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Computational Methods for Protein Structure Prediction and Fold Recognition

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Practical Bioinformatics

Part of the book series: Nucleic Acids and Molecular Biology ((NUCLEIC,volume 15))

Amino acid sequence analysis provides important insight into the structure of proteins,which in turn greatly facilitates the understanding of its biochemical and cellular function. Efforts to use computational methods in predicting protein structure based only on sequence information started 30 years ago (Nagano 1973; Chou and Fasman 1974).However, only during the last decade, has the introduction of new computational techniques such as protein fold recognition and the growth of sequence and structure databases due to modern high-throughput technologies led to an increase in the success rate of prediction methods, so that they can be used by the molecular biologist or biochemist as an aid in the experimental investigations.

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References

  • Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403-410

    PubMed  Google Scholar 

  • Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389-3402

    PubMed  Google Scholar 

  • Aravind L, Koonin EV (1999) Gleaning non-trivial structural, functional and evolutionary information about proteins by iterative database searches. J Mol Biol 287:1023-1040

    PubMed  Google Scholar 

  • Aravind L, Mazumder R, Vasudevan S, Koonin EV (2002) Trends in protein evolution inferred from sequence and structure analysis. Curr Opin Struct Biol 12:392-399

    PubMed  Google Scholar 

  • Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, Eddy SR, Griffiths-Jones S, Howe KL, Marshall M, Sonnhammer EL (2002) The Pfam protein families database. Nucleic Acids Res 30:276-280

    PubMed  Google Scholar 

  • Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164-170

    PubMed  Google Scholar 

  • Bryant SH, Lawrence CE (1993) An empirical energy function for threading protein sequence through the folding motif. Proteins 16:92-112

    PubMed  Google Scholar 

  • Bujnicki JM, Elofsson A, Fischer D, Rychlewski L (2001a) LiveBench-1: continuous benchmarking of protein structure prediction servers. Protein Sci 10:352-361

    PubMed  Google Scholar 

  • Bujnicki JM, Elofsson A, Fischer D, Rychlewski L (2001b) LiveBench-2: Large-scale automated evaluation of protein structure prediction servers. Proteins 45:184-191

    Google Scholar 

  • Bujnicki JM, Elofsson A, Fischer D, Rychlewski L (2001 c) Structure prediction Meta Server. Bioinformatics 17:750-751

    PubMed  Google Scholar 

  • Bystroff C, Thorsson V, Baker D (2000) HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins J Mol Biol 301:173-190

    PubMed  Google Scholar 

  • Chandonia JM, Karplus M (1995) Neural networks for secondary structure and structural class predictions. Protein Sci 4:275-285

    PubMed  Google Scholar 

  • Chothia C (1992) Proteins. One thousand families for the molecular biologist. Nature 357:543-544

    PubMed  Google Scholar 

  • Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823-826

    PubMed  Google Scholar 

  • Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:222-245

    PubMed  Google Scholar 

  • Combet C, Blanchet C, Geourjon C, Deleage G (2000) NPS@: network protein sequence analysis. Trends Biochem Sci 25:147-150

    PubMed  Google Scholar 

  • Cserzo M,Wallin ESimon Ivon Heijne G,Elofsson A (1997) Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. Protein Eng 10:673-676

    PubMed  Google Scholar 

  • Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ (1998) JPred: a consensus secondary structure prediction server. Bioinformatics 14:892-893

    PubMed  Google Scholar 

  • Douguet D, Labesse G (2001) Easier threading through web-based comparisons and cross-validations. Bioinformatics 17:752-753

    PubMed  Google Scholar 

  • Eddy SR (1996) Hidden Markov models. Curr Opin Struct Biol 6:361-365

    PubMed  Google Scholar 

  • Eyrich VA,Rost B (2003) META-PP: single interface to crucial prediction servers.Nucleic Acids Res 31:3308-3310

    PubMed  Google Scholar 

  • Fischer D (2000) Hybrid fold recognition: combining sequence derived properties with evolutionary information. Pac Symp Biocomput , pp 119-130

    Google Scholar 

  • Fischer D, Elofsson A, Rice D, Eisenberg D (1996) Assessing the performance of fold recognition methods by means of a comprehensive benchmark.Pac Symp Biocomput , pp 300-318

    Google Scholar 

  • Frishman D, Argos P (1997) Seventy-five percent accuracy in protein secondary structure prediction. Proteins 27:329-335

