ROSTLAB - Publications Sorted by Author

Andersen Bigelow Bromberg Carter Chen Cokol Eyrich Hegyi Kernytsky Koh Liu Mika Mozes Nair Ofran Przybylski Punta Rost Schlessinger Soong Tan Wrzeszczynski Yachdav

Publications by Authors

CAF Andersen (4): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2008, Secondary structure assignment:in press (abstr, web, Google Scholar)

DSSPcont: continuous secondary structure assignments for proteins
P Carter, CAF Andersen, B Rost
Nucleic Acids Research, 2003, 31:3293-3295 (abstr, web, pdf, Google Scholar)

Automatic secondary structure assignment
CAF Andersen, B Rost
Methods Biochem Anal., 2003, 44:341-363 (abstr, web, Google Scholar)

Continuum secondary structure captures protein flexibility
CAF Andersen, AG Palmer, S Brunak, B Rost
Structure, 2002, 10:175-184 (abstr, web, pdf, Google Scholar)

H Bigelow (5): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Online tools for predicting integral membrane proteins
H Bigelow, B Rost
in: 'Proteomic analysis of membrane proteins: methods and protocols' (eds. MJ Peirce, R Wait), 2008, : Totowa, NJOnline tools for predicting integral membrane proteins: (abstr, web, Google Scholar)

Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007, 41:460-474 (pdf, Google Scholar)

PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins
H Bigelow, B Rost
Nucleic Acids Research, 2006, 34:W186-188 (abstr, web, pdf, Google Scholar)

Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004, 32:2566-2577 (abstr, web, pdf, Google Scholar)

Prediction of protein structure through evolution
B Rost, J Liu, D Przybylski, R Nair, H Bigelow, KO Wrzeszczynski, Y Ofran
in: 'Handbook of Chemoinformatics - from data to knowledge' (eds. J Gasteiger, T Engel), 2003, : WeinheimPrediction of protein structure through evolution:1789-1811

Y Bromberg (3): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Comprehensive in silico mutagenesis highlights functionally improtant residues in proteins
Y Bromberg, B Rost
Bioinformatics, 2008, Comprehensive in silico mutagenesis highlights functionally improtant residues in proteins:in press (abstr, web, Google Scholar)

SNAP: predict effect of non-synonymous polymorphisms on function
Y Bromberg, B Rost
Nucleic Acids Research, 2007, 35:3823-3835 (abstr, web, pdf, Google Scholar)

mlNODS: Split machine learning data sets using graphs
Y Bromberg, B Rost
CUBIC preprint, 2006, : New YorkmlNODS: Split machine learning data sets using graphs:submitted 2006_03_20

P Carter (4): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

The protein target list of the Northeast Structural Genomics Consortium
Z Wunderlich, TB Acton, J Liu, G Kornhaber, J Everett, P Carter, N Lan, N Echols, M Gerstein, B Rost, GT Montelione
Proteins: Structure, Function, and Bioinformatics, 2004, 56:181-187 (abstr, pdf, Google Scholar)

NLSdb: database of nuclear localization signals
R Nair, P Carter, B Rost
Nucleic Acids Research, 2003, 31:397-399 (abstr, web, Google Scholar)

PEP: Predictions for Entire Proteomes
P Carter, J Liu, B Rost
Nucleic Acids Research, 2003, 31:410-413 (abstr, web, pdf, Google Scholar)

DSSPcont: continuous secondary structure assignments for proteins
P Carter, CAF Andersen, B Rost
Nucleic Acids Research, 2003, 31:3293-3295 (abstr, web, pdf, Google Scholar)

CP Chen (3): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Transmembrane helix predictions revisited
CP Chen, A Kernytsky, B Rost
Protein Science, 2002, 11:2774-2791 (abstr, web, pdf, Google Scholar)

State-of-the-art in membrane prediction
CP Chen, B Rost
Applied Bioinformatics, 2002, 1:21-35 (abstr, web, pdf, Google Scholar)

Long membrane helices and short loops predicted less accurately
CP Chen, B Rost
Protein Science, 2002, Long membrane helices and short loops predicted less accurately:2766-2773 (abstr, web, pdf, Google Scholar)

M Cokol (1): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Finding nuclear localisation signals
M Cokol, R Nair, B Rost
EMBO Reports, 2000, 1:411-415 (abstr, web, pdf, Google Scholar)

