Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants
| Autor: | Claire X. Chen, Boyu Pang, Ryan W. Caster, Veasna M. Duong, Carolina V. Ryklansky, Nicole Kim, Harshul Kapoor, Ilias Tagkopoulos, Hosna Mohabbot, Lillian Whithaus, Rachel Teel, Xiaokang Wang, Dylan Alexander Carlin, Stephanie A. Betzenderfer, Justin B. Siegel, Alp Alpekin, Nathan Beaumont |
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| Přispěvatelé: | Hubbard, Timothy J |
| Rok vydání: | 2016 |
| Předmět: |
Models
Molecular 0301 basic medicine Protein Conformation Mutant lcsh:Medicine Physical Chemistry Biochemistry 01 natural sciences Machine Learning Protein structure Models Glycoside hydrolase Amino Acids lcsh:Science chemistry.chemical_classification Alanine Multidisciplinary Organic Compounds Applied Mathematics Simulation and Modeling Enzymes Chemistry Networking and Information Technology R&D Physical Sciences Mutation (genetic algorithm) Algorithms Research Article Computer and Information Sciences Glycoside Hydrolases General Science & Technology Bioengineering Computational biology Research and Analysis Methods 010402 general chemistry Catalysis Machine Learning Algorithms Structure-Activity Relationship 03 medical and health sciences Artificial Intelligence Humans Structure–activity relationship Computer Simulation Enzyme kinetics Enzyme Kinetics Chemical Bonding Organic Chemistry lcsh:R Chemical Compounds Biology and Life Sciences Proteins Molecular Hydrogen Bonding 0104 chemical sciences Kinetics 030104 developmental biology Enzyme Aliphatic Amino Acids chemistry Mutation Enzymology Cognitive Science lcsh:Q Mathematics Function (biology) Neuroscience |
| Zdroj: | PloS one, vol 11, iss 1 PLoS ONE, Vol 11, Iss 1, p e0147596 (2016) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms. |
| Databáze: | OpenAIRE |
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