π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning models often rely on autoregressive generation, which suffers from error accumulation and slow inference speeds.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses.

Characterizing RNA-binding ligands on structures, chemical information, binding affinity and drug-likeness.

Recent studies suggest RNAs act as promising drug targets. However, limited development has been achieved in detecting RNA-ligand interactions. To guide the discovery of RNA-binding ligands, it is necessary to characterize them comprehensively, especially in the binding specificity, binding affinity and drug-like properties.

Systematic analysis to identify transcriptome-wide dysregulation of Alzheimer's disease in genes and isoforms.

Alzheimer's disease (AD) is one of the most common neurodegeneration diseases caused by multiple factors. The mechanistic insight of AD remains limited. To disclose molecular mechanisms of AD, many studies have been proposed from transcriptome analyses.