Nlp pipeline for protein mutation knowledgebase construction
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NLP pipeline for protein mutation knowledgebase construction. Jonas B. Laurila, Nona Naderi, René Witte, Christopher J.O. Baker. Background. Knowledge about mutations is crucial for many applications, e.g. Protein engineering and Biomedicine.

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NLP pipeline for protein mutation knowledgebase construction

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NLP pipeline for protein mutation knowledgebase construction

Jonas B. Laurila, Nona Naderi, René Witte, Christopher J.O. Baker


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.


Example excerpts

"The W125F mutant showed only a slight reduction of activity (Vmax) and a larger increase of Km with 1,2-dibromoethane."

  • Mutation

  • Directionalityofimpact

  • Protein property

"Haloalkane dehalogenase (DhlA) from Xanthobacter autotrophicus GJI0 hydrolyses terminally chlorinated and brominated n-alkanes to the corresponding alcohols."

  • Protein name

  • Gene name

  • Organism name


Mutation impact ontology


NLP framework


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


Named entity recognition

Protein Properties

  • Protein functions

    • Noun phrases extracted with MuNPEx

    • Activity, binding, affinity, specificity as head nouns

  • Kinetic variables

    • Jape rules to extract Km, kcat and Km/kcat in current implementation


Mutation groundingLinking mutations positionally correct to target sequence

  • Important for reuse of mutation mentions

  • Levels of grounding:


mSTRAPviz

Structure annotation visualization

Mutations extracted from text visualized on the protein structure for which mutation grounding is a prerequisite.


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)


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


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


Example query

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


Example query

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


Evaluation

Mutation grounding performance


What’s next?

*Bromberg and Rost, 2007

Modularize into a set of web services

Database (re-)creation

Reuse in phenotype prediction algorithms, (SNAP)*


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

NLP pipeline for protein mutation knowledgebase construction

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


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