Literature DB >> 33433210

Exploring the Evolutionary History of Kinetic Stability in the α-Lytic Protease Family.

Charlotte F Nixon1, Shion A Lim1, Zachary R Sailer2,3, Ivan N Zheludev4, Christine L Gee1,4,5, Brian A Kelch6, Michael J Harms2,3, Susan Marqusee1,4,7,8.   

Abstract

In addition to encoding the tertiary fold and stability, the primary sequence of a protein encodes the folding trajectory and kinetic barriers that determine the speed of folding. How these kinetic barriers are encoded is not well understood. Here, we use evolutionary sequence variation in the α-lytic protease (αLP) protein family to probe the relationship between sequence and energy landscape. αLP has an unusual energy landscape: the native state of αLP is not the most thermodynamically favored conformation and, instead, remains folded due to a large kinetic barrier preventing unfolding. To fold, αLP utilizes an N-terminal pro region similar in size to the protease itself that functions as a folding catalyst. Once folded, the pro region is removed, and the native state does not unfold on a biologically relevant time scale. Without the pro region, αLP folds on the order of millennia. A phylogenetic search uncovers αLP homologs with a wide range of pro region sizes, including some with no pro region at all. In the resulting phylogenetic tree, these homologs cluster by pro region size. By studying homologs naturally lacking a pro region, we demonstrate they can be thermodynamically stable, fold much faster than αLP, yet retain the same fold as αLP. Key amino acids thought to contribute to αLP's extreme kinetic stability are lost in these homologs, supporting their role in kinetic stability. This study highlights how the entire energy landscape plays an important role in determining the evolutionary pressures on the protein sequence.

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Year:  2021        PMID: 33433210      PMCID: PMC8174401          DOI: 10.1021/acs.biochem.0c00720

Source DB:  PubMed          Journal:  Biochemistry        ISSN: 0006-2960            Impact factor:   3.162


  45 in total

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Authors:  Erin L Cunningham; Ted Mau; Stephanie M E Truhlar; David A Agard
Journal:  Biochemistry       Date:  2002-07-16       Impact factor: 3.162

2.  Protease pro region required for folding is a potent inhibitor of the mature enzyme.

Authors:  D Baker; J L Silen; D A Agard
Journal:  Proteins       Date:  1992-04

3.  Conformation of beta-hairpins in protein structures. A systematic classification with applications to modelling by homology, electron density fitting and protein engineering.

Authors:  B L Sibanda; T L Blundell; J M Thornton
Journal:  J Mol Biol       Date:  1989-04-20       Impact factor: 5.469

4.  Molecular analysis of the gene encoding alpha-lytic protease: evidence for a preproenzyme.

Authors:  J L Silen; C N McGrath; K R Smith; D A Agard
Journal:  Gene       Date:  1988-09-30       Impact factor: 3.688

5.  Biological Roles of Protein Kinetic Stability.

Authors:  Wilfredo Colón; Jennifer Church; Jayeeta Sen; Jane Thibeault; Hannah Trasatti; Ke Xia
Journal:  Biochemistry       Date:  2017-11-13       Impact factor: 3.162

6.  SignalP 5.0 improves signal peptide predictions using deep neural networks.

Authors:  José Juan Almagro Armenteros; Konstantinos D Tsirigos; Casper Kaae Sønderby; Thomas Nordahl Petersen; Ole Winther; Søren Brunak; Gunnar von Heijne; Henrik Nielsen
Journal:  Nat Biotechnol       Date:  2019-02-18       Impact factor: 54.908

7.  Features and development of Coot.

Authors:  P Emsley; B Lohkamp; W G Scott; K Cowtan
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2010-03-24

Review 8.  Emerging concepts in pseudoenzyme classification, evolution, and signaling.

Authors:  António J M Ribeiro; Sayoni Das; Natalie Dawson; Rossana Zaru; Sandra Orchard; Janet M Thornton; Christine Orengo; Elton Zeqiraj; James M Murphy; Patrick A Eyers
Journal:  Sci Signal       Date:  2019-08-13       Impact factor: 8.192

9.  How good are my data and what is the resolution?

Authors:  Philip R Evans; Garib N Murshudov
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2013-06-13

10.  The EVcouplings Python framework for coevolutionary sequence analysis.

Authors:  Thomas A Hopf; Anna G Green; Benjamin Schubert; Sophia Mersmann; Charlotta P I Schärfe; John B Ingraham; Agnes Toth-Petroczy; Kelly Brock; Adam J Riesselman; Perry Palmedo; Chan Kang; Robert Sheridan; Eli J Draizen; Christian Dallago; Chris Sander; Debora S Marks
Journal:  Bioinformatics       Date:  2019-05-01       Impact factor: 6.937

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