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Sequence space (evolution)

Protein sequence space can be represented as a space with n dimensions, where n is the number of amino acids in the protein. Each axis has 20 positions representing the 20 amino acids. There are 400 possible 2 amino acid proteins (dipeptide) which can be arranged in a 2D grid. the 8000 tripeptides can be arranged in a 3D cube. Most proteins are longer than 100 amino acids and so occupy large, multidimensional spaces containing an astronomical number protein sequences.
How directed evolution climbs fitness landscapes. Performing multiple rounds of directed evolution is useful not only because a new library of mutants is created in each round, but also because each new library uses better mutants as templates than the previous. The experiment is analogous to climbing a hill on a 'fitness landscape,' where elevation represents the desired property. The goal is to reach the summit, which represents the best achievable mutant. Each round of selection samples mutants on all sides of the starting template (1) and selects the mutant with the highest elevation, thereby climbing the hill. This is repeated until a local summit is reached (2).

In evolutionary biology, sequence space is a way of representing all possible sequences (for a protein, gene or genome).[1][2] The sequence space has one dimension per amino acid or nucleotide in the sequence leading to highly dimensional spaces.[3][4]

Most sequences in sequence space have no function, leaving relatively small regions that are populated by naturally occurring genes.[5] Each protein sequence is adjacent to all other sequences that can be reached through a single mutation.[6] It has been estimated that the whole functional protein sequence space has been explored by life on the Earth.[7] Evolution by natural selection can be visualised as the process of sampling nearby sequences in sequence space and moving to any with improved fitness over the current one.

  1. ^ DePristo, Mark A.; Weinreich, Daniel M.; Hartl, Daniel L. (2 August 2005). "Missense meanderings in sequence space: a biophysical view of protein evolution". Nature Reviews Genetics. 6 (9): 678–687. doi:10.1038/nrg1672. PMID 16074985. S2CID 13236893.
  2. ^ Maynard Smith, John (7 February 1970). "Natural Selection and the Concept of a Protein Space". Nature. 225 (5232): 563–564. Bibcode:1970Natur.225..563M. doi:10.1038/225563a0. PMID 5411867. S2CID 204994726.
  3. ^ Bornberg-Bauer, E.; Chan, H. S. (14 September 1999). "Modeling evolutionary landscapes: Mutational stability, topology, and superfunnels in sequence space". Proceedings of the National Academy of Sciences. 96 (19): 10689–10694. Bibcode:1999PNAS...9610689B. doi:10.1073/pnas.96.19.10689. PMC 17944. PMID 10485887.
  4. ^ Cordes, MH; Davidson, AR; Sauer, RT (Feb 1996). "Sequence space, folding and protein design". Current Opinion in Structural Biology. 6 (1): 3–10. doi:10.1016/S0959-440X(96)80088-1. PMID 8696970.
  5. ^ Hermes, JD; Blacklow, SC; Knowles, JR (Jan 1990). "Searching sequence space by definably random mutagenesis: improving the catalytic potency of an enzyme". Proceedings of the National Academy of Sciences of the United States of America. 87 (2): 696–700. Bibcode:1990PNAS...87..696H. doi:10.1073/pnas.87.2.696. PMC 53332. PMID 1967829.
  6. ^ Romero, Philip A.; Arnold, Frances H. (December 2009). "Exploring protein fitness landscapes by directed evolution". Nature Reviews Molecular Cell Biology. 10 (12): 866–876. doi:10.1038/nrm2805. ISSN 1471-0080. PMC 2997618. PMID 19935669.
  7. ^ Dryden, David T.F; Thomson, Andrew R.; White, John H. (2008). "How much of protein sequence space has been explored by life on Earth?". Journal of the Royal Society Interface. 5 (25): 953–956. doi:10.1098/rsif.2008.0085. PMC 2459213. PMID 18426772.
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