AlgPred: PREDICTION OF ALLERGENIC PROTEINS AND MAPPING OF IgE EPITOPES

Sudipto Saha and G. P. S. Raghava*

Institute of Microbial Technology, Okhla Phase 3, New Delhi, India

Running Tite: Allergens prediction

Address correspondence to: Dr. G. P. S. Raghava, Professor, Department of Computational Biology Institute of Microbial Technology Okhla Phase 3, New Delhi, INDIA, Phone: +91-11-26907444; Fax: +91-172-26907444 E-mail: raghava@iiitd.ac.in

Available at : http://webs.iiitd.edu.in/raghava/algpred

 

Supplemental data

 

Dataset
The protein sequence sets used in this study are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp (Bjorklund et al., 2005). The dataset contains 578 experimental allergens and 700 non allergens protein sequences derived from food. We obtained IgE epitope from SDAP and Bcipep database.

The performance of SVM module based on dipeptide composition

The following table demonstrates the performance of SVM based method using dipeptide composition. The RBF kernel was used and the values are g=100; c=1; and j=1.

 

Table S1: Performance of SVM based method using dipeptide composition

 

Threshold

Sensitivity

Specificity

Accuracy

PPV

NPV

MCC

1.0

0.0957

1.0000

0.5922

1.0000

0.5742

0.2346

0.8

0.1791

1.0000

0.6298

1.0000

0.5978

0.3275

0.6

0.2696

0.9886

0.6643

0.9509

0.6229

0.3852

0.4

0.3913

0.9686

0.7082

0.9109

0.6602

0.4538

0.2

0.5461

0.9429

0.7639

0.8870

0.7174

0.5441

0.0

0.7043

0.9100

0.8173

0.8654

0.7903

0.6353

-0.2

0.8278

0.8500

0.8400

0.8193

0.8586

0.6786

-0.4

0.8922

0.7743

0.8275

0.7645

0.8988

0.6657

-0.6

0.9374

0.6771

0.7945

0.7046

0.9312

0.6259

-0.8

0.9600

0.5657

0.7435

0.6449

0.9474

0.5589

-1.0

0.9757

0.4471

0.6855

0.5918

0.9601

0.4840

 

 

 

The performance of Hybrid approach using SVM based on dipeptide composition and IgE based approach

The following table shows the performance of hybrid approach, which combines SVM module based on dipeptide composition and IgE epitope based approach(PID865). A protein is assigned as allergen if predicted allergen by IgE method (PID865) and also have SVM score -0.5 or protein is assigned allegen or non allergen using SVM approach, when protein have no similarity with known IgE epitopes. The table shows that one might achieve better sensitivity without loosing much specificity at higher threshold.

 

TableS2: The performance of hybrid approach, which combines SVM based approach using dipeptide composition and IgE epitope based approach (PID865).

 

Threshold

Sensitivity

Specificity

Accuracy

PPV

NPV

MCC

1.0

0.2296

0.9943

0.6494

0.9706

0.6116

0.3613

0.8

0.3043

0.9943

0.6831

0.9777

0.6356

0.4283

0.6

0.3878

0.9829

0.7145

0.9489

0.6622

0.4764

0.4

0.4922

0.9643

0.7514

0.9188

0.6988

0.5314

0.2

0.6104

0.9386

0.7906

0.8909

0.7466

0.5922

0.0

0.7322

0.9057

0.8275

0.8645

0.8056

0.6544

-0.2

0.8365

0.8457

0.8416

0.8166

0.8642

0.6823

-0.4

0.8957

0.7743

0.8290

0.7652

0.9018

0.6693

-0.6

0.9374

0.6771

0.7945

0.7046

0.9312

0.6259

-0.8

0.9600

0.5657

0.7435

0.6449

0.9474

0.5589

-1.0

0.9757

0.4471

0.6855

0.5918

0.9601

0.4840

 

 

 

 

 

The performance of hybrid approach, which combines SVM based approach using amino acid composition and motif based approach (MEME ev 0.1).

The following table shows the performance of hybrid approach using SVM based approach using amino acid composition and motif based approach (MEME ev 0.1). No improvement was observed.

