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      Local fitness landscape of the green fluorescent protein

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          Abstract

          Fitness landscapes 1, 2, depictions of how genotypes manifest at the phenotypic level, form the basis for our understanding of many areas of biology 27 yet their properties remain elusive. Studies addressing this issue often consider specific genes and their function as proxy for fitness 2, 4, experimentally assessing the impact on function of single mutations and their combinations in a specific sequence 2, 5, 815 or in different sequences 2, 3, 5, 1618. However, systematic high-throughput studies of the local fitness landscape of an entire protein have not yet been reported. Here, we chart an extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) by measuring the native function, fluorescence, of tens of thousands of derivative genotypes of avGFP. We find that its fitness landscape is narrow, with half of genotypes with two mutations showing reduced fluorescence and half of genotypes with five mutations being completely non-fluorescent. The narrowness is enhanced by epistasis, which was detected in up to 30% of genotypes with multiple mutations arising mostly through the cumulative impact of slightly deleterious mutations causing a threshold-like decrease of protein stability and concomitant loss of fluorescence. A model of orthologous sequence divergence spanning hundreds of millions of years predicted the extent of epistasis in our data, indicating congruence between the fitness landscape properties at the local and global scales. The characterization of the local fitness landscape of avGFP has important implications for a number of fields including molecular evolution, population genetics and protein design.

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          Most cited references23

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          Epistasis and quantitative traits: using model organisms to study gene-gene interactions.

          The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.
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            High Resolution Mapping of Protein Sequence–Function Relationships

            We present a large-scale approach to investigate the functional consequences of sequence variation in a protein. The approach entails the display of hundreds of thousands of protein variants, moderate selection for activity, and high throughput DNA sequencing to quantify the performance of each variant. Using this strategy, we tracked the performance of >600,000 variants of a human WW domain after three and six rounds of selection by phage display for binding to its peptide ligand. Binding properties of these variants defined a high-resolution map of mutational preference across the WW domain; each position possessed unique features that could not be captured by a few representative mutations. Our approach could be applied to many in vitro or in vivo protein assays, providing a general means for understanding how protein function relates to sequence.
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              Missense meanderings in sequence space: a biophysical view of protein evolution.

              Proteins are finicky molecules; they are barely stable and are prone to aggregate, but they must function in a crowded environment that is full of degradative enzymes bent on their destruction. It is no surprise that many common diseases are due to missense mutations that affect protein stability and aggregation. Here we review the literature on biophysics as it relates to molecular evolution, focusing on how protein stability and aggregation affect organismal fitness. We then advance a biophysical model of protein evolution that helps us to understand phenomena that range from the dynamics of molecular adaptation to the clock-like rate of protein evolution.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                2 June 2016
                11 May 2016
                11 May 2016
                11 November 2016
                : 533
                : 7603
                : 397-401
                Affiliations
                [1 ]Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
                [2 ]Nizhny Novgorod State Medical Academy, Minin Sq. 10/1, 603005 Nizhny Novgorod, Russia
                [3 ]Central European Institute of Technology, Masaryk University, Brno, Czech Republic
                [4 ]Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 88 Dr. Aiguader, 08003 Barcelona, Spain
                [5 ]Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
                [6 ]Moscow Institute of Physics and Technology, Institutskiy pereulok 9, g.Dolgoprudny, 141700, Russia
                [7 ]Faculty of Medicine, Moscow State University, Lomonosov ave. 31/5 119192 Moscow, Russia
                [8 ]Laboratory of Protein Physics, Institute of Protein Research of the Russian Academy of Sciences, 4 Institutskaya str., Pushchino, Moscow region, 142290, Russia
                [9 ]Pirogov Russian National Research Medical University, Ostrovitianov 1, Moscow, 117997, Russia
                [10 ]A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
                [11 ]Department of Bioinformatics and Bioengineering, Moscow State University, Moscow, Russia
                [12 ]Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
                [13 ]Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
                [14 ]Institució Catalana de Recerca i Estudis Avançats (ICREA), 23 Pg. Lluís Companys, 08010 Barcelona, Spain
                Author notes
                Correspondence and requests for materials should be addressed to fyodor.kondrashov@ 123456crg.es
                Article
                EMS68321
                10.1038/nature17995
                4968632
                27193686
                2af717ed-7644-441a-9ce3-cff7d76ef8aa

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