Decoding the Molecular Effects of Atovaquone Linked Resistant Mutations on Plasmodium falciparum Cytb-ISP Complex in the Phospholipid Bilayer Membrane.

Atovaquone (ATQ) is a drug used to prevent and treat malaria that functions by targeting the Plasmodium falciparum cytochrome b (PfCytb) protein. PfCytb catalyzes the transmembrane electron transfer (ET) pathway which maintains the mitochondrial membrane potential. The ubiquinol substrate binding site of the protein has heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors that act as redox centers to aid in ET. Recent studies investigating ATQ resistance mechanisms have shown that point mutations of PfCytb confer resistance. Thus, understanding the resistance mechanisms at the molecular level via computational approaches incorporating phospholipid bilayer would help in the design of new efficacious drugs that are also capable of bypassing parasite resistance. With this knowledge gap, this article seeks to explore the effect of three drug resistant mutations Y268C, Y268N and Y268S on the PfCytb structure and function in the presence and absence of ATQ. To draw reliable conclusions, 350 ns all-atom membrane (POPC:POPE phospholipid bilayer) molecular dynamics (MD) simulations with derived metal parameters for the holo and ATQ-bound -proteins were performed. Thereafter, simulation outputs were analyzed using dynamic residue network (DRN) analysis. Across the triplicate MD runs, hydrophobic interactions, reported to be crucial in protein function were assessed. In both, the presence and absence of ATQ and a loss of key active site residue interactions were observed as a result of mutations. These active site residues included: Met 133, Trp136, Val140, Thr142, Ile258, Val259, Pro260 and Phe264. These changes to residue interactions are likely to destabilize the overall intra-protein residue communication network where the proteins' function could be implicated. Protein dynamics of the ATQ-bound mutant complexes showed that they assumed a different pose to the wild-type, resulting in diminished residue interactions in the mutant proteins. In summary, this study presents insights on the possible effect of the mutations on ATQ drug activity causing resistance and describes accurate MD simulations in the presence of the lipid bilayer prior to conducting inhibitory drug discovery for the PfCytb-iron sulphur protein (Cytb-ISP) complex.

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.

Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting.

The impact of pre-existing immunity on the efficacy of artemisinin combination therapy is largely unknown. We performed in-depth profiling of serological responses in a therapeutic efficacy study [comparing artesunate-mefloquine (ASMQ) and artemether-lumefantrine (AL)] using a proteomic microarray. Responses to over 200 Plasmodium antigens were significantly associated with ASMQ treatment outcome but not AL. We used machine learning to develop predictive models of treatment outcome based on the immunoprofile data. The models predict treatment outcome for ASMQ with high (72-85%) accuracy, but could not predict treatment outcome for AL. This divergent treatment outcome suggests that humoral immunity may synergize with the longer mefloquine half-life to provide a prophylactic effect at 28-42 days post-treatment, which was further supported by simulated pharmacokinetic profiling. Our computational approach and modeling revealed the synergistic effect of pre-existing immunity in patients with drug combination that has an extended efficacy on providing long term treatment efficacy of ASMQ.