V Eyrich (3): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Simple jury predicts protein secondary structure best
B Rost, P Baldi, G Barton, J Cuff, V Eyrich, D Jones, K Karplus, R King, M Ouali, G Pollastri, D Przybylski
CUBIC preprint, 2001, Simple jury predicts protein secondary structure best:5 (abstr, web, pdf, Google Scholar)

EVA: large-scale analysis of secondary structure prediction
B Rost, V Eyrich
Proteins: Structure, Function, and Genetics, 2001, 45 Suppl 5:S192-S199 (abstr, web, pdf, Google Scholar)

EVA: continuous automatic evaluation of protein structure prediction servers
V Eyrich, MA Martí-Renom, D Przybylski, A Fiser, F Pazos, A Valencia, A Sali, B Rost
Bioinformatics, 2001, 17:1242-1243 (abstr, web, pdf, Google Scholar)

H Hegyi (1): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Automatic target selection for structural genomics on eukaryotes
J Liu, H Hegyi, TB Acton, GT Montelione, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004, 56:188-200 (abstr, web, pdf, Google Scholar)

A Kernytsky (4): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Using genetic algorithms to select most predictive protein features
A Kernytsky, B Rost
Proteins: Structure, Function, and Bioinformatics, 2008, Using genetic algorithms to select most predictive protein features:in press (abstr, web, Google Scholar)

Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007, 41:460-474 (pdf, Google Scholar)

Static benchmarking of membrane helix predictions
A Kernytsky, B Rost
Nucleic Acids Research, 2003, 31:3642-3644 (abstr, web, pdf, Google Scholar)

Transmembrane helix predictions revisited
CP Chen, A Kernytsky, B Rost
Protein Science, 2002, 11:2774-2791 (abstr, web, pdf, Google Scholar)

IYY Koh (2): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

EVA: evaluation of protein structure prediction servers
IYY Koh, VA Eyrich, MA Marti-Renom, D Przybylski, MS Madhusudhan, E Narayanan, O Grana, A Valencia, A Sali, B Rost
Nucleic Acids Research, 2003, 31:3311-3315 (abstr, web, pdf, Google Scholar)

CAFASP3 in the spotlight of EVA
VA Eyrich, IYY Koh, D Przybylski, O Graña, F Pazos, A Valencia, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003, 53 Suppl 6:548-560 (abstr, web, pdf, Google Scholar)

J Liu (27): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis
JM Aramini, S Sharma, YJ Huang, GV Swapna, CK Ho, K Shetty, K Cunningham, LC Ma, L Zhao, LA Owens, M Jiang, R Xiao, J Liu, MC Baran, TB Acton, B Rost, GT Montelione
Proteins: Structure, Function, and Genetics, 2008, 72:526-530 (pdf, Google Scholar)

Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007, 3:e140 (abstr, web, pdf, som, Google Scholar)

Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007, 41:460-474 (pdf, Google Scholar)

Novel leverage of structural genomics
J Liu, GT Montelione, B Rost
Nature Biotechnology, 2007, Novel leverage of structural genomics:in press (abstr, web, som, Google Scholar)

Solution NMR structure of Escherichia coli ytfP expands the structural coverage of the UPF0131 protein domain family
JM Aramini, YJ Huang, GV Swapna, JR Cort, PK Rajan, R Xiao, R Shastry, TB Acton, J Liu, B Rost, MA Kennedy, GT Montelione
Proteins, 2007, 68:789-95 (pdf, Google Scholar)

Distinguishing protein-coding from non-coding RNA through support vector machines
J Liu, J Gough, B Rost
PLoS Genetics, 2006, 2:e29; DOI: 10.1371/journal.pgen.0020029 (abstr, web, pdf, Google Scholar)

Comparisons of NMR spectral quality and success in crystallization demonstrate that NMR and X-ray crystallography are complementary methods for small protein structure determination
DA Snyder, Y Chen, NG Denissova, T Acton, JM Aramini, M Ciano, R Karlin, J Liu, P Manor, PA Rajan, P Rossi, GV Swapna, R Xiao, B Rost, J Hunt, GT Montelione
J Am Chem Soc, 2005, 127:16505-11 (pdf, Google Scholar)

Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes
R Powers, N Mirkovic, S Goldsmith-Fischman, TB Acton, Y Chiang, YJ Huang, L Ma, PK Rajan, JR Cort, MA Kennedy, J Liu, B Rost, B Honig, D Murray, GT Montelione
Protein Science, 2005, 14:2849-61 (pdf, Google Scholar)

