Estimation of model accuracy in CASP13
| Autor: | Björn Wallner, Karolis Uziela, Torsten Schwede, Arne Elofsson, David Menéndez-Hurtado, Česlovas Venclovas, Jie Hou, Myong‐Ho Choe, Gabriel Studer, Kliment Olechnovič, Liam J. McGuffin, Ali H. A. Maghrabi, Kun‐Sop Han, Jianlin Cheng |
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| Rok vydání: | 2019 |
| Předmět: |
Models
Molecular Protein Folding Protein Conformation Computer science 3d model CAD Machine learning computer.software_genre Biochemistry Article Field (computer science) 03 medical and health sciences Sequence Analysis Protein Structural Biology Server Databases Protein Molecular Biology 030304 developmental biology Estimation 0303 health sciences Single model business.industry Deep learning 030302 biochemistry & molecular biology Computational Biology Proteins Identification (information) Artificial intelligence business Sequence Alignment computer Algorithms Software |
| Zdroj: | Proteins |
| ISSN: | 1097-0134 0887-3585 |
| Popis: | Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods. |
| Databáze: | OpenAIRE |
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