Classification of metabolic reactions based on physicochemical properties and search for enzyme inhibitors [Elektronische Ressource] / vorgelegt von Martin Johann Reitz

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Classification of Metabolic Reactions based on Physicochemical Properties and Search for Enzyme Inhibitors Den Naturwissenschaftlichen Fakultäten der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades vorgelegt von Martin Johann Reitz aus Forchheim Als Dissertation genehmigt von den Naturwissenschaftlichen Fakultäten der Universität Erlangen-Nürnberg Tag der mündlichen Prüfung: 11. Mai 2007 Vorsitzender der Prüfungskomission: Prof. Dr. E. Bänsch Erstberichterstatter: Prof. J. Gasteiger Zweitberichterstatter: Prof. Dr. H. Sticht Danksagung Meinem Doktorvater Prof. Dr. Johann Gasteiger danke ich für die Überlassung des interessanten Themas sowie die vielfältige Unterstützung und die wertvollen Anregungen wodurch das Gelingen der Arbeit erst ermöglicht wurde. Zu besonderem Dank bin ich verpflichtet: Herrn Alexander von Homeyer für die Zusammenarbeit im Rahmen der gemeinschaftlichen Publikation und des Buchkapitels sowie seine Unterstützung bei den Strukturüberlagerungen. Dr. Oliver Sacher für die Zusammenarbeit im Rahmen des BFAM Projektes sowie die stete Hilfsbereitschaft rund um BioPath, Cactvs und Cora. Weiterhin danken möchte ich Dr. Thomas Kleinöder, Dr. Lothar Terfloth und Dr. Yongquan Han für die anregenden wissenschaftlichen Diskussionen sowie zusammen mit Dr. Achim Herwig, Herrn Thomas Tröger und Dr.
Publié le : lundi 1 janvier 2007
Lecture(s) : 21
Tags :
Source : WWW.OPUS.UB.UNI-ERLANGEN.DE/OPUS/VOLLTEXTE/2007/613/PDF/MARTINREITZDISSERTATION.PDF
Nombre de pages : 147
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Classification of Metabolic Reactions based on Physicochemical
Properties and Search for Enzyme Inhibitors





Den Naturwissenschaftlichen Fakultäten
der Friedrich-Alexander-Universität Erlangen-Nürnberg
zur
Erlangung des Doktorgrades













vorgelegt von
Martin Johann Reitz
aus Forchheim
Als Dissertation genehmigt von den Naturwissenschaftlichen Fakultäten der
Universität Erlangen-Nürnberg





















Tag der mündlichen Prüfung: 11. Mai 2007
Vorsitzender der Prüfungskomission: Prof. Dr. E. Bänsch
Erstberichterstatter: Prof. J. Gasteiger
Zweitberichterstatter: Prof. Dr. H. Sticht Danksagung

Meinem Doktorvater

Prof. Dr. Johann Gasteiger

danke ich für die Überlassung des interessanten Themas sowie die vielfältige
Unterstützung und die wertvollen Anregungen wodurch das Gelingen der Arbeit erst
ermöglicht wurde.

Zu besonderem Dank bin ich verpflichtet:

Herrn Alexander von Homeyer für die Zusammenarbeit im Rahmen der
gemeinschaftlichen Publikation und des Buchkapitels sowie seine Unterstützung bei
den Strukturüberlagerungen.

Dr. Oliver Sacher für die Zusammenarbeit im Rahmen des BFAM Projektes sowie die
stete Hilfsbereitschaft rund um BioPath, Cactvs und Cora.

Weiterhin danken möchte ich Dr. Thomas Kleinöder, Dr. Lothar Terfloth und Dr.
Yongquan Han für die anregenden wissenschaftlichen Diskussionen sowie zusammen mit
Dr. Achim Herwig, Herrn Thomas Tröger und Dr. Markus Sitzmann für die tatkräftige
Unterstützung im Rahmen der Linux-Administration.

Danke auch an die Herren Jan Griebsch, Arno Buchner und Hanjo Täubig vom Lehrstuhl
für Effiziente Algorithmen der TU München für die Zusammenarbeit im Rahmen des
BFAM Projektes.

Darüber hinaus danke ich allen derzeitigen und ehemaligen Mitarbeitern des
Arbeitskreises, welche hier nicht namentlich erwähnt sind, sowie den Sekretärinnen für
die stete Hilfsbereitschaft und Bereitstellung einer funktionierenden Infrastruktur sowie
für die stets angenehme Arbeitsatmosphäre.

