posted on 2024-01-23, 00:30authored byAyse A. Bekar-Cesaretli, Omeir Khan, Thu Nguyen, Dima Kozakov, Diane Joseph-Mccarthy, Sandor Vajda
The neural network-based program
AlphaFold2 (AF2) provides high
accuracy structure prediction for a large fraction of globular proteins.
An important question is whether these models are accurate enough
for reliably docking small ligands. Several recent papers and the
results of CASP15 reveal that local conformational errors reduce the
success rates of direct ligand docking. Here, we focus on the ability
of the models to conserve the location of binding hot spots, regions
on the protein surface that significantly contribute to the binding
free energy of the protein–ligand interaction. Clusters of
hot spots predict the location and even the druggability of binding
sites, and hence are important for computational drug discovery. The
hot spots are determined by protein mapping that is based on the distribution
of small fragment-sized probes on the protein surface and is less
sensitive to local conformation than docking. Mapping models taken
from the AlphaFold Protein Structure Database show that identifying
binding sites is more reliable than docking, but the success rates
are still 5% to 10% lower than based on mapping X-ray structures.
The drop in accuracy is particularly large for models of multidomain
proteins. However, both the model binding sites and the mapping results
can be substantially improved by generating AF2 models for the ligand
binding domains of interest rather than the entire proteins and even
more if using forced sampling with multiple initial seeds. The mapping
of such models tends to reach the accuracy of results obtained by
mapping the X-ray structures.