The dimeric dihydropyrimidine dehydrogenase (DPD), metalloenzyme, an adjunct anti-cancer drug target, contains highly specialized 4 × Fe2+4S2−4 clusters per chain. These clusters facilitate the catalysis of the rate-limiting step in the pyrimidine degradation pathway through a harmonized electron transfer cascade that triggers a redox catabolic reaction. In the process, the bulk of the administered 5-fluorouracil (5-FU) cancer drug is inactivated, while a small proportion is activated to nucleic acid antimetabolites. The occurrence of missense mutations in DPD protein within the general population, including those of African descent, has adverse toxicity effects due to altered 5-FU metabolism. Thus, deciphering mutation effects on protein structure and function is vital, especially for precision medicine purposes. We previously proposed combining molecular dynamics (MD) and dynamic residue network (DRN) analysis to decipher the molecular mechanisms of missense mutations in other proteins. However, the presence of Fe2+4S2−4 clusters in DPD poses a challenge for such in silico studies. The existing AMBER force field parameters cannot accurately describe the Fe2+ center coordination exhibited by this enzyme. Therefore, this study aimed to derive AMBER force field parameters for DPD enzyme Fe2+ centers, using the original Seminario method and the collation features Visual Force Field Derivation Toolkit as a supportive approach. All-atom MD simulations were performed to validate the results. Both approaches generated similar force field parameters, which accurately described the human DPD protein Fe2+4S2−4 cluster architecture. This information is crucial and opens new avenues for in silico cancer pharmacogenomics and drug discovery related research on 5-FU drug efficacy and toxicity issues.
Comparison of different approaches to derive classical bonded force-field parameters for a transition metal cofactor: a case study for non-heme iron site of ectoine synthase
