Open AccessAccurate positioning of functional residues with robotics-inspired computational protein design
Author(s) -
Cody Krivacic,
Kale Kundert,
Xingjie Pan,
Roland A. Pache,
Lin Liu,
Shane Ó Conchúir,
Jeliazko R. Jeliazkov,
Jeffrey J. Gray,
Michael C. Thompson,
James S. Fraser,
Tanja Kortemme
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2115480119
Subject(s) - protein design , leverage (statistics) , computer science , artificial intelligence , protein engineering , robotics , computational biology , protein structure , machine learning , biology , biochemistry , robot , enzyme
Significance Computational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein’s preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.