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| Format: | Artículo científico |
| Language: | en |
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Protein science : a publication of the Protein Society
2026
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/41562299/ |
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| _version_ | 1868266097670619136 |
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| author | Liong, Anne Pereira, Leandro de Mattos Leão, Pedro N |
| author_facet | Liong, Anne Pereira, Leandro de Mattos Leão, Pedro N Liong, Anne Pereira, Leandro de Mattos Leão, Pedro N |
| collection | PubMed - marine biology |
| contents | Diversity of FAAL enzymes and prediction of their substrate specificity using FAALPred. Liong, Anne Pereira, Leandro de Mattos Leão, Pedro N Substrate Specificity Fatty Acids Computational Biology Bacteria Ligases Phylogeny Bacterial Proteins Archaea Prediction Algorithms FAALs (fatty acyl-AMP ligases) recruit and incorporate fatty acids during the biosynthesis of secondary metabolites. Their diversity, distribution, and substrate specificity remain poorly understood, which limits functional predictions from sequence data. In this study, we explored the prevalence and diversity of FAAL enzymes across the tree of life and show that these are widely distributed in secondary metabolite-rich bacteria and predominantly in the context of polyketide and non-ribosomal peptide biosynthetic pathways. FAALs were also found to be present in certain archaea and eukaryotic groups. The phylogenetic placement of FAALs was not correlated to the chain length of the fatty acids that they activate and load. Therefore, we developed a bioinformatics and AI workflow (FAALPred) to predict the chain length of the fatty-acid substrate of a given FAAL sequence. The robustness and accuracy of the predictions generated by FAALPred were validated using independent in vitro and in silico data. We anticipate that FAALPred will not only accelerate secondary metabolite structural predictions and subsequent discovery from FAAL-associated pathways but also facilitate engineering of lipoylation. FAALPred is available at https://faalpred.ciimar.up.pt/. |
| format | Artículo científico |
| id | pubmed_41562299 |
| institution | PubMed |
| language | en |
| publishDate | 2026 |
| publisher | Protein science : a publication of the Protein Society |
| record_format | pubmed |
| spellingShingle | Diversity of FAAL enzymes and prediction of their substrate specificity using FAALPred. Liong, Anne Pereira, Leandro de Mattos Leão, Pedro N Substrate Specificity Fatty Acids Computational Biology Bacteria Ligases Phylogeny Bacterial Proteins Archaea Prediction Algorithms Diversity of FAAL enzymes and prediction of their substrate specificity using FAALPred. Liong, Anne Pereira, Leandro de Mattos Leão, Pedro N Substrate Specificity Fatty Acids Computational Biology Bacteria Ligases Phylogeny Bacterial Proteins Archaea Prediction Algorithms FAALs (fatty acyl-AMP ligases) recruit and incorporate fatty acids during the biosynthesis of secondary metabolites. Their diversity, distribution, and substrate specificity remain poorly understood, which limits functional predictions from sequence data. In this study, we explored the prevalence and diversity of FAAL enzymes across the tree of life and show that these are widely distributed in secondary metabolite-rich bacteria and predominantly in the context of polyketide and non-ribosomal peptide biosynthetic pathways. FAALs were also found to be present in certain archaea and eukaryotic groups. The phylogenetic placement of FAALs was not correlated to the chain length of the fatty acids that they activate and load. Therefore, we developed a bioinformatics and AI workflow (FAALPred) to predict the chain length of the fatty-acid substrate of a given FAAL sequence. The robustness and accuracy of the predictions generated by FAALPred were validated using independent in vitro and in silico data. We anticipate that FAALPred will not only accelerate secondary metabolite structural predictions and subsequent discovery from FAAL-associated pathways but also facilitate engineering of lipoylation. FAALPred is available at https://faalpred.ciimar.up.pt/. |
| title | Diversity of FAAL enzymes and prediction of their substrate specificity using FAALPred. |
| topic | Substrate Specificity Fatty Acids Computational Biology Bacteria Ligases Phylogeny Bacterial Proteins Archaea Prediction Algorithms |
| url | https://pubmed.ncbi.nlm.nih.gov/41562299/ |