BINSFinalt

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Muhammad Rehan

1358-MSCS-08

Department of Computer Science

GC University Lahore

MS(CS) Final Thesis

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Background

Literature Review

Problem Statement

Hypothesis

Methodology

Result Future Work

References

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Bioinformatics

Any application of computation in biology

including data management, developing

algorithm and data mining. Bioinformatics is the field of science in which

computer science, information technology,

statistics and various branches of biology merge

to from single discipline.

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What are Proteins

ProteinsAntibody Hormones

Structural

Support &

Transportation

Enzymes

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What are Proteins made of

C

H

COOHH2N

R

Carboxyl groupAmino group

Alpha carbon

General formula of Amino Acid

R

R

H

CH3

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Sr.

No

Amino Acid Single Letter

Code

Three

Letter

Code

1 Alanine A Ala

2 Arginine R Arg

3 Serine S Ser

4 Threonine T Thr

5 Tyrosine Y Tyr

6 Glycine G Gly

7 Histidine H His. . . .

. . . .

20 Ieucine L Ieu

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Experimental Approach

vivo

vitro

silico

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Post Translational Modification(PTMs)

Protein modification is very important for

biological activity and perform the desire task.

This modification is done by the addition ofphosphate, glycosyl or other groups to certain

amino acids.

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PTM Target Amino Acid Description

Phosphorylation S, T, Y, H Addition of a phosphate group, to S,

T, Y, H

Glycosylation S, T, N Addition of a glycosyl group to either

S, T, N.

Sulfation Y Addition of a sulfate group to a Y

Acetylation Addition of an acetyl group, usually at

the N-terminus of the protein

Methylation R Addition of a methyl group, usually at

or R residues

List of PTMs Types

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Database Name Description Reference

PROSITE Database of consensus patterns for

various PTMs

Sigrist et al., (2002)

HPRD Human protein reference database of

disease-related proteins and their

PTMs

Peri et al., (2003)

RESID Database with collection of

annotations and structures for PTMs

Garabelli (2003)

PhosphoBase

ELM

Database of validated

phosphorylation sites.

Diella et al., (2004)

List of PTMs Databases

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Sr.No Statement References

1 Proteins often perform diverse and multiple

functions.

(Jeffery ,1999)

2 The diversity of proteome is higher complex then

genome, in human genome the number of genes

are 22,000 to 25,000 but in contrast number of

proteins more than 10,0000

(Nicolle H et al ,2007)

3 To identify the proteins functions and events

mainly rely on their particular 3-D structure as well

as the occurrence of targeted amino acid

modification.

(Attwood ,2000)

4 PTMs regulate various functions of proteins byeffecting verificational changes such as enzymes

activation.

(Konstantinopoulos et al,2007)

5 Phosphorylated serine, theronine and tyrosine

residues using MS is not easy in VIVO.

(Mann et al ,2002)

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Sr.No Statement References

6 Many methods have been developed within the

field of proteomics but these methods are still in

early stages.

(Blom ,2004)

7 Application of machine learning and statistics inbioinformatics have always played a core role in

understanding proteomics and to analysis of

PTMs.

(Qazi et al,2006)

8 ANN is one such Approach that has been

extensively used in biological sequences analysis.

(Wu, C.H ,1997)

9 Mostly cellular proteins are regulated by reversible

phosphorylation and at least 30% of protein have

such alteration.

(Ficarro et al ,2002)

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DISPHOS

PredPhosPho

GPS

PPSP

KinasePhos 1.0, KinasePhos 2.0

NetPhos, NetPhosK Neural-genetic

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Tools DISPHOS NetPhos Neural-

genetic

BPNN

Method Logistic

regression

ANN ANN ANN

Serine 76% 69% 75% 72%

Threonine 81% 72% 82% 77%

Tyrosine 83% 61% 79% 74%

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Tools KinasePhos

2.0

KinasePhos

1.0

PredPhospho GPS PPSP

Method SVM HMM SVM GPS BDT

Kinase

PKA

Sn=92%

Sp=89%

Acc=90%

Sn=91%

Sp=86%

Acc=85%

Sn=88%

Sp=%91

Ac=90%

Sn=91%

Sp=89%

Acc=90%

Sn=90%

Sp=92%

Acc=91%

Kinase

PKC

Sn=84%

Sp=86%

Acc=85%

Sn=80%

Sp=87%

Acc=83%

Sn=79%

Sp=86%

Ac=83%

Sn=82%

Sp=83%

Acc=82%

Sn=82%

Sp=86%

Acc=84%

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Develop a new method BINS to evolve new

classification model by learning amino acid

sequences data using machine learning

based method artificial neural network. ThisBINS improve the prediction specificity,

efficiency and accuracy for machine learning

simulator called GEARS (Genetic

EvaluationofClassifier by Learning Residue Rules and

Sequences).

