Partitioning Sequences Based on Association Measures

Deborah WeisserCarnegie Mellon University

Feature boundaries

• Need to know form and function of protein sequences to understand complex biological systems

• Not possible to directly determine features or functions directly– estimate feature positions by indirect

laboratory experiments, e.g. hydrophobicity

• Use statistical measures of association to determine feature boundaries

Feature boundaries

• Proteins are comprised of adjacent, non-overlapping features:– helical, cytoplasmic, periplasmic, extracellular,

intracellular, etc.

• GPCR proteins have a fixed feature pattern, although feature positions are only known for one member of the family, Rhodopsin (opsd_human)

C

N

ec1 ec2ec3

cp1 cp2 cp3

1 2 3 4 5 6 7

Cytoplasmic (cp) Domain

Trans-Membrane(helices)Domain

Extracellular (ec) Domain

A.

B.

Segments contributing to extracellular (ec) domainSegments contributing to transmembrane (helices) domainSegments contributing to cytoplasmic (cp) domain

CN

Goal: Statistically determine feature boundaries in sequences of amino acids

S H D E G C L S S E P K P R K Q S D S S T

Association measures

S H D E G C L S S E P K P R K Q S D S S T

2.5

2.5 is a measure of the strength of the association between P and R

Association measures

S H D E G C L S S E P K P R K Q S D S S T

2.34.5

1.10.8 5.51.2 3.70.3 6.21.24.8

4.15.2

2.5 1.80.20.7

1.1 6.23.4 1.1

Association measures

S H D E G C L S S E P K P R K Q S D S S T

2.34.5

1.10.8 5.51.2 3.70.3 6.21.24.8

4.15.2

2.5 1.80.20.7

1.1 6.23.4 1.1

4.2

Adjacent pairs with low association measuresare candidates for partition points.

Association measures are used to quantify correlations between

adjacent amino acids

• Yule’s Q statistic

• Mutual information

EP

MS N

V VV

GFRF

Y

CK

HMI AN

QQ Q

AA

K E A

V F TVQ L

TVRM

SAT TQKA EKE

I I V

E

IM

MY

RG

TT

V QH

KR N

TT

VM

L

C

Cytoplasmic (cp) Domain

T L Y V

N F L I YN LC C

I II III IV V VI VII

LK

P

KN

QF

5575

136 155225 256 306

cp1

cp2cp3

AOOC - PAVQST E T KS V T-

TS AED

DGLP

KN

Cytoplasmic(cp)

Domain

Transmembrane(helices)Domain

Extracellular(ec)

Domain

MI: 39, 63, 76, 94, 115, 136, 155, 176, 205, 233, 255, 279, 287, 301

Hydropathy: 37, 61, 74, 98, 114, 133, 153, 176, 203, 230,253, 276, 285, 309

61309

74

153253

230

133

Hydropathy breaks

301

63

76155255

136 233Cytoplasmic

(cp)Domain

Transmembrane(helices)Domain

Extracellular(ec)

Domain

MI breaks

• The changes in association measure values correspond to feature boundaries

• Goal: automatically detect partition points based on association measures

Partitioning algorithm

• Cluster adjacent association values– each group is represented by its mean value

• Calculate standard deviation of values over all clusters

• Locate partition points in data based on:– deviation from mean– [change between adjacent values]

Parameters

• Cluster adjacent association values– each group is represented by its mean value

window size for computing mean

• Calculate standard deviation of values over all clusters

• Locate partition points in data based on:– deviation from mean– [change between adjacent values]

cutoff distance from mean for a value to be

considered “extreme”

Effect of cutoff threshold on partitioning in opsd_human using mutual information

Effect of window size on partitioningin opsd_human using mutual information

Class A Rhodopsin like Amine Peptide Hormone protein (Rhodopsin

Rhodopsin Vertebrate Rhodopsin Vertebrate type 1 Rhodopsin Vertebrate type 2 Rhodopsin Vertebrate type 3 Rhodopsin Vertebrate type 4 Rhodopsin Vertebrate type 5

Rhodopsin Arthropod Rhodopsin Mollusc Rhodopsin Other

Olfactory Prostanoid Nucleotide-like Cannabis Platelet activating factor Gonadotropin-releasing hormone Thyrotropin-releasing hormone & Secretagogue Melatonin Viral Lysosphingolipid & LPA (EDG) Leukotriene B4 receptor Class A Orphan/other

Class B Secretin like Class C Metabotropic glutamate / pheromone Class D Fungal pheromone Class E cAMP receptors (Dictyostelium) Frizzled/Smoothened family

GPCR: different subfamilies

GPCR: different subfamilies

Size: Hierarchy:

717755 GPCR

371134 Class A

48393 Rhodopsin

33543 Vertebrate

20314 Vertebrate 1

348 opsd_human

39724 Class B

20930 Class C

• Structure of curve is preserved even when the dataset is small.

In progress / Future work

• Set parameters of partition algorithm automatically

• Apply to other sources of data, types of features

• Group amino acids into sub-classes

• Quantify the effect of training set information content and training set size.