Binding Paths: Describing Small Molecule Interactions with Disordered Proteins via a Markov State Model

Disordered proteins are challenging targets for drug discovery because they lack well-defined binding pockets. Although small molecules can form relatively stable complexes with disordered proteins, the highly dynamic nature of these proteins complicates the understanding of their binding mechanism. To address this problem, we analyze the binding of the small molecule 10074-G5 to A{beta}42, which results in the formation of a disordered complex. We describe the binding mechanism in terms of binding paths, which are stochastic trajectories along which small molecule diffuse across disordered protein surfaces, forming transient contacts with overlapping groups of residues. To identify these binding paths, we define them as realizations of a stochastic process defined by a Markov State Model (MSM). The MSM is built from distinct states of the disordered complex and their corresponding transition probabilities, enabling both the dynamic mapping of binding hotspots and targetable regions where static pockets cannot be defined-extending the notion of binding pockets to disordered systems - and the quantitative calculation of binding affinities. The visualization of the MSM via knowledge graphs provides an intuitive representation of the binding paths. We further validated this approach across four additional systems, comprising C-terminal -synuclein with three distinct small-molecule binders, and the full-length peptide (140 residues) with binder fasudil. By generalizing the concept of static binding pockets to dynamic binding paths, our approach rationalizes small-molecule recognition by disordered proteins and establishes a framework for identifying druggable regions on systems that were previously considered intractable, providing insights for future drug design programs.