Ganz besonders herzlichen Dank meiner Freundin Silvia sowie meiner ganzen Familie,
insbesondere meinen Eltern, für die Geduld und Unterstützung in jeglicher Hinsicht beim
Verfassen dieser Arbeit.

Dem Bundesministerium für Bildung und Forschung (BMBF) danke ich für die
Finanzierung der Arbeit durch das BFAM-Projekt. Contents
1 Introduction......................................................................................................................1
1.1 Scientific Background..................................................................................................4
1.1.1 Connection Table4
1.1.2 Atom-Atom Mapping............................................................................................5
1.1.3 Reaction Center Marking ..................................................................................... 6
1.2 Exploiting the Data ...................................................................................................... 7
References .............................................................................................................................. 9
2 Enabling the Exploration of Biochemical Pathways .....................................................11
2.1 General Introduction.................................................................................................11
References....... 11
Abstract ................................................................................................................................ 12
2.2 Introduction............................................................................................................... 12
2.3 The BioPath Database................................................................................................ 15
2.3.1 The Data Model...................................................................................................15
2.3.2 Chemical Structures............................................................................................15
2.3.3 Chemical Reactions.............................................................................................16
2.3.4 Enzymes18
2.3.5 Augmenting the Contents .................................................................................. 18
2.3.6 Data Input............................................................................................................18
2.3.7 Data Processing and Storing ............................................................................... 19
2.4 The C@ROL Retrieval System 21
2.5 Searching in the BioPath Database ........................................................................... 24
2.5.1 Searching in the Molecule Database .................................................................. 24
2.5.2 Name and Name Fragment Searching................................................................ 24
2.5.3 Gross-formula Searches.......................................................................................27
i2.5.4 Full Structure and Substructure Searches.......................................................... 28
2.5.5 Property Retrieval...............................................................................................32
2.5.6 3D-Substructure Searches...................................................................................32
2.5.7 Searching in the Reaction Database................................................................... 34
2.5.8 Searching with Chemical Structures.................................................................. 34
2.5.9 Searches on the Reaction Centre........................................................................ 35
2.5.10 Searching on Enzymes ........................................................................................ 38
2.5.11 Combined Searches.............................................................................................40
2.6 Conclusions................................................................................................................ 41
Acknowledgements ............................................................................................................. 41
Table of Acronyms............................................................................................................... 42
References ............................................................................................................................ 43
2.7 Improvements on the BioPath Database .................................................................. 45
2.7.1 Data Cleanup & Improvement ........................................................................... 45
2.7.2 C@ROL Interface................................................................................................45
References....... 49
3 Query Generation to Search for Inhibitors of Enzymatic Reactions ............................50
3.1 General Introduction.................................................................................................50
References ............................................................................................................................ 51
Abstract ................................................................................................................................ 52
3.2 Introduction............................................................................................................... 53
3.3 Materials and Methods .............................................................................................. 55
3.4 Results and Discussion...............................................................................................60
3.5 Conclusions................................................................................................................ 75
Acknowledgement............................................................................................................... 75
iiReferences ............................................................................................................................ 76
3.6 Further Conclusions..................................................................................................79
References....... 79
4 Database Screening for Enzyme Inhibitors....................................................................80
4.1 Introduction............................................................................................................... 80
4.2 Preparation of data..................................................................................................... 81
4.3 Computation83
4.4 The Fitness Function.................................................................................................84
4.5 Results......................................................................................................................... 87
4.6 Conclusions................................................................................................................ 93
References ............................................................................................................................ 94
5 Classification of Metabolic Reactions Based on Physicochemical Descriptors:
Investigations on Hydrolases ................................................................................................96
5.1 General Introduction.................................................................................................96
Abstract ................................................................................................................................ 98
5.2 Introduction............................................................................................................... 98
5.3 Materials and Methods ............................................................................................ 101
5.3.1 BioPath101
5.3.2 Datasets..............................................................................................................101
5.3.3 Choice of Descriptors........................................................................................ 107
5.3.4 Kohonen Neural network................................................................................. 107
5.4 Results and Discussion.............................................................................................109
5.4.1 EC 3.b.c.d...........................................................................................................109
5.4.2 EC 3.1.c.d118
5.4.3 EC 3.2.c.d121
iii5.4.4 EC 3.5.c.d...........................................................................................................122
5.5 Conclusions..............................................................................................................127
Acknowledgement............................................................................................................. 128
References .......................................................................................................................... 128
5.6 Further Conclusions................................................................................................131
References..... 131
6 Conclusions & Outlook.................................................................................................132
References..... 133
7 Summary .......................................................................................................................134
8 Zusammenfassung.........................................................................................................136
Publikationen .........................................................................................................................A
Lebenslauf............................................................................................................................... B
iv1 Introduction
__________________________________________________________________________________________
1 Introduction

