Predictive Model for Single-Site Mutations That Change Intrinsically Disordered Protein Binding Energetics.

Intrinsically disordered proteins (IDPs) are characterized by structural mobility and lack of stable tertiary structure. These are important for IDP functions and interactions with other protein partners. IDP binding modes are distinctly divergent from globular protein-protein interactions. Comprehensive studies on effects of mutations in this structurally distinct protein class of IDPs are limited, impeding the development of accurate prediction methods for mutational effects in IDPs. Understanding how mutations affect IDP binding affinity is also crucial to peptide drug design, therapeutics engineering, and research on IDP diseases. Previous deep mutational scanning study of intrinsically disordered domain of control of cell death protein A (CcdA) shed light on residue-specific contribution to cognate partner binding energetics and outlined methodology for building IDP-compatible prediction models of binding affinity. The current chapter summarizes our previously described methodology [1] (see Notes 1, 4-7) for development of a linear regression-based predictive model, Intrinsically disordered Protein Affinity prediction (IPApred). Our method incorporates structural information at the residue level as well as experimentally derived mutational penalties while leveraging the distinct physicochemical properties of different amino acids to predict how single-site substitutions affect partner binding in IDPs. We describe methods for data generation and collection, and the steps of selecting features, training a simple mathematical model, outlining a protocol for using IPApred, and finally discussing validation and evaluation of the model. The chapter also highlights current limitations of the model and challenges faced in modeling IDP energetics and discusses possible strategies of addressing them. Despite drawbacks, IPApred provides a useful framework for predicting mutational effects in disordered proteins and for improved understanding of related biological processes and disease mechanisms.