The 2.35 A structure of the TenA homolog from Pyrococcus furiosus supports an enzymatic function in thiamine metabolism
J Benach, WC Edstrom, I Lee, K Das, B Cooper, R Xiao, J Liu, B Rost, TB Acton, GT Montelione, JF Hunt
Acta Crystallogr D Biol Crystallogr, 2005, 61:589-98 (pdf, Google Scholar)

The PredictProtein server
B Rost, G Yachdav, J Liu
Nucleic Acids Research, 2004, 32:W321-W326 (abstr, web, pdf, Google Scholar)

1H, 13C and 15N assignments for the Archaeglobus fulgidis protein AF2095
R Powers, TB Acton, Y Chiang, PK Rajan, JR Cort, MA Kennedy, J Liu, L Ma, B Rost, GT Montelione
Journal of Biomolecular NMR, 2004, 30:107-108 (pdf, Google Scholar)

Sequence-based prediction of protein domains
J Liu, B Rost
Nucleic Acids Research, 2004, 32:3522-3530 (abstr, web, pdf, Google Scholar)

CHOP: parsing proteins into structural domains
J Liu, B Rost
Nucleic Acids Research, 2004, 32:W569-W571 (abstr, web, pdf, Google Scholar)

CHOP proteins into structural domains
J Liu, B Rost
Proteins: Structure, Function, and Bioinformatics, 2004, 55:678-688 (abstr, web, pdf, Google Scholar)

Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004, 32:2566-2577 (abstr, web, pdf, Google Scholar)

The PredictProtein server
B Rost, J Liu
Nucleic Acids Research, 2003, 31:3300-3304 (abstr, web, pdf, Google Scholar)

Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003, 60:2637-2650 (abstr, web, pdf, Google Scholar)

NORSp: predictions of long regions without regular secondary structure
J Liu, B Rost
Nucleic Acids Research, 2003, 31:3833-3835 (abstr, web, pdf, Google Scholar)

Domains, motifs, and clusters in the protein universe
J Liu, B Rost
Current Opinion in Chemical Biology, 2003, 7:5-11 (abstr, web, pdf, Google Scholar)

PEP: Predictions for Entire Proteomes
P Carter, J Liu, B Rost
Nucleic Acids Research, 2003, 31:410-413 (abstr, web, pdf, Google Scholar)

Solution NMR structure of the 30S ribosomal protein S28E from Pyrococcus horikoshii
JM Aramini, YJ Huang, JR Cort, S Goldsmith-Fischman, R Xiao, LY Shih, CK Ho, J Liu, B Rost, B Honig, MA Kennedy, TB Acton, GT Montelione
Protein Science, 2003, 12:2823-2830 (abstr, pdf, Google Scholar)

Target space for structural genomics revisited
J Liu, B Rost
Bioinformatics, 2002, 18:922-933 (abstr, web, pdf, Google Scholar)

Loopy proteins appear conserved in evolution
J Liu, H Tan, B Rost
Journal of Molecular Biology, 2002, 322:53-64 (abstr, web, pdf, Google Scholar)

Comparing function and structure between entire proteomes
J Liu, B Rost
Protein Science, 2001, 10:1970-1979 (abstr, web, pdf, Google Scholar)

S Mika (5): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Protein–protein interactions more conserved within species than across species
S Mika, B Rost
PLoS Computational Biology, 2006, 2:e79 (abstr, web, pdf, som, Google Scholar)

NMPdb: database of nuclear matrix proteins
S Mika, B Rost
Nucleic Acids Research, 2005, 33:D160-163 (abstr, web, pdf, Google Scholar)

Protein names peeled precisely off free text
S Mika, B Rost
Bioinformatics, 2004, 20:I241-I247 (abstr, web, pdf, Google Scholar)

NLProt: extracting protein names and sequences from papers
S Mika, B Rost
Nucleic Acids Research, 2004, 32:W634-W637 (abstr, web, pdf, Google Scholar)

UniqueProt: creating representative protein sequence sets
S Mika, B Rost
Nucleic Acids Research, 2003, 31:3789-3791 (abstr, web, pdf, Google Scholar)

E Mozes (1): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Create and assess protein networks through molecular characteristics of individual proteins
Y Ofran, G Yachdav, E Mozes, T-t Soong, R Nair, B Rost
Bioinformatics, 2006, 22:e402-7 (abstr, web, pdf, Google Scholar)

R Nair (14): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Predicting protein subcellular localization using intelligent systems
R Nair, B Rost
Methods Mol Biol, 2008, 484:435-463 (abstr, web, Google Scholar)