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BINS classification method will reduce thefalse negative and positive prediction.

BINS method show highly accuracy

prediction about PTMs which will affect thespecific site and kinases that act at each site,disclose the important biologicallyinformation from noisy data.

BINS method can gives the best result ascompare to the existing PTMs predictionmethods.

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Empirical research methodology with

Exploratory Development Life Cycle will be used

for the development of BINS Model.

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BINS consists mainly on three parts BINS Data Preparation Module

BINS Bootstrapping Module

BINS ANN Module

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BINS Data Preparation Module

Create Protein grouped by target classes

Create Protein Database grouped by non

modified target classes

Peptide Generator

Removed of duplicate instance

PTMs Database

BINS ANN Module

Topology and Network

Configuration

Validation

[SN] [SP] [Acc] [MCC]

Training

[SN] [SP] [Acc] [MCC]


Peptide dataset grouped by

non modified classes

Merge the Sparse Encoding dataset grouped bymodified and non modified target classes

Sparse Encoding

Peptide dataset grouped by

non modified classes

Training and validation Dataset Generator

Validation dataset

Generator

Training dataset

Generator

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BINS Data Preparation Module BINS Database Inconsistency Analyzing Utility

BINS Balance Inverted Site Application

BINS Peptide Extraction Application

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PID Sequences Position Amino Acid Modification

O08539 ASTSMNSY

TLKSYA.

4 S S

O14543 MVTHSKFP

AAGS.

3 T T

O14746 MPRAPRC

RAVSTA

11 S S

O14920 MSWYPSL

TQTC.

4 Y Y

O15117 ELSFKQGE

QIYTA.

3 S S

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Target

sites

No.of

Proteins

No.of

positive

Peptide

No.of

Negative

Peptide

No.of

Balance

negative

Peptide

No. of

merge pos

and

balanceneg pep

S 5431 14467 326396 14837 29304

T 1940 2907 35795 2983 5890

Y 1156 2208 16273 2325 4533

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BINS Database Inconsistency Analyzing Utility

PID Sequences Position Amino Acid Modification Length

O08539 ASTSMNSY

TLKSYA.

4 S S 350

O14543 MVTHSKFP

AAGS.

3 T T 1030

O14746 MPRAPRC

RAVSTA

11 S S 1250

O14920 MSWYPSLTQTC.

4 Y Y 735

O15117 ELSFKQGE

QIYTA.

3 S S 952

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BINS Invert Application

PID Sequences Position Amino Acid Modification Length

O08539 ASTSMNSY

TLKSYA.

2 S S 350

O08539 ASTSMNSY

TLKSYA.

7 S S 350

O08539 ASTSMNSY

TLKSYA.

12 S S 350

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BINS Peptide Extraction Application

Peptide

ID

Extend

ed

Seque

nces

Class P-10 P-9 P-8 P0 P9 P10

O08539

-2

-,-,-

,A,S,T,

S,M,N

S,Y,T,L

K,S

0.1 - - - A S T K S

O08539-7

-,-,-,A,S,T,

S,M,N,

S,Y,T,L

,K,S,Y

A,-,-,

0.1 - - - N S Y - -

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BINS Bootstrapping Module BINS Training Dataset Encoding Manager

BINS Data Table Merging Utility

BINS Boot Strapping Application

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Sparse Encoding Scheme

Amino Acid Coding Scheme

A 10000000000000000000

C 01000000000000000000

D 00100000000000000000

E 00010000000000000000

F 00001000000000000000

G 00000100000000000000

H 00000010000000000000

I 00000001000000000000

.

.

.

.

- 00000000000000000000

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BINS Training Dataset Encoding Manager

Peptide

ID

Extend

ed

Seque

nces

Class P-10-

1

P-

10-2

P-

10-3

P-

10-

8

P-

10-9

P-

10-

10

O08539

-2

-,-,-

,A,S,T,

S,M,N

S,Y,T,L

K,S

0.1 0 0 0 0 0 0 0 0

O08539

-7

-,-,-

,A,S,T,

S,M,N,

S,Y,T,L

,K,S,Y

A,-,-,

0.1 0 0 0 0 0 0 0 0

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BINS DataTable Merging Utility

Peptide

ID

Extend

ed

Seque

nces

Class P-10-

1

P-

10-2

P-

10-3

P-

10-

8

P-

10-9

P-

10-

10

O08539

-2

-,-,-

,A,S,T,

S,M,N

S,Y,T,L

K,S

0.1 0 0 0 0 0 0 0 0

O08539

-7

-,-,-

,A,S,T,

S,M,N,

S,Y,T,L

,K,S,Y

A,-,-,

0.9 0 1 0 0 0 0 0 0

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BINS Boot Strapping Application

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BINS ANN Module

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Evaluation Strategy

Sn=TP/(TP+FN)

Sp=TN/(TN+FP)

Acc=(Sn+Sp)/2

MCC=

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Evaluation Strategy

PID Sequence Position Target Clarify

O3265 SASNSTSYTS 3 Mod TP

O3265 SASNSTSYTS 10 Mod FN

O3265 SASNSTSYTS 1 Non-mod TN

O3265 SASNSTSYTS 5 Non-mod FP

O3265 SASNSTSYTS 7 Non-mod TN

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BINS Serine Result

Sr.

