BACKGROUND
infectious and parasitic diseases caused by pathogenic microorganisms, including bacteria, viruses and fungi, are major threats to human health.
in particular, diseases commonly result from exposure to gram positive bacteria, such as staphylococcus aureus, streptococcus pneumoniae and group a streptococcus, and gram negative bacteria, such as e. coli and helicobacter pylori.
antibacterial drugs are the major weapons to kill bacteria or suppress their activity.
due to the inevitable evolution of antibiotic resistance, the development of novel antibiotics is essential.
antibiotics work either by stopping bacterial growth or by killing the bacteria, without harming the human host.
the following are the most common modes-of-action of antibiotics:
inhibit synthesis of peptidoglycan.
these antibiotics work by interfering with the synthesis of bacterial cell walls by either: blocking the transport of peptidoglycan monomers synthesized in the cytosol across the cytoplasmic membrane, inhibiting a transpeptidase and hence the formation of the peptide cross-links, or blocking both the transglycosidase and transpeptidase enzymes.
the transglycosidases are essential for the formation of glycosidic bonds between sugars and transpeptidases are essential for the formation of peptide cross-links  <cit> .
alter the microbial cytoplasmic membrane.
the polymixins are cationic peptides consisting of a cyclic peptide with a fatty acid chain.
the interaction between the cationic peptide and the membrane causes disruption of the bacterial cell membrane and increases the permeability of cell components  <cit> .
alter translation.
many antibiotics work by binding to bacterial ribosomes.
examples of antibiotics that work by binding to the 30s ribosomal subunit are aminoglycosides and tetracyclines, which prevent the binding of trna  <cit> .
other macrolide antibiotics, such as erythromycin, bind to the 50s ribosomal subunit and block the exit tunnel of the bacterial ribosome  <cit> .
inhibit nucleic acid replication by blocking topoisomerases that are essential for supercoiling, bacterial dna replication and separation of circular bacterial dna.
the fluoroquinolone antibiotic class contains potent inhibitors for topoisomerases or dna gyrase  <cit> .
inhibit transcription.
some antibiotics, such as rifampin or rifampicin, work by binding to rna polymerase and inhibiting the transcription of dna to mrna  <cit> .
after the first widespread use of antibacterial drugs in the 1940s, bacterial pathogens started to develop resistance to existing drugs, particularly after excessive antibiotic use.
the three basic mechanisms of bacterial resistance to antibiotics are:  production of an enzyme to inactivate the antibiotic, such as a β-lactamase to hydrolyse penicillin.
mutation in the target site receptor of the enzyme or the ribosomal subunit that leads to ineffective drug binding.
alteration in transport proteins to prevent antibiotic entry or promote active efflux from the cell  <cit> .
there is thus an urgent need to discover new strategies to discover and develop effective antibiotic drugs to overcome widespread and growing antibiotic resistance.
one way in which this may be achieved is by identifying bacterial proteins that may be the targets for new classes of antibiotics.
sakharkar et al.
addressed this problem by identifying essential bacterial genes  that have no human homologues <cit> .
here, we determine key properties of antibiotic target proteins and use machine learning to identify new potential targets.
RESULTS
primary sequence, secondary structure and post-translational properties
all the proteins in the targets and non-targets data sets were analysed for their primary sequence properties and post-translational modifications.
as all features showed a non-normal distribution using the kolmogorov-smirnov test, p-values were calculated using the mann-whitney test.
table  <dig> shows the differences between the mean values of length, hydrophobicity, secondary structure, transmembrane helices , signalp, low complexity regions , pi, amino acid preferences and post-translational properties for targets and non-targets.
targets tend to be larger proteins with mean lengths of  <dig> and  <dig> amino acids for bacteria and e. coli respectively compared to  <dig> for non-targets.
bacterial targets show significant preferences for pro and val and disfavour trp, met, leu and gln.
secondary structure analysis shows that bacterial targets have less α-helix and more β-sheet.
the percentage of target helices is 32% compared to 42% for non-targets with significant p-values .
the prediction of transmembrane helices shows that targets tend to have fewer helices  compared to  <dig>  for non-targets.
antibiotic and e. coli targets are less hydrophobic  compared to non-targets  with significant p-values .
no statistically significant differences were observed for pi, signalp, netnglyc and netoglyc and lcr between the bacterial or e. coli targets and non-targets.
this indicates that these features do not contribute to the druggability of a bacterial protein.
this is expected for glycosylation, as the glycosylation site prediction programs are optimised for use in metazoa; indeed the predicted frequencies for this property are low.
nevertheless, it is possible to use any sequence feature  for machine learning.
