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The P. falciparum parasite, responsible for the disease in humans known as malaria, must invade erythrocytes to provide an environment for self-replication and survival. For invasion to occur, the parasite must engage several ligands on the host erythrocyte surface to enable adhesion, tight junction formation and entry. Critical interactions include binding of erythrocyte binding-like ligands and reticulocyte binding-like homologues (Rhs) to the surface of the host erythrocyte. The reticulocyte binding-like homologue 5 (Rh5) is the only member of this family that is essential for invasion and it binds to the basigin host receptor. The essential nature of Rh5 makes it an important vaccine target, however to date, Rh5 has not been targeted by small molecule intervention. Here, we describe the development of a high-throughput screening assay to identify small molecules which interfere with the Rh5-basigin interaction. To validate the utility of this assay we screened a known drug library and the Medicines for Malaria Box and demonstrated the reproducibility and robustness of the assay for high-throughput screening purposes. The screen of the known drug library identified the known leukotriene antagonist, pranlukast. We used pranlukast as a model inhibitor in a post screening evaluation cascade. We procured and synthesised analogues of pranlukast to assist in the hit confirmation process and show which structural moieties of pranlukast attenuate the Rh5 - basigin interaction. Evaluation of pranlukast analogues against P. falciparum in a viability assay and a schizont rupture assay show the parasite activity was not consistent with the biochemical inhibition of Rh5, questioning the developability of pranlukast as an antimalarial. The high-throughput assay developed from this work has the capacity to screen large collections of small molecules to discover inhibitors of P. falciparum Rh5 for future development of invasion inhibitory antimalarials.
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http://dx.doi.org/10.1016/j.ijpddr.2020.10.008 | DOI Listing |
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFNano Lett
September 2025
Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
Precise delivery of nanoliter-scale reagents is essential for high-throughput biochemical assays, yet existing platforms often lack real-time control and selective content fusion. Conventional methods rely on passive encapsulation or stochastic pairing, limiting both throughput and biochemical specificity. Here, we introduce an on-demand nanoliter delivery platform that seamlessly integrates electrical sensing, triggered droplet merging, and passive sorting in a single continuous flow.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Ecology and Evolution, University of Chicago, Illinois, United States of America.
Kobuviruses (family Picornaviridae, genus Kobuvirus) are enteric viruses that infect a wide range of both human and animal hosts. Much of the evolutionary history of kobuviruses remains elusive, largely due to limited screening in wildlife. Bats have been implicated as major sources of virulent zoonoses, including coronaviruses, henipaviruses, lyssaviruses, and filoviruses, though much of the bat virome still remains uncharacterized.
View Article and Find Full Text PDFMol Divers
September 2025
Department of Biotechnology, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492001, India.
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations.
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