Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering
1
Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland
2
International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(8), 2713; https://doi.org/10.3390/ijms21082713
Received: 18 March 2020 / Revised: 10 April 2020 / Accepted: 12 April 2020 / Published: 14 April 2020
(This article belongs to the Special Issue Computational Studies of Biomolecules)
Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.
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Keywords:
protein dynamics; protein engineering; hotspot prediction; mutational analysis; computational design; ligand transport; ensemble-based approach; flexible backbone; de novo design; rational design
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MDPI and ACS Style
Surpeta, B.; Sequeiros-Borja, C.E.; Brezovsky, J. Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering. Int. J. Mol. Sci. 2020, 21, 2713.
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