BACKGROUND
in the past few decades, a large number of bacterial strains have evolved ways to adapt or become resistant to the currently available antibiotic  <cit> .
the widespread resistance of bacterial pathogens to conventional antibiotics has prompted renewed interest in the use of alternative natural microbial inhibitors such as antimicrobial peptides.
antimicrobial peptides  are a family of host-defense peptides most of which are gene-encoded and produced by living organisms of all types  <cit> .
antimicrobial peptides  are small molecular weight proteins with broad spectrum antimicrobial activity against bacteria, viruses, and fungi  <cit> .
these evolutionarily conserved peptides are usually positively charged and have both a hydrophobic and hydrophilic side that enables the molecule to be soluble in aqueous environments yet also enter lipid-rich membranes.
once in a target microbial membrane, the peptide kills target cells through diverse mechanisms  <cit> .
antimicrobial peptides have a broad spectrum of activity and can act as antibacterial, antifungal, antiviral and sometimes even as anticancer peptide  <cit> .
these antibacterial peptides have other properties like antibacterial activity, mitogen activity or act as signaling molecules including pathogen-lytic activities  <cit> .
extensive work has been done in the field of antibacterial peptide, describing their identification, characterization, mechanism of action etc.
keeping in mind their numerous biotechnological applications  <cit> .
lot of work has been done to collect and compile these peptides in form of a database  <cit> .
these antibacterial peptides have very low sequence homology, despite their common function  <cit> .
previously we developed a very robust method antibp  <cit> , for predicting antibacterial peptide using svm, qm  and artificial neural network .
growth of antibacterial peptides in apd database in the last  <dig> years motivated us to develop a prediction method based on the newer and larger  dataset.
we once again analyzed the antibacterial peptides and developed svm based models to predict antibacterial peptides, because our previous study show that svm over perform than other method.
in antibp <dig> we also extracted clean dataset of antibacterial peptide families from swiss-prot and developed classification models for them.
in the following text, we first discuss the method developed to distinguish antibacterial peptides from non-antibacterial peptides  and in the next step describe the method for classifying these peptides on the basis of source and classes .
RESULTS
analysis of the antibacterial peptides
analysis of antibacterial peptides in antibp  <cit>  had shown a preference for certain residues over others at both the termini.
by drawing the plogos  <cit>  it was also seen that there seems to be a residue preference at different position of antibacterial peptides.
as the dataset in antibp <dig> was almost double in size compared to the dataset used in the previous method antibp, we again decided to analyze the antibacterial peptides and look for any change or shift in preference trend.
we again generated sequence logos of  <dig> n-terminal and c-terminal residues using plogo program .
it was seen that the plogos drawn in antibp <dig> showed similar trend as shown in the method antibp  <cit> .
here also in the n-terminus dataset g, f, v, r was predominating at first position and l, i, w, f were frequently present at 2nd position.
similarly, certain residues are preferred at the c-terminus, for example residues k, g, c, and r are preferred at most of the positions.
though both n and c terminus have a higher proportion of positively charged residues but in antibp <dig> analysis also we could notice a higher frequency of positively charged residues at the c-terminus as compared to the n-terminus .
this may be because it is the c-terminus first interacts with the negatively charged membrane of the bacteria and penetrate it  <cit> .
the n-terminus later helps to hamper the crucial bacterial metabolic functions by interacting with intracellular components like dna and rna  <cit> .
antibacterial peptides also have a high propensity of the residues cys which is normally not preferred in most of the proteins
overall amino acids composition comparison of antibacterial and non-antibacterial shows positively charged lys is prominent in antibacterial peptides .
similarly gly and ile propensity is also high in antibacterial peptides
prediction
the performances of nt <dig>  ct <dig>  ntct <dig> and whole peptide based prediction method for antibacterial peptides are given below in table  <dig>  the accuracies achieved by ntct <dig> model and whole peptide based model were almost equal  and is highest among all the models.
the performance of nt <dig> model was better that that of ct <dig> model.
performance on independent or blind dataset
the prediction models developed in this study were evaluated on a  <dig> sequence independent dataset .
these antibacterial peptides in the independent dataset were not used for developing above models either in training or testing.
classification
the result of classification of antibacterial peptides into  <dig> sources is given in table  <dig>  the mcc achieved by the classification model was  <dig> ,  <dig> ,  <dig> ,  <dig>  and  <dig>  for bacteria, frog, insects, mammals and plants respectively.
the dateiled results of classification of insect antibacterial peptides into thie listed  <dig> subfamilies is given in table  <dig>  for classification of insect antibacterial peptides into apidaecin, attacin, cecropin, invertebrate defensin and lebocin, the respective mcc's achieved were  <dig> ,  <dig> ,  <dig>   <dig>   <dig> .
the results of classification of frog's antibacterial peptides and mammalian antibacterial peptides into their respective families  are given in detail in table  <dig> and table  <dig>  the mcc achieved in classification of frog antibacterial peptides into its respective subfamilies  was  <dig> ,  <dig> ,  <dig>   <dig> ,  <dig>  and that for mammalian antibacterial peptide families were  <dig> ,  <dig> ,  <dig> ,  <dig>   <dig> .
discussion
a great deal of interest is shown nowadays in antibacterial peptides or the so called "nature's antibiotics", which seem to be promising to overcome the growing problem of antibiotic resistance  <cit> .
the design of novel peptides with antimicrobial activities requires the development of methods for narrowing down the candidate peptides so as to enable rational experimentation by wet-lab scientists.
attempts have been made to develop methods and strategies for designing effective antimicrobial peptides  <cit> .
antibp is one such method meant to discover efficacious antibacterial peptides that we hope could prove to be a boon to combat the dreadful antibiotic resistant bacteria.
enormous growth of antibacterial peptide data in the databases motivated us to develop an improved version of antibp using the same strategy.
the new version was name antibp <dig>
the n and c terminus sequence logos of antibp <dig> dataset were almost similar to those in the previous method antibp.
this indicates that though there seems to be an absence of great homology or conservation among antibacterial peptides but the pattern of positional preference of certain residues remains constant.
we once again developed the prediction method to classify antibacterial peptide from the non-antibacterial peptide.
but this time the method was developed using a training data that was double in size to the one previously used.
we developed both whole peptide based compositional models as well as binary pattern based terminus approaches.
this time we retained the whole peptide based method also as it becomes difficult to predict peptides that are less than  <dig> residues in length by the binary pattern based terminal models.
in this method also we achieved impressive results with all the above approaches but the best performers were the ntct <dig> and whole peptide based prediction models .
this was followed by the nt <dig> based prediction model while the ct <dig> based model being the poorest performer among all.
this trend is just similar to what was seen in antibp.
the performance evaluation of prediction models on the independent dataset followed the trend shown during development of prediction models .
the ntct <dig> model performed the best followed by nt <dig> and ct <dig> models in respective order.
in antibp <dig> we have also developed models that could classify antibacterial peptides further into families with high accuracy.
first we successfully made an attempt to develop classification models that could assign the source of origin to predicted antibacterial peptides.
the classification models to classify the antibacterial peptides further into corresponding families were also developed.
the results attained in all the classification methods clearly indicate that although the antibacterial peptides do no show a greater conservation or homology, but they become more and more as we go down to the level of a particular family.
this is evident from the high accuracies achieved for each family in various classification models.
therefore, antibp <dig> is an efficient method that can predict and classify the antibacterial peptides.
we hope that our method would help the wet lab scientists to design improved and efficacious antibacterial peptides in future.
CONCLUSIONS
there is a rapid growth in the field of antibacterial peptide research in response to the demand for novel antibacterial agents.
antibp <dig> is one such efficient method that can predict and classify the antibacterial peptides and help to find newer antibacterial peptides more speedily and conveniently.
we hope that our method would promote the research to design improved and efficacious antibacterial peptides in future.

