The first major opportunity lies in advances in deep learning, significantly enhancing both database-dependent (meta)proteomics which combines a graph-based approach with deep learning, and models like PepNet and π-PrimeNovo , which increase inference speed by removing the autoregressive nature of previous models. Although these models show promise, independent benchmarking on metaproteomics datasets is still needed to assess their performance in this context.