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. Additionally, each protein sequences is surrounded by a set of neighbours (point mutants) that are likely to have at least some function. 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. In the Library of Babel, finding any book that made sense was impossible due to the sheer number and lack of order. Protein sequence space has been compared to the Library of Babel, a theoretical library containing all possible books that are 410 pages long. The more overlap between different activities in sequence space, the more cryptic variation for promiscuous activity will be. 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 density of functional proteins in sequence space, and the proximity of different functions to one another is a key determinant in understanding evolvability. Enzyme superfamilies, therefore, exist as tiny clusters of active proteins in a vast empty space of non-functional sequence. Most random protein sequences have no fold or function. Functional sequences in sequence space ĭespite the diversity of protein superfamilies, sequence space is extremely sparsely populated by functional proteins. A fitness landscape is simply a sequence space with an extra vertical axis of fitness added for each sequence. These highly multidimensional spaces can be compressed to 2 or 3 dimensions using principal component analysis. 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. Hence there are 400 possible dipeptides arranged in a 20x20 space but that expands to 10 130 for even a small protein of 100 amino acids arranges in a space with 100 dimensions. 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. Evolution can be visualised as the process of sampling nearby sequences in sequence space and moving to any with improved fitness over the current one.Ī sequence space is usually laid out as a grid. It has been estimated that the whole functional protein sequence space has been explored by life on the Earth. Each protein sequence is adjacent to all other sequences that can be reached through a single mutation. Most sequences in sequence space have no function, leaving relatively small regions that are populated by naturally occurring genes. The sequence space has one dimension per amino acid or nucleotide in the sequence leading to highly dimensional spaces. In evolutionary biology, sequence space is a way of representing all possible sequences (for a protein, gene or genome). This is repeated until a local summit is reached (2). 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. The goal is to reach the summit, which represents the best achievable mutant. The experiment is analogous to climbing a hill on a 'fitness landscape,' where elevation represents the desired property. 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. How directed evolution climbs fitness landscapes.
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