Sequence space (evolution)

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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. Protein Sequence space.svg
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). DE landscape.png
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]

Contents

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.

Representation

A sequence space is usually laid out as a grid. For protein sequence spaces, each residue in the protein is represented by a dimension with 20 possible positions along that axis corresponding to the possible amino acids. [3] [4] Hence there are 400 possible dipeptides arranged in a 20x20 space but that expands to 10130 for even a small protein of 100 amino acids arranged in a space with 100 dimensions. Although such overwhelming multidimensionality cannot be visualised or represented diagrammatically, it provides a useful abstract model to think about the range of proteins and evolution from one sequence to another.

These highly multidimensional spaces can be compressed to 2 or 3 dimensions using principal component analysis. A fitness landscape is simply a sequence space with an extra vertical axis of fitness added for each sequence. [8]

Functional sequences in sequence space

Despite the diversity of protein superfamilies, sequence space is extremely sparsely populated by functional proteins. Most random protein sequences have no fold or function. [9] Enzyme superfamilies, therefore, exist as tiny clusters of active proteins in a vast empty space of non-functional sequence. [10] [11]

The density of functional proteins in sequence space, and the proximity of different functions to one another is a key determinant in understanding evolvability. [12] The degree of interpenetration of two neutral networks of different activities in sequence space will determine how easy it is to evolve from one activity to another. The more overlap between different activities in sequence space, the more cryptic variation for promiscuous activity will be. [13]

Protein sequence space has been compared to the Library of Babel , a theoretical library containing all possible books that are 410 pages long. [14] [15] In the Library of Babel, finding any book that made sense was impossible due to the sheer number and lack of order. The same would be true of protein sequences if it were not for natural selection, which has selected out only protein sequences that make sense. Additionally, each protein sequences is surrounded by a set of neighbours (point mutants) that are likely to have at least some function.

On the other hand, the effective "alphabet" of the sequence space may in fact be quite small, reducing the useful number of amino acids from 20 to a much lower number. For example, in an extremely simplified view, all amino acids can be sorted into two classes (hydrophobic/polar) by hydrophobicity and still allow many common structures to show up. Early life on Earth may have only four or five types of amino acids to work with, [16] and researches have shown that functional proteins can be created from wild-type ones by a similar alphabet-reduction process. [17] [18] Reduced alphabets are also useful in bioinformatics, as they provide an easy way of analyzing protein similarity. [19] [20]

Exploration through directed evolution and rational design

How DNA libraries generated by random mutagenesis sample sequence space. The amino acid substituted into a given position is shown. Each dot or set of connected dots is one member of the library. Error-prone PCR randomly mutates some residues to other amino acids. Alanine scanning replaces each reside of the protein with alanine, one-by-one. Site saturation substitutes each of the 20 possible amino acids (or some subset of them) at a single position, one-by-one. How random DNA libraries sample sequence space.pdf
How DNA libraries generated by random mutagenesis sample sequence space. The amino acid substituted into a given position is shown. Each dot or set of connected dots is one member of the library. Error-prone PCR randomly mutates some residues to other amino acids. Alanine scanning replaces each reside of the protein with alanine, one-by-one. Site saturation substitutes each of the 20 possible amino acids (or some subset of them) at a single position, one-by-one.

A major focus in the field of protein engineering is on creating DNA libraries that sample regions of sequence space, often with the goal of finding mutants of proteins with enhanced functions compared to the wild type. These libraries are created either by using a wild type sequence as a template and applying one or more mutagenesis techniques to make different variants of it, or by creating proteins from scratch using artificial gene synthesis. These libraries are then screened or selected, and ones with improved phenotypes are used for the next round of mutagenesis.

See also

Related Research Articles

<span class="mw-page-title-main">Genetic code</span> Rules by which information encoded within genetic material is translated into proteins

The genetic code is the set of rules used by living cells to translate information encoded within genetic material into proteins. Translation is accomplished by the ribosome, which links proteinogenic amino acids in an order specified by messenger RNA (mRNA), using transfer RNA (tRNA) molecules to carry amino acids and to read the mRNA three nucleotides at a time. The genetic code is highly similar among all organisms and can be expressed in a simple table with 64 entries.

<span class="mw-page-title-main">Mutation</span> Alteration in the nucleotide sequence of a genome

In biology, a mutation is an alteration in the nucleic acid sequence of the genome of an organism, virus, or extrachromosomal DNA. Viral genomes contain either DNA or RNA. Mutations result from errors during DNA or viral replication, mitosis, or meiosis or other types of damage to DNA, which then may undergo error-prone repair, cause an error during other forms of repair, or cause an error during replication. Mutations may also result from substitution,insertion or deletion of segments of DNA due to mobile genetic elements.

<span class="mw-page-title-main">Protein</span> Biomolecule consisting of chains of amino acid residues

Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific 3D structure that determines its activity.

Protein engineering is the process of developing useful or valuable proteins through the design and production of unnatural polypeptides, often by altering amino acid sequences found in nature. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles. It has been used to improve the function of many enzymes for industrial catalysis. It is also a product and services market, with an estimated value of $168 billion by 2017.

