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

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Figure 2.2: Three hyperlinked protein data banks. Illustrated is the size of three databanks, PDB, UniProtKB, and MEDLINE, along with their cross-references. For example, the PDB contains in total 42,943 PDB identifiers (version November 2008) with cross-references to 42,085 out of 333,445 Uniprot identifiers, which in return points to 10,466 biomedical journal articles (PMIDs). Notice that PDB also holds for each record a small number of primary citations, however, these are mainly pointers to crystallographic publications and provide little hints of biological function of the protein or annotation of functional sites. 29
various metal binding sites. The Catalytic Site Atlas (CSA) [PBT04] is another database documenting active sites in enzymes of 3D structures. The data are either manually curated or predicted, based on searches for homologous proteins. Another serious limitation of PDB is its use for statistical analysis of structure data. The PDB represents a redundant and biased snapshot of the protein universe. Redundancy is due to the fact that many highly similar structures or identical folds are deposited in the database leading to an over-representation of some proteins. In the past, structure determination has been guided by hypothesis-driven experiments, short-listed target proteins in the medical or commercial field, and by the methodologically tractable small proteins for crystallisation. Consequently, the fold-space has not been fully explored yet. Although techniques in protein crystallography are improving, there are still other underrepresented proteins, e.g. membrane proteins or large proteins, which define the boundaries of representativeness of the structure data. While there is little we can do about exploring the complete ensemble of folds from a bioinformatics point of view, the over-representation can be filtered. For example, protein sequence based clustering [AGM + 90] [AMS + 97] is the principle method to produce the following datasets: PDB SELECT [HSSS92], PISCES [WD03], UniqueProt [MR03]. However, this approach is limited by the assertion of sequence-structure relation in the so called twilight zone, i.e. below 30 per cent sequence identity proteins may or may not have similar folds [Ros99]. Another critical issue with sequence based clustering is the comparison of protein chain sequences rather than the alignment of segments defined by protein domain boundaries. Structure based approaches cluster the data on the basis of domain structures. Several databases of domain based structure clustering were created with the most prominent ranging from entirely manual work (SCOP [HMBC97]), semi-automatic approach (CATH [OMJ + 97]), to entirely non-supervised methods (FSSP-Dali, [HS94]). Differences in these classification were studied by [HJ99] and [DBAD03]. 30
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 and 26: teins. In contrast, to extract a pr
Page 27 and 28: Chapter 2 Background In the previou
Page 29: Figure 2.1: Data banks in the prote
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
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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
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is the set of 18,427 out of 40,750
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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
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7.2.3 Cross-validation of mined cat
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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
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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
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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
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
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. 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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