    PubMed  Google Scholar 

  • Garnier J, Osguthorpe DJ, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 120:97-120

    PubMed  Google Scholar 

  • Gerstein M, Levitt M (1997) A structural census of the current population of protein sequences. Proc Natl Acad Sci USA 94:11911-11916

    PubMed  Google Scholar 

  • Ginalski K, Elofsson A, Fischer D, Rychlewski L (2003a) 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19:1015-1018

    PubMed  Google Scholar 

  • Ginalski K, Pas J, Wyrwicz LS, von Grotthuss M, Bujnicki JM, Rychlewski L (2003b) ORFeus: detection of distant homology using sequence profiles and predicted secondary structure. Nucleic Acids Res 31:3804-3807

    PubMed  Google Scholar 

  • Godzik A, Kolinski A, Skolnick J (1992) Topology fingerprint approach to the inverse protein folding problem. J Mol Biol 227:227-238

    PubMed  Google Scholar 

  • Grishin NV (2001a) Fold change in evolution of protein structures. J Struct Biol 134:167-185

    PubMed  Google Scholar 

  • Grishin NV (2001b) Treble clef finger-a functionally diverse zinc-binding structural motif. Nucleic Acids Res 29:1703-1714

    PubMed  Google Scholar 

  • Haft DH, Selengut JD, White O (2003) The TIGRFAMs database of protein families. Nucleic Acids Res 31: 371-373

    PubMed  Google Scholar 

  • Henikoff JG, Greene EA, Pietrokovski S, Henikoff S (2000) Increased coverage of protein families with the blocks database servers. Nucleic Acids Res 28:228-230

    PubMed  Google Scholar 

  • Hirokawa T, Boon-Chieng S, Mitaku S(1998) SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 14:378-379

    PubMed  Google Scholar 

  • Hofmann K, Stoffel W (1993) TMbase - a database of membrane spanning proteins segments. Biol Chem 374:166

    Google Scholar 

  • Ikeda M, Arai M, Lao DM, Shimizu T (2002) Transmembrane topology prediction methods: a re-assessment and improvement by a consensus method using a dataset of experimentally-characterized transmembrane topologies. In Silico Biol 2:19-33

    PubMed  Google Scholar 

  • Jones DT (1999a) GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 287:797-815

    PubMed  Google Scholar 

  • Jones DT (1999b) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195-202

    PubMed  Google Scholar 

  • Jones DT, Taylor WR, Thornton JM (1992) A new approach to protein fold recognition. Nature 358:86-89

    PubMed  Google Scholar 

  • Jones DT, Taylor WR, Thornton JM (1994) A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 33:3038-3049

    PubMed  Google Scholar 

  • Karplus K, Barrett C, Hughey R (1998) Hidden Markov models for detecting remote protein homologies. Bioinformatics 14:846-856

    PubMed  Google Scholar 

  • Karplus K, Karchin R, Barrett C, Tu S, Cline M, Diekhans M, Grate L, Casper J, Hughey R (2001) What is the value added by human intervention in protein structure prediction? Proteins 45(Suppl 5):86-91

    Google Scholar 

  • Kaur H, Raghava GP (2003a) A neural-network based method for prediction of gammaturns in proteins from multiple sequence alignment. Protein Sci 12:923-929

    PubMed  Google Scholar 

  • Kaur H, Raghava GP (2003b) Prediction of beta-turns in proteins from multiple alignment using neural network. Protein Sci 12:627-634

    PubMed  Google Scholar 

  • Kelley LA, McCallum CM, Sternberg MJ (2000) Enhanced genome annotation using structural profiles in the program 3D-PSSM. J Mol Biol 299:501-522

    Google Scholar 

  • Kihara D, Lu H, Kolinski A, Skolnick J (2001) TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints. Proc Natl Acad Sci USA 98:10125-10130

    PubMed  Google Scholar 

  • King RD, Ouali M, Strong AT,Aly A, Elmaghraby A, Kantardzic M, Page D (2000) Is it better to combine predictions? Protein Eng 13:15-19

    PubMed  Google Scholar 

  • Kneller DG, Cohen FE, Langridge R (1990) Improvements in protein secondary structure prediction by an enhanced neural network. J Mol Biol 214:171-182

