Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond
AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise diso...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Biomolecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-273X/12/10/1467 |
| _version_ | 1797474859085725696 |
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| author | Apolline Bruley Jean-Paul Mornon Elodie Duprat Isabelle Callebaut |
| author_facet | Apolline Bruley Jean-Paul Mornon Elodie Duprat Isabelle Callebaut |
| author_sort | Apolline Bruley |
| collection | DOAJ |
| description | AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder. |
| first_indexed | 2024-03-09T20:37:01Z |
| format | Article |
| id | doaj.art-7c732232506d4f9bad60bedec45eca8f |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-09T20:37:01Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-7c732232506d4f9bad60bedec45eca8f2023-11-23T23:09:08ZengMDPI AGBiomolecules2218-273X2022-10-011210146710.3390/biom12101467Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and BeyondApolline Bruley0Jean-Paul Mornon1Elodie Duprat2Isabelle Callebaut3Sorbonne Université, Muséum National d’Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, 75005 Paris, FranceSorbonne Université, Muséum National d’Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, 75005 Paris, FranceSorbonne Université, Muséum National d’Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, 75005 Paris, FranceSorbonne Université, Muséum National d’Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, 75005 Paris, FranceAlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder.https://www.mdpi.com/2218-273X/12/10/1467long foldable segmentspyHCAsoluble domainsprotein sequenceconditional orderhidden order |
| spellingShingle | Apolline Bruley Jean-Paul Mornon Elodie Duprat Isabelle Callebaut Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond Biomolecules long foldable segments pyHCA soluble domains protein sequence conditional order hidden order |
| title | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
| title_full | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
| title_fullStr | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
| title_full_unstemmed | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
| title_short | Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond |
| title_sort | digging into the 3d structure predictions of alphafold2 with low confidence disorder and beyond |
| topic | long foldable segments pyHCA soluble domains protein sequence conditional order hidden order |
| url | https://www.mdpi.com/2218-273X/12/10/1467 |
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