AF2Rank Revisited: Reproducing AlphaFold-Based Structure Evaluation and a Hypothesis for Context-Aware Refinement (CAR-AF)

Protein structure prediction has undergone transformative advancements with AlphaFold2 achieving near-experimental accuracy across extensive protein datasets. This study reproduces and validates the AF2Rank pipeline, which utilizes AlphaFolds intrinsic confidence metrics--predicted Local Distance Difference Test (pLDDT) and predicted Template Modeling score (pTM)--to evaluate and rank decoy protein structures without dependence on multiple sequence alignments (MSAs). The pipeline was implemented locally using the Rosetta decoy dataset, overcoming reproducibility challenges such as software dependency conflicts, residue indexing inconsistencies, and system-level execution issues. This framework successfully enabled high-confidence evaluation of over 1000 decoys for a benchmark target (1a32), with ongoing expansion to 133 protein targets. Notably, we discovered that AlphaFold confidence metrics encode protein-specific "fingerprints," enabling reverse classification of structures to their source proteins. Our XGBoost-based classifier achieved 61.5% accuracy across 133 distinct protein targets, substantially exceeding random chance (0.75%). Feature importance analysis revealed that traditional energy functions (Rosetta normalized score, 26%) and derived interaction features (pLDDT-pTM ratio, 16%) provide the strongest discriminative power, suggesting AlphaFold metrics capture meaningful biological and structural information beyond generic quality assessment. Building upon this foundation, a novel hypothesis is introduced: Context-Aware Refinement of AlphaFold Predictions (CAR-AF). This approach postulates that AlphaFold-predicted structures may be further improved through refinement in the presence of their native binding partners--such as receptors or ligands--thereby producing conformations that are both structurally and functionally enhanced. The proposed methodology comprises four stages: generation of initial predictions via AlphaFold, structural modeling of relevant binding partners, docking of predicted proteins into these biological contexts, and refinement of the resulting complexes using molecular modeling tools such as Rosetta or HADDOCK. Structural and energetic metrics including TM-score, RMSD, and binding energy will be used to assess potential improvements in predictive quality.