The fitness peaks are sharp ( Bank et al., 2015 Melamed et al., 2013 Sarkisyan et al., 2016) and the ridges are narrow ( Gong et al., 2013 Kumar et al., 2017 Pokusaeva et al., 2019 Sailer et al., 2020) and, on average, only a few random mutations in a wildtype sequence reduce its fitness to zero ( Hartman and Tullman-Ercek, 2019 Kemble et al., 2019). Nevertheless, only an infinitesimally small fraction of all genotypes are functional (fewer than 10 –11), those that correspond to fitness peaks and ridges, and the remaining genotypes confer low fitness ( Keefe and Szostak, 2001). These extant genotypes had a common ancestor, so they must be connected by ridges of high fitness ( Gong et al., 2013 Smith, 1970 Povolotskaya and Kondrashov, 2010). Each extant genotype, one that is found in an extant species, is a point of high fitness, or a fitness peak, on the highly dimensional and extraordinarily large genotype space ( de Visser and Krug, 2014 Fragata et al., 2019 Smith, 1970 Wright, 1932). Despite some advances in the development of data-driven approaches to protein design ( Biswas et al., 2021 Biswas et al., 2018 Bryant et al., 2021 Kemble et al., 2019), it is still not clear what fraction of the 20 250 sequences of the GFP, or any other gene, must be characterized to approach the coveted absolute knowledge of the fitness landscape ( Kemble et al., 2019 Sailer et al., 2020 Zhou and McCandlish, 2020).ĭespite lack of data, experiments and theory provide some insights on the global fitness landscape ( Fragata et al., 2019 Kemble et al., 2019). However, epistatic interactions between amino acid sites are common ( Russ et al., 2020) and many of them are too complex to predict with available data ( Pokusaeva et al., 2019). Without complex epistatic interactions between amino acid sites the fitness landscape could be deduced from the independent contribution of each amino acid at each site ( Kondrashov and Kondrashov, 2015), requiring just 5000 (20*250) measurements of the effects of all single mutations in GFP. Even for the Green Fluorescent Protein (GFP), which is only ~250 amino acids long, there are 20 250 possible genotypes. While several experimentally characterized fitness landscapes for specific proteins have been reported ( Hartman and Tullman-Ercek, 2019 Jacquier et al., 2013 Kuo et al., 2020 Melamed et al., 2013 Olson et al., 2014 Sarkisyan et al., 2016), such surveys of large proteins are still hindered by the enormity of the genotype space ( de Visser and Krug, 2014 Wright, 1932). However, sparse experimental data, even for specific genes, and the concomitant lack of understanding of the rules by which fitness landscapes are formed, limit the accuracy of phenotype predictions based on sequence alone ( Lässig et al., 2017) but see Bryant et al., 2021 Rocklin et al., 2017 Senior et al., 2020 Wu et al., 2019. Absolute knowledge of the fitness landscape would reveal the phenotypes conferred by any arbitrary genotype ( de Visser and Krug, 2014 Ferretti et al., 2018 Fragata et al., 2019), with immense and obvious practical implications ( Alley et al., 2019 Bryant et al., 2021 Hirabayashi and Arai, 2019 Kemble et al., 2019 Wrenbeck et al., 2017 Wu et al., 2019). ![]() ![]() Over time, the usefulness of the concept of the fitness landscape led to the adaptation of this term to describe the relationship between protein function and its protein-coding gene sequence ( Biswas et al., 2021 Ogden et al., 2019 Romero and Arnold, 2009 Wittmann et al., 2021 Zheng et al., 2020). Originally, the fitness landscape was introduced to describe the relationship between fitness and the entire genome ( de Visser and Krug, 2014 Wright, 1932). The fitness landscape is often conceptualised as a multidimensional surface ( de Visser and Krug, 2014 Ferretti et al., 2018 Kondrashov and Kondrashov, 2015 Wright, 1932) with one dimension representing fitness, or another phenotype, and the other dimensions each representing a genotype’s locus. Understanding the relationship between genotype and phenotype, the fitness landscape, elucidates the fundamental laws of heredity ( Canale et al., 2018 de Visser and Krug, 2014 Ferretti et al., 2018 Fragata et al., 2019 Wright, 1932) and may ultimately create novel methods of protein design ( Alley et al., 2019 Bryant et al., 2021 Hirabayashi and Arai, 2019 Wrenbeck et al., 2017 Wu et al., 2019).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |