{"id":2806,"date":"2023-06-12T23:42:36","date_gmt":"2023-06-12T22:42:36","guid":{"rendered":"https:\/\/archive.belbi.bg.ac.rs\/2023\/?post_type=abstract&p=2806"},"modified":"2023-06-14T18:37:24","modified_gmt":"2023-06-14T17:37:24","slug":"an-agnostic-analysis-of-the-human-alphafold2-proteome-using-local-protein-conformations","status":"publish","type":"abstract","link":"https:\/\/archive.belbi.bg.ac.rs\/2023\/abstract\/an-agnostic-analysis-of-the-human-alphafold2-proteome-using-local-protein-conformations\/","title":{"rendered":"An agnostic analysis of the human AlphaFold2 proteome using local protein conformations"},"content":{"rendered":"\n

Alexandre G. de Brevern1*<\/sup><\/p>\n\n\n\n

1<\/sup> DSIMB Bioinformatics Team, INSERM UMR_S 1134, BIGR, Universit\u00e9 Paris Cit\u00e9 and Universit\u00e9 de la R\u00e9union, 75014 Paris, France<\/p>\n\n\n\n

alexandre.debrevern [at] univ-paris-diderot.fr<\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

For more than 30 years, different computational approaches have been implemented to propose 3D structural models of proteins from their amino acid sequence. Using deep Learning, AlphaFold 2 obtained particularly remarkable results; some models were within the uncertainties of the experimental resolution (Jumper et al., Nature 2021). AlphaFold 2 code is freely avalaible and EBI provides structural model databases (Tunyasuvunakool et al., Nature 2021), i.e. 98.5% of the human proteome is given. 36% of these models are predicted with atomistic quality.<\/p>\n\n\n\n

The human protein models provided by AlphaFold were analyzed using its confidence index (pLDDT score), with classic secondary structure and finer analysis of local protein conformation, e.g. \u03b3-turns, \u03b2-turns and bends, \u03b2-turn types, PolyProline II (PPII), helix curvatures, \u03b2-bulges, and a structural alphabet, namely Protein Blocks (PB). <\/p>\n\n\n\n

As expected, the large majority of \u03b1-helices are well predicted with high pLDDT scores. However, some points are intriguing and could potentially lead to improvements in the future: (i) PPII helices are too often encountered with a low confidence index. They represent 4-5% of all residues and are important in protein-protein interactions; it could so be an issue to be poorly approximated. (ii) In a very surprising way, while \u03b2-turns (turns of 4 residues) are well predicted, 55% of \u03b3-turns (3 residues) have very low pLDDT scores. (iii) Even more strikingly, 94.8% of cis \u03c9 angles associated with low pLDDT scores, i.e. AlphaFold is clearly unable to propose proper cis \u03c9 angles. (iv) \u03b2-sheet occurrence is lower than expected, while PB d (i.e. \u03b2-sheet core geometry) occurrence is completely in accordance with the expected frequencies. There are so potentially \u03b2-sheets that were not founded until the end, which would explain this low frequency (de Brevern, Biochimie 2023). AlphaFold 2 had impacted the structural modeling area but works remained (Tourlet et al., BioMedInformatics 2023)<\/p>\n\n\n\n

Keywords:<\/strong> bioinformatics, deep learning, computer science, protein structure, secondary structures.<\/p>\n","protected":false},"excerpt":{"rendered":"

Alexandre G. de Brevern1*<\/sup><\/p>\n

1<\/sup> DSIMB Bioinformatics Team, INSERM UMR_S 1134, BIGR, Universit\u00e9 Paris Cit\u00e9 and Universit\u00e9 de la R\u00e9union, 75014 Paris, France<\/p>\n

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