Blocs Protéiques

Alexandre de Brevern - Thèse de Bioinformatique Moléculaire

Prochain: À propos de ce Au-dessus: No Title Précédent: Annexe 3 : Les

Références

1: C. André, B. Vincens, J. Boisvieux, and S. Hazout.
Mosaic : segmenting multiple aligned dna sequences.
Bioinfomatics, NA:-, 2001.
2: R. Aurora and G. Rose.
Helix capping.
Protein Science, 7:21-38, 1998.
3: D. Barlow and J. Thornton.
Helix geometry in proteins.
J Mol Biol, 201:601-619, 1988.
4: P. Bates and M. Sternberg.
Model building by comparison at casp3 : using expert knowledge and computer automation.
Proteins, S3:47-54, 1999.
5: I. Berezovsky, A. Grosberg, and E. Trifonov.
Closed loops of nearly standard size: common basic element of protein structure.
FEBS Letters, 466:283-286, 2000.
6: F. Bernstein, T. Koetzle, G. Williams, E. Meyer, M. Brice, J. Rodgers, O. Kennard, T. Shimanouchi, and M. Tasumi.
The protein data bank: a computer-based archival file for macromolecular structures.
J Mol Biol, 112:535-540, 1977.
7: J. Bienkowska, R. j. Rogers, and T. Smith.
Filtered neighbors threading.
Proteins, 37:346-359, 1999.
8: J. Bienkowska, R. j. Rogers, and T. Smith.
Protein fold recognition by total alignement probability.
Proteins, 40:451-462, 2000.
9: T. Blundell, D. Carney, S. Gardner, F. Hayes, B. Howlin, and T. Hubbard.
Knowledge-based protein modelling and design.
Eur. J. Biochem., 172:513-520, 1988.
10: T. Blundell, B. Sibanda, M. Sternberg, and J. Thornton.
Knowledge-based prediction of protein structures and the design of novel molecules.
Nature, 326:347, 1987.
11: J. Boberg, T. Salakoski, and M. Vihinen.
Selection of a representative set of structures from brookhaven protein databank.
Proteins, 14:264-276, 1992.
12: T. bottom line for prediction of residue solvent accessibility.
Richardson, cj and barlow, dj.
Protein Eng, 12:1051-1054, 1999.
13: N. Boutonnet, A. Kajava, and M. Rooman.
Structural classification of alphabetabeta and betabetaalpha supersecondary structure.
Proteins, 30:193-212, 1998.
14: M. Bower, F. Cohen, and R. Dunbrack Jr.
Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: A new homology modeling tool.
J. Mol. Biol., 267:1268-1282, 1993.
15: J. Bowie, R. Luthy, and D. Eisenberg.
A method to identify protein sequences that fold into a known three-dimensional structure.
Science, 253:164-169, 1991.
16: R. Britten.
Precise sequence complementarity between yeast chromosome ends and two classes of just-subtelomeric sequences.
Proc Natl Acad Sci USA, 26:5906-59120, 1998.
17: S. Bryant and C. Lawrence.
An empirical energy function for threading protein sequence through the folding motif.
Proteins, 16:92-112, 1993.
18: C. Bystroff and D. Baker.
Blind predictions of local protein structure in casp2 targets using the i-sites library.
Proteins, supl.1:167-171, 1997.
19: C. Bystroff and D. Baker.
Prediction of local structure in proteins using a library of sequence-structure motif.
J Mol Biol, 281:565-577, 1998.
20: C. Bystroff, V. Thorsson, and D. Baker.
Hmmstr: a hidden markov model for local sequence-structure correlations in proteins.
J Mol Biol, 301:173-190, 2000.
21: A. Camproux, A. de Brevern, P. Tuffery, and S. Hazout.
Exploring the use of a structural alphabet for a structural prediction of protein loops.
Theoretical Chemistery Accounts, page en revision, 2001.
22: A. Camproux, F. Saunier, G. Chouvet, J. Thalabard, and G. Thomas.
A hidden markov model approach to neuron firng patterns.
Biophysical Journal, 71:2404-2412, 1996.
23: A. Camproux, P. Tuffery, L. Buffat, C. Andre, J. Boisvieux, and S. Hazout.
Analyzing patterns between regular secondary structures using short structural blocks defined by a hidden markov model.
Theoretical Chemistery Accounts, 101:33-40, 1999.
24: A. Camproux, P. Tuffery, J. Chevrolat, J. Boisvieux, and S. Hazout.
Hidden markov model approach for identifying the modular framework of the protein backbone.
Protein Eng, 12(12):1063-1073, 1999.
25: O. Carugo.
