Exploiting enzyme evolution for computational protein design
Pinto, Gaspar P ; Corbella, Marina ; Demkiv, Andrey O and Kamerlin, Shina Caroline Lynn LU
(2022)
In Trends in Biochemical Sciences
47(5).
p.375-389
- Abstract
Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary... (More)
Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.
(Less)
- author
- Pinto, Gaspar P
; Corbella, Marina
; Demkiv, Andrey O
and Kamerlin, Shina Caroline Lynn
LU
- publishing date
- 2022-05
- type
- Contribution to journal
- publication status
- published
- keywords
- Computational Biology, Evolution, Molecular, Proteins/genetics
- in
- Trends in Biochemical Sciences
- volume
- 47
- issue
- 5
- pages
- 15 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:34544655
- scopus:85115059339
- ISSN
- 0968-0004
- DOI
- 10.1016/j.tibs.2021.08.008
- language
- English
- LU publication?
- no
- additional info
- Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.
- id
- 4e57c8ac-a98b-4b9a-a197-a5dd6e135386
- date added to LUP
- 2025-01-11 18:49:36
- date last changed
- 2025-07-14 07:54:38
@article{4e57c8ac-a98b-4b9a-a197-a5dd6e135386,
abstract = {{<p>Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.</p>}},
author = {{Pinto, Gaspar P and Corbella, Marina and Demkiv, Andrey O and Kamerlin, Shina Caroline Lynn}},
issn = {{0968-0004}},
keywords = {{Computational Biology; Evolution, Molecular; Proteins/genetics}},
language = {{eng}},
number = {{5}},
pages = {{375--389}},
publisher = {{Elsevier}},
series = {{Trends in Biochemical Sciences}},
title = {{Exploiting enzyme evolution for computational protein design}},
url = {{http://dx.doi.org/10.1016/j.tibs.2021.08.008}},
doi = {{10.1016/j.tibs.2021.08.008}},
volume = {{47}},
year = {{2022}},
}