The increasing resistance to currently available insecticides in the malaria vector, Anopheles mosquitoes, hampers their use as an effective vector control strategy for the prevention of malaria transmission. Therefore, there is need for new insecticides and/or alternative vector control strategies, the development of which relies on the identification of possible targets in Anopheles. Some known and promising targets for the prevention or control of malaria transmission exist among Anopheles metabolic proteins. This review aims to elucidate the current and potential contribution of Anopheles metabolic proteins to malaria transmission and control. Highlighted are the roles of metabolic proteins as insecticide targets, in blood digestion and immune response as well as their contribution to insecticide resistance and Plasmodium parasite development. Furthermore, strategies by which these metabolic proteins can be utilized for vector control are described. Inhibitors of Anopheles metabolic proteins that are designed based on target specificity can yield insecticides with no significant toxicity to non-target species. These metabolic modulators combined with each other or with synergists, sterilants, and transmission-blocking agents in a single product, can yield potent malaria intervention strategies. These combinations can provide multiple means of controlling the vector. Also, they can help to slow down the development of insecticide resistance. Moreover, some metabolic proteins can be modulated for mosquito population replacement or suppression strategies, which will significantly help to curb malaria transmission.

Trehalase inhibitors are considered safe alternatives for insecticides and fungicides. However, there are no studies testing these compounds on Anopheles gambiae, a major vector of human malaria. This study predicted the three-dimensional structure of Anopheles gambiae trehalase (AgTre) and identified potential inhibitors using molecular docking and molecular dynamics methods. Robetta server, C-I-TASSER, and I-TASSER were used to predict the protein structure, while the structural assessment was carried out using SWISS-MODEL, ERRAT, and VERIFY3D. Molecular docking and screening of 3022 compounds was carried out using AutoDock Vina in PyRx, and MD simulation was carried out using NAMD. The Robetta model outperformed all other models and was used for docking and simulation studies. After a post-screening analysis and ADMET studies, uniflorine, 67837201, 10406567, and Compound 2 were considered the best hits with binding energies of −6.9, −8.9, −9, and −8.4 kcal/mol, respectively, better than validamycin A standard (−5.4 kcal/mol). These four compounds were predicted to have no eco-toxicity, Brenk, or PAINS alerts. Similarly, they were predicted to be non-mutagenic, carcinogenic, or hepatoxic. 67837201, 10406567, and Compound 2 showed excellent stability during simulation. The study highlights uniflorine, 67837201, 10406567, and Compound 2 as good inhibitors of AgTre and possible compounds for malaria vector control.

Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control.