Mimicking cellular sorting improves prediction of subcellular localization
R Nair, B Rost
Journal of Molecular Biology, 2005, 348:85-100 (abstr, web, pdf, Google Scholar)

LOCnet and LOCtarget: Sub-cellular localization for structural genomics targets
R Nair, B Rost
Nucleic Acids Research, 2004, 32:W517-W521 (abstr, web, pdf, Google Scholar)

Annotating protein function through lexical analysis
R Nair, B Rost
AI Magazine, 2004, 25:45-56 (abstr, web, Google Scholar)

Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003, 60:2637-2650 (abstr, web, pdf, Google Scholar)

NLSdb: database of nuclear localization signals
R Nair, P Carter, B Rost
Nucleic Acids Research, 2003, 31:397-399 (abstr, web, Google Scholar)

LOC3D: annotate sub-cellular localization for protein structures
R Nair, B Rost
Nucleic Acids Research, 2003, 31:3337-3340 (abstr, web, pdf, Google Scholar)

Better prediction of sub-cellular localization by combining evolutionary and structural information
R Nair, B Rost
Proteins: Structure, Function, and Bioinformatics, 2003, 53:917-930 (abstr, web, pdf, Google Scholar)

Sequence conserved for sub-cellular localization
R Nair, B Rost
Protein Science, 2002, 11:2836-2847 (abstr, web, pdf, Google Scholar)

Inferring sub-cellular localisation through automated lexical analysis
R Nair, B Rost
Bioinformatics, 2002, 18:S78-S86 (abstr, web, pdf, Google Scholar)

Surface profiles predict sub-cellular localisation
R Nair, B Rost
CUBIC preprint, 2001, Surface profiles predict sub-cellular localisation: (abstr, pdf, Google Scholar)

Finding nuclear localisation signals
M Cokol, R Nair, B Rost
EMBO Reports, 2000, 1:411-415 (abstr, web, pdf, Google Scholar)

Y Ofran (12): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Protein-protein interaction hot spots carved into sequences
Y Ofran, B Rost
PLoS Computational Biology, 2007, 3:e119 (abstr, web, pdf, Google Scholar)

Prediction of DNA-binding residues from sequence
Y Ofran, V Mysore, B Rost
Bioinformatics, 2007, 23:i347-i353 (abstr, web, pdf, Google Scholar)

ISIS: Interaction Sites Identified from Sequence
Y Ofran, B Rost
Bioinformatics, 2007, 23:e13-16 (abstr, web, pdf, Google Scholar)

Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006, 34:D777-780 (abstr, web, pdf, Google Scholar)

Predictive methods using protein sequence
Y Ofran, B Rost
in: 'Bioinformatics' (eds. AD Baxevanis, BF Ouellette), 2005, : New YorkPredictive methods using protein sequence:197-222 (pdf, Google Scholar)

Beyond annotation transfer by homology: novel protein function prediction methods that can assist drug discovery
Y Ofran, M Punta, R Schneider, B Rost
Drug Discovery Today, 2005, 10:1475-1482 (pdf, Google Scholar)

Rescue for statistical tests in high-throughput biology
Y Ofran, B Rost
CUBIC preprint, 2004, : New YorkRescue for statistical tests in high-throughput biology:

Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003, 60:2637-2650 (abstr, web, pdf, Google Scholar)

Predict protein-protein interaction sites from local sequence information
Y Ofran, B Rost
FEBS Letters, 2003, 544:236-239 (abstr, web, pdf, Google Scholar)

Analysing six types of protein-protein interfaces
Y Ofran, B Rost
Journal of Molecular Biology, 2003, 325:377-387 (abstr, web, pdf, Google Scholar)

D Przybylski (12): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Predicting simplified features of protein structure
D Przybylski, B Rost
in: 'Bioinformatics – From Genomes to Therapies' (eds. T Lengauer), 2008, : WeinheimPredicting simplified features of protein structure:in press (abstr, web, Google Scholar)

Powerful fusion: PSI-BLAST and consensus sequences
D Przybylski, B Rost
Bioinformatics, 2008, Powerful fusion: PSI-BLAST and consensus sequences:in press (abstr, web, Google Scholar)

Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments
D Przybylski, B Rost
Nucleic Acids Research, 2007, 35:2238-2246 (abstr, web, pdf, som, Google Scholar)

Improving fold recognition without folds
D Przybylski, B Rost
Journal of Molecular Biology, 2004, 341:255-269 (abstr, web, pdf, Google Scholar)

