DataSheet1_Identifying well-folded de novo proteins in the new era of accurate structure prediction.pdf
Supplementary Figure 1. Distributions of AlphaFold2 descriptors calculated for the BoNT dataset. Supplementary Figure 2. Distributions of RoseTTAFold descriptors calculated for the BoNT dataset. Supplementary Figure 3. Distributions of MolProbity and interface descriptors calculated for the BoNT dat...
| Autores: | , |
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| Tipo de recurso: | conjunto de datos |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/331500 |
| Acceso en línea: | http://hdl.handle.net/10261/331500 |
| Access Level: | acceso abierto |
| Palabra clave: | De novo protein design AlphaFold2 RoseTTAFold Machine learning Solubility Protein binding Protein folding Protein monomer |
| Sumario: | Supplementary Figure 1. Distributions of AlphaFold2 descriptors calculated for the BoNT dataset. Supplementary Figure 2. Distributions of RoseTTAFold descriptors calculated for the BoNT dataset. Supplementary Figure 3. Distributions of MolProbity and interface descriptors calculated for the BoNT dataset Supplementary Figure 4. Global and local confidence scores for the AlphaFold2 and RoseTTAFold predictions for the Monomer dataset. (A) Supplementary Figure 5. Fragment quality and MolProbity descriptors calculated for the Monomer dataset. (A) Supplementary Figure 6. Properties of the homodimer interfaces predicted by AlphaFold2 for the Monomer dataset Supplementary Figure 7. Predictions of solvent exposed hydrophobicity and soluble expression for the Monomer dataset. (A) Supplementary Figure 8. Comparison between AlphaFold2 and RoseTTAFold predictions for the two datasets |
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