Protein embedding based alignment
Abstract Purpose Despite the many progresses with alignment algorithms, aligning divergent protein sequences with less than 20–35% pairwise identity (so called "twilight zone") remains a difficult problem. Many alignment algorithms have been using substitution matrices since their creation...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-02-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05699-5 |
| _version_ | 1797273006741913600 |
|---|---|
| author | Benjamin Giovanni Iovino Yuzhen Ye |
| author_facet | Benjamin Giovanni Iovino Yuzhen Ye |
| author_sort | Benjamin Giovanni Iovino |
| collection | DOAJ |
| description | Abstract Purpose Despite the many progresses with alignment algorithms, aligning divergent protein sequences with less than 20–35% pairwise identity (so called "twilight zone") remains a difficult problem. Many alignment algorithms have been using substitution matrices since their creation in the 1970’s to generate alignments, however, these matrices do not work well to score alignments within the twilight zone. We developed Protein Embedding based Alignments, or PEbA, to better align sequences with low pairwise identity. Similar to the traditional Smith-Waterman algorithm, PEbA uses a dynamic programming algorithm but the matching score of amino acids is based on the similarity of their embeddings from a protein language model. Methods We tested PEbA on over twelve thousand benchmark pairwise alignments from BAliBASE, each one extracted from one of their multiple sequence alignments. Five different BAliBASE references were used, each with different sequence identities, motifs, and lengths, allowing PEbA to showcase how well it aligns under different circumstances. Results PEbA greatly outperformed BLOSUM substitution matrix-based pairwise alignments, achieving different levels of improvements of the alignment quality for pairs of sequences with different levels of similarity (over four times as well for pairs of sequences with <10% identity). We also compared PEbA with embeddings generated by different protein language models (ProtT5 and ESM-2) and found that ProtT5-XL-U50 produced the most useful embeddings for aligning protein sequences. PEbA also outperformed DEDAL and vcMSA, two recently developed protein language model embedding-based alignment methods. Conclusion Our results suggested that general purpose protein language models provide useful contextual information for generating more accurate protein alignments than typically used methods. |
| first_indexed | 2024-03-07T14:37:27Z |
| format | Article |
| id | doaj.art-0924ae79c6104d58a44e5fb015f4ef49 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-07T14:37:27Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0924ae79c6104d58a44e5fb015f4ef492024-03-05T20:31:52ZengBMCBMC Bioinformatics1471-21052024-02-0125111610.1186/s12859-024-05699-5Protein embedding based alignmentBenjamin Giovanni Iovino0Yuzhen Ye1Luddy School of Informatics, Computing and Engineering, Indiana UniversityLuddy School of Informatics, Computing and Engineering, Indiana UniversityAbstract Purpose Despite the many progresses with alignment algorithms, aligning divergent protein sequences with less than 20–35% pairwise identity (so called "twilight zone") remains a difficult problem. Many alignment algorithms have been using substitution matrices since their creation in the 1970’s to generate alignments, however, these matrices do not work well to score alignments within the twilight zone. We developed Protein Embedding based Alignments, or PEbA, to better align sequences with low pairwise identity. Similar to the traditional Smith-Waterman algorithm, PEbA uses a dynamic programming algorithm but the matching score of amino acids is based on the similarity of their embeddings from a protein language model. Methods We tested PEbA on over twelve thousand benchmark pairwise alignments from BAliBASE, each one extracted from one of their multiple sequence alignments. Five different BAliBASE references were used, each with different sequence identities, motifs, and lengths, allowing PEbA to showcase how well it aligns under different circumstances. Results PEbA greatly outperformed BLOSUM substitution matrix-based pairwise alignments, achieving different levels of improvements of the alignment quality for pairs of sequences with different levels of similarity (over four times as well for pairs of sequences with <10% identity). We also compared PEbA with embeddings generated by different protein language models (ProtT5 and ESM-2) and found that ProtT5-XL-U50 produced the most useful embeddings for aligning protein sequences. PEbA also outperformed DEDAL and vcMSA, two recently developed protein language model embedding-based alignment methods. Conclusion Our results suggested that general purpose protein language models provide useful contextual information for generating more accurate protein alignments than typically used methods.https://doi.org/10.1186/s12859-024-05699-5Protein embeddingProtein sequence alignmentSmith-Waterman algorithmTwilight zone |
| spellingShingle | Benjamin Giovanni Iovino Yuzhen Ye Protein embedding based alignment BMC Bioinformatics Protein embedding Protein sequence alignment Smith-Waterman algorithm Twilight zone |
| title | Protein embedding based alignment |
| title_full | Protein embedding based alignment |
| title_fullStr | Protein embedding based alignment |
| title_full_unstemmed | Protein embedding based alignment |
| title_short | Protein embedding based alignment |
| title_sort | protein embedding based alignment |
| topic | Protein embedding Protein sequence alignment Smith-Waterman algorithm Twilight zone |
| url | https://doi.org/10.1186/s12859-024-05699-5 |
| work_keys_str_mv | AT benjamingiovanniiovino proteinembeddingbasedalignment AT yuzhenye proteinembeddingbasedalignment |