 in the past few decades, a large number of bacterial strains have evolved ways to adapt or become resistant to the currently available antibiotic. the widespread resistance of bacterial pathogens to conventional antibiotics has prompted renewed interest in the use of alternative natural microbial inhibitors such as antimicrobial peptides. antimicrobial peptides are a family of host-defense peptides most of which are gene-encoded and produced by living organisms of all types. antimicrobial peptides are small molecular weight proteins with broad spectrum antimicrobial activity against bacteria, viruses, and fungi. these evolutionarily conserved peptides are usually positively charged and have both a hydrophobic and hydrophilic side that enables the molecule to be soluble in aqueous environments yet also enter lipid-rich membranes. once in a target microbial membrane, the peptide kills target cells through diverse mechanisms. antimicrobial peptides have a broad spectrum of activity and can act as antibacterial, antifungal, antiviral and sometimes even as anticancer peptide. these antibacterial peptides have other properties like antibacterial activity, mitogen activity or act as signaling molecules including pathogen-lytic activities. extensive work has been done in the field of antibacterial peptide, describing their identification, characterization, mechanism of action etc. keeping in mind their numerous biotechnological applications. lot of work has been done to collect and compile these peptides in form of a database. these antibacterial peptides have very low sequence homology, despite their common function. previously we developed a very robust method antibp, for predicting antibacterial peptide using svm, qm and artificial neural network. growth of antibacterial peptides in apd database in the last years motivated us to develop a prediction method based on the newer and larger dataset. we once again analyzed the antibacterial peptides and developed svm based models to predict antibacterial peptides, because our previous study show that svm over perform than other method. in antibp we also extracted clean dataset of antibacterial peptide families from swiss-prot and developed classification models for them. in the following text, we first discuss the method developed to distinguish antibacterial peptides from non-antibacterial peptides and in the next step describe the method for classifying these peptides on the basis of source and classes.  analysis of the antibacterial peptides analysis of antibacterial peptides in antibp had shown a preference for certain residues over others at both the termini. by drawing the plogos it was also seen that there seems to be a residue preference at different position of antibacterial peptides. as the dataset in antibp was almost double in size compared to the dataset used in the previous method antibp, we again decided to analyze the antibacterial peptides and look for any change or shift in preference trend. we again generated sequence logos of n-terminal and c-terminal residues using plogo program. it was seen that the plogos drawn in antibp showed similar trend as shown in the method antibp. here also in the n-terminus dataset g, f, v, r was predominating at first position and l, i, w, f were frequently present at 2nd position. similarly, certain residues are preferred at the c-terminus, for example residues k, g, c, and r are preferred at most of the positions. though both n and c terminus have a higher proportion of positively charged residues but in antibp analysis also we could notice a higher frequency of positively charged residues at the c-terminus as compared to the n-terminus. this may be because it is the c-terminus first interacts with the negatively charged membrane of the bacteria and penetrate it. the n-terminus later helps to hamper the crucial bacterial metabolic functions by interacting with intracellular components like dna and rna. antibacterial peptides also have a high propensity of the residues cys which is normally not preferred in most of the proteins overall amino acids composition comparison of antibacterial and non-antibacterial shows positively charged lys is prominent in antibacterial peptides. similarly gly and ile propensity is also high in antibacterial peptides prediction the performances of nt ct ntct and whole peptide based prediction method for antibacterial peptides are given below in table the accuracies achieved by ntct model and whole peptide based model were almost equal and is highest among all the models. the performance of nt model was better that that of ct model. performance on independent or blind dataset the prediction models developed in this study were evaluated on a sequence independent dataset. these antibacterial peptides in the independent dataset were not used for developing above models either in training or testing. classification the result of classification of antibacterial peptides into sources is given in table the mcc achieved by the classification model was,,, and for bacteria, frog, insects, mammals and plants respectively. the dateiled results of classification of insect antibacterial peptides into thie listed subfamilies is given in table for classification of insect antibacterial peptides into apidaecin, attacin, cecropin, invertebrate defensin and lebocin, the respective mcc's achieved were,,. the results of classification of frog's antibacterial peptides and mammalian antibacterial peptides into their respective families are given in detail in table and table the mcc achieved in classification of frog antibacterial peptides into its respective subfamilies was,,, and that for mammalian antibacterial peptide families were,,,. discussion a great deal of interest is shown nowadays in antibacterial peptides or the so called "nature's antibiotics", which seem to be promising to overcome the growing problem of antibiotic resistance. the design of novel peptides with antimicrobial activities requires the development of methods for narrowing down the candidate peptides so as to enable rational experimentation by wet-lab scientists. attempts have been made to develop methods and strategies for designing effective antimicrobial peptides. antibp is one such method meant to discover efficacious antibacterial peptides that we hope could prove to be a boon to combat the dreadful antibiotic resistant bacteria. enormous growth of antibacterial peptide data in the databases motivated