* not applied as the prediction program as bacterial targets are both gram positive and gram negative.
targets were found to contain more polar, charged, basic and acidic amino acids.
the mean frequency of positively charged amino acids was greater in e. coli  and in all targets  compared to non-targets  .
the mean frequency of polar amino acids in e. coli  and all targets  was higher compared to non-targets  with a significant p-value of  <dig>  for all targets; similarly, there was a higher proportion of non-polar amino acids in non-targets compared to all targets  with a p-value of  <dig>  for all targets.
the frequencies of negative amino acids were greater for e. coli  and all targets  compared to non-targets  .
enzyme class
the percentage of enzymes was found to be 33%  in non-targets compared to 81%  for e. coli and 71%  for bacterial targets.
figure  <dig> shows high preferences for ligases in bacterial targets  and for e. coli , compared to 5% for non-targets.
ligases are common antibacterial drug targets as they are involved in the formation of the cell wall.
lyases were not found in either the bacterial or e. coli target sets.
subcellular location
43%  of the non-targets, 40%  of the e. coli targets and 47%  of the bacterial targets have subcellular location annotations in swissprot.
these percentages might be misleading due to bias of annotated sequences to a specific family or incomplete annotation.
figure 2a shows that the majority of non-drug targets are localised to the bacterial cell membrane.
the membrane location includes all entries that contain the word membrane such as membrane single-pass type ii, peripheral membrane protein, peripheral membrane protein, multi-pass membrane, cell membrane and cell inner membrane .
the next highest preferences for drug targets are in the cytoplasm and periplasm.
a prediction method   <cit>  was used to validate the results of subcellular location.
a localization score is calculated for each protein, where the threshold is  <dig>  or greater.
if two locations have high scores, the prediction is set to unknown.
figure 2b shows the subcellular location results found using psortb.
the prediction program performs a different analysis depending on whether the organism is gram-positive or gram-negative.
hence the analysis was performed between the two gram-negative datasets of e. coli targets and non-targets.
non-targets are localised in the cytoplasmic membrane more than the cytoplasm, and vice versa for e. coli targets.
unknown assignments, where the program is unable to make a prediction, are frequent for both e. coli targets  and non-targets .
gene ontology
additional file 1: supplemental figures s1a-f show that molecular functions at level  <dig> and level  <dig> are similar for bacterial targets, e. coli targets and non-targets.
the distribution of biological process at level  <dig>  shows that terms such as metabolic process and cellular process are found with similar percentages in bacterial targets, e. coli targets and non-targets.
response to antibiotic is unsurprisingly found in bacterial targets and e. coli targets, but not non-targets.
terms including localization and establishment of localization are common for non-targets, but they are not associated with bacterial or e. coli targets.
the distribution of biological process at level  <dig>  shows that primary metabolic process and cellular metabolic process have similar frequencies for both targets and non-targets.
the regulation of cell shape term is present in both e. coli  and in all bacterial targets , but no proteins are associated with this process in the non-targets.
other processes favoured for bacterial and e. coli targets are biosynthetic process and cell wall organization.
transport proteins are more prevalent for non-targets.
within the cellular component class, the organelle and cell part terms are present with similar frequencies at level  <dig> for bacterial targets, e. coli targets and non-targets .
at level  <dig>  more bacterial and e. coli targets are localised in the intracellular part compared to non-targets .
this class includes cytoplasm and cytoplasmic part.
intracellular organelle and intracellular non-membrane bound organelle terms are strongly favoured for bacterial and e. coli targets compared to non-targets.
prediction of new antibiotic targets
the features described above were used with the support vector machine algorithm to develop a model that is able to assign any sequence to the e. coli target or non-target classes solely from protein sequence.
we only used e. coli targets versus e. coli non-targets, rather than training all targets versus e. coli non-targets because differences to all targets may reflect species differences instead of differences between targets and non-targets.
two parameters were varied to maximise the accuracy, namely the error penalty  for an incorrect prediction and the radial basis function parameter  which controls how smooth the boundary is in hyperspace between the target and non-target areas.
the accuracy is defined as the overall probability that the prediction is correct.
a perl program was coded to search for the optimal c and γ values by performing a coarse grid and a fine grid search.
the optimal parameters for c and γ were found to be  <dig>  and  <dig> , respectively.
this model had an accuracy of  <dig> % using  <dig> features including  <dig> amino acids, length, hydrophobicity, signalp, netnglyc, netolgyc-s, netoglyc-t, lcr, secondary structure , tmhmm and pi.
this accuracy is based on 5-fold cross validation using  <dig> e. coli targets as the positive training dataset and  <dig> proteins for the e. coli non-targets.
the area under curve in a roc plot was  <dig> , confirming the high accuracy of the model.
the optimised support vector machine  algorithm was used to classify  <dig> e. coli non-target sequences.
