Collective dynamics differentiates functional divergence in protein evolution

• Published in: PLoS computational biology Vol. 8; no. 3; p. e1002428
• Main Authors: Glembo, Tyler J, Farrell, Daniel W, Gerek, Z Nevin, Thorpe, M F, Ozkan, S Banu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2012; Public Library of Science (PLoS)
• Subjects: Amino Acid Sequence; Biology; Computational biology; Evolution; Evolution, Molecular; Molecular Sequence Data; Mutation; Mutation - genetics; Phylogenetics; Physics; Physiological aspects; Protein Conformation; Proteins; Receptors, Steroid - chemistry; Receptors, Steroid - genetics; Receptors, Steroid - ultrastructure; Sequence Analysis, Protein - methods; United States; Amino Acid Sequence; Receptors, Steroid; Sequence Analysis, Protein; Molecular Sequence Data; Protein Conformation; Mutation; Evolution, Molecular
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002428

Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ~2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ~60% of permissive mutations necessary to recover ancestral function.