ArticleGenome research2023
Leveraging protein language models for accurate multiple sequence alignments.
Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- Measuring and locating the changes in protein structure using MELO.Nature communications · 2026Article
- Protein embeddings and local alignments.Computational and structural biotechnology journal · 2026Article
- Quest for Orthologs in the era of Data Deluge and AI: Challenges and Innovations in Orthology Prediction and Data Integration.Journal of molecular evolution · 2025Review
- Evaluating Pretrained Protein Language Model Embeddings as Proxies for Functional Similarity.Journal of molecular evolution · 2025Article
- DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models.Bioinformatics (Oxford, England) · 2025Article
- NEAR: neural embeddings for amino acid relationships.Bioinformatics (Oxford, England) · 2025Article
- NEAR: Neural Embeddings for Amino acid Relationships.bioRxiv : the preprint server for biology · 2025Article
- Major advances in protein function assignment by remote homolog detection with protein language models - A review.Current opinion in structural biology · 2025Review
- Fine-tuning protein language models unlocks the potential of underrepresented viral proteomes.PeerJ · 2025Article
- Detection of circular permutations by Protein Language Models.Computational and structural biotechnology journal · 2025Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Protein domain embeddings for fast and accurate similarity search.Genome research · 2024Article
- learnMSA2: deep protein multiple alignments with large language and hidden Markov models.Bioinformatics (Oxford, England) · 2024Article
- A large-scale assessment of sequence database search tools for homology-based protein function prediction.Briefings in bioinformatics · 2024Article
- Protein embedding based alignment.BMC bioinformatics · 2024Article
- Embedding-based alignment: combining protein language models with dynamic programming alignment to detect structural similarities in the twilight-zone.Bioinformatics (Oxford, England) · 2024Article
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3 authors.
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Abstract
Multiple sequence alignment (MSA) is a critical step in the study of protein sequence and function. Typically, MSA algorithms progressively align pairs of sequences and combine these alignments with the aid of a guide tree. These alignment algorithms use scoring systems based on substitution matrices to measure amino acid similarities. Although successful, standard methods struggle on sets of proteins with low sequence identity: the so-called twilight zone of protein alignment. For these difficult cases, another source of information is needed. Protein language models are a powerful new approach that leverages massive sequence data sets to produce high-dimensional contextual embeddings for each amino acid in a sequence. These embeddings have been shown to reflect physicochemical and higher-order structural and functional attributes of amino acids within proteins. Here, we present a novel approach to MSA, based on clustering and ordering amino acid contextual embeddings. Our method for aligning semantically consistent groups of proteins circumvents the need for many standard components of MSA algorithms, avoiding initial guide tree construction, intermediate pairwise alignments, gap penalties, and substitution matrices. The added information from contextual embeddings leads to higher accuracy alignments for structurally similar proteins with low amino-acid similarity. We anticipate that protein language models will become a fundamental component of the next generation of algorithms for generating MSAs.
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