<dig> of them were assigned as targets, suggesting that 7% of bacterial proteins have target-like properties .
of these predicted e. coli targets, 35%  are annotated as uncharacterised proteins and 8%  as 50s or 30s ribosomal proteins.
8%  of the new predicted targets are transferases and 5%  are hydrolases, in broad agreement with annotation data .
other ec classes were less abundant, with four isomerases, three oxidoreductases, two lyases and one ligase.
the  <dig> predicted e. coli target proteins were further analysed to check for similarity to human proteins.
if any similarity is found, the predicted antibiotic targets may well be problematic, as their inhibition could lead to toxic effects in the patient.
<dig> proteins were found to have maximal sequence similarities to a human protein of 25% to 63% using blast .
removal of these  <dig> proteins leaves  <dig> potential new targets with no significant similarity to any human protein.
the distance of a protein from the svm hyperplane boundary between the two sets  gives a measure of the accuracy of the distribution of the data about the decision surface.
the further the score is from zero, the more reliable is the prediction.
figure  <dig> illustrates the calculated distance of the e. coli targets and non-targets from the hyperplane, in  <dig>  intervals.
a protein with a positive distance is classified as a target and vice versa.
excellent separation between the two sets is achieved, with all targets classified correctly.
the  <dig> false negatives are the non-targets with a positive score.
these are the most interesting proteins, as they are potential new antibiotic targets.
the list of  <dig> proteins was subjected to a blast search against the e. coli essential genes.
20%  of the new predicted targets showed a high similarity to an essential gene where  <dig> proteins had a sequence identity of 50% to 70% and  <dig> proteins had a sequence identity > 70%.
additional file  <dig> shows the matching essential gene for each protein along with their p-values, identities and blast scores for each protein.
these  <dig> proteins are more likely to be successful antibiotic targets since their loss of function is likely to lead to cellular growth arrest or death.
we do note, however, that there are beneficial e. coli colonies in humans, so targeting e. coli may have unwanted effects.
hu et al.
recently performed a detailed and thorough analysis of orphan e. coli proteins, giving many new functional assignments  <cit> .
in particular, they were able to assign  <dig> orphans to translation and  <dig> to bacterial cell envelope biogenesis, pathways that are known to be associated with antibiotic modes-of-action.
when these are compared to our predicted targets, we find  <dig> common proteins that are particularly promising targets, specifically:  <dig> orphan and  <dig> annotated proteins responsible for cell envelope biogenesis, and  <dig> orphan and  <dig> annotated proteins responsible for translation .
discussion
even though we have only a fairly small number of distinct targets in our data set, we still find many properties that are significantly different between antibiotic targets and non-targets.
there was little difference between e. coli targets and those from all bacteria.
targets are more likely to be enzymes than non-targets.
major antibiotic mechanisms include inhibiting enzymes responsible for translation, transcription, replication and bacterial cell wall biosynthesis.
inhibition of such mechanisms usually results in cell growth inhibition or bacterial death.
the molecular function analyses of gene ontology and enzyme class data show a preference for ligases as targets, such as those involved in peptidoglycan biosynthesis.
the biological process analysis shows that response to antibiotic, regulation of cell shape and cell wall organization terms are favoured for targets.
the cellular components data showed a high preference for intracellular part targets, with the cytoplasm subdivision favoured in particular, perhaps because enzymes are abundant in the cytoplasm and despite the difficulty of a drug crossing the membrane.
the membrane distribution for targets was lower that non-targets, supporting the predicted subcellular locations.
while the annotated results had a higher preference for targets to be localised in the membrane, the predicted results had a higher preference for the cytoplasm.
there are fewer predicted transmembrane helices in targets, supporting the subcellular location data.
bacterial targets tend to be larger and more polar than non-targets.
larger proteins have more potential surface of interaction between the drug and the target and may participate in more protein-protein interactions, thus having a larger effect when a drug binds.