 

Table S3: The performance of hybrid approach, which combines SVM based approach using amino acid composition and motif based approach (MEME ev 0.1).

 

Threshold

Sensitivity

Specificity

Accuracy

PPV

NPV

MCC

1.0

0.4504

0.8986

0.6965

0.7848

0.6663

0.3973

0.8

0.5270

0.8871

0.7247

0.7932

0.6962

0.4507

0.6

0.6087

0.8757

0.7553

0.8009

0.7324

0.5088

0.4

0.6661

0.8571

0.7710

0.7930

0.7585

0.5378

0.2

0.7235

0.8443

0.7898

0.7924

0.7891

0.5752

0.0

0.7948

0.8186

0.8078

0.7825

0.8304

0.6139

-0.2

0.8748

0.7900

0.8282

0.7738

0.8862

0.6632

-0.4

0.9130

0.7557

0.8267

0.7543

0.9152

0.6699

-0.6

0.9252

0.7000

0.8016

0.7170

0.9211

0.6324

-0.8

0.9565

0.6414

0.7835

0.6866

0.9493

0.6175

-1.0

0.9670

0.5657

0.7467

0.6465

0.9565

0.5677

 

 

 

 

The performance of various methods on Independent dataset

The following table shows the performance of various approaches on independent dataset of 664 allergens obtained from Li et al., 2004 and dataset of 323 proteins, which excludes all those proteins found in Bjorklund et al., 2005. Here, SVMc =SVM based on amino acid compsition; SVMd =SVM based on dipeptide composition; * PID865

 

 

Table S4: The performance of various approaches on independent dataset

 

 

Methods

Sensitivity or percent coverage

664 allergens

323 allergens

SVMc

88.25

84.21

SVMd

89.46

84.82

IgE epitope*

14.78

10.83

Mast

12.65

12.38

ARPs Blast (e-value .001)

83.58

66.56

SVMc + IgE epitope (PID865)

88.86

84.83

SVMd + IgE epitope

90.06

85.14

SVMc+IgE epitope+Mast

89.61

85.76

SVMd+IgE epitope+Mast

90.66

85.76

SVMc+IgE epitope +Mast +BLAST

96.84

93.5

SVMd+IgE epitope+Mast +BLAST

96.08

92.26

 

 

 

 

 

 

 

 

Fig. S1 ROC plot of SVM amino acid and combined approach

 

 

 

 

 

 

 

 

 

 

 

 

Fig. S2 ROC plot of SVM dipeptide and combined approach

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Development of IgE epitope prediction method

There were 183 epitopes collected from SDAP and Bcipep. We examined all the epitope length and their frequency and found epitope length varied from 4 to 22 mers. There were 67 epitopes with 10 mers length, which was the highest frequency. So, we tried to develop a method based on this 67 epitopes and obtained 67 random peptide of 10 mers from nonallergen database (obtained from consumed commodities, such as rice, apple, milk,etc.).

We tried SVM method based on amino acid composition and sequence information and used five fold cross validation technique (four training set and one testing set).

1. SVM method based on amino acid composition

RBF kernel g=.01 c=1000 j=1

 

 

Thres Sen Spe Acc PPV MCC

1.0000 0.2154 0.8769 0.5462 0.7100 0.1412

0.8000 0.2769 0.8462 0.5615 0.7200 0.1691

0.6000 0.3077 0.8154 0.5615 0.6879 0.1626

0.4000 0.4000 0.7077 0.5538 0.5734 0.1123

0.2000 0.5231 0.6308 0.5769 0.5939 0.1577

0.1000 0.6000 0.6154 0.6077 0.6159 0.2173

0.0000 0.6154 0.5538 0.5846 0.5876 0.1720

-0.2000 0.6615 0.4308 0.5462 0.5436 0.0885

-0.4000 0.7077 0.3692 0.5385 0.5321 0.0765

-0.6000 0.8000 0.2769 0.5385 0.5225 0.0997

-0.8000 0.8462 0.2154 0.5308 0.5172 0.0912

-1.0000 0.7385 0.1846 0.4615 0.4360 0.1821

 

Thres= Threshold; Sen=Sensitivity; Spe=Specificity; Acc=Accuracy; PPV= Positive prediction value; MCC= Mathews correlation coefficient

