Computational protein engineering : bridging the gap between rational design and laboratory evolution
Barrozo, Alexandre ; Borstnar, Rok ; Marloie, Gaël and Kamerlin, Shina Caroline Lynn LU
(2012)
In International Journal of Molecular Sciences
13(10).
p.60-12428
- Abstract
Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and... (More)
Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation "hotspots" with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.
(Less)
- author
- Barrozo, Alexandre
; Borstnar, Rok
; Marloie, Gaël
and Kamerlin, Shina Caroline Lynn
LU
- publishing date
- 2012-09-28
- type
- Contribution to journal
- publication status
- published
- keywords
- Catalytic Domain, Computational Biology, Directed Molecular Evolution, Enzymes/chemistry, Kinetics, Molecular Dynamics Simulation, Protein Engineering, Quantum Theory
- in
- International Journal of Molecular Sciences
- volume
- 13
- issue
- 10
- pages
- 33 pages
- publisher
- MDPI AG
- external identifiers
-
- scopus:84867777175
- pmid:23202907
- ISSN
- 1422-0067
- DOI
- 10.3390/ijms131012428
- language
- English
- LU publication?
- no
- id
- 041d2ddf-09bc-4cb4-97fe-1702cb394e07
- date added to LUP
- 2025-01-11 22:06:10
- date last changed
- 2025-04-20 11:55:17
@article{041d2ddf-09bc-4cb4-97fe-1702cb394e07,
abstract = {{<p>Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation "hotspots" with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.</p>}},
author = {{Barrozo, Alexandre and Borstnar, Rok and Marloie, Gaël and Kamerlin, Shina Caroline Lynn}},
issn = {{1422-0067}},
keywords = {{Catalytic Domain; Computational Biology; Directed Molecular Evolution; Enzymes/chemistry; Kinetics; Molecular Dynamics Simulation; Protein Engineering; Quantum Theory}},
language = {{eng}},
month = {{09}},
number = {{10}},
pages = {{60--12428}},
publisher = {{MDPI AG}},
series = {{International Journal of Molecular Sciences}},
title = {{Computational protein engineering : bridging the gap between rational design and laboratory evolution}},
url = {{http://dx.doi.org/10.3390/ijms131012428}},
doi = {{10.3390/ijms131012428}},
volume = {{13}},
year = {{2012}},
}