    PubMed  Google Scholar 

  • Koh IY, Eyrich VA, Marti-Renom MA, Przybylski D, Madhusudhan MS, Eswar N, Grana O, Pazos F,Valencia A, Sali A, Rost B (2003) EVA: evaluation of protein structure prediction servers. Nucleic Acids Res 31:3311-3315

    PubMed  Google Scholar 

  • Koonin EV, Wolf YI, Karev GP (2002) The structure of the protein universe and genome evolution. Nature 420:218-223

    PubMed  Google Scholar 

  • Krieger E, Nabuurs SB, Vriend G (2003) Homology modeling. Methods Biochem Anal 44:509-523

    PubMed  Google Scholar 

  • Kuhlmann UC, Moore GR, James R, Kleanthous C, Hemmings AM (1999) Structural parsimony in endonuclease active sites: should the number of homing endonuclease families be redefined? FEBS Lett 463:1-2

    PubMed  Google Scholar 

  • Kurowski MA, Bujnicki JM (2003) GeneSilico protein structure prediction meta-server. Nucleic Acids Res 31:3305-3307

    PubMed  Google Scholar 

  • Lambert C, Leonard N, De B, X, Depiereux E (2002) ESyPred3D: Prediction of proteins 3D structures. Bioinformatics 18:1250-1256

    PubMed  Google Scholar 

  • Lathrop RH (1994) The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng 7:1059-1068

    PubMed  Google Scholar 

  • Lemer CM, Rooman MJ, Wodak SJ (1995). Protein structure prediction by threading methods: evaluation of current techniques. Proteins 23:337-355

    PubMed  Google Scholar 

  • Letunic I, Goodstadt L, Dickens NJ, Doerks T, Schultz J, Mott R, Ciccarelli F, Copley RR, Ponting CP, Bork P (2002) Recent improvements to the SMART domain-based sequence annotation resource. Nucleic Acids Res 30:242-244

    PubMed  Google Scholar 

  • Levin JM, Pascarella S, Argos P, Garnier J (1993) Quantification of secondary structure prediction improvement using multiple alignments. Protein Eng 6:849-854

    PubMed  Google Scholar 

  • Li W,Pio F,Pawlowski K,Godzik A (2000) Saturated BLAST: an automated multiple intermediate sequence search used to detect distant homology. Bioinformatics 16:1105-1110

    PubMed  Google Scholar 

  • Liakopoulos TD, Pasquier C, Hamodrakas SJ (2001) A novel tool for the prediction of transmembrane protein topology based on a statistical analysis of the SwissProt database: the OrienTM algorithm. Protein Eng 14:387-390

    PubMed  Google Scholar 

  • Liu J, Tan H, Rost B (2002) Loopy proteins appear conserved in evolution. J Mol Biol 322:53-64

    PubMed  Google Scholar 

  • Lundstrom J, Rychlewski L, Bujnicki JM, Elofsson A (2001) Pcons: a neural-network-based consensus predictor that improves fold recognition. Protein Sci 10:2354-2362

    PubMed  Google Scholar 

  • Lupas A,Van Dyke M, Stock J (1991) Predicting coiled coils from protein sequences. Science 252:1162-1164

    Google Scholar 

  • Marchler-Bauer A, Anderson JB, DeWeese-Scott C, Fedorova ND, Geer LY, He S, Hurwitz DI., Jackson JD, Jacobs AR, Lanczycki CJ, Liebert CA, Liu C, Madej T, Marchler GH, Mazumder R, Nikolskaya AN, Panchenko AR, Rao BS, Shoemaker BA, Simonyan V, Song JS, Thiessen PA, Vasudevan S, Wang Y, Yamashita RA, Yin JJ, Bryant SH (2003) CDD: a curated Entrez database of conserved domain alignments. Nucleic Acids Res 31:383-387

    PubMed  Google Scholar 

  • Martelli PL, Fariselli P, Krogh A, Casadio R (2002) A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics 18 (Suppl 1):S46-S53

    PubMed  Google Scholar 

  • Milpetz F, Argos P, Persson B (1995) TMAP: a new email and WWW service for membrane-protein structural predictions. Trends Biochem Sci 20:204-205

