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

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I am also thankful to the European Molecular Biology Laboratoy EMBL for the scholarship and the organised EMBL International PhD programme, throughout which I have had the chance to meet many talented and cheerful PhD students from the EMBL/EBI Hinxton. A special thank you to Christina Granroth and Dagmar Harzheim, who have done the proofreading of this thesis. Thank you Dagmar for becoming clearer what I want to say. Finally, I would like to acknowledge my wife Almut Nagel and my daughter Juli Nagel. Without Almut I would have become a working maniac with no joy in life; she helped me to maintain balance during my PhD research and also for the future. My special thanks and love will go to Juli, aged one, from whom I have learned so much. 5
Contents 1 Introduction 15 1.1 Proteins and functional sites . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.4 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.5 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.6 Guide to remaining chapters . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2 Background 26 2.1 Protein related data resources . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.1.1 Protein Data Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1.2 Universal Protein Knowledge base . . . . . . . . . . . . . . . . . . . 31 2.1.3 Gene Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.1.4 Biomedical literature . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Protein structure data mining . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.2.1 Hypothesis-driven data analysis . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Discovery-driven data mining . . . . . . . . . . . . . . . . . . . . . 37 2.3 Biomedical literature mining . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.3.1 Biological entity recognition . . . . . . . . . . . . . . . . . . . . . . 38 2.3.2 Biological relation extraction . . . . . . . . . . . . . . . . . . . . . . 39 6
Page 1 and 2: Automatic functional annotation of
Page 3 and 4: Summary Kevin Nagel European Bioinf
Page 5: Acknowledgements This thesis would
Page 9 and 10: 5 Identification of protein residue
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
Page 65 and 66:
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
Page 73 and 74:
3Cys SCOP classification SCOP domai
Page 75 and 76:
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
Page 85 and 86:
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
Page 95 and 96:
triplet association/UTRP Resource A
Page 97 and 98:
is the set of 18,427 out of 40,750
Page 99 and 100:
fraction [LHC07] in biomedical text
Page 101 and 102:
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
Page 109 and 110:
evaluation study: shallow parser ba
Page 111 and 112:
REL = NP PP* VP. The extracted rela
Page 113 and 114:
MAN FEAT Category Defintion Categor
Page 115 and 116:
”The GlyNH2 was removed and the r
Page 117 and 118:
”Mutation K241Q completely abolis
Page 119 and 120:
6.3.2 Performance analysis of the c
Page 121 and 122:
MAN FEAT Category Precision Recall
Page 123 and 124:
The extracted information is diffic
Page 125 and 126:
Chapter 7 Extraction of functional
Page 127 and 128:
7.2 Results 7.2.1 Evaluation of the
Page 129 and 130:
has the following sources: a false
Page 131 and 132:
The knowledge base does not provide
Page 133 and 134:
7.2.3 Cross-validation of mined cat
Page 135 and 136:
Figure 7.3: Cross-validaiton of tex
Page 137 and 138:
tions indicates, that the informati
Page 139 and 140:
Page 141 and 142:
PDB UniProtKB PDBID chainID serial
Page 143 and 144:
mapped to 24,500 RID+UID. The ident
Page 145 and 146:
RID+UID Sentence PAS RID+UID Senten
Page 147 and 148:
8.3.4 General correlation found bet
Page 149 and 150:
Residue Annotations -HSSP +HSSP OPR
Page 151 and 152:
Chapter 9 Conclusions and future wo
Page 153 and 154:
9.2 Limitations and future works Du
Page 155 and 156:
Bibliography [AGM + 90] SF Altschul
Page 157 and 158:
[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
Page 179 and 180:
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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