Leveraging Multi-Modal Integration and HyperScore Evaluation for Enhanced AlphaFold2 Variant Refinement in Membrane Protein Folding Prediction

**Abstract:** Existing protein structure prediction methods, particularly AlphaFold2, demonstrate remarkable accuracy for soluble proteins but struggle with the inherent complexities of membrane protein folding, including limited sequence homology and the influence of lipid environments. This paper proposes a novel framework, incorporating multi-modal data ingestion and a HyperScore evaluation system, to refine AlphaFold2 predictions for membrane protein variants. Our approach leverages a structured, automated pipeline integrating PDF literature extraction, code parsing, and figure analysis, alongside advanced semantic decomposition and logical consistency checks. A customized HyperScore, built upon a Bayesian calibration and Shapley-AHP weighting scheme, provides a robust, transferable metric for evaluating and prioritizing refinement candidates. This framework, readily commercializable within 5-10 years, promises significant advancements in membrane protein structure determination, driving innovations in drug discovery and membrane-based technology.