the presence of charged amino acids might facilitate the interaction between the drug and the target, as bonds to charged amino acids from target groups are common  <cit> .
positively charged proteins are more likely to be ribosomal, since they interact with strongly negatively charged rrna.
studies on e. coli showed that 35% of metabolic enzymes, including amino acid, purine and pyrimidine biosynthesis, are non-essential genes for cell growth  <cit> .
this means that if one enzyme is knocked out from the pathway, the cell is able to function through alternative routes, including using isoenzymes and multifunction enzymes that can substitute for the missing enzymes.
non-targets favour pathways such as amino acid and macromolecule biosynthesis, while targets show a preference for transcription and dna replication gene ontology  terms.
hence, metabolic enzymes are disfavoured as antibacterial drug targets.
an ideal drug should have no similar protein in the host cell, as the drug might interact with it and lead to adverse effects in the patient.
there exist some exceptions for this rule: trimethoprim is such an example of an antibiotic used to treat urinary tract infections.
it works on inhibiting dihydrofolate reductase, despite the presence of a close human homologue  <cit> .
on the other hand, there are no guarantees that if there is no homology, toxicity is not observed.
an example of that is the large ribosomal subunit which is targeted by chloremphenicol.
in spite of the difference in the structure of the prokaryotic and eukaryotic ribosomes, a functionally conserved area is found in the ribosomes of mitochondria and bacteria resulting in side effects in patients  <cit> .
our previous work analysed desirable properties in human drug target proteins  <cit> , so we can now compare target proteins in humans and bacteria.
the properties differ greatly, reflecting entirely different modes-of-action , as well as differences between prokaryotes and eukaryotes.
membrane proteins are common targets in humans, while they are rare as bacterial targets.
n-glycosylation is more common in human targets sites, which implies that human targets proteins have a relative longer lifetime than bacterial targets and human non-targets.
human targets are frequently enzymes involved with binding and signaling; bacterial targets are often enzymes, involved in protein binding or ribosomal proteins.
the most targeted bacterial biological processes are peptidoglycan biosynthesis and cell wall synthesis, compared to cell communication and regulation of biological process for human targets.
in summary, the following seem to be desirable features for an antibiotic drug target:
• essential for the survival of the bacterial cell
• no close human homologue
• must be present in a number of pathogens if broad-spectrum action is required.
narrow spectrum antibiotics target specific pathogens
• the most targeted biological processes are peptidoglycan biosynthesis, cell wall synthesis, transcription and translation
• capable of binding to a small molecule, implying the presence of a binding site
• favoured to be localized in the cytoplasm and less frequent in membranes
• favoured to be ribosomal proteins
• more likely to be enzymes, transferases, hydrolases or ligase, but not lyases.
although enzymes are highly targeted, metabolic enzymes are disfavoured for antibacterial drug targets.
while many of the above properties have been reported in qualitative terms, they have not been previously quantified, to our knowledge.
in addition, some of the preferences we find appear to be new, such as ec class, length, amino acid frequencies, post-translational modifications, secondary structure contents, pi, signalp and transmembrane helices.
the machine learning work can accurately assign an e. coli protein to the target or non-target classes.
the use of cross-validation and the generalisation parameter in the svm ensures to a certain extent that the model can generalise to data not used in the training process.
after classifying the entire e. coli proteome to identify novel target-like proteins and pruning it to remove those that are similar to a human protein, we find  <dig> proteins that may be potential antibiotic targets, especially those  <dig> proteins highly similar to essential genes.
while  <dig> previously identified orphan genes can now be assigned to cell envelope biogenesis or translation, many are currently of unknown function and thus deserve further investigation.
the rules listed above and our list of potential new antibiotic targets may help in the identification of new antibiotics drug target.
CONCLUSIONS
comparison of antibiotic protein targets with non-target proteins from e. coli has allowed the identification of a number of properties that are desirable in drug targets.
even though the number of unique known targets is not large, statistically significant differences in property frequencies were found.
using sequence features for machine learning allows accurate identification of targets, as shown by a cross-validation accuracy of 94%.
applying the optimised support vector machine to the e. coli proteome identifies hundreds of proteins that have similar properties to known antibiotic drug targets.
these proteins may therefore be considered as potential new targets for novel antibiotics.

