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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.


Benchmark data set

Fasta files

High-resolution data sethigh_res.fasta.tar.gz
Low-resulotion data setlow_res.fasta.tar.gz


High-resolution data sethigh_res.profiles.tar.gz
Low-resolution data setlow_res.profiles.tar.gz

Correct topologies

High-resolution data sethigh_res.topo.tar.gz
Low-resolution data setlow_res.topo.tar.gz

SCAMPI predicted topologies

High-resolution data set, single-sequence versionhigh_res.pred_topo.SCAMPI_single.tar.gz
High-resolution data set, multiple-sequence versionhigh_res.pred_topo.SCAMPI_multi.tar.gz
Low-resolution data set, single-sequence versionlow_res.pred_topo.SCAMPI_single.tar.gz
Low-resolution data set, multiple-sequence versionlow_res.pred_topo.SCAMPI_multi.tar.gz

TopPredΔG predicted topologies

High-resolution data set, single-sequence versionhigh_res.pred_topo.TopPredDG_single.tar.gz
High-resolution data set, multiple-sequence versionhigh_res.pred_topo.TopPredDG_multi.tar.gz
Low-resolution data set, single-sequence versionlow_res.pred_topo.TopPredDG_single.tar.gz
Low-resolution data set, multiple-sequence versionlow_res.pred_topo.TopPredDG_multi.tar.gz
© 2008 Stockholm University, Stockholm Bioinformatics Center