Skip to yearly menu bar Skip to main content

Workshop: Machine Learning in Structural Biology Workshop

Using artificial sequence coevolution to predict disulfide-rich peptide structures with experimental connectivity in AlphaFold

Gabriella Gerlach · John Nicoludis


We present a novel approach for embedding contact information in Alphafold to predict structures of disulfide-rich peptides (DRPs) with experimental disulfide connectivity. While AlphaFold generates accurate DRP structure prediction in most cases, it sometimes fails at predicting the specific connectivity pattern of the multiple disulfide bonds. Here, we take advantage of the principles of sequence coevolution to directly embed specific connectivity patterns within the MSA by mutating highly conserved cysteines in subsets of the MSA. This approach can be used to incorporate experimental disulfide connectivity patterns from mass spectrometry into DRP structure prediction. Lastly, after minimization of predicted structures by molecular dynamics, we find that predicted DRP structures with native connectivity display favorable peptide properties compared to non-native connectivities, suggesting our approach may be useful for determining the native connectivity of DRPs from sequence alone.

Chat is not available.