 

 

 

 

 

 

 

 

 

 

 

2. SVM method based on sequence information

RBF kernel g=.01 c=10 j=1

 

Thres Sen Spe Acc PPV MCC

1.0000 0.0308 0.3692 0.2000 0.2667 0.0159

0.8000 0.0462 0.5231 0.2846 0.2500 -0.0426

0.6000 0.0462 0.5231 0.2846 0.2500 -0.0426

0.4000 0.1538 0.8154 0.4846 0.5090 -0.0222

0.2000 0.2615 0.6769 0.4692 0.4504 -0.0667

0.0000 0.3692 0.5692 0.4692 0.4611 -0.0627

-0.2000 0.5231 0.4769 0.5000 0.5014 -0.0008

-0.4000 0.6615 0.2615 0.4615 0.4737 -0.0867

-0.6000 0.6462 0.1692 0.4077 0.4038 0.0253

-0.8000 0.6615 0.1077 0.3846 0.3900 -0.0365

-1.0000 0.7385 0.0615 0.4000 0.4000 0.0000

 

Thres= Threshold; Sen=Sensitivity; Spe=Specificity; Acc=Accuracy; PPV= Positive prediction value; MCC= Mathews correlation coefficient

 

We also tried SNNS method (Feed forward network (FNN) and Recurrent neural network (RNN) ) based on amino acid cmposition and sequence information and used five fold cross validation technique (3 training set, one validation set, one testing set).

3. Feed forward neural network (hidden node 5) based on amino acid composition

Thres Sen Spe Acc PPV MCC

1.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.8000 0.0000 0.0000 0.0000 0.0000 0.0000

0.7000 0.0000 0.0000 0.0000 0.0000 0.0000

0.6000 0.9538 0.2615 0.6077 0.5643 0.3028

0.5000 1.0000 0.0769 0.5385 0.5200 0.2000

0.4000 1.0000 0.0769 0.5385 0.5200 0.2000

0.2000 1.0000 0.0769 0.5385 0.5200 0.2000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.2000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.4000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.6000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.8000 0.0000 0.0000 0.0000 0.0000 0.0000

-1.0000 0.0000 0.0000 0.0000 0.0000 0.0000

 

Thres= Threshold; Sen=Sensitivity; Spe=Specificity; Acc=Accuracy; PPV= Positive prediction value; MCC= Mathews correlation coefficient

 

4. Feed forward neural network (hidden node 5) based on sequence information

Thres Sen Spe Acc PPV MCC

1.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.8000 0.0000 0.0000 0.0000 0.0000 0.0000

0.6000 0.2615 0.8769 0.5692 0.7033 0.1838

0.5000 0.6769 0.5846 0.6308 0.6319 0.2606

0.4000 0.8615 0.2615 0.5615 0.5377 0.1607

0.2000 1.0000 0.0769 0.5385 0.5200 0.2000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.2000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.4000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.6000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.8000 0.0000 0.0000 0.0000 0.0000 0.0000

-1.0000 0.0000 0.0000 0.0000 0.0000 0.0000

 

Thres= Threshold; Sen=Sensitivity; Spe=Specificity; Acc=Accuracy; PPV= Positive prediction value; MCC= Mathews correlation coefficient

 

 

5. Recurrent neural network (Hidden node 5) based on Sequence information

Thres Sen Spe Acc PPV MCC

1.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.8000 0.0000 0.0000 0.0000 0.0000 0.0000

0.6000 0.2000 0.7077 0.4538 0.5833 0.1520

0.5000 0.6154 0.6308 0.6231 0.6459 0.2474

0.4000 0.8615 0.2769 0.5692 0.5434 0.1752

0.2000 1.0000 0.0769 0.5385 0.5200 0.2000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.2000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.4000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.6000 0.0000 0.0000 0.0000 0.0000 0.0000

-0.8000 0.0000 0.0000 0.0000 0.0000 0.0000

-1.0000 0.0000 0.0000 0.0000 0.0000 0.0000

 

Thres= Threshold; Sen=Sensitivity; Spe=Specificity; Acc=Accuracy; PPV= Positive prediction value; MCC= Mathews correlation coefficient