Prediction of transmembrane beta-barrels for entire proteomes
H Bigelow, D Petrey, J Liu, D Przybylski, B Rost
Nucleic Acids Research, 2004, 32:2566-2577 (abstr, web, pdf, Google Scholar)

Alignments grow, secondary structure prediction improves
D Przybylski, B Rost
Proteins: Structure, Function, and Bioinformatics, 2002, 46:195-205 (abstr, web, pdf, Google Scholar)

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles
G Pollastri, D Przybylski, B Rost, P Baldi
Proteins: Structure, Function, and Bioinformatics, 2002, 47:228-235 (pdf, Google Scholar)

M Punta (8): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Building a neural network for predicting protein features
M Punta, B Rost
in: 'Application of Artificial Neural Networks to Chemistry and Biology' (eds. D Livingston), 2008, : TotowaBuilding a neural network for predicting protein features:in press (abstr, web, Google Scholar)

Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007, 23:2376-2384 (abstr, web, pdf, som, Google Scholar)

Membrane protein prediction methods
M Punta, LR Forrest, H Bigelow, A Kernytsky, J Liu, B Rost
Methods, 2007, 41:460-474 (pdf, Google Scholar)

Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks
A Passerini, M Punta, A Ceroni, B Rost, P Frasconi
Proteins: Structure, Function, and Bioinformatics, 2006, 65:305-316 (pdf, Google Scholar)

Protein folding rates estimated from contact predictions
M Punta, B Rost
Journal of Molecular Biology, 2005, 348:507-512 (web, pdf, Google Scholar)

PROFcon: novel prediction of long-range contacts
M Punta, B Rost
Bioinformatics, 2005, 21:2960-2968 (abstr, web, pdf, Google Scholar)

CASP6 assessment of contact prediction
O Grana, D Baker, RM Maccallum, J Meiler, M Punta, B Rost, ML Tress, A Valencia
Proteins, 2005, 61:214-224 (pdf, Google Scholar)

B Rost only single-author papers, here (21): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility
B Rost
in: 'Structural Bioinformatics - 2nd Edition' (eds. PE Bourne, H Weissig), 2008, Prediction of protein structure in 1D – Secondary structure, membrane regions, and solvent accessibility: (abstr, web, Google Scholar)

How to use protein 1D structure predicted by PROFphd
B Rost
in: 'The Proteomics Protocols Handbook' (eds. JE Walker), 2005, : Totowa NJHow to use protein 1D structure predicted by PROFphd:875-901

Rising accuracy of protein secondary structure prediction
B Rost
in: 'Protein structure determination, analysis, and modeling for drug discovery' (eds. D Chasman), 2003, : New YorkRising accuracy of protein secondary structure prediction:207-249 (abstr, web, Google Scholar)

Prediction in 1D: secondary structure, membrane helices, and accessibility
B Rost
Methods Biochem Anal, 2003, 44:559-587 (abstr, web, Google Scholar)

Neural networks predict protein structure: hype or hit?
B Rost
in: 'Artificial intelligence and heuristic methods in bioinformatics' (eds. P Frasconi, R Shamir), 2003, : AmsterdamNeural networks predict protein structure: hype or hit?:34-50 (abstr, web, pdf, Google Scholar)

Enzyme function less conserved than anticipated
B Rost
Journal of Molecular Biology, 2002, 318:595-608 (abstr, web, pdf, Google Scholar)

Did evolution leap to create the protein universe?
B Rost
Current Opinion in Structural Biology, 2002, 12:409-416 (abstr, web, pdf, Google Scholar)

Protein secondary structure prediction continues to rise
B Rost
Journal of Structural Biology, 2001, 134:204-218 (abstr, web, pdf, Google Scholar)

Twilight zone of protein sequence alignments
B Rost
Protein Engineering, 1999, 12:85-94 (abstr, web, pdf, Google Scholar)

Short yeast ORFs: expressed protein or not?
B Rost
CUBIC preprint, 1999, Short yeast ORFs: expressed protein or not?: (web, Google Scholar)

Evolution teaches neural networks
B Rost
in: 'Scientific applications of neural nets' (eds. JW Clark, T Lindenau, ML Ristig), 1999, : HeidelbergEvolution teaches neural networks:207-223 (abstr, web, pdf, Google Scholar)

Protein structure prediction in 1D, 2D, and 3D
B Rost
in: 'The Encyclopaedia of Computational Chemistry' (eds. PvR Schleyer, NL Allinger, T Clark, J Gasteiger, PA Kollman, HF Schaefer III, PR Schreiner), 1998, 3:2242-2255 (abstr, web, pdf, Google Scholar)

