AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines randomized  alanine  sequence masking  with shallow multiple sequence alignment  subsampling to expand the conformational diversity of the predicted structural  ensembles and   capture conformational changes between apo and holo protein forms. Using several well-established datasets of  structurally diverse apo-holo protein pairs, the proposed approach enables  robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with  the view  that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes among orthologs. We also found  a significant correlation between conformational flexibility and  AlphaFold2 metric of statistical significance pLDDT for the apo-holo pairs in which ligand binding induced local moderate conformational changes. For apo-holo pairs exhibiting larger structural changes, this relationship  becomes nonlinear, reflecting inability of AlphaFold2 confidence metrics to identify high energy functional conformations. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with  moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AF2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that modeling of multiple functional states of proteins may require more accurate detection of flexible region conformations and cannot solely rely on the pLDDT metric as the major determinant of the prediction accuracy in reproducing functional conformational ensembles.