,5,6, Sergey Ovchinnikov7, Martin Steinegger1,2,8,9 & Milot Mirdita2
1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.
2School of Biological Sciences, Seoul National University, Seoul, South Korea.
3ELKMO, Copenhagen, Denmark.
4Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
5Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, Japan.
6Department of Computational Drug Discovery and Design, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.
7Massachusetts Institute of Technology, Cambridge, MA, USA.
8Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.
9Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea.
10These authors contributed equally: Gyuri Kim, Sewon Lee, Eli Levy Karin.
Corresponding authors
Correspondence to Sergey Ovchinnikov, Martin Steinegger or Milot Mirdita.
Abstract
Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colaboratory and a command-line tool that makes it easy to use AF2 while exposing its advanced options. ColabFold-AF2 shortens turnaround times of experiments because of its optimized usage of AF2’s models. In this protocol, we guide the reader through ColabFold best practices by using three scenarios: (i) monomer prediction, (ii) complex prediction and (iii) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase protein. The third scenario demonstrates an alternative use case of the AF2 models by predicting two conformations of the human alanine serine transporter 2. Users can run the protocol without computational expertise via Google Colaboratory or in a command-line environment for advanced users. Using Google Colaboratory, it takes <2 h to run each procedure. The data and code for this protocol are available at https://protocol.colabfold.com.
논문정보
- Research article
- 2024년 10월 (BRIC 등록일 2024-10-15)
- 국내(교신)+국외 연구진
- 생명과학 > 구조생물 및 생물물리학
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