CIAA: Integrated Proteomics and Structural Modeling for Understanding Cysteine Reactivity with Iodoacetamide Alkyne

Cysteine residues play key roles in protein structure
and function
and can serve as targets for chemical probes and even drugs. Chemoproteomic
studies have revealed that heightened cysteine reactivity toward electrophilic
probes, such as iodoacetamide alkyne (IAA), is indicative of likely
residue functionality. However, while the cysteine coverage of chemoproteomic
studies has increased substantially, these methods still provide only
a partial assessment of proteome-wide cysteine reactivity, with cysteines
from low-abundance proteins and tough-to-detect peptides still largely
refractory to chemoproteomic analysis. Here, we integrate cysteine
chemoproteomic reactivity data sets with structure-guided computational
analysis to delineate key structural features of proteins that favor
elevated cysteine reactivity toward IAA. We first generated and aggregated
multiple descriptors of cysteine microenvironment, including amino
acid content, solvent accessibility, residue proximity, secondary
structure, and predicted pKa. We find
that no single feature is sufficient to accurately predict the reactivity.
Therefore, we developed the CIAA (Cysteine reactivity toward IodoAcetamide
Alkyne) method, which utilizes a Random Forest model to assess cysteine
reactivity by incorporating descriptors that characterize the three-dimensional
(3D) structural properties of thiol microenvironments. We trained
the CIAA model on existing and newly generated cysteine chemoproteomic
reactivity data paired with high-resolution crystal structures from
the Protein Data Bank (PDB), with cross-validation against an external
data set. CIAA analysis reveals key features driving cysteine reactivity,
such as backbone hydrogen bond donor atoms, and reveals still underserved
needs in the area of computational predictions of cysteine reactivity,
including challenges surrounding protein structure selection data
set curation. Thus, our work provides a strong foundation for deploying
artificial intelligence (AI) on cysteine chemoproteomic data sets.