Back

Circular permutants of azurin exhibit molten globule intermediates not observed in WT azurin

Das, D.; Ainavarapu, S. R. K.

2025-10-10 biophysics
10.1101/2025.10.10.681544 bioRxiv
Show abstract

Molten globule (MG) states of proteins have been described as special types of intermediate states within the energy landscape diagram of proteins. In a cell, the MG state has functional significance compared to the native or unfolded states owing to its enhanced side-chain dynamics. In this study, we re-examined the acid-denatured state of the metalloprotein azurin and also investigated whether circular permutation (CP) of azurin can lead to modulation of the energy landscape to yield intermediate or MG states. CP is a protein engineering tool that allows the rearrangement of the secondary structural elements without perturbing the overall three-dimensional protein structure. We carried out comprehensive thermal, chemical, and pH denaturation studies to examine possible MG states in azurin WT. We additionally examined two CPs of azurin, whose energy landscapes in the metal-free and metal-bound forms have been previously characterized. One of the positions of CP is near the metal-binding loop (cpN42) and the other is within the active site of the protein (cpF114). We used thermal denaturation monitored by far-UV and near-UV CD, time-resolved fluorescence anisotropy of a buried Trp, and solvent accessibility of the same Trp residue to evaluate the presence of MG states in these proteins. WT azurin shows binding with ANS at a low pH due to ionic interaction with the fluorophore at this pH. We find that though WT azurin does not adopt an MG state, CP of azurin(s) exhibited an MG state. Our findings highlight the potential of CP to modulate protein energy landscapes and consequently the physiological functions of proteins.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.