a neural network based MHC Class I Binding Peptide Prediction Server

Performence of nHLAPred:-

The analysis of the prediction method provide confidence to the developers about their prediction method.Here we have analysed all the newly generated 47 matrices through single parameter (accuracy).Accuracy is a single value to measure the performence of prediction method.The accuracy represents the probability that a predicted binder or non-binder will be correctly classified from the total number of the binders and non-binders in the dataset.The comprehensive analysis of accuracy of method for different MHC alleles are shown in table below.The data for evalvuating the performence of method has been obtained from MHCBN database. The number of MHC binders for various MHC alleles vary from 15 to 1200 as shown in TableS1 of supplymentary material.The method is analysed through Jack-knife validation test.The value of accuracy vary from 55% to 100% at default threshold of 5% for all MHC alleles. The average value of accuracy for 47 MHC allelesis 85.4

8.9%.This is good accuracy for any prediction method.

The performance of quantitative matrix based prediction method on 47 alleles.

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Analysis of ANN for 30 MHC alleles:-

An artificial neural network based method have been developed for 30 out of above disscused 47 MHC alleles having minimum 40 MHC binders.A feed-forward backpropagation type of ANN with a single hidden layer ( 10 hidden units), 189 (21�9) input units and 1 output unit, has been used in this study. The input layer consists of 189 nodes to repersent the peptide of nine amino acids.The performence of this ANN based method is evalvuated at default cutoff score of 0.5 through Jack-Knief validation test.The value of accuracy for 30 MHC alleles vary from 67% to 96.5%. This is very good accuracy for prediction method. The average value of accuracy for 30 MHC alleles is 87.6

5.9% .The comprehensive analysis of ANN in term of senstivity,specificity,PPV,NPV and accuracy is shown below.

The performance of ANN based method on 30 MHC class I alleles at default cutoff score of 0.5.

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Analysis of hybrid approach for 30 MHC alleles:-

It has been observed from analysis of the newly generated quantitative matrices and ANN that QM performe good for some alleles and poor for other alleles. The similar observation is also true in case of ANN.It has been observed in past by researchers that combination of two prediction approaches can give better prediction than any individual approach (Gulukota et al., 1997).Thus, we have developed a hybrid appoarch based on combination of ANN and QM.This hybrid appoarch results in improvemt in accuracy of prediction.The accuracy of the hybrid appoarch is also evalvuated through Jack-Knife validation test at default cutoff score 0.5 of ANN.The increase in accuracy for various MHC alleles vary from 0.1% to 14%.The hybrid method result in ~5% improvment in means accuracy as compared to both indivisual approaches.The comprehensive analysis of 30 MHC alleles by using hybrid appoarch is shown below.

The performance of hybrid method based on combination of ANN and QM .

The authors also compared the performence of newly generated quantitative matrices with matrices generated by Parker et al.,.The performence of 20 MHC alleles which are common in both methods has been compared. The mean accuracy of newly geneated matrices are nearly ~10% higher as shown below.We have also developed the hybrid method for 20 MHC alleles by using the matrices of Parker et al.,.1994. The hybrid appoarch which used the newly generated matrices performe better as compared to hybrid approach developed by using old matrices.The analysis of 20 alleles which are common in all the methods is shown below.

The performance of different methods that include the performance of individual methods and combination of ANN with old and new QM based methods for 20 common alleles.

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Testing of ComPred( hybrid approach )on PRAME protein:-

The aim behind the development of nHLAPred is to effectively reduce number of wet lab experiments involved in the identification of potential T cell epitopes or suitable vaccine candidates.To test whether the aim is achieved or not authors applied the server on such antigenic protein whose MHC binders and CTL epitopes are experimentally proved.We have choosen the PRAME protein whose MHC binders and CTL epitopes are recently identified by Kessler et al., 2002. The sequence of PRAME antigenic protein was obtained from SWISSPROT database.

MHC binders identification:-The ComPred part of server has been used in identification of HLA-A*0201 binding 19 high affinity and 27 intermidiate affinity MHC binders of PRAME protein.The server has been used at different cutoff scores.The detailed performence of server in correctly identifying the MHC binders is shown in TableS5.The method correctly identified 73% high affinity and 81% intermediate affinity binders at default cutoff score 0.5.The default cutoff score means a value at which the senstivity and specificity are nearly equal.This proves that it is worth to use the server for MHC prediction.

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HLA-A*0201 restricted potential CTL epitope identification:-Kessler et al., 2002 has demonstracted that MHC binders having proteasomal cleavage site at C terminal are mostly recognised by CTL cells.They have identified four regions from PRAME protein which have CTL epitopes. Thus, we have used this server in identification of experimentally proved antigenic regions. The server ComPred has been applied at default cutoff score of ANN and various thresholds of proteasomal filters. The server predict the MHC binders which have proteasomal cleavge sites at C terminal.The number of peptides predicted by above falls in regions identified as T cell epitopes by Kessler et al., 2001, is shown in following Table. ComPred was tested on 4 regions of PRAME (A: 90-116; B:133-159; C: 290-316; D: 415-441) which were identified as T cell epitopes by Kessler et al., (2001). The column 2 shows the number of predicted peptides and regions (in bracket), which agree with the experimentally identified epitopes.

The server is able to identify 75% of experimantally identified regions at default cutoff score of method and 7% threshold of proteasomal filters.. The server is even able to identify all the experimantally identified antigenic regions at 0.01 cutoff score and 10% threshold of standard proteasome filter.This demonstrates that it is worth to use this server for scanning of vaccine candidates from complex genomes.

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