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. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

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. We established a database, RNALID (http://biomed.nscc-gz.cn/RNALID/html/index.html#/database), which collects RNA-ligand interactions validated by low-throughput experiment. RNALID contains 358 RNA-ligand interactions. Comparing to the fellow database, 94.5% of ligands in RNALID are completely or partially novel collections, and 51.78% have novel two-dimensional (2D) structures. Through the analysis of ligand structure, binding affinity and cheminformatic parameters we found that multivalent (MV) ligands mainly binding to RNA repeats are more structurally conserved in both 2D and 3D structures than other ligand types, exhibit higher binding specificity and binding affinity than ligands binding to non-repeat RNAs, but deviate far from the Lipinski's rule of five. In contrary, small molecule (SM) ligands binding to virus RNA exhibit higher affinity and more resemble protein-ligands, but potentially possess low binding specificity. Further analysis on 28 detailed drug-likeness properties indicated that RNA-ligands' development need to balance between the binding affinity and the drug-likeness because of the significant linear co-relationship between the two. Comparing RNALID ligands to FDA-approved drugs and ligands without bioactivity indicated that RNA-binding ligands are different from them in chemical properties, structural properties and drug-likeness. Thus, characterizing the RNA-ligand interactions in RNALID in multiple respects provides new insights into discovering and designing druggable ligands binding with RNA.

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. However, no analysis across multiple levels of transcription has been conducted to discover co-expression networks of AD. We performed gene-level and isoform-level analyses of RNA sequencing (RNA-seq) data from 544 brain tissues of AD patients, mild cognitive impaired (MCI) patients, and healthy controls. Gene and isoform levels of co-expression modules were constructed by RNA-seq data. The associations of modules with AD were evaluated by integrating cognitive scores of patients, Genome-wide association studies (GWAS), alternative splicing analysis, and dementia-related genes expressed in brain tissues. Totally, 29 co-expression modules were found with expressions significantly correlated with the cognitive scores. Among them, two isoform modules were enriched with AD-associated SNPs and genes whose mRNA splicing displayed significant alteration in relation to AD disease. These two modules were further found enriched with dementia-related genes expressed in four brain regions of 125 AD patients. Analyzing expressions of these two modules revealed expressions of 39 isoforms (corresponding to 35 genes) significantly correlated with cognitive scores of AD patients, in which 38 isoforms were significantly up-regulated in AD patients comparing to controls, and 33 isoforms (corresponding to 29 genes) were not reported as AD-related previously. Employing the co-expression modules and the drug-induced gene expression data from Connectivity Map (CMAP), 12 drugs were predicted as significant in restoring the gene expression of AD patients towards health, which include nine drugs reported for relieving AD. In comparison, four of the top 12 significant drugs were known for relieving AD if the drug prediction was performed by the genes expressed significantly different in AD and healthy controls. Analysis of multiple levels of the transcriptomic organization is useful in suggesting AD-related co-expression networks and discovering drugs.