Elucidating protein folding and binding landscapes through computational methods in an experimentally-consistent manner are limited primarily by conformational sampling and force-field deficiencies. Recent advances like machine intensive molecular dynamics (MD) simulations and advanced sampling methods provide avenues to overcome these bottlenecks. Here, we showcase the potential of an Ising-like statistical model in delineating folding-binding phenomena of multiple proteins spanning a range of sizes, topologies and secondary structure content. We expand the scope of the Wako-Saito-Munoz-Eaton (WSME) model to include the effects of inter-molecular interactions (cxWSME) and introduce a novel treatment (bWSME) that approximates the phase space of large proteins (>300aa) to a computationally amenable set of conformational states. The simplified phase space description and experimentally derived energy-entropy function in these models provides a unified framework to explore the conformational landscapes of both ordered and disordered proteins. The model provides unprecedented insights into the structural features of putative intermediates and (un)folding pathways at different structural resolutions, and thus can be employed as a tool to interpret both ensemble and single-molecule experiments. Our work also highlights that the folding landscape of proteins can be tuned as a function of distance from DNA revealing a novel ‘continuous conformational selection’ mechanism of binding. Further improvements to our model and sampling methodology can open up ways to explore general trends in folding-binding processes of not just large macromolecular complexes but also disordered proteins.