ArticleProteins2013
Simultaneous prediction of protein secondary structure and transmembrane spans.
Article in Proteins, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
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- Sparse pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction.Journal of biomolecular NMR · 2023Article
- Computational modeling and prediction of deletion mutants.Structure (London, England : 1993) · 2023Article
- State of the art in epitope mapping and opportunities in COVID-19.Future science OA · 2023Review
- Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins.PLoS computational biology · 2022Article
- A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.Journal of proteome research · 2021Article
- Evolution and Diversity of Semaphorins and Plexins in Choanoflagellates.Genome biology and evolution · 2021Article
- Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure.BMC bioinformatics · 2020Article
- Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13.Proteins · 2019Article
- Structure and Function of the Transmembrane Domain of NsaS, an Antibiotic Sensing Histidine Kinase in Staphylococcus aureus.Journal of the American Chemical Society · 2018Article
- Statistically derived asymmetric membrane potentials from α-helical and β-barrel membrane proteins.Scientific reports · 2018Article
- Finding the needle in the haystack: towards solving the protein-folding problem computationally.Critical reviews in biochemistry and molecular biology · 2018Review
- Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.Circulation. Cardiovascular genetics · 2017Article
- A novel proton transfer mechanism in the SLC11 family of divalent metal ion transporters.Scientific reports · 2017Article
- Article
- Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP.BMC bioinformatics · 2017Article
- Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.PloS one · 2017Article
- Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX.Journal of structural biology · 2016Article
- Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins.Journal of chemical information and modeling · 2016Article
- Implications of Human Transient Receptor Potential Melastatin 8 (TRPM8) Channel Gating from Menthol Binding Studies of the Sensing Domain.Biochemistry · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.