Contact Order and Protein Folding Kinetics
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Topic 16 xco et al (1998), J Mol Biol, 227:985-994. er (2000), Nature, 405:39-42.
description
Contact Order and Protein Folding Kinetics. K Plaxco et al (1998), J Mol Biol , 227:985-994. D Baker (2000), Nature, 405:39-42. Topic 16. Protein folding. Can we use structural bioinformatics to tell us anything about protein folding?. Two-state protein folding. - PowerPoint PPT Presentation
Transcript of Contact Order and Protein Folding Kinetics
Topic 16K Plaxco et al (1998), J Mol Biol, 227:985-994.D Baker (2000), Nature, 405:39-42.
Protein folding
Can we use structural bioinformatics to tell us anything about protein folding?
Two-state protein folding
Cooperativity is a hallmark of protein structure and function.
U F N
Ea
Protein folding is hard (except when it isn’t)
Contact Order
Relative CO is the average sequence distance between all pairs of contacting residues normalized by the total sequence length.
N is the total number of contactsL is the total number of residues in the proteinDSij is the sequence separation (in residues) between contacting residues i & j
Contact Order
The basic idea is that it would take structural contacts that are separated far apart in sequence longer to form than structural contacts that are sequence neighbors.
Low contact order(Faster folder)
High contact order(Slower folder)
Correlating CO and experimental kf
CO webserver
http://depts.washington.edu/bakerpg/contact_order/
Such a simple idea…
…has spawned myriad “Me too!” reports.
Where nij =1, |i - j| > 120, otherwise
Meaning it gives the average number of structural contacts separated by 12 or more sequence positions.
Yet another CO variant…
Istomin, Jacobs, and Livesay (2007). Protein Sci, 16:2564-2569.
Long-range order
Istomin, Jacobs, and Livesay (2007). Protein Sci, 16:2564-2569.
From the abstract:By analyzing correlation of other topological parameters with folding rates of two-state proteins, we find that only the long-range order exhibits correlation with folding rates that is uniform over all three classes.
It is also the only descriptor to provide statistically significant correlations for each of the three structural classes.
Evolutionary Optimization of Protein Folding
Debes et al. (2013). PLoS Computational biology 9(1):e1002861.
Our results show a clear overall increase of folding speed during evolution, with known ultra-fast downhill folders appearing rather late in the timeline.
Evolutionary Optimization of Protein Folding
Debes et al. (2013). PLoS Computational biology 9(1):e1002861.
Our results show a clear overall increase of folding speed during evolution, with known ultra-fast downhill folders appearing rather late in the timeline.
Using phylogenomic and structural analyses, we observe an overall decrease in folding times between 3.8 and 1.5 billion years ago, which can be interpreted as an evolutionary optimization for rapid folding.
Evolutionary Optimization of Protein Folding
Debes et al. (2013). PLoS Computational biology 9(1):e1002861.
Our results show a clear overall increase of folding speed during evolution, with known ultra-fast downhill folders appearing rather late in the timeline.
Using phylogenomic and structural analyses, we observe an overall decrease in folding times between 3.8 and 1.5 billion years ago, which can be interpreted as an evolutionary optimization for rapid folding.
In contrast, we observed an increase in SMCO between 1.5 Gya and the present. Thus, the appearance of many new structures by domain rearrangement 1.5 Gya, also referred to as the “big bang” of the protein world, affected the evolutionary optimization of protein folding.
![Predicting Experimental Quantities in Protein Folding Kinetics ...ai.stanford.edu/~apaydin/recomb06.pdfplied to ligand-protein docking [17], protein folding [3,2], and RNA folding](https://static.fdocuments.in/doc/165x107/60d6bde9a1a7162f153e3cd1/predicting-experimental-quantities-in-protein-folding-kinetics-ai-apaydinrecomb06pdf.jpg)
