A unified ensemble-allosteric framework reconciles gain- and loss-of-function disease mutations in the IP3 receptor

Missense mutations in multidomain signalling proteins often produce divergent functional phenotypes despite preserved structural integrity, posing a fundamental challenge for interpreting human genetic variation. This problem is exemplified by the type 1 inositol 1,4,5-trisphosphate receptor, IP3R1, the principal neuronal IP3-gated Ca2+ release channel and a recurrent locus of pathogenic variation in ITPR1-associated ataxias and neurodevelopmental disorders. Although disease mutations cluster within the N-terminal suppression domain (SD) and IP3-binding core (IBC), how neighbouring substitutions can drive either gain- or loss-of-function remains unresolved. Here, we establish a unified ensemble-allosteric framework for IP3R1 channelopathy by integrating mutational constraint analysis, ensemble structural modelling, adaptive molecular dynamics, Markov state modelling and dynamical network analysis. We show that pathogenic variants preferentially occupy a stability-preserving regime, perturbing local sequence-structure compatibility without inducing global structural collapse. Thus, disease arises not from loss of fold, but from corruption of the conformational and allosteric logic that links ligand recognition to channel gating. The IP3-binding pocket forms a discrete multistate ensemble whose populations, physicochemical properties and kinetic connectivity are selectively remodelled by mutation. The loss-of-function variant R269W disrupts a cationic IP3-coordinating site, enriches non-permissive binding-pocket states and diverts conformational exchange through indirect kinetic routes. By contrast, the gain-of-function variant R36C preserves local pocket competence but weakens suppressor-domain restraint by rerouting long-range communication through extended, less efficient allosteric pathways. These findings reconcile opposing disease phenotypes within a single mechanistic model, showing that pathogenic variation can be encoded in altered ensemble probabilities and information flow rather than in static structural lesions.