ArticleBioinformatics (Oxford, England)2022
Highly significant improvement of protein sequence alignments with AlphaFold2.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 41 citations in OpenAlex.
- PARAS: High-Accuracy Machine Learning of Substrate Specificities in Nonribosomal Peptide Synthetases.JACS Au · 2026Article
- An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study.NAR genomics and bioinformatics · 2025Article
- Protein Structural Phylogenetics.Genome biology and evolution · 2025Review
- Arabidopsis GH3.10 conjugates jasmonates.Plant biology (Stuttgart, Germany) · 2025Article
- multistrap: boosting phylogenetic analyses with structural information.Nature communications · 2025Article
- Parallel loss of sexual reproduction in field populations of a brown alga sheds light on the mechanisms underlying the emergence of asexuality.Nature ecology & evolution · 2024Article
- Seqrutinator: scrutiny of large protein superfamily sequence datasets for the identification and elimination of non-functional homologues.Genome biology · 2024Article
- OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.Nature methods · 2024Article
- Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper.Bioinformatics (Oxford, England) · 2024Article
- AQcalc: A web server that identifies weak molecular interactions in protein structures.Protein science : a publication of the Protein Society · 2023Article
- Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD.Genome biology · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
motivationProtein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis.
resultsHere, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis. AVAILABILITY AND IMPLEMENTATION: All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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