MS2MetGAN: Latent-space adversarial training for metabolite-spectrum matching in MS/MS database search
arXiv:2603.13342v1 Announce Type: new Abstract: Database search is a widely used approach for identifying metabolites from tandem mass spectra (MS/MS). In this strategy, an experimental spectrum is matched against a user-specified database of candidate metabolites, and candidates are ranked such that true metabolite-spectrum matches receive the highest scores. Machine-learning methods have been widely incorporated into database-search-based identification tools and have substantially improved performance. To further improve identification accuracy, we propose a new framework for generating negative training samples. The framework first uses autoencoders to learn latent representations of metabolite structures and MS/MS spectra, thereby recasting metabolite-spectrum matching as matching between latent vectors. It then uses a GAN to generate latent vectors of decoy metabolites and constructs decoy metabolite-spectrum matches as negative samples for training. Experimental results show th
arXiv:2603.13342v1 Announce Type: new Abstract: Database search is a widely used approach for identifying metabolites from tandem mass spectra (MS/MS). In this strategy, an experimental spectrum is matched against a user-specified database of candidate metabolites, and candidates are ranked such that true metabolite-spectrum matches receive the highest scores. Machine-learning methods have been widely incorporated into database-search-based identification tools and have substantially improved performance. To further improve identification accuracy, we propose a new framework for generating negative training samples. The framework first uses autoencoders to learn latent representations of metabolite structures and MS/MS spectra, thereby recasting metabolite-spectrum matching as matching between latent vectors. It then uses a GAN to generate latent vectors of decoy metabolites and constructs decoy metabolite-spectrum matches as negative samples for training. Experimental results show that our tool, MS2MetGAN, achieves better overall performance than existing metabolite identification methods.
Executive Summary
MS2MetGAN, a novel framework for metabolite-spectrum matching in MS/MS database search, leverages latent-space adversarial training to improve identification accuracy. By utilizing autoencoders to learn latent representations of metabolite structures and MS/MS spectra, MS2MetGAN generates negative training samples through a Generative Adversarial Network (GAN). Experimental results demonstrate superior performance compared to existing metabolite identification methods. This advancement has significant implications for the fields of metabolomics and mass spectrometry.
Key Points
- ▸ MS2MetGAN employs latent-space adversarial training for improved metabolite-spectrum matching
- ▸ Autoencoders learn latent representations of metabolite structures and MS/MS spectra
- ▸ GAN generates negative training samples for enhanced training efficacy
Merits
Improved identification accuracy
MS2MetGAN outperforms existing metabolite identification methods, enhancing confidence in metabolite-spectrum matches
Enhanced training efficacy
The use of GAN-generated negative training samples improves model robustness and generalizability
Demerits
Computational complexity
The requirement for complex autoencoder and GAN architectures may increase computational costs and processing times
Interpretability challenges
The latent-space representation may lack transparency, making it difficult to understand the decision-making process
Expert Commentary
The proposed MS2MetGAN framework represents a significant advancement in metabolite-spectrum matching, leveraging the power of latent-space adversarial training to improve identification accuracy. While challenges related to computational complexity and interpretability remain, the demonstrated superiority of MS2MetGAN over existing methods underscores its potential to transform the field of metabolomics. As the metabolomics community continues to evolve, it is essential to address these challenges and explore the broader implications of MS2MetGAN's innovations.
Recommendations
- ✓ Future research should focus on developing more efficient and interpretable latent-space representations
- ✓ The metabolomics community should prioritize the development of standardized protocols and guidelines for the implementation and validation of MS2MetGAN and similar technologies