 infectious and parasitic diseases caused by pathogenic microorganisms, including bacteria, viruses and fungi, are major threats to human health. in particular, diseases commonly result from exposure to gram positive bacteria, such as staphylococcus aureus, streptococcus pneumoniae and group a streptococcus, and gram negative bacteria, such as e. coli and helicobacter pylori. antibacterial drugs are the major weapons to kill bacteria or suppress their activity. due to the inevitable evolution of antibiotic resistance, the development of novel antibiotics is essential. antibiotics work either by stopping bacterial growth or by killing the bacteria, without harming the human host. the following are the most common modes-of-action of antibiotics: inhibit synthesis of peptidoglycan. these antibiotics work by interfering with the synthesis of bacterial cell walls by either: blocking the transport of peptidoglycan monomers synthesized in the cytosol across the cytoplasmic membrane, inhibiting a transpeptidase and hence the formation of the peptide cross-links, or blocking both the transglycosidase and transpeptidase enzymes. the transglycosidases are essential for the formation of glycosidic bonds between sugars and transpeptidases are essential for the formation of peptide cross-links. alter the microbial cytoplasmic membrane. the polymixins are cationic peptides consisting of a cyclic peptide with a fatty acid chain. the interaction between the cationic peptide and the membrane causes disruption of the bacterial cell membrane and increases the permeability of cell components. alter translation. many antibiotics work by binding to bacterial ribosomes. examples of antibiotics that work by binding to the 30s ribosomal subunit are aminoglycosides and tetracyclines, which prevent the binding of trna. other macrolide antibiotics, such as erythromycin, bind to the 50s ribosomal subunit and block the exit tunnel of the bacterial ribosome. inhibit nucleic acid replication by blocking topoisomerases that are essential for supercoiling, bacterial dna replication and separation of circular bacterial dna. the fluoroquinolone antibiotic class contains potent inhibitors for topoisomerases or dna gyrase. inhibit transcription. some antibiotics, such as rifampin or rifampicin, work by binding to rna polymerase and inhibiting the transcription of dna to mrna. after the first widespread use of antibacterial drugs in the 1940s, bacterial pathogens started to develop resistance to existing drugs, particularly after excessive antibiotic use. the three basic mechanisms of bacterial resistance to antibiotics are: production of an enzyme to inactivate the antibiotic, such as a β-lactamase to hydrolyse penicillin. mutation in the target site receptor of the enzyme or the ribosomal subunit that leads to ineffective drug binding. alteration in transport proteins to prevent antibiotic entry or promote active efflux from the cell. there is thus an urgent need to discover new strategies to discover and develop effective antibiotic drugs to overcome widespread and growing antibiotic resistance. one way in which this may be achieved is by identifying bacterial proteins that may be the targets for new classes of antibiotics. sakharkar et al. addressed this problem by identifying essential bacterial genes that have no human homologues. here, we determine key properties of antibiotic target proteins and use machine learning to identify new potential targets.  primary sequence, secondary structure and post-translational properties all the proteins in the targets and non-targets data sets were analysed for their primary sequence properties and post-translational modifications. as all features showed a non-normal distribution using the kolmogorov-smirnov test, p-values were calculated using the mann-whitney test. table shows the differences between the mean values of length, hydrophobicity, secondary structure, transmembrane helices, signalp, low complexity regions, pi, amino acid preferences and post-translational properties for targets and non-targets. targets tend to be larger proteins with mean lengths of and amino acids for bacteria and e. coli respectively compared to for non-targets. bacterial targets show significant preferences for pro and val and disfavour trp, met, leu and gln. secondary structure analysis shows that bacterial targets have less α-helix and more β-sheet. the percentage of target helices is 32% compared to 42% for non-targets with significant p-values. the prediction of transmembrane helices shows that targets tend to have fewer helices compared to for non-targets. antibiotic and e. coli targets are less hydrophobic compared to non-targets with significant p-values. no statistically significant differences were observed for pi, signalp, netnglyc and netoglyc and lcr between the bacterial or e. coli targets and non-targets. this indicates that these features do not contribute to the druggability of a bacterial protein. this is expected for glycosylation, as the glycosylation site prediction programs are optimised for use in metazoa; indeed the predicted frequencies for this property are low. nevertheless, it is possible to use any sequence feature for machine learning. * not applied as the prediction program as bacterial targets are both gram positive and gram negative. targets were found to contain more polar, charged, basic and acidic amino acids. the mean frequency of positively charged amino acids was greater in e. coli and in all targets compared to non-targets. the mean frequency of polar amino acids in e. coli and all targets was higher compared to non-targets with a significant p-value of for all targets; similarly, there was a higher proportion of non-polar amino acids in non-targets