Predicting residue solvent accessibility from protein sequence by considering the sequence environment.
Protein Eng, 13:607-609, 2000.
26: H. Chan.
Folding alphabet.
Nature Structural Biology, 6:994-996, 1999.
27: J. Chandonia and M. Karplus.
The importance of larger data sets for protein secondary structure prediction with neural networks.
Protein Sci., 5:768-774, 1996.
28: J. Chandonia and M. Karplus.
New methods for accurate prediction of protein secondary structure.
Proteins, 35:293-306, 1999.
29: P. Chou and G. Fasman.
Prediction of protein conformation.
Biochemistry, 13:222-245, 1974.
30: M. Claessens, E. Cutsen, I. Lasters, and S. Wodak.
Modelling the polypeptide backbone with 'spare parts' from known protein structures.
Prot. Eng., 4:335, 1989.
31: N. Colloc'h, C. Etchebest, E. Thoreau, B. Henrissat, and J. Mornon.
Comparaison of three algorithms for the assignement of secondary structure in proteins: the advantages of a concenssus assignement.
Protein Eng., 6:377-382, 1993.
32: L. Conte and J. Chothia, C andJanin.
The atomic structure of protein-protein recognition sites.
J Mol Biol, 5:2177-2199, 1999.
33: J. Cuff and G. Barton.
Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.
Proteins, 34:508-519, 1999.
34: J. Cuff, M. Clamp, A. Siddiqui, M. Finlay, and G. Barton.
Jpred: a consensus secondary structure prediction server.
Proteins, 14:892-893, 1998.
35: A. de Brevern, C. Etchebest, and S. Hazout.
Bayesian probabilistic approach for prediction backbone structures in terms of protein blocks.
Proteins, 41(3):271-287, 2000.
36: A. de Brevern and S. Hazout.
Hybrid protein model (hpm): a method to compact protein 3d-structures information and physicochemical properties.
IEEE - Computer Society : Proceedings of the 7th Symposium on String Processing and Information Retrieval, 1(1):49-57, 2000.
37: A. de Brevern and S. Hazout.
Compacting local protein folds with a hybrid protein.
Theoretical Chemistry Accounts, NA:-, 2001.
38: C. Deane and T. Blundell.
A novel exhaustive search algorithm for predicting the conformation of polypeptide segments in proteins.
Proteins, 40:135-144, 2000.
39: T. Defay and F. Cohen.
Evaluation of current techniques for ab initio protein structure prediction.
Proteins, 23:431-445, 1995.
40: G. Deléage, C. Blanchet, and C. Geourjon.
Protein structure prediction. implications for the biologist.
Biochimie, 79:681-686, 1997.
41: P. Derreumaux.
Folding a 20 amino acid $\alpha$ $\beta$ peptide with the diffusion process-controled monte-carlo method.
J. Chem. Phys., 107:1941-1947, 1997.
42: V. Di Francesco, J. Garnier, and P. Munson.
Improving protein secondary structure prediction with aligned homologous sequences.
Protein Science, 5:106-113, 1996.
43: C. Dodge, R. Schneider, and C. Sander.
The hssp database of protein structure-sequence alignments and family profiles.
Nucleic Acid Res, 26:313-315, 1998.
44: L. Donate, S. Rufino, L. Canard, and T. Blundell.
Conformational analysis and clustering of short and medium size loops connecting secondary structures: a database for modeling and prediction.
Protein Science, 5:2600-2616, 1996.
45: R. Dunbrack Jr. and M. Karplus.
Backbone-dependent rotamer library for proteins: Application to side-chain prediction.
J. Mol. Biol., 230:543-574, 1993.
46: A. Efimov.
Super-secondary structures involving triple-strand beta-sheets.
FEBS Lett, 334:253-256, 1993.
47: N. Eswar and C. Ramakrishan.
Secondary structures without backbone : an analysis of backbone mimicry by ploar side chains in protein structures.
Protein Eng, 12:447-455, 1999.
48: P. Fariselli and R. Casadio.
A neural network based predictor of residue contacts in proteins.
Protein Eng, 12:15-21, 1999.
49: P. Fariselli and R. Casadio.
Prediction of the number of residue contacts in proteins.
Ismb. 2000, 8:146-151, 2000.
50: P. Fariselli, P. Riccobelli, and R. Casadio.
Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins.
Proteins, 36:340-346, 1999.
51: J. Fetrow.
Omega loops: nonregular secondary structures significant in protein function and stability.
FASEB J, 9:708-717, 1995.
52: J. Fetrow and G. Berg.
Using information theory to discover side chain rotamer classes: analysis of the effects of local backbone structure.