No

Training Validation

Ac Sn Sp MCC Ac Sn Sp MCC

10.965 1 0.931 0.932 0.497 0 1 None

2 0.984 0.982 0.987 0.969 0.805 0.612 0.999 0.662

3 0.996 0.996 0.996 0.992 0.807 0.619 0.995 0.663

4 0.995 0.995 0.996 0.991 0.807 0.622 0.995 0.664

5 0.998 0.998 0.998 0.996 0.807 0.616 0.999 0.665

6 0.99

6 0.99

6 0.99

6 0.99

2 0.809

0.628 0.991

0.663

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BINS Threonine ResultSr.

No

Training Validation


10.972 0.963 0.981 0.946 0.826 0.688 0.965 0.680

20.987 0.986 0.989 0.975 0.834 0.737 0.932 0.683

3 0.987 0.986 0.988 0.974 0.825 0.750 0.901 0.658

4 0.987 0.986 0.987 0.974 0.827 0.771 0.884 0.659

5 0.987 0.986 0.987 0.974 0.824 0.774 0.875 0.653

6 0.986 0.986 0.986 0.972 0.822 0.772 0.872 0.648

7 0.988 0.990 0.987 0.977 0.825 0.761 0.890 0.657

8 0.989 0.989 0.990 0.979 0.823 0.768 0.880 0.652

90.990 0.990 0.990 0.980 0.822 0.770 0.874 0.647

10 0.990 0.989 0.991 0.980 0.821 0.770 0.871 0.645

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BINS Tyrosine ResultSr.

No

Training Validation


10.966 0.952 0.979 0.933 0.846 0.735 0.951 0.705

20.972 0.961 0.983 0.945 0.843 0.741 0.939 0.697

3 0.977 0.975 0.979 0.955 0.836 0.778 0.891 0.675

4 0.976 0.975 0.977 0.953 0.837 0.780 0.890 0.676

5 0.975 0.973 0.976 0.950 0.831 0.779 0.881 0.665

6 0.973 0.970 0.976 0.947 0.829 0.779 0.877 0.661

7 0.974 0.971 0.977 0.948 0.828 0.778 0.876 0.659

8 0.974 0.973 0.975 0.948 0.828 0.779 0.875 0.659

90.974 0.974 0.975 0.949 0.826 0.778 0.872 0.654

10 0.977 0.974 0.980 0.955 0.825 0.768 0.879 0.652

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BINS Comparison with other Method

Algorithm Y T S

Acc Sn Sp Acc Sn Sp Acc Sn Sp

BINS 85% 74% 95% 83% 74% 93% 81% 63% 99%

NetPhos 69% 70% 68% 72% 66% 77% 69% 81% 57%

DISPHOS 83% NA NA 81% NA NA 76% NA NA

BPNN 75% 75% 75% 78% 78% 77% 72% 72% 72%

Neural-genetic 79% 81% 78% 83% 81% 84% 75% 76% 74%

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BINS is a developed as Desktop Application, technically, there is no online WWW supportavailable in the current version, nevertheless, increasing opportunities over the internet

urges the need to develop an online version of this application for its wider scope and

availability to multiple clients in different regions of the world. This effort would not only

help us to enhance the embedded capability of BINS for efficient PTMs but also could be

major resource for multi-nation research collaborations.

BINS are the sub module of GEARS so in next version learn and optimize the parameters

and weights of ANN with genetic algorithm. In next, BINS integrate with other GEARS

modules like MAPRes and HMM for best classification of proteins data using pros and

cons of each technique.

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Jeffery C.J. Moonlighting proteins, Trends Biochem. Sci., 24:8--11, 1999.

Bork P., Dansekar T., Diaz-Lazcoz Y., Eisenhaber F., Huynen M.and Yuan Y. Predicting function: from genes to genome and back.J. Mol. Biol., 283:707--725, 1998.

Attwood T. The quest to deduce protein function from sequence:the role of pattern databases, Int. J. Biochem. CellBiol., 32:139--155, 2000.

Mann, M., Ong, S., Gronborg. M, .Steen, H. et al., TrendsBiotechnol. 2002, 20, 261-268.

Wu, C. H., Comput, Chem, 1997, 21, 237-256.

Blom N., Sicheritz-Protein T., Gupta R., Gammeltoft S., andBrunak S. Prediction of post-translational glycosylation andphosphorylation of proteins from the amino acid sequence,Proteomics, 4: 1633--1649, 2004.

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