A Structural Proteomics Exploration of Synphilin-1 and Alpha-Synuclein Interaction in Pathogenesis of Parkinson's Disease

Pathological significance of interaction of Synphilin-1 with mutated alpha-synuclein is well known to have serious consequences in causing the formation of inclusion bodies that are linked to Parkinson's disease (PD). Information extracted so far pointed out that specific mutations, A53T, A30P, and E46K, in alpha-synuclein promote such interactions. However, a detailed structural study of this interaction is pending due to the unavailability of the complete structures of the large protein Synphilin-1 of chain length 919 residues and the mutated alpha-synuclein having all the reported specific mutations so far. In this study, a semi-automatic pipeline-based meta-predictor, AlphaLarge, is created to predict high-fidelity structures of large proteins like Synphilin-1 given the limitations of the existing protocols. AlphaLarge recruits a novel augmented AlphaFold model that uses a divide and conquer based strategy on the foundation of a self-sourced template dataset to choose the best structure model through their standard validations. The structure models were re-validated by a Protein Mediated Interaction Analysis (PMIA) formalism that uses the existing structurally relevant information of these proteins. For the training dataset, the new method, AlphaLarge, performed reasonably better than AlphaFold. Also, the new residue- and domain-based structural details of interactions of resultant best structure models of Synphilin-1 and both wild and mutated alpha-synuclein are extracted using PMIA. This result paves the way for better screening of target specific drugs to control the progression of PD, in particular, and research on any kind of pathophysiology involving large proteins of unknown structures, in general.