Biology subjects

Bo, W.

Publications and source records attributed to Bo, W..

4 recordsLinked to original sources

bioRxiv2026

Integrating Narrow-Window DIA with AI-Powered Search Enables Deep and Reliable Functional Proteomics

Narrow-window data-independent acquisition (nDIA) is emerging as a powerful technique for bottom-up proteomics. Here, we systematically benchmarked nDIA, wide-window DIA (wDIA), narrow-window data-dependent acquisition (nDDA), and wide-window DDA (wDDA) for rapid, single-shot proteomic analysis. For data processing, we introduced Tesorai Search, a new search engine leveraging a large pre-trained model and compared it with DIA-NN and FragPipe across both DIA and DDA datasets. Among 12 acquisition-analysis pipelines evaluated, nDIA combined with DIA-NN and Tesorai Search delivered the highest proteome coverage, identifying 10,255 and 10,766 protein groups from benchmark samples, respectively. Both search engines maintained rigorous false-discovery rate (FDR) control. While nDIA generally outperformed nDDA in sensitivity, FragPipe-DDA+ approach proved to be the most sensitive within the nDDA pipelines. However, entrapment analyses indicate that this sensitivity comes at the cost of less robust FDR control compared to Tesorai Search. As a proof of concept, we applied nDIA-MS to 17 cancer cell lines harboring DNA damage response (DDR) gene knockouts, successfully detecting significant downregulation of all targeted proteins and uncovering 81 DDR-related proteins modulated in at least one cell line. These results underscore nDIA-MS, together with DIA-NN and Tesorai Search, as a robust and scalable platform for high-throughput functional proteomic screening.

systems biology↗

bioRxiv2023

AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery

Ionizable lipid nanoparticles (LNPs) have seen widespread use in mRNA delivery for clinical applications, notably in SARS-CoV-2 mRNA vaccines. Despite their successful use, expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored to different target cell types. The traditional process of LNP development remains labor-intensive and cost-inefficient, relying heavily on trial and error. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines the iterative development of ionizable lipids, crucial components for LNP-mediated mRNA delivery. This approach brings forth three significant features: efficient design and synthesis of combinatorial lipid libraries, comprehensive in silico lipid screening employing deep neural networks, and adaptability to diverse cell lines. Using AGILE, we were able to rapidly design, synthesize, and evaluate new ionizable lipids for mRNA delivery in muscle and immune cells, selecting from a library of over 10,000 candidates. Importantly, AGILE has revealed cell-specific preferences for ionizable lipids, indicating the need for different tail lengths and head groups for optimal delivery to varying cell types. These results underscore the potential of AGILE in expediting the development of customized LNPs. This could significantly contribute to addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies. One Sentence SummaryAI and combinatorial chemistry expedite ionizable lipid creation for mRNA delivery.

biochemistry↗

bioRxiv2023

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

bioinformatics↗

bioRxiv2021

Haemosporidian Blood Parasites in nestling birds of prey in Mongolia

Haemosporidians are vector-transmitted intracellular parasites that happen in numerous bird species worldwide and may possibly have important effects for wild bird populations. Studies of haemosporidians most dedicated on Europe and North America, and only newly some study in the Neotropics has been done, where the occurrence and influences of the disease have been less considered and are not understood well. In this study we designed a study in the nestling birds of prey in Mongolia. We sampled blood from 72 raptors at 2 different species and evaluated avian haemosporidian infection by two nested PCR protocol and one Real time PCR protocol. Sequencing a portion of the cytochrome b (cyt b) gene of the parasite. From the sampled birds, 10 % were infected by Plasmodium. Inclusive, our findings advocate a high haemosporidian species richness in the bird community of Mongolia. In view of the frequency of local habitat loss that in this area is living, recognize how avian haemosporidians affect bird populations it is very important; in addition, more exhaustive sampling is required to fully understand the range of avian haemosporidian infection in this area.

microbiology↗