us to develop an improved version of antibp using the same strategy. the new version was name antibp the n and c terminus sequence logos of antibp dataset were almost similar to those in the previous method antibp. this indicates that though there seems to be an absence of great homology or conservation among antibacterial peptides but the pattern of positional preference of certain residues remains constant. we once again developed the prediction method to classify antibacterial peptide from the non-antibacterial peptide. but this time the method was developed using a training data that was double in size to the one previously used. we developed both whole peptide based compositional models as well as binary pattern based terminus approaches. this time we retained the whole peptide based method also as it becomes difficult to predict peptides that are less than residues in length by the binary pattern based terminal models. in this method also we achieved impressive results with all the above approaches but the best performers were the ntct and whole peptide based prediction models. this was followed by the nt based prediction model while the ct based model being the poorest performer among all. this trend is just similar to what was seen in antibp. the performance evaluation of prediction models on the independent dataset followed the trend shown during development of prediction models. the ntct model performed the best followed by nt and ct models in respective order. in antibp we have also developed models that could classify antibacterial peptides further into families with high accuracy. first we successfully made an attempt to develop classification models that could assign the source of origin to predicted antibacterial peptides. the classification models to classify the antibacterial peptides further into corresponding families were also developed. the results attained in all the classification methods clearly indicate that although the antibacterial peptides do no show a greater conservation or homology, but they become more and more as we go down to the level of a particular family. this is evident from the high accuracies achieved for each family in various classification models. therefore, antibp is an efficient method that can predict and classify the antibacterial peptides. we hope that our method would help the wet lab scientists to design improved and efficacious antibacterial peptides in future.  there is a rapid growth in the field of antibacterial peptide research in response to the demand for novel antibacterial agents. antibp is one such efficient method that can predict and classify the antibacterial peptides and help to find newer antibacterial peptides more speedily and conveniently. we hope that our method would promote the research to design improved and efficacious antibacterial peptides in future. 
among antibacterial peptides, there is preference for certain residues at n and c terminus, which helps to discriminate them from non-antibacterial peptides. amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. during analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the n and c terminus. finally we developed svm based model using amino acid composition of whole peptide and achieved % accuracy with mcc. we further classify antibacterial peptides according to their sources and achieved an overall accuracy of %. we further classify antibacterial peptides in their respective family and got a satisfactory result. antibacterial peptides are one of the effecter molecules of innate immune system. over the last few decades several antibacterial peptides have successfully approved as drug by fda, which has prompted an interest in these antibacterial peptides. in our recent study we analyzed antibacterial peptides, which were collected from antibacterial peptide database. we have also developed methods to predict and classify these antibacterial peptides using support vector machine. these observation and increased data of antibacterial peptide in apd encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. in this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. first, the binary patterns of the n terminus residues were used for predicting antibacterial peptide using svm and achieved accuracy of % with mathew's correlation coefficient. antibp will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. then we used the binary pattern of c terminus residues and achieved accuracy of % with mcc, latter on we developed prediction method by combining n & c terminus and achieved an accuracy of % with mcc.
 antibacterial peptides are one of the effecter molecules of innate immune system. over the last few decades several antibacterial peptides have successfully approved as drug by fda, which has prompted an interest in these antibacterial peptides. in our recent study we analyzed antibacterial peptides, which were collected from antibacterial peptide database. we have also developed methods to predict and classify these antibacterial peptides using support vector machine.  during analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the n and c terminus. these observation and increased data of antibacterial peptide in apd encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. first, the binary patterns of the n terminus residues were used for predicting antibacterial peptide using svm and achieved accuracy of % with mathew's correlation coefficient. then we used the binary pattern of c terminus residues and achieved accuracy of % with mcc, latter on we developed prediction method by combining n & c terminus and achieved an accuracy of % with mcc. finally we developed svm based model using amino acid composition of whole peptide and achieved % accuracy with mcc. in this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. we further classify antibacterial peptides according to their sources and achieved an overall accuracy of %. we further classify antibacterial peptides in their respective family and got a satisfactory result.  among antibacterial peptides, there is preference for certain residues at n and c terminus, which helps to discriminate them from non-antibacterial peptides. amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. antibp will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. we also developed user friendly web server for the biological community. 18- january the eighth asia pacific bioinformatics conference bangalore, india 