期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 109, 期 24, 页码 E1540-E1547出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1120036109
关键词
structural bioinformatics; protein modeling; compressed sensing; amino acid contacts
资金
- UK Medical Research Council (MRC)
- BBSRC [BB/E023533/1] Funding Source: UKRI
- MRC [G0902087] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/E023533/1] Funding Source: researchfish
- Medical Research Council [G0902087] Funding Source: researchfish
A new de novo protein structure prediction method for transmembrane proteins (FILM3) is described that is able to accurately predict the structures of large membrane proteins domains using an ensemble of two secondary structure prediction methods to guide fragment selection in combination with a scoring function based solely on correlated mutations detected in multiple sequence alignments. This approach has been validated by generating models for 28 membrane proteins with a diverse range of complex topologies and an average length of over 300 residues with results showing that TM-scores > 0.5 can be achieved in almost every case following refinement using MODELLER. In one of the most impressive results, a model of mitochondrial cytochrome c oxidase polypeptide I was obtained with a TM-score > 0.75 and an rmsd of only 5.7 angstrom over all 514 residues. These results suggest that FILM3 could be applicable to a wide range of transmembrane proteins of as-yet-unknown 3D structure given sufficient homologous sequences.
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