Pacific Symposium, 1:-, 1998.
53: J. Fetrow, M. Palumbo, and G. Berg.
Patterns, structures, and amino acid frequencies in structural building blocks, a protein secondary structure classification scheme.
Proteins, 27:249-271, 1997.
54: A. Finkelstein and B. Reva.
A search for the most stable folds of protein chains.
Nature, 351:497-499, 1991.
55: A. Finkelstein and B. Reva.
Search for the most stable folds of protein chains: I. application of a self-consistent molecular field to a problem of protein three-dimensional structure prediction.
Protein Eng, 9:387-397, 1996.
56: A. Fiser, N. Dosztányi, and S. I.
The role of long-range interactions in defining the secondary structure of proteins is overestimated.
Comput. Apll. Biosci, 13:297-301, 1997.
57: A. Fiser and I. Simon.
Predicting the oxidation state of cysteines by multiple sequence alignment.
Bioinformatics, 16:251-256, 2000.
58: D. Frishman and P. Argos.
Knowledge-based protein secondary structure.
Proteins, 23:566-579, 1995.
59: D. Frishman and P. Argos.
The future of protein secondary structure accuracy.
Fold. Des., 2:159-162, 1997.
60: D. Frishman and P. Argos.
Seventy-five percent accuracy in protein secondary structure prediction.
Proteins, 27:97-120, 1997.
61: J. Garnier, J. Gibrat, and B. Robson.
Gor method for predicting protein secondary structure from amino acid sequence.
Methods. Enzymol, 266:540-553, 1996.
62: J. Garnier, D. Osguthorpe, and B. Robson.
Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins.
J Mol Biol, 120:97-120, 1978.
63: V. Geetha and P. Munson.
Linkers of secondary structures in proteins.
Protein Science, 6:2538-2547, 1997.
64: C. Geourjon and G. Deléage.
Sopma: significant improvements in protein secondary structure prediction by concensus prediction from multiple alignements.
CABIOS, 6:681-684, 1995.
65: J.-F. Gibrat, J. Garnier, and B. Robson.
Further developments of protein secondary structure prediction using information theory.
J Mol Biol, 198:425-443, 1987.
66: J.-F. Gibrat, B. Robson, and J. Garnier.
Inluence of the local amino acid sequence upon the zones of the torsional angles $\phi$ and $\psi$ adopted by residues in proteins.
Biochemistery, 30:1578-1586, 1991.
67: J. Gorodkin, O. Lund, C. Andersen, and S. Brunak.
Using sequence motifs for enhanced neural network prediction of protein distance constraints.
Proceedings of the seventh international conference for molecular biology (ISMB'99), 1:95-105, 1999.
68: C. Gourgeon and G. Deleage.
Sopm: a self-optimized method for protein secondary structure prediction.
Prot. Eng., 7:157-164, 1994.
69: S. Govindarajan and R. Goldstein.
Why are some proteins structures so common ?
Proc. Natl. Acad. Sci. USA, 93:3341-3345, 1996.
70: S. Govindarajan, R. Recabarren, and R. Goldstein.
Estimating the total number of protein folds.
Proteins, 35:408-414, 1999.
71: M. Gribskov, A. McLachlan, and D. Eisenberg.
Profile analysis: detection of distantly related proteins.
Proc Natl Acad Sci U S A, 84:4355-4358, 1987.
72: Y. Guermeur, C. Geourjon, P. Gallinari, and G. Deléage.
Improved performance in protein secondary structure prediction by inhomogeneous score combination.
Bioinformatics, 15:413-421, 1999.
73: C. Hadley and D. Jones.
A systematic comparison of protein structure classications scop, cath and fssp.
Structure, 7:1099-1112, 1999.
74: K. Han and D. Baker.
Recurring local sequence motifs in proteins.
J Mol Biol, 241:176-187, 1995.
75: K. Han and D. Baker.
Global properties of the mapping between local amino acid sequence and local structure in proteins.
Proc. Natl. Acad. Sci. USA, 93:5814-5818, 1996.
76: K. Han, C. Bystroff, and D. Baker.
Three dimensional structures and contexts associated with recurrent amino acid sequence patterns.
Protein Science, 6:1587-1590, 1997.
77: J. Hanke and J. Reich.
Kohonen map as a visualization tool for the analysis of protein sequences: multiple alignments, domains and segments of secondary structures.
Comput. Appl. Biosci., 12:447-454, 1996.
78: J. Hanke and J. Reich.
Self-organizing hierarchic networks for pattern recognition in protein sequence.
Protein Science, 5:72-82, 1996.
79: J. Hartigan and M. Wong.
k-means.
