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

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Figure 3.1: Overview of processes and evaluation methods of the developed 3D pattern identification system. 43
3.1.1 Structural feature extraction Theory Residue triplet as spatial pattern unit. The presented protein structure data mining algorithm aims to identify significant interaction of residues within a triplet configuration. The rational of analysing residue triplets is described in the following. In order to form a functional site in a protein structure, residues need to be physically in closed contact. In other words there exists a mutual dependency or interaction among the residues. The interaction can be studied on a two-residue basis (doublet 3D pattern). However, regarding the size of structure data the probability of any two-residue configurations is too high to be detected as specific. Hence, the signal/noise ratio issue is the reason why a two-residue 3D pattern is not the target of protein structure data mining [Old02]. A two residue contact is completely defined by a scalar property, while a three residue contact is defined by vectors. Consequently, a three residue constellation encodes much more information. This makes information theory based methods tractable to find conserved residue interactions as signals. In reality, functional sites can be composed of more than three residues, e.g. various metal binding sites used four coordinative cysteine residues. However, data sparseness and the mathematical complexity [CL64] [Sin04] in modelling four or larger residue interactions makes it infeasible. In principle, the more variables are introduced in modelling residue interactions, the more specific the data mining. It should be noted, that the identification of N-body interactions of residues can be solved from a combinatorial approach. Two triplets are combined, if there is equality in two out of three residues from each triplet [Old02]. This approach was adopted in this study to demonstrate that larger interaction configurations are extractable. However, this investigation concentrates mainly on the identification of three residue interactions. The assumption is, that if the output of a data mining provides valid result, the approach is justified and more complex residue configurations may inherit this property. 44
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 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: Chapter 3 Mining residue interactio
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
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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
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Figure 6.1: Overview of processes a
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discussion on semantic relation and
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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
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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
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Figure 8.1: Overview of processes a
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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:/
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[DCG + 04] F Diella, S Cameron, C G
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[Gue96] F Guenthner. Electronic lex
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[JK95] J Justeson and S Katz. Techn
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[MB99] Y Matsuo and SH Bryant. Iden
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[POHS05] M Pesu, J O’Shea, L Henn
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[STB06] MH Saier, CV Tran, and RD B
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[WK07] R Witte and T Kappler. Enhan
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Table A.1: Examples of errors in th
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. RID+UID Table B.1: Comparison of
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. . . 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
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Table F.1: Identified catalytic tri
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Appendix G Glossary 3D pattern - a
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