Abstract

The interpretation of Small-Angle Scattering (SAXS) data for biological macromolecules often encounters an inherent **ambiguity issue** in solving the inverse problem, particularly when determining the low-resolution structure of complex membrane-associated proteins. This study addresses the challenge of obtaining a consistent and unique structural model from SAXS data, focusing on the **Insulin Receptor-Related Receptor (IRR)** as a biologically relevant example. IRR, a transmembrane protein crucial for cell signaling, presents a significant challenge due to its multi-domain architecture and conformational flexibility. Our objective is to develop and validate a robust methodology for interpreting SAXS data that minimizes the ambiguity arising from multiple possible solutions. We introduce a novel approach that integrates advanced computational methods for SAXS data analysis, including enhanced regularization techniques and the incorporation of known structural constraints. Application to the IRR demonstrates that this consistent approach yields a more reliable and biologically plausible structural model, specifically resolving the arrangement of its extracellular domains. This work provides essential methodological advancements for the structural characterization of challenging biological systems, offering a clearer path to understanding the structure-function relationship of key cell surface receptors.