  Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly
advanced protein tertiary structure prediction, achieving results comparable to
real biological experimental methods. While AF2 has shown limitations in
predicting the effects of mutations, its robustness against sequence mutations
remains to be determined. Starting with the wild-type (WT) sequence, we
investigate adversarial sequences generated via an evolutionary approach, which
AF2 predicts to be substantially different from WT. Our experiments on CASP14
reveal that by modifying merely three residues in the protein sequence using a
combination of replacement, deletion, and insertion strategies, the alteration
in AF2's predictions, as measured by the Local Distance Difference Test (lDDT),
reaches 46.61. Moreover, when applied to a specific protein, SPNS2, our
proposed algorithm successfully identifies biologically meaningful residues
critical to protein structure determination and potentially indicates
alternative conformations, thus significantly expediting the experimental
process.
