Small G proteins can be classified into two groups: high-level of GTP loading (GTP/(GTP+GDP)) 40-60% or low-level of GTP loading (2-5%). The former examples are Rnd and Rab7. The latter examples are Ras and Rab3. This is usually explained by the statement that high or low GAP activity specific for particular GTPases. But why high or low GAP activity exists? One possibility may be one is low-pass filter and the other is high-pass filter. In that case, do such filters work spatially or temporally? This may be an open question, I think.
Small G Proteins as Signal Filters: Understanding GTP Loading Levels and Regulatory Mechanisms
Small G proteins serve as molecular switches in cellular signaling, cycling between active GTP-bound and inactive GDP-bound states. Intriguingly, they exhibit dramatically different steady-state GTP loading levels, creating distinct signaling properties. This fascinating dichotomy between high and low GTP loading may represent evolved filtering mechanisms that process cellular signals in unique ways, operating across both spatial and temporal dimensions. @type to T Nakamura, Summary of the present answer by Perplexity (May 10th, 2025, 14:30 Japan) : The GTP Loading Spectrum: Molecular Basis for Signal Processing, Beyond GAP Activity: Why the Dichotomy Exists, The Filter Hypothesis: Signal Processing through G Protein Dynamics, Conclusion: A Dynamic Perspective on G Protein Regulation
This is a summary of the present answer by Perplexity (May 10th, 2025, 14:30, JAPAN)
The reply by Perplexity 1: "Small G Proteins as Signal Filters: A Category Theoretic Formalization" Conclusion
This categorical reframing reveals deep structural parallels between:
1. Compositionality of regulatory networks
2. Universality of filtering mechanisms
3. Duality between temporal and spatial dynamics
The formalism enables rigorous analysis of design principles in GTPase signaling through limits/colimits, adjunctions, and universal properties[2][3][9]. Future work should explore ∞-categorical extensions for multi-scale modeling[3][11]. Social biology of the cell, revisited!
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