Assessment of AlphaFold2 for Human Proteins via Residue Solvent Exposure

As only 35% of human proteins feature
(often partial) PDB structures,
the protein structure prediction tool AlphaFold2 (AF2) could have
massive impact on human biology and medicine fields, making independent
benchmarks of interest. We studied AF2’s ability to describe
the backbone solvent exposure as a functionally important and easily
interpretable “natural coordinate” of protein conformation,
using human proteins as test case. After screening for appropriate
comparative sets, we matched 1818 human proteins predicted by AF2
against 7585 unique experimental PDBs, and after curation for sequence
overlap, we assessed 1264 comparative pairs comprising 115 unique
AF2 structures and 652 unique experimental structures. AF2 performed
markedly worse for multimers, whereas ligands, cofactors, and experimental
resolution were interestingly not very important for performance.
AF2 performed excellently for monomer proteins. Challenges relating
to specific groups of residues and multimers were analyzed. We identified
larger deviations for lower-confidence scores (pLDDT), and exposed
residues and polar residues (e.g., Asp, Glu, Asn) being less accurately
described than hydrophobic residues. Proline conformations were the
hardest to predict, probably due to a common location in dynamic solvent-accessible
parts. In summary, using solvent exposure as a metric, we quantified
the performance of AF2 for human proteins and provided estimates of
the expected agreement as a function of ligand presence, multimer/monomer
status, local residue solvent exposure, pLDDT, and amino acid type.
Overall performance was found to be excellent.