August 2, 20250 citationsOpen Access

Evaluating the Significance of Embedding-Based Protein Sequence Alignment with Clustering and Double Dynamic Programming for Remote Homology

RSRobert SpicerAuburn University at Montgomery NRNilanjana RaychawdharyAuburn University CSCheryl SealsAuburn University

Key Points

MAIN FINDING: The embedding-based protein sequence alignment method improves detection of remote homology significantly.
KEY EVIDENCE: Experimental results indicate this method outperforms traditional approaches with pLMs on several benchmarks.
APPROACH: The technique uses K-means clustering and double dynamic programming to refine similarity metrics.
SIGNIFICANCE: This approach enhances our understanding of protein evolution and function prediction in challenging similarity scenarios.

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 programming (DDP). We show that the incorporation of clustering and DDP contributes substantially to the improved performance in detecting remote homology. Experimental results demonstrate that our approach outperforms both traditional and state-of-the-art approaches based on pLMs on several benchmarks. Our study illustrates embedding-based alignment refined with clustering and DDP offers a powerful approach for identifying remote homology, with potential to evolve further as pLMs continue to advance.

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