Force Field Parameters for Fe2+4S2-4 Clusters of Dihydropyrimidine Dehydrogenase, the 5-Fluorouracil Cancer Drug Deactivation Protein: A Step towards In Silico Pharmacogenomics Studies.

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.

Human OMICs and Computational Biology Research in Africa: Current Challenges and Prospects.

Following the publication of the first human genome, OMICs research, including genomics, transcriptomics, proteomics, and metagenomics, has been on the rise. OMICs studies revealed the complex genetic diversity among human populations and challenged our understandings of genotype-phenotype correlations. Africa, being the cradle of the first modern humans, is distinguished by a large genetic diversity within its populations and rich ethnolinguistic history. However, the available human OMICs tools and databases are not representative of this diversity, therefore creating significant gaps in biomedical research. African scientists, students, and publics are among the key contributors to OMICs systems science. This expert review examines the pressing issues in human OMICs research, education, and development in Africa, as seen through a lens of computational biology, public health relevant technology innovation, critically-informed science governance, and how best to harness OMICs data to benefit health and societies in Africa and beyond. We underscore the disparities between North and Sub-Saharan Africa at different levels. A harmonized African ethnolinguistic classification would help address annotation challenges associated with population diversity. Finally, building on the existing strategic research initiatives, such as the H3Africa and H3ABioNet Consortia, we highly recommend addressing large-scale multidisciplinary research challenges, strengthening research collaborations and knowledge transfer, and enhancing the ability of African researchers to influence and shape national and international research, policy, and funding agendas. This article and analysis contribute to a deeper understanding of past and current challenges in the African OMICs innovation ecosystem, while also offering foresight on future innovation trajectories.