The Evolutionary Paradox: Why Protein Mutations Face Severe Constraints in Driving Major Evolutionary Changes
In 2008, Tokuriki et al. wrote a paper: "How Protein Stability and New Functions Trade Off". The key conclusions were: The majority of both new-function mutations and general mutations are destabilizing. As quoted from the paper:
"The majority of both all- and new-function mutations are destabilizing (43% of mutations exhibit DDG values higher than 1 kcal/mol [DDG represents the change in protein stability energy, where positive values indicate destabilization]), and a significant fraction of mutations are actually stabilizing (7% of mutations exhibit DDG <-1 kcal/mol)."1
This finding highlights a fundamental challenge: proteins must maintain their structural stability to function, yet most mutations that might confer new functions are inherently destabilizing. Like climbing a mountain, evolution must find paths that maintain stability at each step while moving toward new functions.
Stability loss needs to be compensated for during evolution. The compensation mechanism often involves "silent" mutations - changes that don't directly affect function but help maintain structural integrity. As quoted:
"Indeed, our analysis indicates that many of the other mutations seen in directed evolution experiments might play an essential role in compensating for loss of stability, and are thus involved in the process despite having no direct role in altering the activity of the evolving enzyme."
This compensation has been observed in some cases. For example, in the evolution of antibiotic resistance enzymes like TEM-1 β-lactamase, researchers found that while function-altering mutations allowed the enzyme to break down new antibiotics, these mutations also destabilized the protein. To compensate, secondary stabilizing mutations arose, which did not directly contribute to the new function but helped maintain structural stability. One specific mutation, Met181Thr in TEM-1 β-lactamase, was shown to stabilize the enzyme by approximately 2.67 kcal/mol, allowing it to tolerate further destabilizing mutations essential for adapting to third-generation antibiotics.
However, such successful compensation is the exception rather than the rule. A study on temperature-sensitive mutants of the yeast enzyme orotidine 5′-phosphate decarboxylase (ODCase) 2 found that, despite extensive screening, compensatory mutations were rare and often provided only partial restoration of function. This finding reveals that compensatory stabilizing mutations face several critical constraints:
- They must occur in precisely the right location
- They must use appropriate residues
- They must emerge under conditions where they confer a selective advantage
- They must navigate the local fitness landscape without getting trapped at suboptimal peaks
These requirements make successful compensation relatively rare, even in directed evolution studies where specific conditions and goals guide the process. The challenge becomes even greater in natural settings, where proteins that acquire destabilizing function-altering mutations without compensation are often not viable and may be selected against if they lose stability to the point of non-functionality.
Recent research using AlphaFold (artificial intelligence protein structure prediction) has provided additional insights into this stability-function relationship. According to Pak et al. (2023), analyzing predicted protein structures (measured by pLDDT scores, which indicate prediction confidence and correlate with structural stability):
"Protein stability is intimately coupled with protein functionality. Thus, a reasonable hypothesis holds that the loss of protein functionality due to mutations in most cases results from reduced stability." The authors found that "...the destabilizing effect of mutations was associated with decreasing pLDDTs: 87% of destabilizing mutations had negative ΔpLDDT (p-value = 10−22)." 3
This tight coupling between stability and function creates multiple layers of constraints that severely limit evolutionary possibilities:
1. Most mutations (43%) are destabilizing, with only 7% being stabilizing, creating significant probabilistic hurdles for functional innovations.
2. Compensatory mutations are rare and often only partially effective, even in controlled laboratory settings with directed selection.
3. Natural settings impose even stricter constraints, as proteins that lose stability tend to be eliminated before they can acquire compensatory mutations.
4. While some adaptations are possible (like the thermophilic adaptations described by Cea et al. 2024) 4, these represent special cases under very strong selection pressures rather than a general mechanism for major evolutionary changes.
5. Multiple coordinated mutations would often be needed for new complex functions, while maintaining stability and avoiding non-functional intermediates.
6. The local fitness landscape problem further constrains possibilities - populations tend to get trapped at local fitness peaks, making it extremely unlikely to traverse the fitness valleys required to reach higher peaks, especially when stability must be maintained throughout the journey.
7. Experimental work by Axe and Gauger demonstrated that even modest functional modifications of existing proteins require at least seven coordinated mutations. As Axe concludes: "It turns out once you get above the number six [changes] -- and even at lower numbers actually -- but once you get above the number six you can pretty decisively rule out an evolutionary transition because it would take far more time than there is on planet Earth and larger populations than there are on planet Earth."
This combination of constraints - the need to maintain stability, the rarity of compensatory mutations, and the challenges of navigating fitness landscapes - creates formidable barriers to major evolutionary innovations through protein mutations alone. The evidence suggests that while protein mutations can contribute to minor adaptations, they face severe limitations in driving major evolutionary changes.
References:
1. Tokuriki, N., Stricher, F., Serrano, L., & Tawfik, D. S. (2008). How Protein Stability and New Functions Trade Off. PLoS Computational Biology, 4(2), e1000002. Link. (This paper provides a comprehensive computational analysis of how mutations that confer new protein functions impact protein stability, demonstrating that while such mutations are generally destabilizing, proteins can evolve compensatory mechanisms through additional stabilizing mutations.)
2. Jakubowska, A., & Korona, R. (2019). Limits to Compensatory Mutations: Insights from Temperature-Sensitive Mutants of Yeast Orotidine 5′-Phosphate Decarboxylase.** *Molecular Biology and Evolution*, 36(9), 1874–1886. Link . This study investigates the rarity and partial effectiveness of compensatory mutations in yeast ODCase mutants, highlighting that such mutations are not universal and depend on specific contexts.
3. Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023). Using AlphaFold to predict the impact of single mutations on protein stability and function. PLOS ONE, 18(3), e0282689. Link. (This paper evaluates AlphaFold's ability to predict how mutations affect protein stability and function.)
4. Cea, P. A., Pérez, M., Herrera, S. M., Muñoz, S. M., Fuentes-Ugarte, N., Coche-Miranda, J., Maturana, P., Guixé, V., & Castro-Fernandez, V. (2024). *Deciphering Structural Traits for Thermal and Kinetic Stability across Protein Family Evolution through Ancestral Sequence Reconstruction.* Molecular Biology and Evolution, 41(7), msae127. Link. ( explores how ancestral sequence reconstruction (ASR) can elucidate the structural features that promote thermal and kinetic stability in proteins.)
