Weighting method for domain combination pairs

considering PDB crystal structures

Hyun, Bora

ContentsIntroductionBackground & MotivationPreSPI++

Evaluation of PreSPI++Method

DCPPW++EvaluationConclusion

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IntroductionDomain combination based approach

Uses domain combination and domain combi-nation pair information for the prediction of the protein interactions.

PreSPI++A protein interaction prediction system based

the Interaction Significance(IS) matrix which quantified an influence of domain combination pair on a protein interaction.

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Motivation & ObjectiveIn PreSPI++,

Consider Possibilities of domain collaboration and, Weighted Domain Combination Pair(WDCP)

Consider being the main body on a protein in-teraction Domain Combination Pair’s coupling

Power(DCPPW)

However,No explanation and evaluation of relationship

between DCPPW and physical interaction structures

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Motivation & ObjectiveVerify whether value of DCPPW is given con-

sistently based on PDB crystal structures, es-pecially in protein interactions which have multi-domain interactions

Propose advanced weighting method for do-main combination pairs considering PDB crystal structures

We can provide more reliable and meaningful weighted domain combination information for prediction of protein-protein interaction

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PreSPI++IS matrix contains

Possibilities of domain collaboration Using all-confidence

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PreSPI++IS matrix contains

Possibilities of domain collaborationPossibilities of being main body on interaction

WDCP

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a

b

A

e

D

a

c

B

e

D

a

d

C

e

D

Weight of <a, e>= Weight of <b, e>, <c, e>, <d. e>?

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PreSPI++IS matrix contains

Possibilities of domain collaborationPossibilities of being main body on interaction

WDCP DCPPW

Using frequency information as relative power

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힘의아닌가힘의힘의

),( ,,

,, , qppairdcdcdcdcdc

dcdcqpDCPPW

jiji

jiji

Evaluation of PreSPI++Using PDB crystal structure information

PPIs have single domain interaction Among 169 pairs, 146 pair correctly predicted

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DCPPWCompare with PDB crystal struc-

tureMatch Un-Match

0.0< DCPPW<0.1

8 4.73% 0 0.00%

0.1< DCPPW<0.2

8 4.73% 6 3.55%

0.2< DCPPW<0.3

5 2.96% 3 1.78%

0.3< DCPPW<0.4

10 5.92% 0 0.00%

0.4< DCPPW<0.5

8 4.73% 6 3.55%

0.5< DCPPW<0.6

4 2.37% 0 0.00%

0.6< DCPPW<0.7

1710.06

%1 0.59%

0.7< DCPPW<0.8

2 1.18% 0 0.00%

0.8< DCPPW<0.9

6 3.55% 1 0.59%

0.9< DCPPW<1.0

1710.06

%1 0.59%

DCPPW=161

36.09%

5 2.96%

Total 146 86% 23 14%

Evaluation of PreSPI++Using PDB crystal structure information

PPIs have multi domain interaction Among 56 pairs, only 1 pair correctly predicted

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DCPPWCompare with PDB crystal struc-

tureMatch Un-Match

0.0< DCPPW<0.1 0 0% 33 58.93

%0.1<

DCPPW<0.2 0 0% 18 32.14%

0.2< DCPPW<0.3 1 1.79% 0 0%

0.3< DCPPW<0.4 0 0% 4 7.14%

Total 1 1.79% 55 98.21%

Evaluation of PreSPI++WDCP

Since the weight of single domain is always one, the weight of a DC made by two or more domains is not higher than one of single do-main.

DCPPWFreq ({a,b,c}) ≤ Freq ({a}), Freq({b}),

Freq({c})So, DCPPW({a,b,c}) ≤ DCPPW({a}), DCPPW

({b}), DCPPW({c})It is rational because PDB has only few inter-

action pairs caused by multi-domain interac-tion

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MethodWe need additional processing and weight

scheme to compensate DCPPW for multi-do-main interactionsPre-processing

Filter out DCs have low all-confidence valueWeight scheme

Reduce effect caused by difference of all-confidence and frequency between single and multi domain pairs

All coefficient will experimentally deter-mined. 12ISI LABORATORY

MethodUsing rooted all-confidence value to compen-

sate large difference between single and multi DC

Distribution of all-confidence by DC size change

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MethodUsing size of DC information

Multiply of each size of DC

Sum of each size of DC

C=Coefficient

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),(,,

,, ))1|||(||),((|,

))1|||(||),((|,,

qppairdcdcdcqpvuvu

qpjijiji

vu

CDCDCdcdcpairIqpWDCP

CDCDCdcdcpairIqpWDCPqpDCPPW

),(,,

,, ))2|||(||),((|,

))2|||(||),((|,,

qppairdcdcdcqpvuvu

qpjijiji

vu

CDCDCdcdcpairIqpWDCP

CDCDCdcdcpairIqpWDCPqpDCPPW

EvaluationData (iPfam)

PPI with multi-domain interaction : 65개PPI with single-domain interaction : 169개

EvaluationCoefficient change from 0 to 30All-confidence + multiplyRooted all-confidence + multiplyRooted all-confidence + sumPre-processing (filter out all-confidence is

lower than 0.2, 0.3, 0.4)

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EvaluationPPIs have Multi-domain interaction

Best : Rooted AC +sumWorst: AC>0.4 + multiply

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EvaluationPPIs have single-domain interaction

Best : AC>0.4 + multiplyWorst : Rooted AC + sum

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Future worksEffect of modified learning method is not significant

Even coefficient is 100, there are 13-15 matched re-sults

Matched results in single DDI reduced by coefficient increase

About +13 / -24

Need other processing Directly using PDB information to weight their DCUsing all inter/intra DC

How much larger than other DC? How to evaluate?

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Future worksNeed to update domain dataNeed more evaluation

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