1. PathoLogic Pathway Predictor

2. Inference of Metabolic Pathways
Annotated Genomic
Sequence
Pathway/Genome
Database
Genes/ORFs
Gene Products
DNA Sequences
Pathways
Reactions
PathoLogic Software
Integrates genome and pathway data to identify putative metabolic networks
Compounds
Multi-organism Pathway
Database (MetaCyc)
Gene Products
Reactions
Pathways
Compounds
Genes
Genomic Map

3. PathoLogic Functionality
Initialize schema for new PGDB
Transform existing genome to PGDB form
Infer metabolic pathways and store in PGDB
Infer operons and store in PGDB
Assemble Overview diagram
Assist user with manual tasks
Assign enzymes to reactions they catalyze
Identify false-positive pathway predictions
Build protein complexes from monomers
Infer transport reactions

4. PathoLogic Input/Output
Inputs:
File listing genetic elements
Files containing DNA sequence for each genetic element
Files containing annotation for each genetic element
MetaCyc database
Output:
Pathway/genome database for the subject organism
Reports that summarize:
Evidence contained in the input genome for the presence of reference pathways
Reactions missing from inferred pathways

5. PathoLogic Analysis Phases
Trial parsing of input data files [few days]
Initialize schema of new PGDB [3 min]
Create DB objects for replicons, genes, proteins [5 min]
Assign enzymes to reactions they catalyze
ferrochelatase [10 min / 1 week]
glutamate 1-semialdehyde 2,1-aminomutase
porphobilinogen deaminase E1 E2
A C G B D E F

6. PathoLogic Analysis Phases
From assigned reactions, infer what pathways are present [5 min / few days]
Define metabolic overview diagram [30 min]
Define protein complexes [few days]

7. genetic-elements.dat ID TEST-CHROM-1 NAME Chromosome 1 TYPE :CHRSM
CIRCULAR? N
ANNOT-FILE chrom1.pf
SEQ-FILE chrom1.fsa //
ID TEST-CHROM-2
NAME Chromosome 2
ANNOT-FILE /mydata/chrom2.gbk
SEQ-FILE /mydata/chrom2.fna

8. File Naming Conventions
One pair of sequence and annotation files for each genetic element
Sequence files: FASTA format
suffix fsa or fna
Annotation file:
Genbank format: suffix .gbk
PathoLogic format: suffix .pf

9. Typical Problems Using Genbank Files With PathoLogic
Wrong qualifier names used: read PathoLogic documentation!
Extraneous information in a given qualifier
Check results of trial parse carefully

10. GenBank File Format Accepted feature types: CDS, tRNA, rRNA, misc_RNA
Accepted qualifiers:
/locus_tag Unique ID [recm]
/gene Gene name [req]
/product [req]
/EC_number [recm]
/product_comment [opt]
/gene_comment [opt]
/alt_name Synonyms [opt]
/pseudo Gene is a pseudogene [opt]
For multifunctional proteins, put each function in a separate /product line

11. PathoLogic File Format
Each record starts with line containing an ID attribute
Tab delimited
Each record ends with a line containing //
One attribute-value pair is allowed per line
Use multiple FUNCTION lines for multifunctional proteins
Lines starting with ‘;’ are comment lines
Valid attributes are:
ID, NAME, SYNONYM
STARTBASE, ENDBASE, GENE-COMMENT
FUNCTION, PRODUCT-TYPE, EC, FUNCTION-COMMENT
DBLINK
INTRON

12. PathoLogic File Format
ID TP0734
NAME deoD
STARTBASE
ENDBASE
FUNCTION purine nucleoside phosphorylase
DBLINK PID:g
PRODUCT-TYPE P
GENE-COMMENT similar to GP: percent identity: 57.51; identified by sequence similarity; putative //
ID TP0735
NAME gltA
STARTBASE
ENDBASE
FUNCTION glutamate synthase
DBLINK PID:g

13. Before you start: What to do when an error occurs
Most Navigator errors are automatically trapped – debugging information is saved to error.tmp file.
All other errors (including most PathoLogic errors) will cause software to drop into the Lisp debugger
Unix: error message will show up in the original terminal window from which you started Pathway Tools.
Windows: Error message will show up in the Lisp console. The Lisp console usually starts out iconified – its icon is a blue bust of Franz Liszt
2 goals when an error occurs:
Try to continue working
Obtain enough information for a bug report to send to pathway-tools support team.

