Abstract
The molecular recognition process between proteins is the foundation of complex biological functions, driven by residue-level interactions between regulatory and functional domains. Therefore, change in network is the root cause of normal physiology to pathophysiology. Since the network can only be traced through structural data, such insights are essential. However, identifying the critical structural and conformational determinants facilitating signalling cascades remains a major challenge for protein-protein interactions (PPIs) based therapeutic interventions. This challenge is further compounded by the absence of structural data, which makes deciphering the intricate web of PPIs even more difficult. Structural insights are paramount, as PPIs are inherently flexible, exploring a dynamic conformational space characterized by low-energy states interconnected by high-energy transition paths. Autophagy is a cellular process heavily reliant on PPIs, and researchers from academia and industry are targeting them for therapeutic intervention due to their beneficial role in the modulation of multiple diseases, including cancer, neurodegenerative and metabolic diseases. In autophagy pathway, Beclin 1 is a pivotal protein in the signalling cascade. However, targeting Beclin 1 for therapeutic purposes and understanding its role in the signalling cascades remain challenging, primarily due to the lack of structural insights into the mechanisms governing its interactions with its regulatory partners. To overcome these challenges, we integrate AlphaFold predicted models with experimentally resolved PDB structures to construct a comprehensive, domain wise and residue level map of Beclin 1 interactome capturing both structured and unstructured regions, identifying critical interaction interfaces, and uncovering pivotal determinants for Beclin 1 specific therapeutic interventions.