Tao Jiang

Mass spectrometry-based metabolomics generates complex, high-dimensional data that holds vast potential for biological discovery but remains difficult to i

Motivation: The quality of tandem mass spectra critically determines metabolite identifiability in untargeted metabolomics, yet optimizing MS2 acquisition parameters experimentally is costly, time-consuming, and infeasible across the full diversity of samples and instruments. A key obstacle to computational quality assessment is the absence of reliable negative labels: most MS2 spectra remain unannotated not because they are low quality, but because the corresponding compounds are absent from reference libraries. This label ambiguity fundamentally limits supervised learning approaches and complicates acquisition-time decision making. Results: We present a deep learning framework that predicts the probability that an MS2 scan will be identifiable using only the preceding MS1 spectrum and instrument acquisition parameters, without inspecting the MS2 spectrum itself. The problem is formulated as positive-unlabeled learning and addressed using a non-negative positive-unlabeled objective, enabling robust training despite missing negative labels. Trained on over eight million MS2 scans from public Orbitrap metabolomics datasets and evaluated on laboratory-disjoint benchmarks, the model recovered 90% of known identifiable spectra in a held-out test set. Predicted probabilities generalized to unseen laboratories and stratified unlabeled spectra in a physicochemically meaningful manner. Independent validation demonstrated that high predicted quality is associated with increased structural explainability, richer fragmentation patterns, canonical precursor charge states, and reduced spectral interference. These results indicate that MS2 identifiability can be anticipated from precursor context and acquisition settings alone. Availability and implementation: Code is available at https://github.com/bittremieuxlab/pu_ms2_identifiability . Model weights and processed datasets are available at 10.5281/zenodo.18266932 .

Mass spectrometry metabolomics generates complex data that overwhelms traditional analysis approaches. MetaboT, a multi-agent Large Language Model (LLM)-based framework, converts natural-language questions into needed SPARQL queries, enabling effective knowledge graph navigation (https://holobiomicslab.github.io/MetaboT/). We validate MetaboT’s performance on a large plant dataset with 50 representative queries. MetaboT's modular design facilitates advanced data mining, biological interpretation, and the discovery of novel compounds without specialized programming expertise.

Crystallophores are lanthanide complexes that have demonstrated outstanding induction of crystallization for various proteins. This article explores the effect of tailored modifications of the crystallophore first generation and studies their impact on the nucleating properties, and protein crystal structures. Through high-throughput crystallization experiments and dataset analysis, we evaluated the effectiveness of these variants, in comparison to the first crystallophore generation G1. In particular, the V1 variant, featuring a propyl-3-ol pendant arm, demonstrated the ability to produce new crystallization conditions for the proteins tested (hen-egg white lysozyme, proteinase K and thaumatin). Structural analysis performed in the case of hen egg-white lysozyme along with Molecular Dynamics simulations, highlights V1's unique behavior, taking advantage of the flexibility of its propyl-3-ol arm to explore different protein surfaces and form versatile supramolecular interactions.

The stimulator of interferon genes (STING) protein is a cornerstone of the human immune response. Its activation by cGAMP in the presence of cytosolic DNA stimulates the production of type Iinterferons and inflammatory cytokines. In the human population, several STING variants exist and exhibit dramatic differences in their activity, impacting the efficiency of the host defense against infections. Understanding the molecular mechanisms of these variants opens perspectives for personalized medicine treatments against diseases such as viral infections, cancers, or autointlammatory diseases. Through microsecond-scale molecular modeling simulations, contact analyses, and machine learning techniques, we reveal the dynamic behavior of four STING variants (wild type, G230A, R293Q, and G230A/R293Q) and rationalize the variability of efficiency observed experimentally. Our results show that the decrease in STING activity is linked to a stiffening of key structural elements of the binding cavity together with changes in the interaction patterns within the protein.

Apurinic/apyrimidinic sites are the most common forms of DNA damage under physiological conditions, yet their structural and dynamical behavior within nucleosome core particles has just begun to be investigated and is dramatically different from that of abasic sites in B-DNA. Clusters of two or more abasic sites are repaired even less efficiently and hence constitute hot spots of high mutagenicity notably due to enhanced double-strand break formation. On the basis of an X-ray structure of a 146 bp DNA wrapped onto a histone core, we investigate the structural behavior of two bistranded abasic sites positioned at mutational hot spots during microsecond-range molecular dynamics simulations. Our simulations allow us to probe interactions of histone tails at clustered abasic site locations, with a definitive assignment of the key residues involved in the NCP-catalyzed formation of DNA–protein cross-linking in line with recent experimental findings, and pave the way for a systematic assessment of the response of histone tails to DNA lesions.

Functionalized supramolecular cages are of growing importance in biology and biochemistry. They have recently been proposed as efficient auxiliaries to obtain high-resolution cocrystallized proteins. Here, we propose a molecular dynamics investigation of the supramolecular association of sulfonated calix-[8]-arenes to cytochrome c starting from initially distant proteins and ligands. We characterize two main binding sites for the sulfonated calixarene on the cytochrome c surface which are in perfect agreement with the previous experiments with regard to the structure (comparison with the X-ray structure PDB 6GD8) and the binding free energies [comparison between the molecular mechanics Poisson–Boltzmann surface area analysis and the isothermal titration calorimetry measurements]. The per-residue decomposition of the interaction energies reveals the detailed picture of this electrostatically driven association and notably the role of arginine R13 as a bridging residue between the two main anchoring sites. In addition, the analysis of the residue behavior by means of a supervised machine learning protocol unveils the formation of a hydrogen bond network far from the binding sites, increasing the rigidity of the protein. This study paves the way toward an automated procedure to predict the supramolecular protein–cage association, with the possibility of a computational screening of new promising derivatives for controlled protein assembly and protein surface recognition processes.