The complexity of proteins is substantially simplified by regarding them as archetypical examples of self-organized criticality (SOC). To test this idea and elaborate on it, this article applies the Moret-Zebende SOC hydrophobicity scale to the large-scale scaffold repeat protein of the HEAT superfamily, PR65/A. Hydrophobic plasticity is defined and used to identify docking platforms and hinges from repeat sequences alone. The difference between the MZ scale and conventional hydrophobicity scales reflects longrange conformational forces that are central to protein functionality.hydrophobicity ͉ repeat ͉ scaling T here are many scales (hydrophobicity, charge, helix-forming propensity, etc.) that bioinformatically describe various aspects of amino acid interactions in proteins and their relations to sequence, structure, stability, and functionality, all justified by combining plausible constructs with parameters adjusted through statistical searches. Are all such scales equally useful, and are the substantial differences among them attributable to their inevitably heuristic character? Here, I discuss differences in methodology, with special emphasis on holistic vs. reductionist approaches. I suggest that a better understanding of the limitations and potential of scaling analysis of proteins will result from systematic studies of simple cases first, such as repeat proteins and their associates. Scaling analysis may prove to be a valuable tool in designing proteins with desired functions.Many physical systems exhibit power-law distributions over limited ranges (hence the popularity of log-log graph paper), and power-law distributions are the characteristic feature of the modern theory of phase transitions near a critical point. Self-organized criticality (SOC) is a methodology that attempts to explain why so many complex systems exhibit power-law distributions and appear to be ''accidentally'' located near critical points. It is argued that the critical points are dynamical fixed points (''tipping points'') toward which the system evolves without tuning external parameters (1). The critical points are extrema in some property (or properties) with respect to which the system has been optimized, especially with respect to long-range, highly-cooperative interactions, such as conformational changes.Given the widespread occurrence of power laws, SOC has great intuitive appeal: it has achieved an enduring popularity among theorists (Ͼ2,500 papers discussing SOC, Ͼ25 current books from a single publisher), notably in modeling the critical stability of sand piles against avalanches, but its concrete applications have been limited largely to seismic phenomena. A cautionary remark: power laws, especially in the context of dissipative reactions, have often been interpreted in terms of SOC, but a simpler interpretation involves only slow sweeping of a control parameter toward a global extremal, as could occur in the context of commercial product optimization (2). However, more recently Boolchand and coworkers (3) discovered a r...