Best α‐helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

By Håkan Viklund, Arne Elofsson. 2004

302 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bfc9c1e977e54a1166028ed7afa811e06f290d0b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

HHsenser: exhaustive transitive profile search using HMM–HMM comparison (2006, background): “Examples are the prediction of secondary structure (1), solvent accessebility (2), disorder (3), transmembrane helices ( 4 ), intraprotein contacts (5), protein‐protein interactions (6), subcellular localization (7), internal repeats (8), deleterious mutations (9) and conserved or subtype-specific f”
PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes (2010, methodology): “The transmembrane α-helix predictor module HMMTOP (Tusnady and Simon, 2001) was replaced with S-TMHMM, an open source transmembrane α-helix predictor (Viklund and Elofsson, 2004).”
TOPCONS: consensus prediction of membrane protein topology (2009, methodology): “Using a dataset of 163 sequences with known topology, the performance of TOPCONS was benchmarked against eight other topology prediction algorithms (Table 1). ConPredII (7) is an earlier consensus transmembrane topology prediction approach, and the other methods [OCTOPUS (9), MEMSAT3 (13), SCAMPI (1”
GENCODE: The reference human genome annotation for The ENCODE Project (2012, methodology): “(cid:129) THUMP makes conservative predictions of trans -membrane helices by analyzing the output of three locally installed trans - membrane prediction methods, MemSat (Jones 2007), PRODIV (Viklund and Elofsson 2004), and PHOBIUS (Kall et al. 2004).”
The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides (2015, unknown): “Along these lines, several algorithms for the prediction of α-helical transmembrane protein topology exist, either as single-sequence-based methods (8-16) or with the inclusion of homologous sequences in the prediction process (17-22).”
An HMM posterior decoder for sequence feature prediction that includes homology information (2005, methodology): “Other approaches for incorporating homologs in feature prediction with HMMs (Viklund and Elofsson, 2004), use homologs in their training procedure and not just in the decoding.”
CCTOP: a Consensus Constrained TOPology prediction web server (2015, methodology): “Ten methods were selected based on their availability and performance on different benchmark sets: HMMTOP (28,40), MemBrain (41), MEMSAT-SVM (42), Octopus (43), Philius (44), Phobius (37), Pro-and Prodiv-TMHMM (45), Scampi-MSA (38) and TMHMM (2,39).”
Prediction of membrane-protein topology from first principles (2008, methodology): “Becauseit had previously been found that topology predictions can be improved by taking evolutionary information into account (5, 6, 16), we also developed a version of TopPred (cid:1) G that takes a multiple-sequence alignment as input and calculates (cid:1) G app contributions from a weighted…”