Viroid-like RNAs (vdlRNAs) are infectious agents characterized by single-stranded circular genomes folding into compact secondary structures, often containing self-cleaving ribozymes involved in replication. Recent metatranscriptomic studies have expanded their diversity across multiple ecological niches and revealed n...
N. Serale, M. Chiumenti, P. Mussano et al.· bioRxiv· 0 citations
Large-scale mining of public transcriptomic datasets can reveal viral diversity that remains invisible to conventional virus-surveillance approaches, while increasingly powerful structure-prediction methods provide a complementary route to characterizing highly divergent viral proteins. Here, we combine sequence detect...
N. Bejerman, D. Huebert, R. Alvarez-Quinto et al.· bioRxiv· 0 citations
ESMCHalo is a frozen protein-sequence predictor that combines ESMC-600M layer-36 canonical mean representations with five compact student heads trained using out-of-fold teacher targets with documented cross-fold limitations. The release provides frozen inference code, model checkpoints, calibration, a fixed threshold,...
Fang Lv, Hutao Jin, Qishuai Ma et al.· Zenodo (CERN European Organi...· 0 citations
The mainstream approach to splicing therapy today involves synthetic antisense oligonucleotides. ASOs interfere with spliceosomal ribozyme assembly by blocking splice sites or influence protein regulators by blocking or adding their binding sites. ASOs are not compatible with human biology, which limits their efficacy...
Olga V. Artemyeva-Isman· Preprints.org· 0 citations
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U2, U6 and U5 are the components of spliceosomal ribozymes that descend from mobile self-splicing introns. The fragmentation of ancestral introns in eukaryogenesis led to the evolution of the ribozyme assembly in trans using common snRNAs and protein-chaperones. This stepwise process provides opportunities to target di...
Olga V. Artemyeva-Isman· Preprints.org· 0 citations
Start with the six-page PDF. On 19 September 2026 Edison Scientific and FutureHouse published twelve Millennium Problems for Biology with explicit acceptance criteria. This note takes two of them, Problem 3 (the reverse translatase) and Problem 10 (the protein amplification chain reaction), and proves what the criteria...
John Nader· Zenodo (CERN European Organi...· 0 citations
BACKGROUND
Walnut meal, a major by-product of oil extraction, is rich in protein and bioactive compounds but suffers from poor palatability and low digestibility due to anti-nutritional factors (ANFs) such as phytic acid, tannins, and trypsin inhibitors. Conventional processing methods fail to address these limitations...
Meng-Yu Yang, Jie Zhou, Thanh Ninh Le et al.· The Journal of the Science o...· 0 citations
There was a positive correlation between TLR-2 expression and inflammatory and disease activity indicators, and TLR-2 expression was considerably higher in RA patients, suggesting that TLR-2 might be a useful biomarker for RA activity and development.
A. M. Hussein, Muntaha A. Abdulkareem, Nadia T. Hefdhi· Israa University Journal for...· 0 citations
Start with the six-page PDF. On 19 September 2026 Edison Scientific and FutureHouse published twelve Millennium Problems for Biology with explicit acceptance criteria. This note takes two of them, Problem 3 (the reverse translatase) and Problem 10 (the protein amplification chain reaction), and proves what the criteria...
John Nader· Zenodo (CERN European Organi...· 0 citations
AlphaFold 3 (AF3) has transformed structural bioinformatics through generative diffusion modeling of biomolecular assemblies. However, intrinsically disordered regions (IDRs)—which comprise over 30% of the human proteome and mediate essential cell signaling, oncogenesis, and phase separation—violate the classic thermod...
Genetically proxied smoking was associated with increased risks of uIAs and aSAH, and smoking-related proteins with the risk of unruptured intracranial aneurysms and aneurysmal subarachnoid hemorrhage were assessed.
Ya-Qi Liu, Hao Yuan, Chi Huang et al.· Tobacco Induced Diseases· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
