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

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textual features. Chapter 7 presents the third part of the three text mining chapters. Both text mining modules from the previous chapters (protein residue identification, and contextual feature extraction) are combined to form the functional annotation extraction system. The overall performance of this information extraction system is studied. The validity of the extracted information as functional annotation is demonstrated by manual analysis on two example proteins (p53 and Jak2), and by cross-validation of identified catalytic or binding residues with two reference databases: CSA and MSDsite. Chapter 8 presents results on combining protein structure data with literature data. The validity is studied by examining the correlation of predicted active site residues with enzyme-related functional annotations. Chapter 9 summarises the thesis and presents limitations and open questions for follow up research. 25
Chapter 2 Background In the previous chapter, I have presented the motivation and objective of this thesis. The purpose of this chapter is to familiarise the reader with relevant concepts in protein science, data mining, and literature mining. The limitations of each reviewed data resource or methodology are discussed in context of this research work. 2.1 Protein related data resources Proteins are both building blocks of cellular structures and the major machinery in cells. In order to perform their functions, proteins need to fold into their three-dimensional structures and thereby form functional sites. The prediction of a structural pattern associated with a biological function is an important aspect in protein bioinformatics. To interpret the multiple functions of proteins, annotations are linked with results from bioinformatics analysis tools. In addition, data are extracted from generic and specific databases, biological knowledge accumulated in literature, and data from genome-wide experiments, such as transcriptomics and proteomics, are collected. One major goal is to describe protein function within biological context by using a standardised hierarchical classification scheme and controlled vocabulary. The biological community has developed databases and functional annotation schemes 26
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 and 8: Contents 1 Introduction 15 1.1 Prot
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: teins. In contrast, to extract a pr
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-
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that were found in this study. The
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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
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matches the protein sequence; (2) s
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abstract texts was drawn from the U
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Unique residue entities Reference D
Page 93 and 94:
Unique resi.-prot.-org.-association
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triplet association/UTRP Resource A
Page 97 and 98:
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
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data collation method is necessary
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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
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”The GlyNH2 was removed and the r
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”Mutation K241Q completely abolis
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6.3.2 Performance analysis of the c
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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
Page 145 and 146:
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
Page 155 and 156:
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
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[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
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[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
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. 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
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. . . continuation of table C.1 PMI
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. . . continuation of table C.1 RID
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Table D.1: Examples of literature m
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Appendix E Examples of extracted fu
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. . . 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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