
The Caspase-based fusion protein technology (CASPON) allows for universal cleavage of fusion tags from proteins of interest, to reconstitute the native N-terminus. While the CASPON enzyme has been optimized to be promiscuous against a diversity of N-terminal peptides, the cleavage efficacy for larger proteins can be surprisingly low. We develop an efficient means to rationalize and predict the cleavage efficiency based on a structural representation of the intrinsically disordered N-terminal peptides, and their putative interactions with the CASPON enzyme. The number of favourably interacting N-terminal conformations shows a very good agreement with the experimentally observed cleavage efficiency, in agreement with a conformational selection model. The method relies on computationally cheap molecular dynamics simulations to efficiently generate a diverse collection of N-terminal conformation, followed by a simple fitting procedure into the CASPON enzyme. It can be readily used to assess CASPON cleavability a priori.
This dataset contains all files necessary to set up the simulations conducted in this work. It further contains the scripts that were used to do the stitching and combining of the CASPON-tag and the N-termini of the POIs. The manuscript was just submitted and accepted: 10.1021/acs.jcim.4c00316
molecular modeling, Computational Modeling, CASPON, Substrate Cleavability, Proteins, IDP, protease, Conformational Selection Model, IDR, Protein Engineering, Local Elevation, molecular dynamics, molecular modelling, Intrinsically Disordered Proteins, GROMOS, Intrinsically Disordered Regions, Protein Cleavage, Caspase-Based Fusion Protein Technology, Statistical mechanics, N-terminal Peptides
molecular modeling, Computational Modeling, CASPON, Substrate Cleavability, Proteins, IDP, protease, Conformational Selection Model, IDR, Protein Engineering, Local Elevation, molecular dynamics, molecular modelling, Intrinsically Disordered Proteins, GROMOS, Intrinsically Disordered Regions, Protein Cleavage, Caspase-Based Fusion Protein Technology, Statistical mechanics, N-terminal Peptides
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