Iron-sulfur (Fe-S) metalloproteins play vital roles in cellular processes, including DNA replication and repair. Though Fe-S protein dysregulation has been suggested to be associated with developmental and cancerous diseases, only a few Fe-S proteins with genome maintenance roles have been identified experimentally, likely because Fe-S cluster is susceptible to degradation under oxic environment. In vitro, Fe-S clusters could be substituted by other metal ions during protein purification, resulting in misannotation of Fe-S proteins as apoproteins or other metalloproteins. In silico, Fe-S proteins feature cysteines with atypical spacings, rendering motif prediction based solely on sequence signature difficult. Thus, in this study, a three-pronged bioinformatic approach is developed to discover putative [4Fe-4S] proteins in the human proteome: (i) a triamino acid motif involved in Fe-S biogenesis and reconstitution, (ii) cysteine geometric coordinates, and (iii) cysteine mutations with clinical relevance. Here, 21 novel proteins are uncovered as cancer-associated [4Fe-4S] protein candidates while using MUTYH, a known [4Fe-4S] protein, as positive control. Specifically, this study predicts 6 receptor proteins, 3 growth factors, and 5 histone lysine methyltransferases with SET domains to potentially contain [4Fe-4S] metallocofactors. This work establishes a bioinformatic framework for systematic identification of new metalloproteins and discovery of novel disease biomarkers.