Table I. Prediction methods and online servers for membrane proteins.

Method name	REF	Laboratory	Comment
Secondary structure prediction
PsiPred	37	David Jones	three states: helix, strand, coil
BCL∷Jufo9D	39	Jens Meiler	combined withTMspan prediction
ProfPhd	40	BurkhardRost	part of Predict Protein suite
JPred	41	Geoffrey Barton	informs about homologous sequences
Topology prediction and TMspan prediction
BCL∷Jufo9D	39	Jens Meiler	SSPred and TMPred for helical bundles and beta-barrels
OCTOPUS	54	Arne Elofsson	TM helix prediction
TMMOD	55	GuangGao	TM helix prediction
TMHMM	56	Anders Krogh	TM helix prediction
Memsat-SVM	57	David Jones	TM helix prediction
MemBrain	86	James Chou	TM helix prediction, contact prediction
BOCTOPUS	62	ArneElofsson	TM beta-barrel prediction
BETAWARE	63	RitaCasadio	TM beta-barrel prediction
TMBeta-Net	64	Makiko Suwa	TM beta-barrel prediction
TMBHMM	65	Sikander Hayat	TM beta-barrel prediction and exposure
ProfTMB	66	BurkhardRost	TM beta-barrel prediction, part of PredictProtein suite
TMDET	274	Gabor Tusnady	membrane position from 3D structure
PPM	275	Henry Mosberg	membrane position from 3D structure
Other sequence-based predictors
AmphipaSeek	69	GilbertDeleage	amphipathic helix prediction
HeliQuest	70	Bruno Antonny	amphipathic helix prediction
TMkink	71	James Bowie	prediction of TM kinks
ASAP	73	Rohan Teasdale	solvent accessibility for MPs (α and β) and soluble proteins
MPRAP	191	Arne Elofsson	solvent accessibility for helical bundles
aTMX	74	Sikander Hayat	solvent accessibility for helical bundles
bTMX	74	Sikander Hayat	solvent accessibility for beta-barrels
PRIMSILPR	75	Volkhard Helms	prediction of pore-lining residues
LIPS	77	Jie Liang	lipid accessibility for helical bundles
PREDDIMER	88	Roman Efremov	dimerization of TM helices
PRALINETM	91	JaapHeringa	multiple sequence alignments for MPs
PHAT	92	Steven Henikoff	substitution matrices for MPs
JSUBST	94	Kenji Mizuguchi	substitution matrices for MPs
MP-T	93	Charlotte Deanne	sequence-structure alignment
Databases
PDBTM	276	Gabor Tusnady	MPs from PDB, transformed into membrane coordinates, bilayer thickness, updated weekly
OPM	275	Henry Mosberg	topology database with bilayer thickness
TOPDB	59	Gabor Tusnady	topology database
ExTopoDB	60	Stavros Hamodrakas	topology database
TOPDOM	61	Gabor Tusnady	topology, sequence motifs, domains
MeMotif	84	Michael Schroeder	sequence motifs in helical bundles
HOMEP	277	Barry Honig	homologous MP dataset
CGDB	95	Mark Sansom	coarse-grained molecular dynamics models
3D structure prediction
RosettaMembrane	96, 97	Vladimir Yarov-Yarovoy Patrick Barth	homology modeling, de novo prediction of helicalbundles
MODELLER	100	Andrej Sali	homology modeling (no focus on MPs)
MEDELLER	102	Charlotte Deane	homology-based coordinate generation
MEMOIR	103	Charlotte Deane	homology modeling, fold-recognition
HHPred	104	Johannes Soeding	homology modeling (no focus on MPs)
Swissmodel	105	TorstenSchwede	homology modeling (no focus on MPs)
GoMoDo	278	Alejandro Giorgetti	GPCR modeling and docking
FREAD	279	Charlotte Deane	loop building from MP fragments
FUGUE	94	Kenji Mizuguchi	fold-recognition
iTASSER	106	Yang Zhang	fold-recognition (no focus on MPs)
BCL∷MP-Fold	114	Jens Meiler	de novo prediction of MP helical bundles
FILM3	116	David Jones	de novo prediction of MP helical bundles with correlated mutations
EVfold_membrane	115	Deborah Marks	de novo prediction of MP helical bundles with correlated mutations
transFold	147	Peter Clote	MP beta-barrel predictor: SSPred, topology, contacts
partiFold	148	Bonnie Berger	MP beta-barrel predictor
TMBPro	150	Pierre Baldi	de novo prediction of MP beta-barrels
TOBMODEL	151	Arne Elofsson	de novo prediction of MP beta-barrels
TMBB-Explorer	152	Jie Liang	de novo prediction of MP beta-barrels
RosettaNMR	155	David Baker	de novo prediction with NMR restraints
CABS-fold	157	AndrzejKolinski	de novo prediction, can use NMR restraints
GeNMR	161	David Wishart	structure prediction with NMR restraints
CS-Rosetta	163	Oliver Lange	structure prediction using Chemical Shift NMR restraints
ProQM	197	BjoernWallner	quality assessment of 3D models
MD simulations
MARTINI	209	Siewert-Jan Marrink	coarse-grained MD force field for lipids and proteins
AMBER	179	Peter Kollman	all-atom force field
OPLS	180	William Jorgensen	all-atom force field
CHARMM	178	Martin Karplus	all-atom force field
GROMOS	280	Hermann Berendsen	all-atom force field
GROMACS	230	Hermann Berendsen	MD software package
NAMD	231	Klaus Schulten	MD software package
Desmond	232	DE Shaw	MD software package
CHARMM	228	Martin Karplus	MD software package