Create and assess Create and assess protein networks protein networks through molecular through molecular characteristics of characteristics of individual proteinsindividual proteinsYanay Ofran et al. ISMB ’06Yanay Ofran et al. ISMB ’06

Presenter: Danhua GuoPresenter: Danhua Guo12/07/200612/07/2006

RoadmapRoadmap MotivationMotivation IntroductionIntroduction MethodsMethods Results and DiscussionResults and Discussion ConclusionConclusion

MotivationMotivation Study of biological systems relies Study of biological systems relies

on network topology.on network topology. Integrating protein information Integrating protein information

into the network enhance the into the network enhance the analysis of biological systems.analysis of biological systems.

IntroductionIntroduction Protein-Protein Interaction (PPI) Protein-Protein Interaction (PPI)

NetworkNetwork– Help identify process or functionsHelp identify process or functions– Major problemMajor problem

Generation problemGeneration problem– Experimental errors: should not be in the networkExperimental errors: should not be in the network– ““In vitroIn vitro”: should be include in the network”: should be include in the network

Data representation problemData representation problem– Essential connection between PPI and proteinEssential connection between PPI and protein

IntroductionIntroduction An ideal frameworkAn ideal framework

– Macro level: network topologyMacro level: network topology– Micro level: characteristics of each Micro level: characteristics of each

proteinprotein LocalizationLocalization Functional annotationFunctional annotation

IntroductionIntroduction Protein interaction Network Protein interaction Network

Assessment Tool (PiNAT)Assessment Tool (PiNAT)

MethodsMethods Large-scale Assessment of PPIsLarge-scale Assessment of PPIs

– Based on localizationBased on localization– Based on GO annotation (if applicable)Based on GO annotation (if applicable)

Automatic generation of networksAutomatic generation of networks– Get submitted list of proteins from userGet submitted list of proteins from user– Search DIP and IntActSearch DIP and IntAct

Display of networks in the cellular Display of networks in the cellular contextcontext

Alzheimer’s disease related pathwayAlzheimer’s disease related pathway

MethodsMethods Localization criteriaLocalization criteria

– LOCtree: classify eukaryotic proteins LOCtree: classify eukaryotic proteins (60%)(60%) Threshold: confidence score >=4Threshold: confidence score >=4

– PHDhtm: predict transmembrane helices PHDhtm: predict transmembrane helices (7%)(7%) Threshold: average score among 20 reliable Threshold: average score among 20 reliable

predictions >8.5predictions >8.5– Experiment on 4800 interactions (2191 Experiment on 4800 interactions (2191

proteins)proteins) High-confidence prediction: 2312 (1482 proteins)High-confidence prediction: 2312 (1482 proteins) Total protein pairs: 1,097,421Total protein pairs: 1,097,421 Binomial approximation to the cumulative Binomial approximation to the cumulative

hypergeometric probability distribution to get a p-hypergeometric probability distribution to get a p-value for over and under representationvalue for over and under representation

MethodsMethods GO criteriaGO criteria

– The functionality annotation of a proteinThe functionality annotation of a protein– Distance between 2 GO terms measure the Distance between 2 GO terms measure the

similaritysimilarity

m,n: respective numbers of annotations in i and jm,n: respective numbers of annotations in i and j simGo: GO similarity defined by Lord et al.simGo: GO similarity defined by Lord et al. Ck, Cp: respective individual annotation in protein Ck, Cp: respective individual annotation in protein

i and ji and j Cjmax: Ck’s most similar term in jCjmax: Ck’s most similar term in j Cimax: Cp’s most similar term in iCimax: Cp’s most similar term in i

MethodsMethods Display of networks in the cellular Display of networks in the cellular

contextcontext– Based on LOCtree and PHDhtm Based on LOCtree and PHDhtm

predictionspredictions– Generate Graph Markup Language Generate Graph Markup Language

(GML)(GML)– Localization overide rule:Localization overide rule:

High PHDhtm > High LOCtree > Low PHDhtm > Low High PHDhtm > High LOCtree > Low PHDhtm > Low LOCtreeLOCtree

ResultsResults Interactions across subcellular Interactions across subcellular

compartmentscompartments

– Intra-compartment interactions: high scoreIntra-compartment interactions: high score– Distant compartment: low scoreDistant compartment: low score– Nearby compartment: likelyNearby compartment: likely

ResultsResults Likely and unlikely interactions Likely and unlikely interactions

across GOacross GO– Likely: >Likely: >3.253.25– Unlikely: Unlikely: <1.3<1.3– Neutral: elseNeutral: else

ResultResult Alzheimer in the Alzheimer in the

perspective of PiNATperspective of PiNAT– Reflects the unclarity Reflects the unclarity

regarding Amyloid regarding Amyloid beta A4 protein (APP) beta A4 protein (APP) ’s localization’s localization

– APP interacts APP interacts extensively with extensively with almost every almost every compartment of the compartment of the cellcell

ResultResult APP’s role in APP’s role in

AlzheimerAlzheimer– APP-related PPI APP-related PPI

deemed deemed “unlikely”“unlikely”

– Conflicts Conflicts between 2 between 2 scoring systemsscoring systems

ConclusionConclusion Molecular knowledge and network Molecular knowledge and network

structure can enhance our structure can enhance our understanding of biological understanding of biological processes.processes.

PiNAT is efficient and meaningful.PiNAT is efficient and meaningful.