    PubMed  Google Scholar 

  • Mulder NJ,Apweiler R,Attwood TK, Bairoch A, Barrell D, Bateman A, Binns D, Biswas M, Bradley P, Bork P, Bucher P, Copley RR, Courcelle E, Das U, Durbin R, Falquet L, Fleischmann W, Griffiths-Jones S, Haft D, Harte N, Hulo N, Kahn D, Kanapin A, Krestyaninova M, Lopez R, Letunic I, Lonsdale D, Silventoinen V, Orchard SE, Pagni M, Peyruc D, Ponting CP, Selengut JD, Servant F, Sigrist CJ,Vaughan R, Zdobnov EM (2003) The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Res 31:315-318

    PubMed  Google Scholar 

  • Murzin AG (1998) How far divergent evolution goes in proteins. Curr Opin Struct Biol 8 380-387

    PubMed  Google Scholar 

  • Nagano K (1973) Logical analysis of the mechanism of protein folding. I. Predictions of helices, loops and beta-structures from primary structure. J Mol Biol 75:401-420

    PubMed  Google Scholar 

  • Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48:443-453

    PubMed  Google Scholar 

  • Ouali M, King RD (2000) Cascaded multiple classifiers for secondary structure prediction. Protein Sci 9:1162-1176

    PubMed  Google Scholar 

  • Ouzounis C, Sander C, Scharf M, Schneider R (1993) Prediction of protein structure by evaluation of sequence-structure fitness. Aligning sequences to contact profiles derived from three-dimensional structures. J Mol Biol 232:805-825

    PubMed  Google Scholar 

  • Pagni M, Jongeneel CV (2001) Making sense of score statistics for sequence alignments. Brief Bioinform 2:51-67

    PubMed  Google Scholar 

  • Park J, Karplus K, Barrett C, Hughey R, Haussler D, Hubbard T, Chothia C (1998) Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. J Mol Biol 284:1201-1210

    PubMed  Google Scholar 

  • Park J,Teichmann SA,Hubbard T,Chothia C (1997).Intermediate sequences increase the detection of homology between sequences. J Mol Biol 273:349-354

    PubMed  Google Scholar 

  • Pasquier C, Promponas VJ, Palaios GA, Hamodrakas JS, Hamodrakas SJ (1999) A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm. Protein Eng 12:381-385

    PubMed  Google Scholar 

  • Pearson WR (1998) Empirical statistical estimates for sequence similarity searches. J Mol. Biol 276:71-84

    PubMed  Google Scholar 

  • Pearson WR,Lipman DJ (1988) Improved tools for biological sequence comparison.Proc Natl Acad Sci U. S.A. 85:2444-2448

    PubMed  Google Scholar 

  • Pizzi E, Frontali C.(2001) Low-complexity regions in Plasmodium falciparum proteins. Genome Res 11:218-229

    PubMed  Google Scholar 

  • Pollastri G, Przybylski D, Rost B, Baldi P (2002) Improving the prediction of protein sec- ondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47:228-235

    PubMed  Google Scholar 

  • Rost B, Fariselli P, and Casadio R (1996) Topology prediction for helical transmembrane proteins at 86 % accuracy. Protein Sci 5:1704-1718

    PubMed  Google Scholar 

  • Rost B, Sander C, Schneider R (1994) PHD-an automatic mail server for protein secondary structure prediction. Comput Appl Biosci 10:53-60

    PubMed  Google Scholar 

  • Rychlewski L, Jaroszewski L, Li W, Godzik A (2000) Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Sci 9:232-241

    PubMed  Google Scholar 

  • Salamov AA, Solovyev VV (1995) Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments. J Mol Biol 247: 11-15

    PubMed  Google Scholar 

  • Samudrala R, Levitt M (2002) A comprehensive analysis of 40 blind protein structure predictions. BMC Struct Biol 2:3

    PubMed  Google Scholar 

  • Sanchez R, Sali A (2000) Comparative protein structure modeling. Introduction and practical examples with modeller. Methods Mol Biol 143:97-129

    PubMed  Google Scholar 

  • Schaffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF (2001) Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 29:2994-3005

    PubMed  Google Scholar 

  • Servant F, Bru C, Carrere S, Courcelle E, Gouzy J, Peyruc D, Kahn D (2002) ProDom: automated clustering of homologous domains. Brief Bioinform 3(3):246-251

    PubMed  Google Scholar 

  • Shi J, Blundell TL, Mizuguchi K (2001) Fugue: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol 310:243-257

    PubMed  Google Scholar 

  • Sigrist CJ, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, Bairoch A, Bucher P (2002) PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform 3:265-274

    PubMed  Google Scholar 

  • Simons KT, Kooperberg C, Huang E, Baker D (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268:209-225