Applied Statistics, 28(1):100-115, 1979.
80: C. Haseman, R. Kurumbail, N. Boddupalli, J. Peterson, and N. Deisenhofer.
Structure and function of cytochromes p450: a comparative analysis of three crystal structures.
Structure, 2:41-62, 1995.
81: C. Hasemann, K. Ravichandran, J. Peterson, and J. Deisenhofer.
Crystal structure and refinement of cytochrome p450terp at 2.3 a resolution.
J Mol Biol, 4:1169-1185, 1994.
82: S. Hayward and J. Collins.
Limits on $\alpha$ -helix prediction with neural networks models.
Proteins, 14:372-381, 1992.
83: U. Hobohm and C. Sander.
Enlarged representative set of protein structures.
Prot Sci, 3:522-524, 1994.
84: U. Hobohm, F. Scharf, R. Schneider, and C. Sander.
Selection of a representative set of structures from the brookhaven protein databank.
Prot Sci, 1:409-417, 1992.
85: L. Holley and M. Karplus.
Protein secondary structure prediction with a neural network.
Proc. Natl. Acad. Sci., 86:152-156, 1989.
86: L. Holm and C. Sander.
The fssp database of structurally aligned protein fold families.
Nucl. Acid. Res., 22:3600-3609, 1994.
87: L. Holm and C. Sander.
Dali/fssp classification of three-dimensional protein folds.
Nucl Acids Res, 25:231-234, 1997.
88: E. Huang, R. Samudrala, and J. Ponder.
Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions.
J Mol Biol, 290:267-281, 1999.
89: T. Hubbard, A. Murzin, S. Brenner, and C. Chotia.
Scop : a structural classification of proteins database.
Nucleic Acids Research, 25:236-239, 1997.
90: E. Hutchinson and J. Thornton.
A revised set of potentials for $\beta$ -turn formation in protein.
Prot Sci, 3:2207-2216, 1994.
91: J. Janin.
Surface area of globular proteins.
J. Mol. Biol., 105:13, 1976.
92: J. Janin.
Wet and dry interfaces: the role of solvent in protein-protein and protein-dna recognition.
Structure Fold Des, 7:R277-279, 1999.
93: L. Jaroszewski, L. Rychlewski, B. Zhang, and A. Godzik.
Fold prediction by a hierarchy of sequence, threading, and modeling methods.
Prot Sci, 7:1431-40, 1998.
94: P. Jean, J. Pothier, P. Dansette, D. Mansuy, and A. Viari.
Automated multiple analysis of protein structures: application to homology modeling of cytochromes p450.
Proteins, 28:388-404, 1997.
95: D. Jones, W. Taylor, and J. Thornton.
A new approach to protein fold recognition.
Nature, 358:86-89, 1992.
96: D. Jones and J. Thornton.
Protein fold recognition.
Journal of Computer-Aided Molecular Design, 7:439-456, 1993.
97: D. Jones, M. Tress, K. Bryson, and C. Hadley.
Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure.
Proteins, S3:104-111, 1999.
98: S. Jones, M. Stewart, A. Michie, M. Swindells, C. Orengo, and J. Thornton.
Domain assignment for protein structures using a concensus approach ; characterization and analysis.
Prot Sci, 7:233-242, 1998.
99: T. Jones and S. Thirup.
Using know substructures in protein model building and crystallography.
EMBO J, 5:819-822, 1986.
100: W. Kabsh and C. Sander.
Dictionary of protein secondary structure: Pattern recognition of hydrogen bonded and geometrical features.
Biopolymers, 22:2577, 1983.
101: N. Kannan and S. Vishveshwara.
Identification of side-chain clusters in protein structures by a graph spectral method.
J. Mol. Biol, 292:441-464, 1999.
102: M. Karpen, P. de Haseth, and K. Neet.
Comparing short protein substructures by a method based on backbone torsion angles.
Proteins, 6:155-167, 1989.
103: M. Karpen, P. de Haseth, and K. Neet.
Differences in the amino acid distributions of 3₁0-helices and $\alpha$ -helices.
Protein Science, 1:1333-1342, 1992.
104: T. Kawabata and J. Doi.
Improvement of protein secondary structure prediction using binary word encoding.
Proteins, 27:36-46, 1997.
105: R. King and M. Sternberg.
Identification and application of the concepts important fro accurate and reliable protein secondary structure prediction.
Protein Science, 5:2298-2310, 1996.
106: I. Koch, T. Lengauer, and E. Wanke.
An algoritm for finding maximal common subtopologies in a set of protein structures.
Journal of Computational Biology, 3:289-306, 1996.
