A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation.

Scaffold proteins drive liquid-liquid phase separation (LLPS) to form biomolecular condensates and organize various biochemical reactions in cells. Dysregulation of scaffolds can lead to aberrant condensate assembly and various complex diseases. However, bioinformatics predictors dedicated to scaffolds are still lacking and their development suffers from an extreme imbalance between limited experimentally identified scaffolds and unlabeled candidates. Here, using the joint distribution of hybrid multimodal features, we implemented a positive unlabeled (PU) learning-based framework named PULPS that combined ProbTagging and penalty logistic regression (PLR) to profile the propensity of scaffolds. PULPS achieved the best AUC of 0.8353 and showed an area under the lift curve (AUL) of 0.8339 as an estimation of true performance. Upon reviewing recent experimentally verified scaffolds, we performed a partial recovery with 2.85% increase in AUL from 0.8339 to 0.8577. In comparison, PULPS showed a 45.7% improvement in AUL compared with PLR, whereas 8.2% superiority over other existing tools. Our study first proved that PU learning is more suitable for scaffold prediction and demonstrated the widespread existence of phase separation states. This profile also uncovered potential scaffolds that co-drive LLPS in the human proteome and generated candidates for further experiments. PULPS is free for academic research at http://pulps.zbiolab.cn.

Identification of a broad sarbecovirus neutralizing antibody targeting a conserved epitope on the receptor-binding domain.

Omicron, as the emerging variant with enhanced vaccine tolerance, has sharply disrupted most therapeutic antibodies. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the subgenus Sarbecovirus, members of which share high sequence similarity. Herein, we report one sarbecovirus antibody, 5817, which has broad-spectrum neutralization capacity against SARS-CoV-2 variants of concern (VOCs) and SARS-CoV, as well as related bat and pangolin viruses. 5817 can hardly compete with six classes of receptor-binding-domain-targeted antibodies grouped by structural classifications. No obvious impairment in the potency is detected against SARS-CoV-2 Omicron and subvariants. The cryoelectron microscopy (cryo-EM) structure of neutralizing antibody 5817 in complex with Omicron spike reveals a highly conserved epitope, only existing at the receptor-binding domain (RBD) open state. Prophylactic and therapeutic administration of 5817 potently protects mice from SARS-CoV-2 Beta, Delta, Omicron, and SARS-CoV infection. This study reveals a highly conserved cryptic epitope targeted by a broad sarbecovirus neutralizing antibody, which would be beneficial to meet the potential threat of pre-emergent SARS-CoV-2 VOCs.