ArticleProteins2016
TMSEG: Novel prediction of transmembrane helices.
Article in Proteins, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 69 citations in OpenAlex.
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- A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Sperm-Specific CatSper is Not Conserved in All Vertebrates and May Not be the Only Progesterone-Responsive Ion Channel Present in Sperm.The Journal of membrane biology · 2024Article
- Uncovering the Interaction Interface Between Harpin (Hpa1) and Rice Aquaporin (OsPIP1;3) Through Protein-Protein Docking: An In Silico Approach.Molecular biotechnology · 2024Article
- Inconspicuous Yet Indispensable: The Coronavirus Spike Transmembrane Domain.International journal of molecular sciences · 2023Review
- Three Novel Antisense Overlapping Genes in E. coli O157:H7 EDL933.Microbiology spectrum · 2023Article
- Expression of glycerol-3-phosphate acyltransferase increases non-polar lipid accumulation in Nannochloropsis oceanica.Microbial cell factories · 2023Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Non-structural genes of novel lemur adenoviruses reveal codivergence of virus and host.Virus evolution · 2023Article
- Analysis of microbiota-host communication mediated by butyrate in Atlantic salmon.Computational and structural biotechnology journal · 2023Article
- A Uniquely Stable Trimeric Model of SARS-CoV-2 Spike Transmembrane Domain.International journal of molecular sciences · 2022Article
- TMbed: transmembrane proteins predicted through language model embeddings.BMC bioinformatics · 2022Article
- Improving the topology prediction of α-helical transmembrane proteins with deep transfer learning.Computational and structural biotechnology journal · 2022Article
- Genetic determinants of antimicrobial resistance in three multi-drug resistant strains ofAccess microbiology · 2022Article
- Exploring the Topology of Cytoplasmic Membrane Proteins Involved in Lipopolysaccharide Biosynthesis by in Silico and Biochemical Analyses.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Spectrum of Protein Location in Proteomes Captures Evolutionary Relationship Between Species.Journal of molecular evolution · 2021Article
- SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms.Molecular systems biology · 2021Article
- PredictProtein - Predicting Protein Structure and Function for 29 Years.Nucleic acids research · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non-redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state-of-the-art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98 ± 2% and a false positive rate as low as 3 ± 1%. Individual TMHs were predicted with a precision of 87 ± 3% and recall of 84 ± 3%. Furthermore, in 63 ± 6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the second and third best method available, and 4400 fewer mistakes than by a simple hydrophobicity-based method. Second, TMSEG provides an add-on improvement for any existing method to benefit from. Proteins 2016; 84:1706-1716. © 2016 Wiley Periodicals, Inc.
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