14. The Lisp Debugger
Sample error (details and number of restart actions differ for each case)
Error: Received signal number 2 (Keyboard interrupt)
Restart actions (select using :continue):
0: continue computation
1: Return to command level
2: Pathway Tools version 10.0 top level
3: Exit Pathway Tools version 10.0
[1c] EC(2):
To generate debugging information (stack backtrace):
:zoom :count :all
To continue from error, find a restart that takes you to the top level – in this case, number 2
:cont 2
To exit Pathway Tools:
:exit

15. How to report an error
Determine if problem is reproducible, and how to reproduce it (make sure you have all the latest patches installed)
Send to containing:
Pathway Tools version number and platform
Description of exactly what you were doing (which command you invoked, what you typed, etc.) or instructions for how to reproduce the problem
error.tmp file, if one was generated
If software breaks into the lisp debugger, the complete error message and stack backtrace (obtained using the command :zoom :count :all, as described on previous slide)

16. Using the PPP GUI to Create a Pathway/Genome Database
Input Project Information
Organism -> Create New

17. Input Project Information

18. PathoLogic Command Menus
Organism
Select
Create New
Save KB
Revert KB
Reinitialize KB
Specify Reference PGDB(s)
Exit
Build
Trial Parse
Automated Build
Refine
Assign Probable Enzymes
Assign Modified Proteins
Create Protein Complexes
Re-run Name Matcher
Rescore Pathways
Predict transcription units
Transport Identification Parser
Update Overview
Pathway Hole Filler

19. Next Steps Trial Parse Build -> Trial Parse
Fix any errors in input files
Build pathway/genome database
Build -> Automated Build

20. PathoLogic Parser Output

21. Assign Enzymes to Reactions
Gene product
MetaCyc
UDP-glucose-4-epimerase
Match
yes no
Probable enzyme
-ase
Assign
UDP-D-glucose  UDP-galactose no
yes
Manually search
Not a metabolic enzyme no
yes
Assign
Can’t Assign

22. Enzyme Name Matcher Matches on full enzyme name
Match is case-insensitive and removes the punctuation characters “ -_(){}',:”
Also matches after removal of prefixes and suffixes such as:
“Putative”, “Hypothetical”, etc
alpha|beta|…|catalytic|inducible chain|subunit|component
Parenthetical gene name

23. Enzyme Name Matcher
For names that do not match, software identifies probable metabolic enzymes as those
Containing “ase”
Not containing keywords such as
“sensor kinase”
“topoisomerase”
“protein kinase”
“peptidase”
Etc
Research unknown enzymes
MetaCyc, Swiss-Prot, PubMed

24. Enzyme Name to Reaction Mapping
See also file PTools Tutorial/PathoLogic Reports/name-matching-report.txt

25. Manual Polishing Refine -> Assign Probable Enzymes  Do this first
Refine -> Rescore Pathways  Redo after assigning enzymes
Refine -> Create Protein Complexes  Can be done at any time
Refine -> Assign Modified Proteins  Can be done at any time
Refine -> Transport Identification Parser  Can be done at any time
Refine -> Pathway Hole Filler
Refine -> Predict Transcription Units
Refine -> Update Overview  Do this last, and repeat after any material changes to PGDB

26. Assign Probable Enzymes

27. How to find reactions for probable enzymes
First, verify that enzyme name describes a specific, metabolic function
Search for fragment of name in MetaCyc – you may be able to find a match that PathoLogic missed
Look up protein in SwissProt or other DBs
Search for gene name in PGDB for related organism (bear in mind that gene names are not reliable indicators of function, so check carefully)
Search for function name in PubMed
Other…

28. Manual Polishing Refine -> Assign Probable Enzymes
Refine -> Rescore Pathways
Refine -> Create Protein Complexes
Refine -> Assign Modified Proteins
Refine -> Transport Identification Parser
Refine -> Pathway Hole Filler
Refine -> Predict Transcription Units
Refine -> Run Consistency Checker
Refine -> Update Overview

29. Automated Pathway Inference
All pathways in MetaCyc for which there is at least one enzyme identified in the target organism are considered for possible inclusion.
Algorithm errs on side of inclusivity – easier to manually delete a pathway from an organism than to find a pathway that should have been predicted but wasn’t.

30. Considerations taken into account when deciding whether or not a pathway should be inferred:
Is there a unique enzyme – an enzyme not involved in any other pathway?
Does the organism fall in the expected taxonomic domain of the pathway?
Is this pathway part of a variant set, and, if so, is there more evidence for some other variant?
If there is no unique enzyme:
Is there evidence for more than one enzyme?
If a biosynthetic pathway, is there evidence for final reaction(s)?
If a degradation pathway, is there evidence for initial reaction(s)?
If an energy metabolism pathway, is there evidence for more than half the reactions?