<span class="mw-page-title-main">Protein structure prediction</span> Type of biological prediction

Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design.

<span class="mw-page-title-main">Protein family</span> Group of evolutionarily-related proteins

A protein family is a group of evolutionarily related proteins. In many cases, a protein family has a corresponding gene family, in which each gene encodes a corresponding protein with a 1:1 relationship. The term "protein family" should not be confused with family as it is used in taxonomy.

Evolvability is defined as the capacity of a system for adaptive evolution. Evolvability is the ability of a population of organisms to not merely generate genetic diversity, but to generate adaptive genetic diversity, and thereby evolve through natural selection.

Protein design is the rational design of new protein molecules to design novel activity, behavior, or purpose, and to advance basic understanding of protein function. Proteins can be designed from scratch or by making calculated variants of a known protein structure and its sequence. Rational protein design approaches make protein-sequence predictions that will fold to specific structures. These predicted sequences can then be validated experimentally through methods such as peptide synthesis, site-directed mutagenesis, or artificial gene synthesis.

<span class="mw-page-title-main">Catalytic triad</span> Set of three coordinated amino acids

A catalytic triad is a set of three coordinated amino acids that can be found in the active site of some enzymes. Catalytic triads are most commonly found in hydrolase and transferase enzymes. An acid-base-nucleophile triad is a common motif for generating a nucleophilic residue for covalent catalysis. The residues form a charge-relay network to polarise and activate the nucleophile, which attacks the substrate, forming a covalent intermediate which is then hydrolysed to release the product and regenerate free enzyme. The nucleophile is most commonly a serine or cysteine amino acid, but occasionally threonine or even selenocysteine. The 3D structure of the enzyme brings together the triad residues in a precise orientation, even though they may be far apart in the sequence.

<span class="mw-page-title-main">Directed evolution</span> Protein engineering method

Directed evolution (DE) is a method used in protein engineering that mimics the process of natural selection to steer proteins or nucleic acids toward a user-defined goal. It consists of subjecting a gene to iterative rounds of mutagenesis, selection and amplification. It can be performed in vivo, or in vitro. Directed evolution is used both for protein engineering as an alternative to rationally designing modified proteins, as well as for experimental evolution studies of fundamental evolutionary principles in a controlled, laboratory environment.

<span class="mw-page-title-main">TIM barrel</span> Protein fold

The TIM barrel, also known as an alpha/beta barrel, is a conserved protein fold consisting of eight alpha helices (α-helices) and eight parallel beta strands (β-strands) that alternate along the peptide backbone. The structure is named after triose-phosphate isomerase, a conserved metabolic enzyme. TIM barrels are ubiquitous, with approximately 10% of all enzymes adopting this fold. Further, five of seven enzyme commission (EC) enzyme classes include TIM barrel proteins. The TIM barrel fold is evolutionarily ancient, with many of its members possessing little similarity today, instead falling within the twilight zone of sequence similarity.

Neutral mutations are changes in DNA sequence that are neither beneficial nor detrimental to the ability of an organism to survive and reproduce. In population genetics, mutations in which natural selection does not affect the spread of the mutation in a species are termed neutral mutations. Neutral mutations that are inheritable and not linked to any genes under selection will be lost or will replace all other alleles of the gene. That loss or fixation of the gene proceeds based on random sampling known as genetic drift. A neutral mutation that is in linkage disequilibrium with other alleles that are under selection may proceed to loss or fixation via genetic hitchhiking and/or background selection.

<span class="mw-page-title-main">Expanded genetic code</span> Modified genetic code

An expanded genetic code is an artificially modified genetic code in which one or more specific codons have been re-allocated to encode an amino acid that is not among the 22 common naturally-encoded proteinogenic amino acids.

<span class="mw-page-title-main">Saturation mutagenesis</span>

Site saturation mutagenesis (SSM), or simply site saturation, is a random mutagenesis technique used in protein engineering, in which a single codon or set of codons is substituted with all possible amino acids at the position. There are many variants of the site saturation technique, from paired site saturation (saturating two positions in every mutant in the library) to scanning site saturation (performing a site saturation at every site in the protein, resulting in a library of size [20^(number of residues in the protein)] that contains every possible point mutant of the protein).

<span class="mw-page-title-main">Circular permutation in proteins</span> Arrangement of amino acid sequence

A circular permutation is a relationship between proteins whereby the proteins have a changed order of amino acids in their peptide sequence. The result is a protein structure with different connectivity, but overall similar three-dimensional (3D) shape. In 1979, the first pair of circularly permuted proteins – concanavalin A and lectin – were discovered; over 2000 such proteins are now known.

Enzyme promiscuity is the ability of an enzyme to catalyze an unexpected side reaction in addition to its main reaction. Although enzymes are remarkably specific catalysts, they can often perform side reactions in addition to their main, native catalytic activity. These wild activities are usually slow relative to the main activity and are under neutral selection. Despite ordinarily being physiologically irrelevant, under new selective pressures, these activities may confer a fitness benefit therefore prompting the evolution of the formerly promiscuous activity to become the new main activity. An example of this is the atrazine chlorohydrolase from Pseudomonas sp. ADP evolved from melamine deaminase, which has very small promiscuous activity toward atrazine, a man-made chemical.