107: T. Kohonen.
Learning vector quantization.
Neural Networks, 1(suppl. 1):303, 1989.
108: T. Kohonen.
Statistical pattern recognition revisited.
Advanced Neural Computers,R. Eckmiller (editor),Elesevier Science publisher (Holland), pages -, 1990.
109: T. Kohonen.
Self-Organizing Maps.
Springer-Verlag, Berlin, Germany, 1997.
110: A. Kolinski, P. Rotkiewicz, B. Ilkowski, and J. Skolnick.
A method for the improvement of threading-based protein models.
Proteins, 37:592-610, 1999.
111: J. Kraulis.
Molscript: A program to produce both detailed and schematic plots of protein structures.
J Appl Cryst, 24:946-950, 1991.
112: S. Kullback and R. Leibler.
On information and sufficiency.
Ann Math Stat, 22:79-86, 1951.
113: S. Kumar and M. Bansal.
Geometrical and sequence characterisitics of $\alpha$ -helics in globular proteins.
Biophysical Journal, 78:1935-1944, 1998.
114: J.-M. Kwasigroch, J. Chomilier, and J.-P. Mornon.
A global taxonomy of loops in globular proteins.
J Mol Biol, 259:855-872, 1996.
115: J. Kyte and R. Doolittle.
A simple method for displaying the hydropathic character of a protein.
J Mol Biol, 157(1):105-132, 1982.
116: G. Labesse, N. Colloc'h, J. Pothier, and J.-P. Mornon.
P-sea: a new efficient assignement of secondary structure from c $\alpha$ .
Comput. Appl. Biosci., 13:291-295, 1997.
117: R. Lathrop, R. Rogers Jr., T. Smith, and J. White.
A bayes-optimal sequence-structure theory that unifies protein sequence-structure recognition and alignement,.
Bulletin of Mathematical Biology, 60:1039-1071, 1998.
118: B. Lee and F. Richards.
The interpretation of protein structures: Estimation of static accessibility.
J. Mol. Biol., 55:379-392, 1971.
119: C. Levinthal.
Molecular model-building by computer.
Sci Am, 214:42-52, 1966.
120: M. Levitt.
Accurate modeling of protein conformation by automatic segment matching.
J. Mol. Biol., 226:507-533, 1992.
121: R. Luthy, D. MCLachlan, and D. Eisenberg.
Secondary structure-based profiles: use of structure-conserving scoring tables in searching protein sequence databases for structural similarities.
Proteins, 10:229-239, 1991.
122: M. Liebman, C. Venanzi, and H. Weinstein.
Structural analysis of carboxypeptidase a and its complexes with inhibitors as a basis for modeling enzyme recognition and specificity.
Biopolymers, 24:1721-1758, 1985.
123: L. Lo Conte, B. Ailey, T. Hubbard, S. Brenner, A. Murzin, and C. Chotia.
Scop : a structural classification of proteins database.
Nucleic Acid Research, 28:257-259, 2000.
124: O. Lund, K. Frimand, J. Gorodkin, H. Bohr, J. Hansen, and S. Brunak.
Protein distance constraints predicted by neural networks and probability density functions.
Protein Engineering, 10:1241-1248, 1997.
125: M. MacArthur and J. Thornton.
Deviations from planarity of peptide bond in peptides and proteins.
J Mol Biol, 264:1180-1195, 1996.
126: T. Madej, J.-F. Gibrat, and S. Bryant.
Threading a database of protein cores.
Proteins, 23:356-369, 1995.
127: A. Michie, C. Orengo, and J. Thornton.
Analysis of domain structural class using an automated class assignement protocol.
J Mol Biol, 262:168-85, 1996.
128: Mucchielli-Giorgi.
Thèse.
Paris 7, 5:70-122, 1999.
129: M. Mucchielli-Giorgi, S. Hazout, and T. P.
Predacc: prediction of solvent accessibility.
Bioinformatics, 15:176-177, 1999.
130: M. Mucchielli-Giorgi, T. P, and S. Hazout.
Prediction of solvent accessibility of amino acid residues: critical aspects.
Theoretical chemistry accounts, 101:186-193, 1999.
131: A. Murzin.
Structural classification of proteins: new superfamilies.
Current opinion in Structural Biology, 6:386-394, 1996.
132: A. Murzin, S. Brenner, T. Hubbard, and C. Chotia.
Scop : a structural classification of proteins database for the investigation of sequences and structures.
JMB, 247:526-540, 1995.
133: S. Muskal and S. Kim.
Predicting protein secondary structure content. a tandem neural network approach.
J Mol Biol, 225:713-727, 1992.