31. Assigning Evidence Scores to Predicted Pathways
X|Y|Z denotes score for P in O
where:
X = total number of reactions in P
Y = enzymes catalyzing number of reactions for which there is evidence in O
Z = number of Y reactions that are used in other pathways in O

32. Manual Pruning of Pathways
Use pathway evidence report
Coloring scheme aids in assessing pathway evidence
Phase I: Prune extra variant pathways
Rescore pathways, re-generate pathway evidence report
Phase II: Prune pathways unlikely to be present
No/few unique enzymes
Most pathway steps present because they are used in another pathway
Pathway very unlikely to be present in this organism
Nonspecific enzyme name assigned to a pathway step

33. Caveats Cannot predict pathways not present in MetaCyc
Evidence for short pathways is hard to interpret
Since many reactions occur in multiple pathways, some false positives

34. Output from PPP Pathway/genome database Summary pages
Pathway evidence page
Click “Summary of Organisms”, then click organism name, then click “Pathway Evidence”, then click “Save Pathway Report”
Missing enzymes report
Directory tree containing sequence files, reports, etc.

35. Resulting Directory Structure
ROOT/ptools-local/pgdbs/user/ORGIDcyc/VERSION/
input
organism.dat
organism-init.dat
genetic-elements.dat
annotation files
sequence files
reports
name-matching-report.txt
trial-parse-report.txt kb
ORGIDbase.ocelot
data
overview.graph
released -> VERSION

36. Manual Polishing Refine -> Assign Probable Enzymes
Refine -> Rescore Pathways
Refine -> Create Protein Complexes
Refine -> Assign Modified Proteins
Refine -> Transport Identification Parser
Refine -> Pathway Hole Filler
Refine -> Predict Transcription Units
Refine -> Run Consistency Checker
Refine -> Update Overview

37. Creating Protein Complexes

38. Complex Subunits Stoichiometries

39. Manual Polishing Refine -> Assign Probable Enzymes
Refine -> Re-run Name Matcher
Refine -> Create Protein Complexes
Refine -> Assign Modified Proteins
Refine -> Transport Identification Parser
Refine -> Pathway Hole Filler
Refine -> Predict Transcription Units
Refine -> Run Consistency Checker
Refine -> Update Overview

40. Proteins as Reaction Substrates

41. Manual polishing Refine -> Assign Probable Enzymes
Refine -> Re-run Name Matcher
Refine -> Create Protein Complexes
Refine -> Assign Modified Proteins
Refine -> Transport Identification Parser
Refine -> Pathway Hole Filler
Refine -> Predict Transcription Units
Refine -> Run Consistency Checker
Refine -> Update Overview

42. nicotinate nucleotide
What are pathway holes?
At least one reaction in the pathway has an enzyme assigned. The reactions in the pathway without enzymes assigned are holes.
L-aspartate
iminoaspartate
No EC#
quinolinate
holes
n.n. pyrophosphorylase
nadC, RV1596
deamido-NAD
deamido-NAD
nicotinate nucleotide
NAD

43. Algorithm for identifying candidates and consolidating data…
Step III & IV: Consolidate hits and evaluate evidence using a Bayes classifier
Step II: BLAST against target genome
Step I: collect query isozymes of function A
3 queries have low-scoring hits to sequence X
Resulting P(has-function) is low
gene X
organism 1 enzyme A
organism 2 enzyme A
organism 3 enzyme A
8 queries have high-scoring hits to sequence Y
Resulting P(has-function) is high
organism 4 enzyme A
organism 5 enzyme A
gene Y
organism 6 enzyme A
organism 7 enzyme A
organism 8 enzyme A
5 queries have low-scoring hits to sequence Z
Resulting P(has-function) is low
gene Z
target genome

44. Reference for the Pathway Hole Filler…
Green, ML and Karp, PD. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 2004, 5:76.

