Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913581480542208 |
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| author | Sandhu, Mahakaran Chen, John Matthews, Dana Spence, Matthew A Pulsford, Sacha B Gall, Barnabas Nichols, James Tokuriki, Nobuhiko Jackson, Colin J |
| author_facet | Sandhu, Mahakaran Chen, John Matthews, Dana Spence, Matthew A Pulsford, Sacha B Gall, Barnabas Nichols, James Tokuriki, Nobuhiko Jackson, Colin J |
| contents | Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex functions or under multiple selection pressures, exist on rugged fitness landscapes. Here we discuss the theoretical framework that underpins our understanding of fitness landscapes, alongside recent work that has advanced our understanding - particularly the biophysical basis for smoothness versus ruggedness. Finally, we address the rapid advances that have been made in computational and experimental exploration and exploitation of fitness landscapes, and how these can identify efficient routes to protein optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains Sandhu, Mahakaran Chen, John Matthews, Dana Spence, Matthew A Pulsford, Sacha B Gall, Barnabas Nichols, James Tokuriki, Nobuhiko Jackson, Colin J Populations and Evolution Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex functions or under multiple selection pressures, exist on rugged fitness landscapes. Here we discuss the theoretical framework that underpins our understanding of fitness landscapes, alongside recent work that has advanced our understanding - particularly the biophysical basis for smoothness versus ruggedness. Finally, we address the rapid advances that have been made in computational and experimental exploration and exploitation of fitness landscapes, and how these can identify efficient routes to protein optimization. |
| title | Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains |
| topic | Populations and Evolution |
| url | https://arxiv.org/abs/2411.12957 |