this is for holding javascript data
Alex Carlin Deleted File
almost 8 years ago
Commit id: 119c3a7109c3271f60cdee1592574ae9f671a7cb
deletions | additions
diff --git a/Abstract_A_key_part_of__.md b/Abstract_A_key_part_of__.md
deleted file mode 100644
index 426d3dc..0000000
--- a/Abstract_A_key_part_of__.md
+++ /dev/null
...
# Abstract
A key part of understanding how enzymes work is teasing out the complex interplay between how each residue in the protein contributes to functional parameters kcat, KM, and kcat/km, protein stability. So far, purely computational methods have failed to provide predictions suitable for predicting the effect of missense mutations on protein stability. An important part of the effort to understand enzymes must rely on the collection of experimental data in a standardized fashion such that results can be directly compared. We have previously reported a data-driven approach to predicting the functional effects of mutations in enzymes based on standardized laboratory techniques, and reported kinetic constants for 100 single point mutants of a family 1 glycoside hydrolase along with a computational model with a PCC of 0.76 for kcat/km. In order to investigate our predictive ability for another important property of enzymes, thermal denaturation temperature, we experimentally determine thermal stability for 120 single point mutations of the same enzyme. Our results shed light on the relationships between structure and thermal stability in this important class of enzymes. We also propose a method for the prediction of enzyme thermal stability using a combination of molecular modeling and machine learning.
