Methods for identifying drug targets based on genomic sequence data

• US 8,606,553 B2
• Filed: 08/06/2001
• Issued: 12/10/2013
• Est. Priority Date: 02/02/1999
• Status: Expired due to Fees

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Palsson, Bernhard
• Primary Examiner(s): NEGIN, RUSSELL SCOTT
• Application Number:
  US09/923,870
• Publication Number:
  US 20020012939A1
• Time in Patent Office: 4,509 Days
• Field of Search: 435/6, 435/34, 702/19, 702/20
• US Class Current: 703/5
• CPC Class Codes: C12N 15/1034 Isolating an individual clone by screening libraries
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 35/00 ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• CPC Class Codes: G16B 35/20 Screening of libraries
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16C 20/60 In silico combinatorial chemistry

Abstract

This invention provides a computational approach to identifying potential antibacterial drug targets based on a genome sequence and its annotation. Starting from a fully sequenced genome, open reading frame assignments are made which determine the metabolic genotype for the organism. The metabolic genotype, and more specifically its stoichiometric matrix, are analyzed using flux balance analysis to assess the effects of genetic deletions on the fitness of the organism and its ability to produce essential biomolecules required for growth.

Claims

1. A method performed in a computer of simulating a metabolic capability of an in silico strain of a microbe, comprising:

obtaining a plurality of DNA sequences comprising most metabolic genes in a genome of the microbe to produce an in silico representation of a microbe;

determining open reading frames of genes of unknown function in the microbe in said plurality of DNA sequences;

assigning a potential function to proteins encoded by said open reading by determining the homology of said open reading frames to gene sequences encoding proteins of known function in a different organism;

determining which of said open reading frames potentially correspond to metabolic genes by determining if the assigned function of said proteins is involved in cellular metabolism;

determining substrates, products and stoichiometry of the reaction for each of the gene products of said metabolic genes having an assigned potential function;

producing a genome specific stoichiometric matrix of said microbe produced by incorporating said substrates, products and stoichiometry into a stoichiometric matrix;

determining a metabolic demand corresponding to a biomass composition of said microbe;

calculating uptake rates of metabolites of said microbe;

combining said metabolic demands and said uptake rates with said stoichiometric matrix to produce an in silico representation of said microbe;

incorporating a general linear programming problem to produce an in silico strain of said microbe;

performing a flux balance analysis on said in silico strain, and

providing a visual output to a user of said analysis that simulates a metabolic capability of said strain predictive of said microbe'"'"'s phenotype.

2. The method of claim 1, wherein said microbe is Escherichia coli.

3. The method of claim 1, wherein said genes involved in cellular metabolism comprise genes involved in central metabolism, amino acid metabolism, nucleotide metabolism, fatty acid metabolism, lipid metabolism, vitamin and cofactor biosynthesis, energy and redox generation or carbohydrate assimilation.

4. The method of claim 3, wherein said genes are involved in central metabolism.

5. The method of claim 3, wherein said genes are involved in amino acid metabolism.

6. The method of claim 3, wherein said genes are involved in nucleotide metabolism.

7. The method of claim 3, wherein said genes are involved in fatty acid metabolism.

8. The method of claim 3, wherein said genes are involved in lipid metabolism.

9. The method of claim 3, wherein said genes are involved in vitamin and cofactor biosynthesis.

10. The method of claim 3, wherein said genes are involved in energy and redox generation.

11. The method of claim 3, wherein said genes are involved in carbohydrate assimilation.

12. The method of claim 1, wherein assigning a function comprises performing a homology search using the Basic Local Alignment Search Tool (BLAST).

13. The method of claim 1, wherein said uptake rates are calculated by measuring the depletion of substrate from growth media of said microbe.

14. A method performed in a computer for simulating a metabolic capability of an in silico strain of a microbe, comprising:

a) providing a nucleotide sequence of a potential metabolic gene in the microbe;

b) determining substrates, products and stoichiometry of the reaction for the gene product of said potential metabolic gene, wherein said gene product having an unknown function in the microbe is assigned a potential function by determining homology of said nucleotide sequence to gene sequences encoding gene products of known function in a different organism;

c) repeating steps a) and b) for most potential metabolic genes of said microbe to produce an in silico representation;

d) producing a genome specific stoichiometric matrix produced by incorporating said substrates, products and stoichiometry of the potential metabolic gene products in said microbe into a stoichiometric matrix;

e) determining a metabolic demand corresponding to a biomass composition of said microbe;

f) calculating uptake rates of metabolites of said microbe;

g) combining said metabolic demands and said uptake rates with said stoichiometric matrix to produce an in silico representation of said microbe;

h) incorporating a general linear programming problem to produce an in silico strain of said microbe;

i) performing a flux balance analysis on said in silico strain; and

j) providing a visual output to a user of said analysis that simulates a metabolic capability of said strain predictive of said microbe'"'"'s phenotype.

15. The method of claim 14, wherein the microbe is Escherichia coli.

16. The method of claim 14, wherein said metabolic gene is selected from the group consisting of:

genes involved in central metabolism, amino acid metabolism, nucleotide metabolism, fatty acid metabolism, lipid metabolism, vitamin and cofactor biosynthesis, energy and redox generation and carbohydrate assimilation.

17. The method of claim 16, wherein said genes are involved in central metabolism.

18. The method of claim 16, wherein said genes are involved in amino acid metabolism.

19. The method of claim 16, wherein said genes are involved in nucleotide metabolism.

20. The method of claim 16, wherein said genes are involved in fatty acid metabolism.

21. The method of claim 16, wherein said genes are involved in lipid metabolism.

22. The method of claim 16, wherein said genes are involved in vitamin and cofactor biosynthesis.

23. The method of claim 16, wherein said genes are involved in energy and redox generation.

24. The method of claim 16, wherein said genes are involved in carbohydrate assimilation.

25. The method of claim 14, wherein assigning a function comprises performing a homology search using the Basic Local Alignment Search Tool (BLAST).

26. The method of claim 14, wherein said uptake rates are calculated by measuring the depletion of substrate from growth media of said microbe.