Mass spectrometry has become a vital tool in the field of proteomics. This technique enables the study of proteins on a much larger scale, facilitating further biological insights. However, there is a need for advanced algorithms that can accurately interpret mass spectrometry data and reliably identify proteins, including post-translational modifications and sequence variations present in a sample. One major issue with current algorithms is their frequent inability to detect unexpected post-translational modifications and sequence variations in proteins. To address this challenge, we present a novel mass-based alignment algorithm implemented in OpenSea. This algorithm utilizes de novo sequencing results to robustly identify post-translational modifications and sequence variations in proteins.