Unknown

Dataset Information

0

Prediction of membrane-protein topology from first principles.


ABSTRACT: The current best membrane-protein topology-prediction methods are typically based on sequence statistics and contain hundreds of parameters that are optimized on known topologies of membrane proteins. However, because the insertion of transmembrane helices into the membrane is the outcome of molecular interactions among protein, lipids and water, it should be possible to predict topology by methods based directly on physical data, as proposed >20 years ago by Kyte and Doolittle. Here, we present two simple topology-prediction methods using a recently published experimental scale of position-specific amino acid contributions to the free energy of membrane insertion that perform on a par with the current best statistics-based topology predictors. This result suggests that prediction of membrane-protein topology and structure directly from first principles is an attainable goal, given the recently improved understanding of peptide recognition by the translocon.

SUBMITTER: Bernsel A 

PROVIDER: S-EPMC2438223 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

altmetric image

Publications

Prediction of membrane-protein topology from first principles.

Bernsel Andreas A   Viklund Håkan H   Falk Jenny J   Lindahl Erik E   von Heijne Gunnar G   Elofsson Arne A  

Proceedings of the National Academy of Sciences of the United States of America 20080513 20


The current best membrane-protein topology-prediction methods are typically based on sequence statistics and contain hundreds of parameters that are optimized on known topologies of membrane proteins. However, because the insertion of transmembrane helices into the membrane is the outcome of molecular interactions among protein, lipids and water, it should be possible to predict topology by methods based directly on physical data, as proposed >20 years ago by Kyte and Doolittle. Here, we present  ...[more]

Similar Datasets

| S-EPMC3077071 | biostudies-literature
| S-EPMC4560821 | biostudies-literature
| S-EPMC4489233 | biostudies-literature
| S-EPMC4820709 | biostudies-literature
| S-EPMC2143870 | biostudies-other
| S-EPMC6211794 | biostudies-literature
| S-EPMC14966 | biostudies-literature
| S-EPMC2700806 | biostudies-literature
| S-EPMC5975840 | biostudies-literature
| S-EPMC4468522 | biostudies-other