Marrying structure and genomics
B Rost
Structure, 1998, 6:259-263 (abstr, web, pdf, Google Scholar)

Protein structures sustain evolutionary drift
B Rost
Folding & Design, 1997, 2:S19-S24 (abstr, web, pdf, Google Scholar)

NN which predicts protein secondary structure
B Rost
in: 'Handbook of Neural Computation' (eds. E Fiesler, R Beale), 1997, : New YorkNN which predicts protein secondary structure:G4.1 (abstr, pdf, Google Scholar)

Learning from evolution to predict protein structure
B Rost
in: 'BCEC97: Bio-Computing and Emergent Computation' (eds. B Olsson, D Lundh, A Narayanan), 1997, : Skövde, SwedenLearning from evolution to predict protein structure:87-101 (abstr, web, pdf, Google Scholar)

Better 1D predictions by experts with machines
B Rost
Proteins: Structure, Function, and Genetics, 1997, Suppl. 1:192-197 (abstr, web, pdf, Google Scholar)

PHD: predicting one-dimensional protein structure by profile based neural networks
B Rost
Methods in Enzymology, 1996, 266:525-539 (abstr, web, pdf, Google Scholar)

TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures
B Rost
in: 'Third International Conference on Intelligent Systems for Molecular Biology' (eds. C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, S Wodak), 1995, : Cambridge, EnglandTOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures:314-321 (abstr, pdf, Google Scholar)

Fitting 1-D predictions into 3-D structures
B Rost
in: 'Protein folds: a distance based approach' (eds. H Bohr, S Brunak, S Brunak), 1995, : Boca Raton, FloridaFitting 1-D predictions into 3-D structures:132-151 (abstr, pdf, Google Scholar)

Neural networks and evolution - advanced prediction of protein secondary structure
B Rost
Thesis, 1993, Neural networks and evolution - advanced prediction of protein secondary structure:

A Schlessinger (5): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Natively unstructured regions in proteins identified from contact predictions
A Schlessinger, M Punta, B Rost
Bioinformatics, 2007, 23:2376-2384 (abstr, web, pdf, som, Google Scholar)

Natively unstructured loops differ from other loops
A Schlessinger, J Liu, B Rost
PLoS Computational Biology, 2007, 3:e140 (abstr, web, pdf, som, Google Scholar)

PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006, 22:891-893 (abstr, web, pdf, som, Google Scholar)

Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006, 34:D777-780 (abstr, web, pdf, Google Scholar)

Protein flexibility and rigidity predicted from sequence
A Schlessinger, B Rost
Proteins: Structure, Function, and Bioinformatics, 2005, 61:115-126 (abstr, web, pdf, Google Scholar)

T-T Soong (1): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

H Tan (1): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Loopy proteins appear conserved in evolution
J Liu, H Tan, B Rost
Journal of Molecular Biology, 2002, 322:53-64 (abstr, web, pdf, Google Scholar)

KO Wrzeszczynski (4): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

Cataloguing proteins in cell cycle control
KO Wrzeszczynski, B Rost
Methods in Molecular Biology, 2004, 241:219-233 (abstr, web, pdf, Google Scholar)

Annotating proteins from Endoplasmic reticulum and Golgi apparatus in eukaryotic proteomes
KO Wrzeszczynski, B Rost
Cellular and Molecular Life Sciences, 2004, 61:1341-1353 (abstr, web, Google Scholar)

Automatic prediction of protein function
B Rost, J Liu, R Nair, KO Wrzeszczynski, Y Ofran
Cellular and Molecular Life Sciences, 2003, 60:2637-2650 (abstr, web, pdf, Google Scholar)

G Yachdav (4): abstr=Abstract, www=paper in HTML, pdf=paper in PDF, som=Supporting online material

PROFbval: predict flexible and rigid residues in proteins
A Schlessinger, G Yachdav, B Rost
Bioinformatics, 2006, 22:891-893 (abstr, web, pdf, som, Google Scholar)

Epitome: Database of structure-inferred antigenic epitopes
A Schlessinger, Y Ofran, G Yachdav, B Rost
Nucleic Acids Research, 2006, 34:D777-780 (abstr, web, pdf, Google Scholar)

The PredictProtein server
B Rost, G Yachdav, J Liu
Nucleic Acids Research, 2004, 32:W321-W326 (abstr, web, pdf, Google Scholar)