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Gerald N, Dutta D, Brajesh RG, Saini S. Mathematical modeling of movement on fitness landscapes. BMC SYSTEMS BIOLOGY 2019; 13:25. [PMID: 30819150 PMCID: PMC6394095 DOI: 10.1186/s12918-019-0704-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 02/12/2019] [Indexed: 11/17/2022]
Abstract
Background Movement of populations on fitness landscapes has been a problem of interest for a long time. While the subject has been extensively developed theoretically, reconciliation of the theoretical work with recent experimental data has not yet happened. In this work, we develop a computational framework and study evolution of the simplest transcription network between a single regulator, R and a single target protein, T. Results Through our simulations, we track evolution of this transcription network and comment on its dynamics and statistics of this movement. Significantly, we report that there exists a critical parameter which controls the ability of a network to reach the global fitness peak on the landscape. This parameter is the fraction of all permissible values of a biochemical parameter that can be accessed from its current value via a single mutation. Conclusions Overall, through this work, we aim to present a general framework for analysis of movement of populations (and particularly regulatory networks) on landscapes. Electronic supplementary material The online version of this article (10.1186/s12918-019-0704-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nishant Gerald
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India
| | - Dibyendu Dutta
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India
| | - R G Brajesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India
| | - Supreet Saini
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400 076, India.
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Currin A, Swainston N, Day PJ, Kell DB. Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chem Soc Rev 2015; 44:1172-239. [PMID: 25503938 PMCID: PMC4349129 DOI: 10.1039/c4cs00351a] [Citation(s) in RCA: 251] [Impact Index Per Article: 27.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Indexed: 12/21/2022]
Abstract
The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.
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Affiliation(s)
- Andrew Currin
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
| | - Neil Swainston
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- School of Computer Science , The University of Manchester , Manchester M13 9PL , UK
| | - Philip J. Day
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
- Faculty of Medical and Human Sciences , The University of Manchester , Manchester M13 9PT , UK
| | - Douglas B. Kell
- Manchester Institute of Biotechnology , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK . ; http://dbkgroup.org/; @dbkell ; Tel: +44 (0)161 306 4492
- School of Chemistry , The University of Manchester , Manchester M13 9PL , UK
- Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) , The University of Manchester , 131, Princess St , Manchester M1 7DN , UK
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Aita T. Hierarchical distribution of ascending slopes, nearly neutral networks, highlands, and local optima at the dth order in an NK fitness landscape. J Theor Biol 2008; 254:252-63. [DOI: 10.1016/j.jtbi.2008.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2007] [Revised: 06/06/2008] [Accepted: 06/07/2008] [Indexed: 10/21/2022]
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