1. NLP pipeline for protein mutation knowledgebase construction Jonas B. Laurila, Nona Naderi, René Witte, Christopher J.O. Baker

2. Background Knowledge about mutations is crucial for many applications, e.g. Protein engineering and Biomedicine. Protein mutations are described in scientific literature. The amount of Information grow faster than manual database curation can handle. Automatic reuse of mutation impact information from documents needed.

3. Example excerpts "Haloalkane dehalogenase (DhlA) from Xanthobacter autotrophicus GJI0 hydrolyses terminally chlorinated and brominated n-alkanes to the corresponding alcohols." "The W125F mutant showed only a slight reduction of activity (Vmax) and a larger increase of Km with 1,2-dibromoethane." Directionality of impact Protein property Mutation Protein name Gene name Organism name

4. Mutation impact ontology

5. NLP framework

6. Named entity recognition Protein-, gene- and organism names – Gazetteer lists based on SwissProt – Mappings encoded in the MGDB Mutation mentions – MutationFinder ~700 regular expressions – normalize into wNm-format

7. Named entity recognition Protein Properties 1.Protein functions – Noun phrases extracted with MuNPEx – Activity, binding, affinity, specificity as head nouns 2.Kinetic variables – Jape rules to extract K m, k cat and K m /k cat in current implementation

8. Mutation grounding Linking mutations positionally correct to target sequence Important for reuse of mutation mentions Levels of grounding: 1. 2. 3.

9. mSTRAPviz Structure annotation visualization Mutations extracted from text visualized on the protein structure for which mutation grounding is a prerequisite.

10. Protein function grounding Mentions of protein functions are linked to correct Gene Ontology concepts. Previously grounded proteins and mutations provide us with hints. Grounding scored based on string similarity (later used during impact extraction)

11. Relation detection Impacts – Words describing directionality + protein properties Mutants – Set of mutations giving rise to altered proteins Mutant – Impacts – The causal relation between mutants and their impacts

12. OwlExporter Translates GATE Annotations to OWL instances Application independent Literature Specifications added automatically Used here to populate our Mutation impact ontology to create a mutation knowledgebase

13. Example query Retrieve mutations that do not have an impact on haloalkane dehalogenase activity (also retrieve the Swissprot identifier of the protein beeing mutated).

14. Example query Retrieve mutations on Haloalkane Dehalogenase that do not impact negatively on the Michaelis Constant.

15. Evaluation Mutation grounding performance

16. What’s next? Modularize into a set of web services Database (re-)creation Reuse in phenotype prediction algorithms, (SNAP)* *Bromberg and Rost, 2007

17. NLP pipeline for protein mutation knowledgebase construction Jonas B. Laurila CSAS, UNB, Saint John j02h9@unb.ca Nona Naderi CSE, Concordia University, Montréal n_nad@encs.concordia.ca René Witte CSE, Concordia University, Montréal rwitte@cse.concordia.ca Christopher J.O. Baker CSAS, UNB, Saint John bakerc@unb.ca Acknowledgement This research was funded in part by : New Brunswcik Innovation Foundation, New Brunswick, Canada NSERC, Discovery Grant, Canada Quebec -New Brunswick University Co-operation in Advanced Education - Research Program, Government of New Brunswick, Canada