Artificial Intelligence Captures Pseudo-Intelligence Evolved in the Chemical Architecture of the Multifaceted Signal Transducer, TAK1.

TAK1, a multifunctional kinase, possesses intramolecular interactions that are capacitated to recognize and process diverse forms of chemical and mechanical forces, which ultimately translate into a convergent catalytic function. By elucidating a dual-switching mechanism in TAK1, this computational study represents a pioneering effort in unveiling the dynamic adaptability encoded in multifunctional proteins. Activation-triggers, from S192-phosphorylation (chemical) or from TAB1-binding (mechanical) reroute signal transduction in distinct spatiotemporal ways to reach a shared catalytic outcome─analogous to an electronic circuit with multiple input sensors that switch on the same light. Our findings create new portals of multifunctional structural investigations by advocating TAK1's ability to deploy two context-dependent internal strategies. (1) The " → " is an efficient spatial task allocation strategy, which enabled TAK1's activation loop to mold its shape distinctly to process each activation cue and an allosteric ATP → substrate cleft hydrogen-bond network (circuit's core) to converge these structural Morse codes for basal activity. (2) The "" strategy allowed structural elements-like activation loop and αF helix-to be repurposed in different temporal sequences, creating distinct internal activation pathways. These mechanisms became lustrously perceivable as conventional "black-box" AI/ML methods were improvised to extract hidden interaction fingerprints with a novel pipeline of differential contact-based prescreening, selection, and VMD-based 3D-visualization of features. Notably, this study also introduces one of the first known applications of Kolmogorov-Arnold Networks (KAN) for feature selection in protein's mechanistic studies. Evolution has precisely balanced unique and conserved residues (e.g., K63, D175, C174, R44, E70) to form TAK1's core allosteric interaction circuit, with R44-E70 drawing key attention in terms of rivaling the conserved K-E salt-bridge for αC helix assembly. Our study not only shows how AI/ML complements the new age study of complex chemical systems of "life" but also reveals novel structural principles that could inform customized design of therapeutics for multifunctional proteins.