Methods for identifying drug targets based on genomic sequence data

• US 8,635,031 B2
• Filed: 10/29/2007
• Issued: 01/21/2014
• 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:
  US11/980,199
• Publication Number:
  US 20080270096A1
• Time in Patent Office: 2,276 Days
• Field of Search: None
• US Class Current: 702/27
• 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 genomic 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 producing a genome specific stoichiometric matrix of a microbe, comprising:

obtaining a plurality of DNA sequences in a genome, wherein said plurality comprises a number of DNA sequences in a genome sufficient to produce an in silico representation of a microbe;

determining open reading frames of genes in said plurality of DNA sequences;

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

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 for each of said metabolic genes having an assigned potential function; and

providing an output to a user of a genome specific stoichiometric matrix of said microbe produced from said substrates, products and stoichiometry.

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

5. A method of producing an in silico representation of a microbe in a computer, comprising:

producing a genome specific stoichiometric matrix of said microbe by the method of claim 1;

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

calculating uptake rates of metabolites of said microbe; and

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

6. The method of claim 5, further comprising incorporating a general linear programming problem into said in silico representation to produce an in silico strain.

7. The method of claim 5, wherein said uptake rates are calculated by measuring the depletion of substrate from growth media of said microbe.

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

producing a genome specific stoichiometric matrix of said microbe by the method of claim 1;

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, and

performing a flux balance analysis on said in silico strain.

9. A method performed in a computer for producing a genome specific stoichiometric matrix of a microbe, comprising:

a) assigning a potential function to a protein encoded by an open reading frame by determining a homology of said open reading frame to a gene sequence encoding a protein of known function, thereby providing a nucleotide sequence of a potential metabolic gene in the microbe;

b) determining substrates, products and stoichiometry for a metabolic gene product encoded by said potential metabolic gene based on its assigned function;

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

d) providing an output to a user of a genome specific stoichiometric matrix produced from said substrates, products and stoichiometry of the metabolic genes product in said microbe.

10. The method of claim 9, wherein the microbe is Escherichia coli.

11. The method of claim 9, 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.

12. The method of claim 9, wherein said homology is determined by performing a homology search using the Basic Local Alignment Search Tool (BLAST).

13. The method of claim 9, further comprising incorporating a general linear programming problem into said in silico representation to produce an in silico strain.

14. A method performed in a computer of producing an in silico representation of a microbe, comprising:

producing a genome specific stoichiometric matrix of said microbe by the method of claim 9;

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

calculating uptake rates of metabolites of said microbe; and

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

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

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

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

producing a genome specific stoichiometric matrix of said microbe by the method of claim 14;

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

calculating uptake rates of metabolites of said microbe, and

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, and

performing a flux balance analysis on said in silico strain.