Benchmarking AlphaFold-Generated Structures of Chemokine–Chemokine Receptor Complexes

AlphaFold
and AlphaFold-Multimer have become two essential tools
for the modeling of unknown structures of proteins and protein complexes.
In this work, we extensively benchmarked the quality of chemokine–chemokine
receptor structures generated by AlphaFold-Multimer against experimentally
determined structures. Our analysis considered both the global quality
of the model, as well as key structural features for chemokine recognition.
To study the effects of template and multiple sequence alignment parameters
on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer
correctly predicted differences in chemokine binding orientation and
accurately reproduced the unique binding orientation of the CXCL12-ACKR3
complex. Further, the predictions of the full receptor N-terminus
provided insights into a putative chemokine recognition site 0.5.
The accuracy of chemokine N-terminus binding mode prediction varied
between complexes, but the confidence score permitted the distinguishing
of residues that were very likely well positioned. Finally, we generated
a high-confidence model of the unsolved CXCL12-CXCR4 complex, which
agreed with experimental mutagenesis and cross-linking data.