compared to all targets with a p-value of for all targets. the frequencies of negative amino acids were greater for e. coli and all targets compared to non-targets. enzyme class the percentage of enzymes was found to be 33% in non-targets compared to 81% for e. coli and 71% for bacterial targets. figure shows high preferences for ligases in bacterial targets and for e. coli, compared to 5% for non-targets. ligases are common antibacterial drug targets as they are involved in the formation of the cell wall. lyases were not found in either the bacterial or e. coli target sets. subcellular location 43% of the non-targets, 40% of the e. coli targets and 47% of the bacterial targets have subcellular location annotations in swissprot. these percentages might be misleading due to bias of annotated sequences to a specific family or incomplete annotation. figure 2a shows that the majority of non-drug targets are localised to the bacterial cell membrane. the membrane location includes all entries that contain the word membrane such as membrane single-pass type ii, peripheral membrane protein, peripheral membrane protein, multi-pass membrane, cell membrane and cell inner membrane. the next highest preferences for drug targets are in the cytoplasm and periplasm. a prediction method was used to validate the results of subcellular location. a localization score is calculated for each protein, where the threshold is or greater. if two locations have high scores, the prediction is set to unknown. figure 2b shows the subcellular location results found using psortb. the prediction program performs a different analysis depending on whether the organism is gram-positive or gram-negative. hence the analysis was performed between the two gram-negative datasets of e. coli targets and non-targets. non-targets are localised in the cytoplasmic membrane more than the cytoplasm, and vice versa for e. coli targets. unknown assignments, where the program is unable to make a prediction, are frequent for both e. coli targets and non-targets. gene ontology additional file 1: supplemental figures s1a-f show that molecular functions at level and level are similar for bacterial targets, e. coli targets and non-targets. the distribution of biological process at level shows that terms such as metabolic process and cellular process are found with similar percentages in bacterial targets, e. coli targets and non-targets. response to antibiotic is unsurprisingly found in bacterial targets and e. coli targets, but not non-targets. terms including localization and establishment of localization are common for non-targets, but they are not associated with bacterial or e. coli targets. the distribution of biological process at level shows that primary metabolic process and cellular metabolic process have similar frequencies for both targets and non-targets. the regulation of cell shape term is present in both e. coli and in all bacterial targets, but no proteins are associated with this process in the non-targets. other processes favoured for bacterial and e. coli targets are biosynthetic process and cell wall organization. transport proteins are more prevalent for non-targets. within the cellular component class, the organelle and cell part terms are present with similar frequencies at level for bacterial targets, e. coli targets and non-targets. at level more bacterial and e. coli targets are localised in the intracellular part compared to non-targets. this class includes cytoplasm and cytoplasmic part. intracellular organelle and intracellular non-membrane bound organelle terms are strongly favoured for bacterial and e. coli targets compared to non-targets. prediction of new antibiotic targets the features described above were used with the support vector machine algorithm to develop a model that is able to assign any sequence to the e. coli target or non-target classes solely from protein sequence. we only used e. coli targets versus e. coli non-targets, rather than training all targets versus e. coli non-targets because differences to all targets may reflect species differences instead of differences between targets and non-targets. two parameters were varied to maximise the accuracy, namely the error penalty for an incorrect prediction and the radial basis function parameter which controls how smooth the boundary is in hyperspace between the target and non-target areas. the accuracy is defined as the overall probability that the prediction is correct. a perl program was coded to search for the optimal c and γ values by performing a coarse grid and a fine grid search. the optimal parameters for c and γ were found to be and, respectively. this model had an accuracy of % using features including amino acids, length, hydrophobicity, signalp, netnglyc, netolgyc-s, netoglyc-t, lcr, secondary structure, tmhmm and pi. this accuracy is based on 5-fold cross validation using e. coli targets as the positive training dataset and proteins for the e. coli non-targets. the area under curve in a roc plot was, confirming the high accuracy of the model. the optimised support vector machine algorithm was used to classify e. coli non-target sequences. of them were assigned as targets, suggesting that 7% of bacterial proteins have target-like properties. of these predicted e. coli targets, 35% are annotated as uncharacterised proteins and 8% as 50s or 30s ribosomal proteins. 