45. Features used to calculate the probability that a protein has the desired function…
Candidate is in a contiguous set of genes transcribed in one direction with another gene in the pathway
Best E-value
Avg. rank
Avg % aligned
Number of query sequences aligned
Potential operon?
Adjacent reactions?
Candidate is adjacent to the gene assigned to an adjacent reaction in the pathway

46. Navigating to the Pathway Hole Filler

47. Steps that must be completed before running the Pathway Hole Filler
Install BLAST executable (should already be installed on training room machines)
Prepare BLAST protein db
Need FASTA format genome nucleotide sequence (see instructor if you have something different, like ESTs, or have no sequence data file)
In general, the more pathways in your PGDB, the more the pathway hole filler will have to search for

48. Steps for operating the pathway hole filler
Prepare training data for Bayes classifier
Collect feature data for known rxns in PGDB
Calculate probability distributions for classifier
Identify and evaluate candidates
Collect feature data for each candidate
Use classifier to determine P(has-function)
Choose holes to fill in KB
Either select all above a cutoff or manually review candidates

49. Step 1: Prepare Training Data…
Calculate training data from your organism or use existing training data…
Once Step 1 has been completed, the training data are saved and can be reused (even in another Pathway Tools session).
If using existing data from E. coli the training data are based on data from the literature.

50. Step 2: Identify & Evaluate Candidates…

51. Step 2: Identify & Evaluate Candidates
Select reactions from a list
Select pathways from a list
A list of all pathways in the PGDB with holes
A list of all pathway holes in the PGDB

52. Modes of operation… Fully automatic No interaction required from user
CAUTION!!
Fully automatic
No interaction required from user
All default values used
Prepare training data – all known rxns in KB
Identify and evaluate candidates – all pathways with pathway holes
Choose holes to fill in KB – all holes with P>0.9 filled

53. Modes of operation… Wizard Power-user mode
Wizard prompts user for training data source and for which holes to make predictions. Wizard runs Steps 1 & 2, then prompts user to complete Step 3.
Power-user mode
User must proceed through each step in order. Program still prompts user for required parameters, but each step must be completed before advancing to next step.

54. Step 3: Choose Holes to Fill in KB

55. Step 3: Choose Holes to Fill in KB

59. Output from Pathway Hole Filler - from “Prepare Training Data” step
ROOT/aic-export/ecocyc/ORGIDcyc/VERSION/data/
(e.g., ROOT/aic-export/ecocyc/caulocyc/1.0/data/)
rxn-list = data retrieved from ORGID for calculating training data
priors/ = directory containing training data that is loaded when using existing data from ORGID
These files contain the training data computed in Step 1. If either file is available, the user may use “existing” training data in Step 1.
* Each file is overwritten each time you run this step.

60. Output from Pathway Hole Filler - from “Identify and Evaluate Candidates” step
ROOT/aic-export/ecocyc/ORGIDcyc/VERSION/reports/
(e.g., ROOT/aic-export/ecocyc/caulocyc/1.0/reports/)
ORGIDholesX-Y.html (e.g., CAULOholes0-10.html)
ORGID_filled-holes.html = the list of holes that user selected to fill in the KB in Step 3.
blasterrors.log = log of each rxn describing whether or not any candidates were found
hole-data = file containing data (in a Lisp structure) found for each rxn, used to generate list in “Choose holes to fill in KB” dialogue. If this file is available, step 3 can be initiated without repeating Step 2.
* Each file is overwritten each time you run this step.

61. Manual polishing Refine -> Assign Probable Enzymes
Refine -> Rescore Pathways
Refine -> Create Protein Complexes
Refine -> Assign Modified Proteins
Refine -> Transport Identification Parser
Refine -> Pathway Hole Filler
Refine -> Predict Transcription Units
Refine -> Run Consistency Checker
Refine -> Update Overview

62. Nomenclature WO pair = pair of genes within an operon
TUB pair = pair of genes at a transcription unit boundary (delineate operons)

63. Operation of the operon predictor
For each contiguous gene pair, predict whether gene pairs are within the same operon or at a transcription unit boundary
Use pairwise predictions to identify potential operons
AB = TUB pair
BC = WO pair operon = BCD
CD = WO pair
DE = TUB pair A B C D E

64. Operon predictor Predicts operon gene pairs based on:
intergenic distance between genes
genes in the same functional class
Typically used for operon prediction
We use method from Salgado et al, PNAS (2000) as a starting point.
Uses E. coli experimentally verified data as a training set.
Compute log likelihood of two genes being WO or TUB pair based on intergenic distance.

65. Operon predictor Additional features easily computed from a PGDB
both genes products enzymes in the same metabolic pathway
both gene products monomers in the same protein complex
one gene product transports a substrate for a metabolic pathway in which the other gene product is involved as an enzyme
a gene upstream or downstream from the gene pair (and within the same directon) is related to either one of the genes in the pair as per features 1, 2 and 3 above.