'Intelligent' proteins.

We present an idea of protein molecules that challenges the traditional view of proteins as simple molecular machines and suggests instead that they exhibit a basic form of "intelligence". The idea stems from suggestions coming from Integrated Information Theory (IIT), network theory, and allostery to explore how proteins process information, adapt to their environment, and even show memory-like behaviors. We define protein intelligence using IIT and focus on how proteins integrate information (in terms of the parameter Φ coming from IIT) and balance their core (stable, ordered regions) and periphery (flexible, disordered regions). This balance allows proteins to remain stable while adapting to changes and operating in a critical state where order and disorder coexist. We summarize recent findings on conformational memory, allosteric regulation, protein intrinsic disorder, liquid-liquid phase separation, and critical transitions, and compare protein behavior to other complex systems like ecosystems and neural networks. While our perspective offers a unified framework to understand proteins, it also raises questions about applying intelligence concepts to molecular systems. We discuss how this understanding could advance protein engineering, drug design, and synthetic biology, while at the same time acknowledging the challenges of creating adaptive, "intelligent" proteins. This concept bridges the gap between mechanistic and systems-level views of proteins and offers a comprehensive understanding of their dynamic and adaptive nature. We have tried to redefine the traditionally metaphorical concept of "intelligence" in biochemistry as a measurable property while simultaneously establishing the material foundation of protein intelligence through the identification of fundamental elements such as memory and learning in molecular systems.