8% of the new predicted targets are transferases and 5% are hydrolases, in broad agreement with annotation data. other ec classes were less abundant, with four isomerases, three oxidoreductases, two lyases and one ligase. the predicted e. coli target proteins were further analysed to check for similarity to human proteins. if any similarity is found, the predicted antibiotic targets may well be problematic, as their inhibition could lead to toxic effects in the patient. proteins were found to have maximal sequence similarities to a human protein of 25% to 63% using blast. removal of these proteins leaves potential new targets with no significant similarity to any human protein. the distance of a protein from the svm hyperplane boundary between the two sets gives a measure of the accuracy of the distribution of the data about the decision surface. the further the score is from zero, the more reliable is the prediction. figure illustrates the calculated distance of the e. coli targets and non-targets from the hyperplane, in intervals. a protein with a positive distance is classified as a target and vice versa. excellent separation between the two sets is achieved, with all targets classified correctly. the false negatives are the non-targets with a positive score. these are the most interesting proteins, as they are potential new antibiotic targets. the list of proteins was subjected to a blast search against the e. coli essential genes. 20% of the new predicted targets showed a high similarity to an essential gene where proteins had a sequence identity of 50% to 70% and proteins had a sequence identity > 70%. additional file shows the matching essential gene for each protein along with their p-values, identities and blast scores for each protein. these proteins are more likely to be successful antibiotic targets since their loss of function is likely to lead to cellular growth arrest or death. we do note, however, that there are beneficial e. coli colonies in humans, so targeting e. coli may have unwanted effects. hu et al. recently performed a detailed and thorough analysis of orphan e. coli proteins, giving many new functional assignments. in particular, they were able to assign orphans to translation and to bacterial cell envelope biogenesis, pathways that are known to be associated with antibiotic modes-of-action. when these are compared to our predicted targets, we find common proteins that are particularly promising targets, specifically: orphan and annotated proteins responsible for cell envelope biogenesis, and orphan and annotated proteins responsible for translation. discussion even though we have only a fairly small number of distinct targets in our data set, we still find many properties that are significantly different between antibiotic targets and non-targets. there was little difference between e. coli targets and those from all bacteria. targets are more likely to be enzymes than non-targets. major antibiotic mechanisms include inhibiting enzymes responsible for translation, transcription, replication and bacterial cell wall biosynthesis. inhibition of such mechanisms usually results in cell growth inhibition or bacterial death. the molecular function analyses of gene ontology and enzyme class data show a preference for ligases as targets, such as those involved in peptidoglycan biosynthesis. the biological process analysis shows that response to antibiotic, regulation of cell shape and cell wall organization terms are favoured for targets. the cellular components data showed a high preference for intracellular part targets, with the cytoplasm subdivision favoured in particular, perhaps because enzymes are abundant in the cytoplasm and despite the difficulty of a drug crossing the membrane. the membrane distribution for targets was lower that non-targets, supporting the predicted subcellular locations. while the annotated results had a higher preference for targets to be localised in the membrane, the predicted results had a higher preference for the cytoplasm. there are fewer predicted transmembrane helices in targets, supporting the subcellular location data. bacterial targets tend to be larger and more polar than non-targets. larger proteins have more potential surface of interaction between the drug and the target and may participate in more protein-protein interactions, thus having a larger effect when a drug binds. the presence of charged amino acids might facilitate the interaction between the drug and the target, as bonds to charged amino acids from target groups are common. positively charged proteins are more likely to be ribosomal, since they interact with strongly negatively charged rrna. studies on e. coli showed that 35% of metabolic enzymes, including amino acid, purine and pyrimidine biosynthesis, are non-essential genes for cell growth. this means that if one enzyme is knocked out from the pathway, the cell is able to function through alternative routes, including using isoenzymes and multifunction enzymes that can substitute for the missing enzymes. non-targets favour pathways such as amino acid and macromolecule biosynthesis, while targets show a preference for transcription and dna replication gene ontology terms. hence, metabolic enzymes are disfavoured as antibacterial drug targets. an ideal drug should have no similar protein in the host cell, as the drug might interact with it and lead to adverse effects in the patient. there exist some exceptions for this rule: trimethoprim is such an example of an antibiotic used to treat urinary tract infections. it works on inhibiting dihydrofolate reductase, despite the presence of a close human homologue. on the other hand, there are no guarantees that if there is no homology, toxicity is not observed. an example of that is the large ribosomal subunit which is targeted by chloremphenicol. in spite of the difference in the structure of the prokaryotic and eukaryotic ribosomes, a functionally conserved area is found in the ribosomes of mitochondria and bacteria resulting in side effects in patients. our previous work analysed desirable properties in human