NLP pipeline for protein mutation knowledgebase construction Jonas B. Laurila, Nona Naderi, René...
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Transcript of NLP pipeline for protein mutation knowledgebase construction Jonas B. Laurila, Nona Naderi, René...
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
"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
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 Properties1. Protein functions
– Noun phrases extracted with MuNPEx– Activity, binding, affinity, specificity as
head nouns
2. 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:1.
2.
3.
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?
• Modularize into a set of web services
• Database (re-)creation
• Reuse in phenotype prediction algorithms, (SNAP)*
*Bromberg and Rost, 2007
NLP pipeline for protein mutation knowledgebase construction
Jonas B. LaurilaCSAS, UNB, Saint [email protected]
Nona NaderiCSE, Concordia University, Montré[email protected]é WitteCSE, Concordia University, Montré[email protected] J.O. BakerCSAS, UNB, Saint [email protected]
AcknowledgementThis 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