134: K. Nadassy, S. Wodak, and J. Janin.
Structural features of protein-nucleic acid recognition sites.
Biochemistry, 38:1999-2017, 1999.
135: O. Olmea and A. Valencia.
Improving contact predictions by the combination of correlated mutations and other sources of sequence information.
Fold Des, 2:S25-32, 1997.
136: C. Orengo.
Classification of protein folds.
Current opinion in Structural Biology, 4:429-44, 1994.
137: C. Orengo, J. Bray, T. Hubbard, L. LoConte, and I. Sillitoe.
Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction.
Proteins, suppl.3:149-170, 1999.
138: C. Orengo, S. Jones, and J. Thornton.
Protein superfamilies and domain superfolds.
Nature, 372:631-634, 1994.
139: C. Orengo, A. Michie, J. Jones, M. Swinells, and J. Thornton.
Cath- a hierarchic classification of protein domain structures.
Structure, 5:1093-1098, 1997.
140: C. Orengo, F. Peral, J. Bray, A. Todd, A. Martin, L. Lo Conte, and J. Thornton.
The cath database provides insights into protein structure/function relationships.
Nucleic Acids Research, 27:275-279, 1999.
141: D. Osguthorpe.
ab initio protein folding.
Current Opinion in Structural Biology, 10:146-152, 2000.
142: M. Ouali and K. RD.
Cascaded multiple classifiers for secondary structure prediction.
Protein Science, 9:1136-1176, 2000.
143: A. Panchenko, A. Marchler-Bauer, and S. Bryant.
Threading with explicit models for evolutionary conservation of structure and sequence.
Proteins, Suppl 3:133-140, 1999.
144: T. Petersen, C. Lundegaard, M. Nielsen, H. Bohr, J. Bohr, S. Brunak, G. Gippert, and O. Lund.
Prediction of protein secondary structure at 80% accuracy.
Proteins, 41:17-20, 2000.
145: L. Presta and G. Rose.
Helix signals in proteins.
Science, 240:1632-1641, 1988.
146: S. Prestelski, A. Williams Jr., and M. Liebman.
Generation of a substructure library for the description and classification of protein secondary structure. i. overview of the methods ans results.
Proteins, 14:430-439, 1992.
147: S. Prestelski, A. Williams Jr., and M. Liebman.
Generation of a substructure library for the description and classification of protein secondary structure. ii. application to spectra-structure correlations in fourrier transform infrared spectroscopy.
Proteins, 14:440-450, 1992.
148: N. Qian and T. Sejnowski.
Prediction of secondary structure of globular proteins using a neural network.
J. Mol. Biol., 202:865-884, 1988.
149: L. Rabiner.
A tutorial on hidden markov models and selected applications in speech recognition.
Proc. of the IEEE, 77:257-285, 1989.
150: K. Rajashankar and S. Ramakumar.
Pi-turns in proteins and peptides: classification, conformation, occurence, hydratation and sequence.
Protein Sci, 5:932-946, 1996.
151: K. Ravichandran, S. Boddupalli, C. Hasemann, J. Peterson, and J. Deisenhofer.
Crystal structure of hemoprotein domain of p450bm-3, a prototype for microsomal p450's.
Science, 6:731-736, 1993.
152: B. Reva, A. Finkelstein, and J. Skolnick.
What is the probability of a chance prediction of a protein structure with an rmsd of 6 A?
Fold Des, 3:141-147, 1998.
153: B. Reva, J. Skolnick, and A. Finkelstein.
Averaging interaction energies over homologs improves protein recognition in gapless threading.
Proteins, 35:353-359, 1999.
154: J. Richardson and D. Richardson.
Amino acid preferences for specific locations at the end of $\alpha$ helices.
Science, 240:1648-1652, 1988.
155: T. Richmond.
Solvent accessible surface area and excluded volume in proteins.
J. Mol. Biol., 178:63-89, 1984.
156: F. Ridchards and C. Kundrot.
Identification of structural motifs from protein coordinate data: secondary structure and first-level supersecondary structure,.
Proteins, 3:71-84, 1988.
157: C. Rohl and A. Doig.
Models for the 3.10-helix/coil, $\pi$ -helix-coil and $\alpha$ -helix/3.10-helix/coil transitions in isolated peptides.
Protein Science, 5:1689-1696, 1996.
158: J. Rooman, MJ, S. Wodak, and J. Thronton.
Amino acid sequence templates derived from recurrent turn motifs in proteins: critical evaluation of their predictive power.
Protein Eng, 3:23-27, 1989.
159: M. Rooman, J.-P. Kocher, and S. Wodak.
prediction of protein backbone conformation based on seven structure assignements. influence of local intercations.
