KIF – Key Interactions Finder: A Program to Identify the Key Molecular Interactions that Regulate Protein Conformational Changes

03 January 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Simulation datasets of proteins (e.g., those generated by molecular dynamics simulations) are filled with information about how the non-covalent interaction network within a protein regulates the conformation and thus function of said protein. Most proteins contain thousands of non-covalent interactions, with most of these being largely irrelevant to any single conformational change. The ability to automatically process any protein simulation dataset to identify the non-covalent interactions that are strongly associated with a single, defined conformational change would be a highly valuable tool for the community. Furthermore, the insights generated from this tool could be applied to both basic research, in order to improve understanding of a mechanism of action, or for protein engineering, to identify candidate mutations to improve/alter the functionality of any given protein. The open-source Python package Key Interactions Finder (KIF) enables users to identify those non-covalent interactions that are strongly associated with any conformational change of interest for any protein simulated. KIF gives the user full control to define the conformational change of interest as either a continuous or categorical variable, and methods from statistics or machine learning can be applied to identify and rank the interactions and residues distributed throughout the protein which are relevant to the conformational change. Finally, KIF has been applied to three diverse model systems (protein tyrosine phosphatase 1B, the PDZ3 domain, and the KE07 series of Kemp eliminases) in order to showcase its power to identify key features that regulate functionally important conformational dynamics.


protein allostery
conformational changes
non-covalent interactions
machine learning
mutual information

Supplementary materials

Supporting Information
Additional information about methodology, supplementary results, and supplementary analysis.

Supplementary weblinks


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