Transport Inference Parser: Inferring Transport Reactions from Protein Data for PGDBs

1. Transport Inference Parser:Inferring Transport Reactions from Protein Data for PGDBs

2. Running the Transport Inference Parser 1. Run Pathway Tools. 2. Make the organism of interest the current organism. 3. [Run operon predictor]. 4. Select Tools/Pathologic. 5. From Pathologic, select Refine/Transport Inference Parser. 6. If running TIP for the first time on the organism, optionally provide its aerobicity. 7. Wait and observe progress. 8. When complete, Probable Transporter Table window appears. 9. You may now review and modify the inferred transporters.

3. Background Implemented in consultation with Ian Paulsen Reference: Annotation-based inference of transporter function. Thomas J. Lee, Ian Paulsen and Peter Karp. Bioinformatics, vol. 24, pp. 259-67, 2008.

4. Purpose of TIP Infer transport reactions from protein data and construct them in BioCyc PGDBs. Present results for review so that predictions can be reviewed for acceptance, rejection, and modification.

5. Results of running TIP Add the following to the PGDB for each inferred transported substrate: • Transport-Reaction frame of correct subclass • Assign compartments – use simple assumptions • Enzymatic-Reaction frame linking protein to reaction Construct Protein-Complexes as required Evidence codes and provenance data added to these

6. Sequence of internal operations 1. Find candidate transporter proteins. 2. Filter out candidates. 3. Identify substrate(s). 4. Assign an energy coupling to transporter. 5. Identify compartment of each substrate. 6. Group subunits of transporter complexes. 7. Construct full compartmental reaction from substrate and coupling. 8. Construct enzymatic reaction linking each reaction with protein.

7. 1. Find candidate transporter proteins • Input: all protein frames of organism • Output: internal data structures for each candidate • Annotation must contain an indicator. Exs:"transport”, “export”, “permease”, “channel” • Exclude proteins with long annotations (default: 12 words)

8. 2. Filter candidates • Exclude if annotation matches a list of regular expressions of counterindicator phrases and patterns • Ex:“transport associated domain” • Exclude if annotation containscounterindicator word • Exs: “regulator”, “nuclear-export”

9. 3. Identify substrate(s) Search annotation for names of MetaCyc compounds. Details: Multiple substrates indicate multiple reactions, symport/antiport pair, or both. Exs: “cytosine/purines/uracil/thiamine/allantoin permease family protein” “magnesium and cobalt transport protein cora, putative” “sodium:sulfate symporter transmembrane domain protein” “probable agcs sodium/alanine/glycine symporter” Exclude non-substrates that look like compounds via an exception list. Exs: “as”“be”“c”“i”

10. 3. Identify substrate(s) (cont.) Name canonicalization. Ex: strip plurals. Affixed substrates. Exs: “-transporting”“-specific” Lookup special ionic forms. Exs: “cuprous”“ferric”“hydrogen” Resolve multivalent options using aerobicity. Exs: “FE”“CR”“MN” Two-word substrates, substrate classes (no 3+ word substrates). Ex: “amino acid”

11. 4. Assign an energy coupling. Couplings: Channel, Secondary, ATP, PTS, Unknown Search annotation for prioritized list of indicators. Exs: "atp-binding" => ATP "mfs" => SECONDARY "pts" => PTS "phosphotransferase" => PTS "carrier" => SECONDARY "channel" => CHANNEL Some substrates imply a coupling. Ex: protoheme => ATP Absence of indicator => UNKNOWN

12. 5. Identify compartment of each substrate. Use keywords to determine compartment of primary substrate (Exs: “export”, “antiporter”) Otherwise assume primary substrate is transported into cell (periplasm => cytoplasm) Deferred complex compartment analysis: • Assume E.coli-like cellular structure

13. 6. Group subunits of transporter complexes. Many transporters are systems of several proteins. These are grouped into complexes Grouping criteria; all must be met: • Predicted coupling is ATP or PEP • Predicted substrates are identical • Genes of proteins have a common operon (NOTE requirement on operon availability) Resulting complex is added to PGDB as a frame Protein-Complexes.

