Evaluating the significance of embedding-based protein sequence alignment with clustering and double dynamic programming for remote homology.

ABSTRACT: Accurate detection of protein sequence homology is essential for understanding evolutionary relationships and predicting protein functions, particularly for detecting remote homology in the "twilight zone" (20-35% sequence similarity), where traditional sequence alignment methods often fail. Recent studies show that embeddings from protein language models (pLM) can improve remote homology detection over traditional methods. Alignment-based approaches, such as those combining pLMs with dynamic programming alignment, further improve performance but often suffer from noise in the resulting similarity matrices. To address this, we evaluate a newly developed embedding-based sequence alignment approach that refines residue-level embedding similarity using K-means clustering and double dynamic pr

SUBMITTER: Spicer R 

PROVIDER: S-EPMC12612178 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature