Journal
NUCLEIC ACIDS RESEARCH
Volume 51, Issue D1, Pages D517-D522Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkac928
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AI-driven protein structure prediction, particularly AlphaFold2, has significant implications in the field of structural biology, especially for transmembrane proteins. The TmAlphaFold database provides a valuable tool for accurately assessing protein localization and structure.
AI-driven protein structure prediction, most notably AlphaFold2 (AF2) opens new frontiers for almost all fields of structural biology. As traditional structure prediction methods for transmembrane proteins were both complicated and error prone, AF2 is a great help to the community. Complementing the relatively meager number of experimental structures, AF2 provides 3D predictions for thousands of new alpha-helical membrane proteins. However, the lack of reliable structural templates and the fact that AF2 was not trained to handle phase boundaries also necessitates a delicate assessment of structural correctness. In our new database, Transmembrane AlphaFold database (TmAlphaFold database), we apply TMDET, a simple geometry-based method to visualize the likeliest position of the membrane plane. In addition, we calculate several parameters to evaluate the location of the protein into the membrane. This also allows TmAlphaFold database to show whether the predicted 3D structure is realistic or not. The TmAlphaFold database is available at https://tmalphafold.ttk.hu/..
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