J Mol Biol, 221:961-979, 1991.
160: M. Rooman, J.-P. Kocher, and S. Wodak.
Extracting information on folding from amino acid sequence: accurate predictions for protein regions with preferred conformation in the absence of tertiary interactions.
Biochemistery, 27:10226-10238, 1992.
161: M. Rooman, J.-P. Kocher, and S. Wodak.
Extracting information on folding from amino acid sequence: concensus regions with preferres conformation in homologous proteins.
Biochemistery, 27:10239-10249, 1992.
162: M. Rooman, J. Rodriguez, and S. Wodak.
Automatic definition of reccurent local structure motifs in proteins.
J Mol Biol, 213:327-336, 1990.
163: M. Rooman, J. Rodriguez, and S. Wodak.
Relations between protein sequece and structure and their significance.
J Mol Biol, 213:337-350, 1990.
164: M. Rooman and S. Wodak.
Identification of predictive sequence motifs limited by protein structure data base size.
Nature, 335:45-49, 1988.
165: B. Rost.
Phd : predicting one-dimensional protein structure by profile-based neural networks.
Methods. Enzymol, 232:525-539, 1996.
166: B. Rost and C. Sander.
Prediction of protein secondary structure at better than 70% accuracy.
J Mol Biol, 232:584-599, 1993.
167: B. Rost, C. Sander, and R. Schneider.
Phd - an automatic mail server for protein secondary structure prediction.
CABIOS, 10:53-60, 1994.
168: A. Salamov and V. Solovyev.
Protein secondary structure prediction using local alignenments.
J Mol Biol, 268:31-36, 1997.
169: A. Sali and T. Blundell.
Compartative protein modelling by satisfaction of spatial restraints.
J Mol Biol, 234:779-815, 1993.
170: R. Samudrala and J. Moult.
A graph-theoric algorithm for comparative modeling of protein structure.
J Mol Biol, 279:287-302, 1998.
171: R. Samudrala, Y. Xia, E. Huanh, and L. M.
Ab initio protein structure prediction using a combined hierarchical approach.
Proteins, 3 suppl.:194-198, 1999.
172: C. Sander and R. Schneider.
Database of homology-derived protein structures and the structural meaning of sequence alignment.
Proteins, 9:56-68, 1991.
173: F. Sasagawa and K. Tajima.
Prediction of protein secondary structures by a neural network.
Comput. Appl. Biosci, 9:147-152, 1993.
174: R. Schneider, A. de Daruvar, and C. Sander.
The hssp database of protein structure-sequence alignements.
Nucl Acids Res, 25:226-230, 1997.
175: J. Schuchhardt, G. Schneider, J. Reichelt, D. Schomburg, and P. Wrede.
Local structural motifs of protein backbones are classified by self-organizing neural networks.
Protein Eng, 9(10):833-842, 1996.
176: J. Segrest, H. De Loof, J. Dohlman, C. Brouillette, and G. Anantharamaiah.
Amphipathic helix motif: classes and properties.
Proteins, 8:103-117, 1990.
177: E. Shakhanovich.
Modeling protein folding: the beauty of power and simplicity.
Fold. Des., 1:50-54, 1996.
178: K. Simons, R. Bonneau, I. Ruczinski, and D. Baker.
Ab initio protein structure prediction of casp 3 targets using rosetta.
Proteins, 34(suppl. 3):171-176, 1999.
179: K. Simons, C. Kooperberg, E. Huang, and B. D.
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions.
J Mol Biol, 268:209-225, 1997.
180: K. Simons, I. Ruczinski, C. Kooperberg, B. Fox, C. Bystroff, and D. Baker.
Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins.
Proteins, 34:82-95, 1999.
181: H. Sklenar, C. Etchebest, and R. Lavery.
Describing protein structure: a general algorithm yielding complete helicoidal parameters and a unique overall axis.
Proteins, 6:46-60, 1989.
182: V. Solovyev and A. Salamov.
Predictiong $\alpha$ -helix and $\beta$ -strand segments of globular proteins.
CABIOS, 10:661-669, 1994.
183: R. Srinivasan and G. Rose.
Linus : a hierarchic procedure to predict the fold of a protein.
Proteins, 22:81-99, 1997.
184: R. Srinivasan and G. Rose.
A physical basis for protein secondary structure.
PNAS, 96:14258-14263, 1999.
185: M. Sternberg, P. Bates, L. Kelley, and M. MacCalllum.
Progress in protein structure predicition : assessment of casp 3.
Current opinion in Structural Biology, 9:368-373, 1999.
186: M. Sternberg and S. Islam.
