While recent advancements in deep learning have substantially improved the accuracy and efficiency of these tools, some challenges remain, particularly in handling poor-quality spectra, managing the large search space inherent in de novo approaches, and developing robust validation methods -similar to FDR estimation in database-dependent methods. Despite these challenges, the future of de novo sequencing in metaproteomics looks promising. Continued innovation in algorithms, hardware, and software will be essential to unlocking its full potential, making it a critical tool for advancing our understanding of microbial diversity and function in complex environments.