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Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011
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Page 1: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Alessandro Pedretti

MetaPies,an annotated database formetabolism analysis and prediction:results and future perspectives

L’AquilaNovember 21, 2011

Page 2: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

The MetaPies project

1. Database structure definition and input forms.

Objective: classification and analysis of metabolic reactions of different substrates with a view to developing reliable prediction models.

Research phases:

2. Collecting data: exploited criteria and rules.

4. Property profiling: enzyme’s substrate/metabolite property space.

3. First general analysis: relative relevance of each metabolic reaction.

Page 3: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Phase 1: MetaPies database

Microsoft Access database application.

Reports for:

generic analysis;

metabolite analysis;

generation analysis.

Forms to simplify the input.

Page 4: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Phase 2: meta-analysis of the literature

A systematic search of metabolic studies was carried out in the primary literature, namely Chem Res Tox (2004-2009), Drug Metab Dispos (2004-2009), and Xenobiotica (2004-2009).

The focus was on drugs and other xenobiotics at the exclusion of endogenous compounds, except when the latter are used as drugs (e.g. steroids).

The meta-analysis involved studies in humans or mammalian animals, carried out either: 1) in vivo; 2) in cellular systems; 3) at subcellular or enzymatic level.

Each substrate was analyzed separately, avoiding duplicates. Regio- and stereo-isomers were considered as distinct substrates (substrate selectivity) or metabolites (product selectivity).

Page 5: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Metabolite classification

In each paper, the reported metabolites were classified according to:

the type of reaction that produced them (28 different types);

the enzyme (super)family or category that produced them (16 families);

the metabolic generation to which they belonged (1st, 2nd, and 3rd or more);

whether they were pharmacologically active;

whether they were reactive and/or toxic.

General counts

Number of analyzed papers: 903

Number of distinct substrates: 1107

Number of distinct metabolites: 6767

Metabolites per substrate: 5.8

Number of active metabolites: 201

Number of toxic metabolites: 473

Page 6: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Phase 3: distribution of metabolites into thethree major reaction classes

Global distribution

Distribution for each generation

Page 7: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Distribution of metabolites withineach reaction class

Red-ox reactions Conjugations

Hydrolisis

Page 8: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Reactions and metabolites

Active metabolites

Reactive / toxicmetabolites

Page 9: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Phase 4: substrate profiling

MetaPies

SubstratesMetabolitesReactions

ODBC

PubChem

3D structures

HTTP

3D structures of substrates and metabolitesMolecular descriptorsReactions

New database

ODBC

Microsoft ExcelMolecular descriptors

DDE

Page 10: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Substrate profiling2

For each molecule in the database, a set of 2D/3D properties are automatically calculated:

Descriptive: SMILES, InChI, InChI Key, functional group codes, molecular formula.

Constitutional: number of atoms, heavy atoms, chiral atoms, bonds, unsatured bonds, angles, torsions, flexible torsions, rings, H-bond acceptors and donors.

Structural: mass, volume, radius of gyration, SAS, PSA, ovality.

Physicochemical: charge, dipole, lipole, virtual logP.

Page 11: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Enzyme specific property space

Marketed

All substrates

Red-ox

Conjugation

Hydrolase

Rotors

Virtual logP

Oxidoreductases prefer more rigid and less polar substrates.

Conjugation enzymes prefer more rigid and more polar substrates.

Hydrolases prefer more flexible and less polar substrates.

Metabolic substrates are more apolar and more rigid molecules.

Page 12: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Redox and property space

Csp3

Csp2/1

C=O

Nox

Rotors

Virtual logPSubstrates of C=O/CH-OH oxidations are in general more polar.

Substrates of Csp3 oxidation are in general more flexible.

Page 13: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Conclusions

Recently, we used the MetaPies data to predict the probability to generate specific metabolites starting from substrates not included in the database.

Considering the substrate property space required for each type of reaction, it was possible to identify the most probable metabolites and these results were in agreement with the experimental data.

MetaPies is a valuable source of metabolic information and we explored only the iceberg tip.

Page 14: Alessandro Pedretti MetaPies, an annotated database for metabolism analysis and prediction: results and future perspectives L’Aquila November 21, 2011.

Acknowledgements

www.vegazz.net3.0

Bernard Testa

Giulio Vistoli


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