Local protein sequence similarity does not imply a structural relationship.
Protein Eng, 4:125-131, 1990.
187: P. Storloz, A. Lapedes, and Y. Xia.
Predicting protein secondary structure using neural net and statistical methods.
J. Mol. Biol., 225:363-377, 1992.
188: J. Sun and A. Doig.
Addition of side-chain interactions to 3₁₀-helix/coil and $\alpha$ -helix/3₁₀-helix/coil theory.
Protein Science, 7:2374-2383, 1998.
189: X. Sun, X. Rao, L. Peng, and D. Xu.
Prediction of protein supersecondary structures based on the artificial neural network method.
Protein Eng, 10:763-769, 1997.
190: W. Taylor.
The classification of amino acid conservation.
J Theor Biol, 119:205-218, 1986.
191: W. Taylor.
Protein structural domain identification.
Protein Eng, 12:203-216, 1999.
192: M. Thompson and R. Goldstein.
Predicting solvent accessibility: higher accuracy using bayesian statistics and optimized residue substitution classes.
Proteins, 25:38-47, 1996.
193: M. Thompson and R. Goldstein.
Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information.
Protein Sci, 6:1963-1975, 1997.
194: P. Tuffery.
Xmmol : an x11 and motif program for macromolecular visualization and modeling.
J Mol Graphics, 72:67-72, 1995.
195: P. Tufféry, C. Etchebest, and S. Hazout.
Prediction of protein side chains conformations : a study on the influence of backbone accuracy on conformation stability in the rotamer space.
Prot. Eng., 10:361-372, 1997.
196: P. Tufféry, C. Etchebest, S. Hazout, and R. Lavery.
A new approach to the rapid determination of protein side chain conformations.
J. Biomol. Struct. Dynam., 6:1267-1289, 1991.
197: P. Tufféry, C. Etchebest, S. Hazout, and R. Lavery.
A critical comparison of search algorithms applied to the optimisation of protein side-chain conformations.
J. Comp. Chem., 14:790-798, 1993.
198: R. Unger, D. Harel, W. S, and S. JL.
A 3d building blocks approach to analyzing and predicting structure of proteins.
Proteins, 5:355-373, 1989.
199: R. Unger, D. Harel, W. S, and S. JL.
Analysis of dihedral angles distribution: the doublets distribution determines polypeptides conformation.
Biopolymers, 30:499-508, 1990.
200: R. Unger and S. JL.
The importance of short structural motifs in protein structure analysis.
J Comput Aid Mol Des, 7:457-472, 1993.
201: L. Wernish, M. Hunting, and S. Wodak.
Identification of structural domains in proteins by a graph heuristic.
Proteins, 36:338-352, 1999.
202: C. Wilmot and J. Thornton.
Analysis and prediction of the different types of beta-turn in proteins.
J Mol Biol, 203:221-232, 1988.
203: R. Wintjens, M. Rooman, and S. Wodak.
Automatic classification and analysis of alpha alpha-turn motifs in proteins.
J. Mol. Biol, 255:235-253, 1996.
204: J. Wodjick, J.-P. Mornon, and J. Chomilier.
New efficient statistical sequence-dependent structure prediction of short to medium-sized protein loops based on an exhaustive loop classification.
J Mol Biol, 289:1469-90, 1999.
205: Y. Xu, D. Xu, and E. Uberbacher.
An efficient computational method for globally optimal threading.
J Comput Biol, 5:597-614, 1998.
206: Q. Yi, C. Bystroff, P. Rajagopal, R. Klevit, and D. Baker.
Prediction and structural characterization of an independant folding substructure in the src sh3 domain.
J Mol Biol, 283:293-300, 1998.
207: A. Zamyatin.
Protein volume in solution.
Prog Biophys Mol Biol, 24:107-123, 1972.
208: X. Zhang, J. Fetrow, W. Rennie, D. Waltz, and G. Berg.
Automatic derivation of substructures yoelds novel structure building blocks in globular proteins.
ISMB 93, 1:438-446, 1993.
209: X. Zhang, J. Fetrow, W. Rennie, D. Waltz, and G. Berg.
Design of an auto-associative neural network with hidden layer activation that were used to reclassify local protein structures.
Techniques in protein chemistery V, 1:397-404, 1994.
210: K. Zimmermann.
When awaiting 'bio' champollion; dynamic programming regularization of the protein secondary structure predictions.
Protein Eng, 7:1197-1202, 1994.
211: K. Zimmermann and J. Gibrat.
In unison : regularization of protein secondary structure predictions that makes use of multiple alignements.
Prot. Eng., 11:865-865, 1998.

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