Ancestral sequence reconstruction (ASR) – also known as ancestral gene/sequence reconstruction/resurrection – is a technique used in the study of molecular evolution. The method uses related sequences to reconstruct an "ancestral" gene from a multiple sequence alignment.

A neutral network is a set of genes all related by point mutations that have equivalent function or fitness. Each node represents a gene sequence and each line represents the mutation connecting two sequences. Neutral networks can be thought of as high, flat plateaus in a fitness landscape. During neutral evolution, genes can randomly move through neutral networks and traverse regions of sequence space which may have consequences for robustness and evolvability.

<i>De novo</i> gene birth Evolution of novel genes from non-genic DNA sequence

De novo gene birth is the process by which new genes evolve from non-coding DNA. De novo genes represent a subset of novel genes, and may be protein-coding or instead act as RNA genes. The processes that govern de novo gene birth are not well understood, although several models exist that describe possible mechanisms by which de novo gene birth may occur.

Erich Bornberg-Bauer is an Austrian biochemist, theoretical biologist and bioinformatician.

References

  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. 1 2 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. 1 2 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.
  8. ↑ Romero, PA; Arnold, FH (Dec 2009). "Exploring protein fitness landscapes by directed evolution". Nature Reviews Molecular Cell Biology. 10 (12): 866–76. doi:10.1038/nrm2805. PMC   2997618 . PMID   19935669.
  9. ↑ Keefe, AD; Szostak, JW (Apr 5, 2001). "Functional proteins from a random-sequence library". Nature. 410 (6829): 715–8. Bibcode:2001Natur.410..715K. doi:10.1038/35070613. PMC   4476321 . PMID   11287961.
  10. ↑ Stemmer, Willem P. C. (June 1995). "Searching Sequence Space". Bio/Technology. 13 (6): 549–553. doi:10.1038/nbt0695-549. S2CID   20117819.
  11. ↑ Bornberg-Bauer, E (Nov 1997). "How are model protein structures distributed in sequence space?". Biophysical Journal. 73 (5): 2393–403. Bibcode:1997BpJ....73.2393B. doi:10.1016/S0006-3495(97)78268-7. PMC   1181141 . PMID   9370433.
  12. ↑ Bornberg-Bauer, E; Huylmans, AK; Sikosek, T (Jun 2010). "How do new proteins arise?". Current Opinion in Structural Biology. 20 (3): 390–6. doi:10.1016/j.sbi.2010.02.005. PMID   20347587.
  13. ↑ Wagner, Andreas (2011-07-14). The origins of evolutionary innovations : a theory of transformative change in living systems. Oxford [etc.]: Oxford University Press. ISBN   978-0199692590.
  14. ↑ Arnold, FH (2000). "The Library of Maynard-Smith: My Search for Meaning in the protein universe". Advances in Protein Chemistry. 55: ix–xi. doi:10.1016/s0065-3233(01)55000-7. PMID   11050930.
  15. ↑ Ostermeier, M (March 2007). "Beyond cataloging the Library of Babel". Chemistry & Biology. 14 (3): 237–8. doi:10.1016/j.chembiol.2007.03.002. PMID   17379136.
  16. ↑ Dryden, DT; Thomson, AR; White, JH (6 August 2008). "How much of protein sequence space has been explored by life on Earth?". Journal of the Royal Society, Interface. 5 (25): 953–6. doi:10.1098/rsif.2008.0085. PMC   2459213 . PMID   18426772.
  17. ↑ Akanuma, S.; Kigawa, T.; Yokoyama, S. (2 October 2002). "Combinatorial mutagenesis to restrict amino acid usage in an enzyme to a reduced set". Proceedings of the National Academy of Sciences. 99 (21): 13549–13553. Bibcode:2002PNAS...9913549A. doi: 10.1073/pnas.222243999 . PMC   129711 . PMID   12361984.
  18. ↑ Fujishima, Kosuke; Wang, Kendrick M.; Palmer, Jesse A.; Abe, Nozomi; Nakahigashi, Kenji; Endy, Drew; Rothschild, Lynn J. (29 January 2018). "Reconstruction of cysteine biosynthesis using engineered cysteine-free enzymes". Scientific Reports. 8 (1): 1776. Bibcode:2018NatSR...8.1776F. doi: 10.1038/s41598-018-19920-y . PMC   5788988 . PMID   29379050.
  19. ↑ Bacardit, Jaume; Stout, Michael; Hirst, Jonathan D; Valencia, Alfonso; Smith, Robert E; Krasnogor, Natalio (6 January 2009). "Automated Alphabet Reduction for Protein Datasets". BMC Bioinformatics. 10 (1): 6. doi: 10.1186/1471-2105-10-6 . PMC   2646702 . PMID   19126227.
  20. ↑ Solis, Armando D. (30 July 2019). "Reduced alphabet of prebiotic amino acids optimally encodes the conformational space of diverse extant protein folds". BMC Evolutionary Biology. 19 (1): 158. doi: 10.1186/s12862-019-1464-6 . PMC   6668081 . PMID   31362700.
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