Exploring the binding free energy landscape of intrinsically disordered protein–protein interactions: insights into the AF9–BCOR complex implicated in leukemia

Abstract

Chromosomal rearrangements involving the mixed-lineage leukemia (MLL) gene are implicated in acute leukemias with poor prognosis. In MLL-rearranged leukemias, the aberrant recruitment of transcriptional and epigenetic modifier complexes is driven primarily by the MLL–AF9 fusion protein. AF9 typically inhibits transcription by recruiting BCL-6 corepressor (BCOR); however, the direct fusion of AF9 with MLL results in a loss of context dependence in AF9 recruitment and causes oncogenic transformation of hematopoietic cells. Notably, the E531R mutation in AF9, which disrupts the binding between the MLL–AF9 fusion protein and BCOR, abrogates the leukemogenic potential in a mouse model, underscoring its significance as a therapeutic target in leukemia. AF9 and BCOR interact through their intrinsically disordered regions (IDRs), which undergo conformational folding upon complex formation. Understanding this conformational transition is critical for guiding drug discovery efforts but interactions mediated by IDRs remain challenging to study due to their dynamic nature. We propose a hybrid method by combining conventional and replica exchange molecular dynamics (REMD) simulations, to investigate the binding free energy landscape (BFEL) of wild type (WT) and mutant (MT) AF9–BCOR complexes. REMD simulations of WT AF9 alone revealed a significant loss of β-sheets and mutation accelerated the rate of β-sheet disappearance due to the formation of non-native contacts. BFEL of WT AF9–BCOR complex exhibited several local minima, highlighting C-terminal BCOR interactions as potential target for therapeutic intervention. Mutation disrupted the native interactions in AF9–BCOR complex and showed poor binding affinity. Our study uncovers the interaction dynamics of AF9–BCOR and introduces an innovative approach for mapping protein–protein interaction energy landscapes, offering valuable insights to advance targeted drug design.