Douglas R. Houston

The control of malaria, caused by Plasmodium falciparum, is hampered by the relentless evolution of drug resistance. Because artemisinin derivatives are now used in the most effective anti-malarial therapy, resistance to artemisinin would be catastrophic. Indeed, studies suggest that artemisinin resistance has already appeared in natural infections. Understanding the mechanisms of resistance would help to prolong the effective lifetime of these drugs. Genetic markers of resistance are therefore required urgently. Previously, a mutation in a de-ubiquitinating enzyme was shown to confer artemisinin resistance in the rodent malaria parasite Plasmodium chabaudi. Here, for a mutant P. chabaudi malaria parasite and its immediate progenitor, the in vivo artemisinin resistance phenotypes and the mutations arising using Illumina whole-genome re-sequencing were compared. An increased artemisinin resistance phenotype is accompanied by one non-synonymous substitution. The mutated gene encodes the μ-chain of the AP2 adaptor complex, a component of the endocytic machinery. Homology models indicate that the mutated residue interacts with a cargo recognition sequence. In natural infections of the human malaria parasite P. falciparum, 12 polymorphisms (nine SNPs and three indels) were identified in the orthologous gene. An increased artemisinin-resistant phenotype occurs along with a mutation in a functional element of the AP2 adaptor protein complex. This suggests that endocytosis and trafficking of membrane proteins may be involved, generating new insights into possible mechanisms of resistance. The genotypes of this adaptor protein can be evaluated for its role in artemisinin responses in human infections of P. falciparum.

Structure-based virtual screening relies on scoring the predicted binding modes of compounds docked into the target. Because the accuracy of this scoring relies on the accuracy of the docking, methods that increase docking accuracy are valuable. Here, we present a relatively straightforward method for improving the probability of identifying accurately docked poses. The method is similar in concept to consensus scoring schemes, which have been shown to increase ranking power and thus hit rates, but combines information about predicted binding modes rather than predicted binding affinities. The pose prediction success rate of each docking program alone was found in this trial to be 55% for Autodock, 58% for DOCK, and 64% for Vina. By using more than one docking program to predict the binding pose, correct poses were identified in 82% or more of cases, a significant improvement. In a virtual screen, these more reliably posed compounds can be preferentially advanced to subsequent scoring stages to improve hit rates. Consensus docking can be easily introduced into established structure-based virtual screening methodologies.