Glycophorin B-PfEMP1 interaction mediates robust rosetting in Plasmodium falciparum.

P. falciparumerythrocyte membrane protein 1 (PfEMP1) is the major parasite protein responsible for rosetting by binding to host receptors such as heparan sulfate, CR1 on RBC surface. Usually monomeric protein-carbohydrate interactions are weak [1], therefore PfEMP1 binds to plasma proteins like IgM or α2-macroglobulin that facilitate its clustering on parasitized RBC surface and augment rosetting [2,3]. We show that 3D7A expresses PfEMP1, PF3D7_0412900, and employs its CIDRγ2 domain to interact with glycophorin B on uninfected RBC to form large rosettes but more importantly even in the absence of plasma proteins. Overall, we established the role of PF3D7_0412900 in rosetting as antibodies against CIDRγ2 domain reduced rosetting and also identified its receptor, glycophorin B which could provide clue why glycophorin B null phenotype, S-s-U- RBCs prevalent in malaria endemic areas is protective against severe malaria.

PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction.

Recent advances in language models have enabled the protein modeling community with a powerful tool that uses transformers to represent protein sequences as text. This breakthrough enables a sequence-to-property prediction for peptides without relying on explicit structural data. Inspired by the recent progress in the field of large language models, we present PeptideBERT, a protein language model specifically tailored for predicting essential peptide properties such as hemolysis, solubility, and nonfouling. The PeptideBERT utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. Through fine-tuning the pretrained model for the three downstream tasks, our model is state of the art (SOTA) in predicting hemolysis, which is crucial for determining a peptide's potential to induce red blood cells as well as nonfouling properties. Leveraging primarily shorter sequences and a data set with negative samples predominantly associated with insoluble peptides, our model showcases remarkable performance.