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| Title: | Prediction In 1D: secondary structure, membrane helices, and accessibility |
| Author: | Burkhard Rost |
| Quote: | B Rost (2002) Prediction In 1D: secondary structure, membrane helices, and accessibility In Structural Bioinformatics. Philip Bourne and Helge Weissig (eds.), city: Wiley, pp. |
Predictions of simplified aspects of protein structure are often the first step to gaining some insight into the function of a protein. Furthermore, proteome analysis and methods predicting 3D structure are increasingly based upon 1D predictions. Developing 1D prediction methods may be one of the most active and most successful disciplines of bioinformatics. Here, I summarised some of the major ideas of available methods. Particular focus is on evaluating the performance of methods. Recent advances are reviewed and some hints for using methods for sequence analysis are given.
Abbreviations used: 1D structure, one-dimensional,
e.g. sequence, or strings of secondary structure or solvent accessibility; 2D
structure, two-dimensional (e.g. inter-residue
distances); 3D structure, three-dimensional
co-ordinates of protein structure; ASP, method
identifying regions of structure ambivalent in response to global changes [xxx 1; 2]; BLAST, fast sequence alignment method [xxx 3]; CASP, Critical Assessment of Protein Structure Prediction [xxx 4]; COILS, coiled-coil prediction [xxx 5]; DSSP, program and database assigning secondary structure and solvent
accessibility for proteins of known 3D structure [xxx 6]; EVA, server automatically evaluating structure prediction methods [xxx 7; 8]; HMM, Hidden Markov Model; HMMSTR, Hidden
Markov model-based prediction of secondary structure [xxx 9]; HMMTOP, Hidden Markov model predicting transmembrane helices [xxx 10]; JPred2, divergent profile (PSI-BLAST) based neural network prediction of
secondary structure and solvent accessibility [xxx 11]; MEMSAT, dynamic-programming based prediction of transmembrane helices [xxx 12]; META-PP, internet service allowing to access a variety of bioinformatics
tools through one single interface [xxx 13]; PHD, Profile based neural network prediction of secondary structure,
solvent accessibility and transmembrane helices [xxx 14]; PHDpsi, divergent profile (PSI-BLAST) based neural network prediction [xxx 15]; PSI-BLAST, position specific iterated database search [xxx 16]; PROFphd, Advanced profile-based neural network prediction of secondary
structure [xxx 17]; PSIPRED, divergent profile (PSI-Blast) based neural network prediction [xxx 18]; SAM-T99sec, neural network prediction, using hidden Markov models as input [xxx 19]; SOSUI, hydrophobicity and amphiphilicity based transmembrane helix
prediction [xxx 20]; SPLIT, transmembrane helix prediction [xxx 21]; SSpro, profile-based advanced neural network prediction method [xxx 22]; SSpro2, divergent profile-based advanced neural network prediction method [xxx 23]; TM, transmembrane; TMAP,
alignment-based prediction of transmembrane helices [xxx 24]; TMH, transmembrane helix; TMHMM,
Transmembrane prediction using cyclic hidden Markov models [xxx 25]; TMpred, prediction of transmembrane helices [xxx 26]; TopPred2, hydrophobicity-based membrane helix prediction [xxx 27; 28];
Symbols used: secondary
structure: H =
helix, E = strand, L = other; transmembrane helices: T =
transmembrane, N = globular; solvent
accessibility: e =
exposed (≥ 16% relative accessible surface), b = buried (<16%);