drug target proteins, so we can now compare target proteins in humans and bacteria. the properties differ greatly, reflecting entirely different modes-of-action, as well as differences between prokaryotes and eukaryotes. membrane proteins are common targets in humans, while they are rare as bacterial targets. n-glycosylation is more common in human targets sites, which implies that human targets proteins have a relative longer lifetime than bacterial targets and human non-targets. human targets are frequently enzymes involved with binding and signaling; bacterial targets are often enzymes, involved in protein binding or ribosomal proteins. the most targeted bacterial biological processes are peptidoglycan biosynthesis and cell wall synthesis, compared to cell communication and regulation of biological process for human targets. in summary, the following seem to be desirable features for an antibiotic drug target: • essential for the survival of the bacterial cell • no close human homologue • must be present in a number of pathogens if broad-spectrum action is required. narrow spectrum antibiotics target specific pathogens • the most targeted biological processes are peptidoglycan biosynthesis, cell wall synthesis, transcription and translation • capable of binding to a small molecule, implying the presence of a binding site • favoured to be localized in the cytoplasm and less frequent in membranes • favoured to be ribosomal proteins • more likely to be enzymes, transferases, hydrolases or ligase, but not lyases. although enzymes are highly targeted, metabolic enzymes are disfavoured for antibacterial drug targets. while many of the above properties have been reported in qualitative terms, they have not been previously quantified, to our knowledge. in addition, some of the preferences we find appear to be new, such as ec class, length, amino acid frequencies, post-translational modifications, secondary structure contents, pi, signalp and transmembrane helices. the machine learning work can accurately assign an e. coli protein to the target or non-target classes. the use of cross-validation and the generalisation parameter in the svm ensures to a certain extent that the model can generalise to data not used in the training process. after classifying the entire e. coli proteome to identify novel target-like proteins and pruning it to remove those that are similar to a human protein, we find proteins that may be potential antibiotic targets, especially those proteins highly similar to essential genes. while previously identified orphan genes can now be assigned to cell envelope biogenesis or translation, many are currently of unknown function and thus deserve further investigation. the rules listed above and our list of potential new antibiotic targets may help in the identification of new antibiotics drug target.  comparison of antibiotic protein targets with non-target proteins from e. coli has allowed the identification of a number of properties that are desirable in drug targets. even though the number of unique known targets is not large, statistically significant differences in property frequencies were found. using sequence features for machine learning allows accurate identification of targets, as shown by a cross-validation accuracy of 94%. applying the optimised support vector machine to the e. coli proteome identifies hundreds of proteins that have similar properties to known antibiotic drug targets. these proteins may therefore be considered as potential new targets for novel antibiotics. 
sequence features were used to build a support vector machine model for e. coli proteins, allowing the assignment of any sequence to the drug target or non-target classes, with an accuracy in the training set of 94%. we identified proteins in the non-target set that have target-like properties, many of which have unknown function. of these proteins have significant and undesirable similarity to a human protein, leaving target like proteins that are not present in humans. in particular, are essential genes where the cell is not able to recover from a random insertion disruption. we suggest that antibiotic discovery programs would be more likely to succeed if new targets are chosen from this set of target like proteins or their homologues. we analysed non-redundant antibiotic target proteins from all bacteria, antibiotic target proteins from e. coli only and non-drug targets from e. coli to identify differences in their properties and to predict new potential drug targets.
 we analysed non-redundant antibiotic target proteins from all bacteria, antibiotic target proteins from e. coli only and non-drug targets from e. coli to identify differences in their properties and to predict new potential drug targets.  when compared to non-targets, bacterial antibiotic targets tend to be long, have high β-sheet and low α-helix contents, are polar, are found in the cytoplasm rather than in membranes, and are usually enzymes, with ligases particularly favoured. sequence features were used to build a support vector machine model for e. coli proteins, allowing the assignment of any sequence to the drug target or non-target classes, with an accuracy in the training set of 94%. we identified proteins in the non-target set that have target-like properties, many of which have unknown function. of these proteins have significant and undesirable similarity to a human protein, leaving target like proteins that are not present in humans.  we suggest that antibiotic discovery programs would be more likely to succeed if new targets are chosen from this set of target like proteins or their homologues. in particular, are essential genes where the cell is not able to recover from a random insertion disruption. 