BMES546 Chao Yu Bin Zhang Qing Li

 Background  ion channel protein  Method  Codes  Results  Challenges and Future Work

 Ion Channel Proteins “pore-forming proteins that help establish and control the voltage gradient across the plasma membrane of cells by allowing the flow of ions down their electrochemical gradient.” Classification By Gating Voltage-gated : are activated by changes in electrical potential difference near the channel Voltage-gated sodium channels Voltage-gated calsium channels Voltage-gated potassium channels Voltage-gated anion channels Ligand-gated: are opened or closed in response to the binding of a chemical messenger

Understanding Life  gene regulation by Ca channels Opportunities for Therapy  novel pharmaceutical agents  - neuropathic path (blocking NaV1.7 channels)  - capitalizing on molecular information in calcium channels

 Building Database  148 voltage-gated ion channels, 150 ligand-gated ion channels and 300 non ion channel membrane proteins from the Universal Protein Resource  Selecting Particular Tri-peptides from The Database  Binomial distribution (CL value)  Getting Frequencies of Particular Tri-peptide in the Unknown Sequences  Predicting the Unknown Sequences  Support vector machine(SVM) : used for classifying data  - training : get 5 fold cross-validation accuracy  - prediction: get ion/non-ion, subclass(pie chart)

train: predict: known sequences Our predictor have two functions: validate our prediction method accuracy unknown sequences predict these types with our method probability of each types

Users imput the known sequences from our database By training and scaling through SVM, get the file which should be used in prediction Users imput unknown sequences By using libSVM, predict the unknown sequences wether are ion channels or not. If it is, show their types in a pie chart.

input: the file name (store these unknown sequences ) the three modelnames ion-non, V-L, k-ca-an-na ( According to the modelnames, the predictor will know which sets of tri-peptides should be used) output: the accuracies of the three situations 5-fold cross-validation accuracy for distinguishing ion channel proteins or non-ion channel proteins voltage-gated ion channels or ligand-gated ion channels subclasses of voltage-gated ion channel proteins SVM-train.exe from a software package named libSVM

The format of the file to be trained: label+ the frequency*10000 of a tripeptide in the first sequence label+ the frequency*10000 of a tripeptide in the second sequence c cost: set the parameter C of C-SVC,epsilon-SVR and nu-SVR -g gamma: set gamma in kernal function -v n : n-fold cross validation mode got by analyzing these file by grid.py in libSVM

Label: show which types are these sequences should be input: the file name (store these unknown sequences ) groupnames (can be one or more of 'ion,nonion,k,ca,an,na,v,l') output: structure{headers, sequences, labels}

Similar with ionchantrain.m + SVM-train.exe The user provides the file of unknown sequences and it will be changed into that particular format. Then they are analyzed by SVM-predict.exe, it will show three results. probability of ion and non voltage and ligand k, ca, an,na

ion non V Lk ca an na combine

Tried to combine the training part and testing part together. function ionchantrain (ps,modelname,dotrain) if dotrain is true, then we do the training part if dotrain is false, then we do the testing part. But we didn’t get that…