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Updated: Dec 27, 2025
07:08
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
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Evolution Rapidly Optimizes Stability and Aggregation in Lattice Proteins Despite Pervasive Landscape Valleys and
Jason Bertram1,2, Joanna Masel3
1Environmental Resilience Institute, Indiana University, Bloomington, Indiana 47401 jxb@iu.edu masel@email.arizona.edu.
Genetics
|February 29, 2020
Summary
Evolutionary paths can be maze-like, hindering adaptation. However, indirect routes can help proteins overcome challenges and reach high fitness.
Area of Science:
- Evolutionary Biology
- Computational Biology
- Biophysics
Background:
- Genetic "fitness" landscapes are high-dimensional and "rugged" due to sign epistasis, making adaptation difficult.
- Adaptive trajectories can become trapped at low-fitness local peaks, and extremely long paths may be needed to reach even local peaks.
- The impact of these "maze-like" landscapes on evolution is poorly understood due to empirical and modeling limitations.
Purpose of the Study:
- To investigate the prevalence, scale, and evolutionary consequences of landscape mazes in protein evolution.
- To explore how competing interactions, specifically "stability" and aggregation propensity, shape these landscapes.
Main Methods:
- Development of a biophysically grounded computational model of protein evolution.
- Analysis of a "stability-aggregation" landscape model exhibiting extensive sign epistasis and local peaks.
- Examination of adaptive paths from low to high fitness, identifying key mediating factors.
Main Results:
- The model landscape shows extensive sign epistasis and numerous local peaks, obstructing adaptive ascent and reducing reproducibility.
- Despite challenges, many adaptive paths successfully reach high fitness, with hydrophobicity playing a critical role.
- Successful adaptive paths demonstrate "maze-like" properties on a global scale, using indirect routes to bypass local optima.
Conclusions:
- Protein evolution navigates a balance between difficult but possible adaptation, influenced by competing biophysical constraints.
- "Maze-like" landscape topography is a significant factor in protein evolution, guiding successful adaptive trajectories.
- The findings suggest that such "hard but possible" adaptation dynamics may be relevant in other biological systems with competing interactions.
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