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Abstract Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, 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, which makes it easy to use AF2, while exposing its advanced options. ColabFold-AF2 shortens turn-around times of experiments due to its optimized usage of AF2’s models. In this protocol, we guide the reader through ColabFold best-practices using three scenarios: (1) monomer prediction, (2) complex prediction, and (3) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase (GPIT) protein. The third scenario demonstrates an alternative use-case of the AF2 models by predicting two conformations of the human Alanine Serine Transporter 2 (ASCT2). Users can run the protocol without command-line knowledge via Google Colaboratory or in a command-line environment. The protocol is available at https://protocol.colabfold.com. 

Abstract ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .