Automatic functional annotation of predicted active sites - European ...

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2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3 Mining residue interactions as triads from PDB 42 3.1 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.1.1 Structural feature extraction . . . . . . . . . . . . . . . . . . . . . . 44 3.1.2 Detection of significant configurations as interactions . . . . . . . . 47 3.1.3 Grouping and selecting frequent configurations . . . . . . . . . . . . 52 3.2 Analysing available non-redundant protein structure sets . . . . . . . . . . 53 3.3 Evaluation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4.1 Identification of residue interactions is dependent on data selection 55 3.4.2 The interaction distance correlates with the distribution of residue triads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.4.3 Interaction classification is sensitive to the size of cross-validation . 59 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 Prediction of functions for mined residue triads 63 4.1 Evaluation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.2.1 Identification of homologous metal binding sites . . . . . . . . . . . 66 4.2.2 Validation of convergent metal binding sites . . . . . . . . . . . . . 67 4.2.3 Recovering active sites and catalytic triads from the dataset . . . . 73 4.2.4 Discovering the conserved serine residue in the catalytic triad (quartet) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7
5 Identification of protein residues in MEDLINE 79 5.1 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.1.1 Protein and organism entity recognition . . . . . . . . . . . . . . . 81 5.1.2 Entity recognition of protein residue . . . . . . . . . . . . . . . . . 82 5.1.3 Association identification of the entity triplet organism, protein, and residue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.2 The construction of evaluation test corpora . . . . . . . . . . . . . . . . . . 86 5.3 Evaluation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.4.1 Evaluation of organism, protein, and residue entity recognition . . . 90 5.4.2 Performance study on the entity triplet association . . . . . . . . . 92 5.4.3 Cross-validation of identified residues with UniProtKB . . . . . . . 93 5.4.4 Identified residues in MEDLINE for Uniprot/PDB proteins . . . . . 94 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6 Information extraction from the context of a residue in text 101 6.1 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.1.1 Extraction of contextual features . . . . . . . . . . . . . . . . . . . 103 6.1.2 Categorisation of contextual features . . . . . . . . . . . . . . . . . 110 6.2 Evaluation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.3.1 Contextual feature extraction evaluated . . . . . . . . . . . . . . . . 117 6.3.2 Performance analysis of the classifiers . . . . . . . . . . . . . . . . . 118 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 8
Page 1 and 2: Automatic functional annotation of
Page 3 and 4: Summary Kevin Nagel European Bioinf
Page 5 and 6: Acknowledgements This thesis would
Page 7: Contents 1 Introduction 15 1.1 Prot
Page 11 and 12: B Examples of extracted functional
Page 13 and 14: 4.5 Re-discovery of the catalytic t
Page 15 and 16: 6.3 Evaluation of syntactical langu
Page 17 and 18: Amino Acid 3-Letter 1-Letter Side-c
Page 19 and 20: site 1. evolutionary site 1.1. cons
Page 21 and 22: Figure 1.3: The protein universe an
Page 23 and 24: oth approaches, results from data m
Page 25 and 26: teins. In contrast, to extract a pr
Page 27 and 28: Chapter 2 Background In the previou
Page 29 and 30: Figure 2.1: Data banks in the prote
Page 31 and 32: various metal binding sites. The Ca
Page 33 and 34: Key INIT MET SIGNAL PROPEP TRANSIT
Page 35 and 36: Annotation Sentence Manual GO ”Th
Page 37 and 38: process consists of the following m
Page 39 and 40: esult actually incurs some bias, be
Page 41 and 42: the extraction that would describe
Page 43 and 44: Chapter 3 Mining residue interactio
Page 45 and 46: 3.1.1 Structural feature extraction
Page 47 and 48: from the input data set by providin
Page 49 and 50: factorised, we attempt to approxima
Page 51 and 52: whereas in a system with one-pair i
Page 53 and 54: 3.1.3 Grouping and selecting freque
Page 55 and 56: key features of each dataset. The m
Page 57 and 58: esults from data mining on OLDFIELD
Page 59 and 60:
Figure 3.5: Comparison of extracted
Page 61 and 62:
Figure 3.6: The effect of varying t
Page 63 and 64:
probabilistic classification approa
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4.1 Evaluation methods The biologic
Page 67 and 68:
MSDsite Reference Dataset Determine
Page 69 and 70:
PDBID Description Bound metal 1h2r
Page 71 and 72:
PDBID Description Bound metal 1iml
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3Cys SCOP classification SCOP domai
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3D pattern (k=2) Cross-validated Pa
Page 77 and 78:
PDBID Asp-His-Ser His-2Ser Ala-His-
Page 79 and 80:
that were found in this study. The
Page 81 and 82:
Figure 5.1: Overview of processes a
Page 83 and 84:
5.1.2 Entity recognition of protein
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RANGE-TO = ("-"+ ("to" "-+")? | "to
Page 87 and 88:
matches the protein sequence; (2) s
Page 89 and 90:
abstract texts was drawn from the U
Page 91 and 92:
Unique residue entities Reference D
Page 93 and 94:
Unique resi.-prot.-org.-association
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triplet association/UTRP Resource A
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is the set of 18,427 out of 40,750
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fraction [LHC07] in biomedical text
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identifier annotations in combinati
Page 103 and 104:
Page 105 and 106:
discussion on semantic relation and
Page 107 and 108:
data collation method is necessary
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evaluation study: shallow parser ba
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REL = NP PP* VP. The extracted rela
Page 113 and 114:
MAN FEAT Category Defintion Categor
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”The GlyNH2 was removed and the r
Page 117 and 118:
”Mutation K241Q completely abolis
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6.3.2 Performance analysis of the c
Page 121 and 122:
MAN FEAT Category Precision Recall
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The extracted information is diffic
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Chapter 7 Extraction of functional
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7.2 Results 7.2.1 Evaluation of the
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has the following sources: a false
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The knowledge base does not provide
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7.2.3 Cross-validation of mined cat
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Figure 7.3: Cross-validaiton of tex
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tions indicates, that the informati
Page 139 and 140:
Page 141 and 142:
PDB UniProtKB PDBID chainID serial
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mapped to 24,500 RID+UID. The ident
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RID+UID Sentence PAS RID+UID Senten
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8.3.4 General correlation found bet
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Residue Annotations -HSSP +HSSP OPR
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Chapter 9 Conclusions and future wo
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9.2 Limitations and future works Du
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Bibliography [AGM + 90] SF Altschul
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[BMC08] BMC. Biomed central. http:/
Page 159 and 160:
[DCG + 04] F Diella, S Cameron, C G
Page 161 and 162:
[Gue96] F Guenthner. Electronic lex
Page 163 and 164:
[JK95] J Justeson and S Katz. Techn
Page 165 and 166:
[MB99] Y Matsuo and SH Bryant. Iden
Page 167 and 168:
[POHS05] M Pesu, J O’Shea, L Henn
Page 169 and 170:
[STB06] MH Saier, CV Tran, and RD B
Page 171 and 172:
[WK07] R Witte and T Kappler. Enhan
Page 173 and 174:
Table A.1: Examples of errors in th
Page 175 and 176:
. RID+UID Table B.1: Comparison of
Page 177 and 178:
. . . continuation of table B.1 PAS
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Table C.1: Examples of literature m
Page 181 and 182:
. . . continuation of table C.1 PMI
Page 183 and 184:
. . . continuation of table C.1 RID
Page 185 and 186:
Table D.1: Examples of literature m
Page 187 and 188:
Appendix E Examples of extracted fu
Page 189 and 190:
. . . continuation of table E.1 Sen
Page 191 and 192:
Table F.1: Identified catalytic tri
Page 193 and 194:
Appendix G Glossary 3D pattern - a
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