14. 7. Construct full compartmental reaction from substrate and coupling. Determine set of transported substrates for this transporter: • For SECONDARY coupling: • Identify auxiliary substrate providing ion gradient (H+, Na+) • Remove from transported substrate list • Place on side of reaction indicated by symport/antiport clues • For other couplings: • Determined previously in substrate analysis

15. 7. Construct full compartmental reaction from substrate and coupling (cont). For each transported substrate of this transporter, either import reaction (from E.coli) or to create new one. • Search importKB for reaction with matching substrates:(find-rxn-by-substrates) • Transported substrate added with indicated compartment • Auxiliary substrates determined by coupling. Ex: • CHANNEL have none • ATP have ATP/H2O  ADP/phosphate • If one reaction is found, import: (import-reactions trxns src-kb dst-kb …) • If multiple reactions found, retain all. • Else if reaction is not present in PGDB, create new rxn

16. 7. Construct full compartmental reaction from substrate and coupling (cont). Create new reaction: • Create reaction frame, subclass determined by coupling: • (create-instance-w-generated-id rxn-class) • Add transported and auxiliary substrates to appropriate sides of reaction

17. 8. Construct enzymatic reaction linking each reaction with protein. For each created reaction: • (add-reactions-to-protein …) • Added evidence code, history string arguments • Subordinates new [(import-reactions) handles import of enzymatic-reactions]

18. Running the Transport Inference Parser 1. Run Pathway Tools. 2. Make the organism of interest the current organism. 3. [Run operon predictor]. 4. Select Tools/Pathologic. 5. From Pathologic, select Refine/Transport Inference Parser. 6. If running TIP for the first time on the organism, optionally provide its aerobicity. 7. Wait and observe progress. 8. When complete, Probable Transporter Table window appears. 9. You may now review and modify the inferred transporters.

19. GUI Overview • Window is titled: Probable Transporter Table for Organism • Table of inferred transporters is organized into columns: • Status • Gene • Substrate • Coupling • Reaction / Function 3. Each row contains a transport reaction description: • Multiple reactions per transport protein are possible • Sort by Gene (the default) to keep together visually 4. Aggregate pane shows counts by status. 5. Mousing over a reaction shows details in bottom pane.

20. Probable Transporter Table

21. Notional Probable Transporter Table

22. Reviewing and Editing • Left-click on a row • Dialog box appears • May edit: • Function (name) • Energy coupling • May invoke Reaction Editor on reaction • May retract reaction • May update status

23. TIP Dialog

24. Transporter Status • Unreviewed: • Initial value of status • Accepted: • Preserves edits • Incorporates transporter into PGDB upon save • Rejected: • Discard transporter upon save Accept and Reject are undoable

25. Table row after rejection 

26. Dialog after rejection

27. Filtering and Sorting • Filtering excluded transporters from display: • Filter low- or high-confidence transporters (low-confidence usually means ‘no substrate’) • Filter by status • Filter by number of reactions per substrate • Sort transporters by columns like a spreadsheet: • Gene • Energy Coupling • Substrate number/name • Status (e.g., Accepted, Rejected)

28. Group Operations TIP permits en masse acceptance or rejection of remaining predictions being shown: Edit / Accept all Unreviewed predictions being shown Edit / Reject all Unreviewed predictions being shown

29. Saving Your Work TIP has made in-memory modifications to the PGDB; nothing is saved until exit from TIP. Exit / Save saves all predictions & edits. Exit / Cancel reverts to most recent save. Must exit to save work!

30. Multisession Workflow • TIP remembers accepted predictions in the KB. • TIP remembers rejected transporters in a file under the organism directory. • To continue, re-run TIP and resume session. • If you don’t resume (i.e., start from scratch): • Will not re-predict Accepteds (they are in KB) • Will re-predict Rejecteds

31. Batch Mode • TIP supports batch mode operation as well as interactive • Run by BRG for all Tier 3 PGDBs (>3000 KBs) • To support both automated and user-controlled operation: • Distinguish high- and low-confidence inferences • Automated mode accepts all high-confidence inferences