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Paper text:
the peptide bond linking adjacent amino acids in protein structures can adopt either the cis or the trans conformation .
the cis conformation occurs rarely in polypeptides because of the higher intrinsic energy compared to the trans conformation . despite their infrequent occurrence ,
cis peptide bonds are very important in a variety of biological processes , such as protein folding , regulation , cell signaling and splicing of protein molecules ( 1 ) .
recent studies have indicated that prolyl cis / trans isomerization can act as a molecular timer to help control the cellular process , making it a new target for therapeutic interventions ( 2 ) .
furthermore , cis peptide bonds , especially the ones between non - proline residues , are located near the active sites of proteins , or have roles in the function of the protein molecules 1 .
( 5 ) trained a support vector machine ( svm ) using only the primary sequence as input in order to discriminate between the two conformations of proline peptide bonds .
( 6 ) predicted the isomerization of proline peptide bonds using multiple sequence alignment profiles and secondary structure as input .
the cops algorithm ( 7 ) aimed to predict the peptide bond formation between any two amino acids employing an extension of the chou - fasman parameters .
most of the aforementioned studies focus only on the proline residues , ignoring the rare but highly important non - proline cis peptide bonds . here , we make a further distinction of the peptide bonds into four classes , namely cis imide ( cis - pro ) , cis amide ( cis - nonpro ) , trans imide ( trans - pro ) and trans amide ( trans - nonpro ) , by developing the pbond web server . hence , pbond not only predicts the peptide bond conformation between any two amino acids , but also designates potential cis - nonpro formations .
furthermore , a reliability index is computed , which represents the confidence assigned to each prediction .
a majority voting scheme is also available , which provides consensus prediction of 10 svm classifiers .
pbond has been developed using 3,050 high - quality protein sequences with resolution < 2.0 , r - factor < 0.25 and sequence identity < 25% .
the pbond web server graphical interface , as shown in figure 1 , consists of six fields .
the numbers placed next to every field in the figure follow the same notation as below .
1 . processing : the user may choose to process either a single sequence or upload multiple sequences for batch processing ; there is no upper limit to the number of sequences submitted for batch processing .
2 . upload sequence : amino acid sequences can be provided in fasta format either by pasting them in the text box or uploading them within a text file .
more specifically , multiple sequence alignment profiles , in the form of position - specific scoring matrices ( pssms ) , are obtained after running psi - blast ( 8) against one of the provided protein databases ; the choice of the database highly affects the computational time , whereas only slight perturbations are expected in terms of performance .
next , the predicted secondary structure of every residue in the query sequence is computed using psipred ( 9 ) ; real valued predictions of solvent accessibility are obtained from rvp - net ( 10 ) ; six widely used physicochemical properties are also employed for every residue ( volume , hydrophobicity , polarity , charge , aromatic and aliphatic character ) .
, 11 . , centered at each residue , whose peptide bond with the preceding amino acid we are trying to predict ; outside this range , the influence of the surrounding residues towards the peptide bonds formation decreases .
4 . feature selection : next , the user may choose either to employ the whole feature vector for the prediction or an optimal reduced set of features ( 12 ) identified in our previous study ( 11 ) .
voting : the user may choose to invoke either a single svm model or 10 svm models , each one trained with a different dataset . in the latter , each model independently assigns a label ( cis - pro , cis - nonpro , trans - pro , trans - nonpro ) to every residue in the query sequence and then a linear time majority voting algorithm calculates the consensus of the 10 predictions .
submit sequence : if a valid e - mail address is supplied , the results are submitted in a compressed file ; otherwise , if the e - mail field is left blank , the prediction results can be downloaded directly from the web page , where they will be available for 10 days .
the output of the pbond server consists of a compressed file containing either a single text file , or multiple text files , in case of batch processing .
these files contain plain text with the predictions for every sequence uploaded , along with a reliability index for every prediction .
the first and last five peptide bonds of every sequence are labeled as  n / a  since there are not enough residues in the sliding window to make a prediction .
due to the scarcity of cis peptide bonds ( both cis - pro and cis - nonpro ) , a severe class imbalance problem emerges , posing a tradeoff between the identification of as many potential cis formations and certain false positive predictions .
hence , special attention was given during the training and evaluation of the pbond server so that important cis formations are not neglected .
for this purpose , the predictive models of pbond server have been trained using fully balanced datasets , in which all four classes are equally represented .
the evaluation of pbond has been performed on fully balanced disjoint data segments coming from the initial unbalanced dataset 13 .
, 14 .. table 1 presents the performance achieved using the initial feature vector , with and without performing majority voting .
sensitivity and positive predictive value ( ppv ) are also provided for the two general classes ( cis / trans ) .
the performance achieved using the initial input vector is in general quite poor , even though voting slightly improves the results . in table 2 , the performance achieved using the optimal reduced set of features is shown , with and without the employment of the majority voting algorithm .
it is clear that the feature selection improves the classification outcome to a certain extent ; a further increment in the results is achieved using the consensus prediction of the 10 models .
furthermore , the reliability index associated with every prediction can be used for post processing the prediction results .
a detailed comparison of the available prediction methods in the literature and pbond is presented in table 3 .
based on the physicochemical properties of the 6 surrounding amino acids , frmmel et al .
although the reported results are promising , such refined dataset ( 242 proline bonds ) diminishes the credibility of the proposed method .
( 5 ) as well , focused only on the proline residues and employed single sequence information coded in binary form in order to predict the conformation of the peptide bond .
the prediction accuracy achieved by this method is 70% and 77% when evaluated with independent datasets and the jackknife test , respectively .
( 6 ) provided multiple sequence alignment profiles coupled with secondary structure information as input to an svm in order to predict the proline cis / trans isomerization .
the overall reported accuracy is 71% after performing five - fold cross validation . only pahlke et al .
( 7 ) aimed to predict the peptide bond conformation between any two amino acids , using the secondary structure of amino acid triplets ; however , the reported results ( overall accuracy 66% ) are quite unsatisfactory .
this could be attributed to the refined length of the sliding window , as well as to the small number of employed features .
the performance of pbond compares well with previously published studies , albeit validated on different datasets and using different evaluation methods . moreover , pbond is able to identify the scarce but highly important non - proline cis peptide bonds .
tpe and cp provided valuable comments and suggestions throughout the study and helped in the manuscript preparation .
tpe and cp provided valuable comments and suggestions throughout the study and helped in the manuscript preparation .
pbond is a web server that predicts the conformation of the peptide bond between any two amino acids . 
 pbond classifies the peptide bonds into one out of four classes , namely cis imide ( cis - pro ) , cis amide ( cis - nonpro ) , trans imide ( trans - pro ) and trans amide ( trans - nonpro ) . 
 moreover , for every prediction a reliability index is computed . 
 the underlying structure of the server consists of three stages : ( 1 ) feature extraction , ( 2 ) feature selection and ( 3 ) peptide bond classification . 
 pbond can handle both single sequences as well as multiple sequences for batch processing . 
 the predictions can either be directly downloaded from the web site or returned via e - mail . 
 the pbond web server is freely available at http://195.251.198.21/pbond.html .