Metabolic reactions are of high interest as these processes keep us alive. Misbalances or
failures in these highly regulated reaction networks can lead to severe diseases. In
biotechnology and agricultural industry, it is also of high importance to understand the
metabolism and get hand on its regulation in order to enhance the output of desired
products by living organisms.
Metabolic reactions are very energy-intensive processes in the organism. For example, a
human at rest consumes about 65 kilograms of ATP per day, nearly the weight of his own
body, which has to be regenerated; a human doing activity consumes even much more [1].

Figure 1.1 Example of a metabolic reaction network. The rectangular boxes represent the catalyzing
enzyme with EC number, oval boxes indicate connected pathways, and small circles stand for
chemical compounds. Filled arrows indicate a direct molecular interaction while unfilled
arrows indicate a connection to another pathway. The example was taken from the KEGG
website [2].

11 Introduction
__________________________________________________________________________________________
This effective network of reactions is driven and regulated by enzymes. These proteins
enable the reactions at the required reaction rate in water solution at body temperature.
Therefore, to understand the enzymes and how they work enables us to understand
metabolism which may then lead to new drugs. An example for such a complex reaction
network is given in Figure 1.1.
In the past, much effort has been spent on the investigation of metabolic reactions and
pathways [3]. One of the first researchers who performed systematic and quantitative
studies on metabolism by using a balance was the Italian physiologist Santorio Santorio in
th th ththe 16 century [4]. Then, in the 19 and 20 centuries, the basic concepts of metabolism,
including the major metabolic pathways and enzymes, were investigated. Nowadays, a
large amount of metabolic data, often stored in electronic databases, is available [5]. Since
the 1990’s, an enormous increase of biochemistry and molecular biology data can be
observed as a consequence of new methods in genomics, proteomics, and high-throughput
technology. Starting with the sequencing of the Haemophilus influenza genome in 1995
[6], a milestone in genomics was reached with the complete sequence of the human
genome in 2001 [7]. Nowadays, the sequencing of genomes is an established tool and
commercially interesting plant and livestock genomes are under sequencing or already
uncovered. Parallel to the growth of genomic data, also the knowledge on proteomics data
rises fast and is collected and stored in databases. One of the most comprehensive
databases in the field of proteomics is the Protein Data Bank (PDB) [8] founded in 1971.
As a consequence of the huge amounts of data now available, bioinformatics has emerged
as an important discipline in biosciences. The methods provided by bioinformatics help to
understand the data and to generate new knowledge from them. Even the simulation of
whole cells in silico is under development [9]. However, to view metabolism from the
standpoint of a biologist is only one side of the medal. The whole machinery of all the
genes and proteins involved in metabolism is at the very end only a tool, optimized to
enable the essential task of metabolism: the chemical reaction. Therefore, if one wants to
completely understand metabolism, one also has to view it from a chemical standpoint.
Here, modern technology helped to accumulate a multitude of chemical data related to
metabolism, like reaction data or data on chemical compounds, which is often also
21 Introduction
__________________________________________________________________________________________
available in computer readable form. A quite comprehensive database in this field is the
KEGG database [2].
As a counterpart to bioinformatics, on the chemistry side, the discipline of
chemoinformatics arose in order to analyze these data by computer methods. Therefore,
to understand metabolism which is the basis for the development of useful drugs for
curing metabolic disorders, one has to integrate the knowledge of both worlds:
bioinformatics and chemoinformatics (Figure 1.2).


Figure 1.2 To gain a complete understanding of metabolism, both methods, bioinformatics and
chemoinformatics, have to interact. While bioinformatics investigates the situation from the
view of genes and proteins, chemoinformatics handles the data on the chemical compounds
and reactions involved in metabolism.

In this work, a database of metabolic reactions, BioPath, was used as a starting point. This
database is suited to act as a link between bioinformatics and chemoinformatics and
derived from a preceding project funded by the ‘Bundesministerium fuer Bildung und
Forschung’ (BMBF) [10]. In this project, the data from the well known Boehringer
Biochemical Pathways wall charts [11] was turned into computer-readable form by
storing the compounds and reactions on atomic level. This was done by marking the sites
where the enzymes act on the chemical compounds, the reaction center, and by atom-
atom mapping, matching the individual atom of substrate and product together. In the
following section 1.1, some basic concepts this database relies on are explained. For a
detailed explanation refer to Ref. 12 and 13.

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