    PubMed  Google Scholar 

  • Sippl MJ,Weitckus S (1992) Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a data base of known protein conformations. Proteins 13:258-271

    PubMed  Google Scholar 

  • Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147:195-197

    PubMed  Google Scholar 

  • Sonnhammer EL, von Heijne G, Krogh A (1998) A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol 6:175-182

    PubMed  Google Scholar 

  • Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV (2001) The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res 29:22-28

    PubMed  Google Scholar 

  • Taylor PD,Attwood TK, Flower DR (2003) BPROMPT: a consensus server for membrane protein prediction. Nucleic Acids Res 31:3698-3700

    PubMed  Google Scholar 

  • Thornton JM, Orengo CA, Todd AE, Pearl FM (1999) Protein folds, functions and evolution. J Mol Biol 293:333-342

    PubMed  Google Scholar 

  • Tompa P (2002) Intrinsically unstructured proteins. Trends Biochem Sci 27:527-533

    PubMed  Google Scholar 

  • Tusnady GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849-850

    PubMed  Google Scholar 

  • Vlahovicek K, Kajan L, Murvai J, Hegedus Z, Pongor S (2003) The SBASE domain sequence library, release 10: domain architecture prediction. Nucleic Acids Res 31:403-405

    PubMed  Google Scholar 

  • von Heijne G (1986) The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology. EMBO J 5:3021-3027

    Google Scholar 

  • von Heijne G (1992) Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol. Biol 225:487-494

    PubMed  Google Scholar 

  • Wallner B, Elofsson A (2003) Can correct protein models be identified? Protein Sci 12:1073-1086

    PubMed  Google Scholar 

  • Webber C,Barton GJ (2003) Increased coverage obtained by combination of methods for protein sequence database searching. Bioinformatics 19:1397-1403

    PubMed  Google Scholar 

  • Wolf YI, Grishin NV, Koonin EV (2000) Estimating the number of protein folds and families from complete genome data. J Mol Biol 299:897-905

    PubMed  Google Scholar 

  • Wootton JC (1994) Sequences with “unusual” amino acid composition. Curr Opin Struct Biol 4:413-421

    Google Scholar 

  • Wootton JC, Federhen S (1996) Analysis of compositionally biased regions in sequence databases. Methods Enzymol 266:554-571

    PubMed  Google Scholar 

  • Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293:321-331

    PubMed  Google Scholar 

  • Xu J, Li M, Lin G, Kim D, Xu Y (2003) Protein structure prediction by linear programming. Pac Symp Biocomput 264:75

    Google Scholar 

  • Xu Y, Xu D (2000) Protein threading using PROSPECT: design and evaluation. Proteins 40 (3):343-354

    PubMed  Google Scholar 

  • Yona G, Levitt M (2002) Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. J Mol Biol 315:1257-1275

    PubMed  Google Scholar 

  • Zhai Y, Saier MH Jr (2001) A web-based program (WHAT) for the simultaneous prediction of hydropathy, amphipathicity, secondary structure and transmembrane topology for a single protein sequence. J Mol Microbiol Biotechnol 3:501-502

    PubMed  Google Scholar 

  • Zhai Y, Saier MH Jr (2002) The beta-barrel finder (BBF) program, allowing identification of outer membrane beta-barrel proteins encoded within prokaryotic genomes. Protein Sci 11:2196-2207

    PubMed  Google Scholar 

  • Zhang C, DeLisi C (1998) Estimating the number of protein folds. J Mol. Biol 284:1301-1305

    PubMed  Google Scholar 

  • Zhou H,Zhou Y (2003) Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method. Protein Sci 12:1547-1555

    PubMed  Google Scholar 

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  1. Bioinformatics Laboratory, International Institute of Molecular and Cell Biology in Warsaw, Trojdena 4, 02-109, Warsaw, Poland

    Iwona A. Cymerman, Marcin Feder, Marcin PawŁowski, Michal A. Kurowski & Dr. Janusz M. Bujnicki

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    Janusz M. Bujnicki

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Cymerman, I.A., Feder, M., PawŁowski, M., Kurowski, M.A., Bujnicki, J.M. (2008). Computational Methods for Protein Structure Prediction and Fold Recognition. In: Bujnicki, J.M. (eds) Practical Bioinformatics. Nucleic Acids and Molecular Biology, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74268-5_1

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