Redesigning Protein Cavities as a Strategy for Increasing Affinity in Protein-Protein Interaction: Interferon-γReceptor 1 as a Model

Abstract

Combining computational and experimental tools, we present a new strategy for designing high affinity variants of a binding protein. The affinity is increased by mutating residues not at the interface, but at positions lining internal cavities of one of the interacting molecules. Filling the cavities lowers flexibility of the binding protein, possibly reducing entropic penalty of binding. The approach was tested using the interferon-γ receptor 1 (IFNγR1) complex with IFNγ as a model. Mutations were selected from 52 amino acid positions lining the IFNγR1 internal cavities by using a protocol based on FoldX prediction of free energy changes. The final four mutations filling the IFNγR1 cavities and potentially improving the affinity to IFNγ were expressed, purified, and refolded, and their affinity towards IFNγ was measured by SPR. While individual cavity mutations yielded receptor constructs exhibiting only slight increase of affinity compared to WT, combinations of these mutations with previously characterized variant N96W led to a significant sevenfold increase. The affinity increase in the high affinity receptor variant N96W+V35L is linked to the restriction of its molecular fluctuations in the unbound state. The results demonstrate that mutating cavity residues is a viable strategy for designing protein variants with increased affinity.

Flexible regions govern promiscuous binding of IL‐24 to receptors IL‐20R1 and IL‐22R1

“…Two such algorithms PROSS [14] and FireProt [15] that are already available on web servers, have made laborious, timeconsuming, expert-requiring analysis of the sequence and structural data accessible to molecular biologists and protein chemists. These servers, as well as other approaches [16,17], combine force-field homology modeling by Rosetta [18] or FoldEX [19] with evolutionary consensus inferred from phylogeny to propose a large number of stabilizing mutations with a low rate of false positives.…”

Analysis of changes of cavity volumes in predefined directions of protein motions and cavity flexibility

“…[3][4][5] On the contrary, cavity-filling mutations can lower the flexibility of the binding protein, possibly reducing conformational entropic penalty of binding and, therefore, generating high affinity variants of a binding protein. [3][4][5][6] The connection between protein fluctuations and cavity changes has been the subject of a variety of previous works. 7,8 Most of them are focused on cavity rearrangements during ligand binding and the effect of mutations on cavity shapes and sizes and their ultimate impact on ligand affinity.…”

“…The flexibility of cavities are commonly investigated by studying changes among ensembles of structures from sources such as molecular dynamics (MD) snapshots, crystallography under different conditions, NMR ensembles, and homologous protein structures. On one hand, databases of conformational diversity in the native state of proteins (CoDNaS; PDBFlex [3][4][5][6][9][10][11] ) provide nonredundant collections of three-dimensional structures for the same sequence where each structure could be taken as a snapshot of the conformational ensemble of the protein. In this way, variations of cavities among conformers have been revealed as part of conformational mechanisms related with biological functions and shared by subsets of proteins with common flexibility features, degree of conformational diversity and disordered regions.…”

“…In this way, variations of cavities among conformers have been revealed as part of conformational mechanisms related with biological functions and shared by subsets of proteins with common flexibility features, degree of conformational diversity and disordered regions. [3][4][5][6]9,10,12 On the other hand, numerous computational methods have been developed to provide extended explorations of the conformational space of proteins. 13 These methods are mainly based on MD techniques including different accelerated conformational sampling techniques 14,15 and Normal mode analysis (NMA) using elastic network models (ENMs).…”