BACKGROUND
more than half a century has passed since the discovery that reactive electrophilic metabolites derived from xenobiotic agents covalently modify endogenous cellular proteins  <cit> .
since then such covalent binding by reactive metabolites has been strongly correlated with, and is widely believed to be responsible for, the acute organ-damaging effects of a wide range of xenobiotic agents including drugs and natural products  <cit> .
tissue injury is a complex phenomenon.
most tissues are comprised of more than one cell type, and macroscopic tissue injury often involves various secreted chemical mediators such as cytokines, tumor necrosis factor, nitric oxide and pro-inflammatory cells such as leukocytes and macrophages or kupffer cells.
thus it is important to note that the same compounds that can cause organ damage in vivo can also cause acute cytotoxicity, correlated to protein covalent binding, in isolated metabolically-competent cells in vitro.
it has generally been presumed that protein adduction impairs protein function, leading to disruption of metabolic or signaling pathways, organelle failure, loss of cellular homeostasis, aberrant cell-cell interactions, macroscopic tissue damage through necrosis and/or apoptosis, and in the extreme, organ failure and death.
we know that some compounds that give rise to covalently bound residues do not cause toxicity, i.e., none of their adducts trigger events leading to cytotoxicity.
likewise some, perhaps many, of the adducts of toxic compounds are ineffective at causing toxicity, but some of the adducts of toxic compounds do trigger events leading to cytotoxicity .
the challenge is to identify which adduct structure on which amino acid residue of which protein are important for toxicity, and the mechanism by which their appearance triggers toxicity while others do not.
despite extensive investigation, the mechanisms by which covalent binding events trigger cytotoxic outcomes remain largely unclear  <cit> .
a major reason for this gap is that only recently has it become technically feasible to identify numbers of individual proteins targeted by xenobiotic reactive metabolites.
early target protein identifications were based on isolating individual adduct-bearing proteins, one at a time, using traditional protein separation methods.
by  <dig>  only  <dig> proteins targeted by xenobiotic reactive metabolites had been isolated and identified, largely by n-terminal sequencing  <cit> .
in  <dig>  however, the coupling of 2d gel electrophoresis with mass spectrometric methods of protein identification literally revolutionized the field  <cit> .
since then both the number of known target proteins and the number of small-molecule adduct-forming xenobiotics studied have increased nearly ten-fold.
to help keep track of this information and to facilitate its analysis, we recently built the reactive metabolite target protein database   <cit> .
all proteins listed in this database came from studies using live animals or intact living cells.
as the number of known target proteins grew, so did the hope of elucidating mechanistic pathways connecting covalent adduction events to the observed cytotoxic outcomes.
for example, we and others  <cit>  have attempted to make sense of lists of target proteins by arbitrarily grouping them into categories according to function, but this approach has done little to reveal to a unifying mechanism of toxicity caused by a variety of reactive metabolites  <cit> .
another way to analyze target proteins is to sort them into gene ontology  categories  <cit>  and determine whether any show an over-abundance of target proteins relative to statistical expectations.
similarly, kegg biological pathway analysis  <cit>  can identify metabolic pathways in which target proteins are over-represented.
such analyses could potentially indicate a functional connection between target protein adduction and biological consequence in the context of systems biology  <cit> .
in living cells, proteins interact extensively with other proteins, forming protein-protein interaction  networks that sense and respond to the abundance or status of other network proteins.
since endogenous post-translational modifications of proteins are well-known to perturb ppi networks comprising intracellular signaling cascades  <cit> , it is plausible to hypothesize that protein modification through adduction by xenobiotic reactive metabolites could constitute an aberrant form of signaling leading to cytotoxic consequences.
thus, inspection of the interacting partners of reactive metabolite target proteins might shed new light on the path from protein adduction to cytotoxicity.
in addition to the question of how protein adduction leads to cytotoxicity, it is also of interest to know what features of a protein, beyond simple abundance, determine whether or not it is likely to be a target for reactive metabolites.
electrophilic xenobiotic metabolites can be classified broadly as having acylating or alkylating activity  <cit> .
the former tend to attack lysine side chains, while the latter tend to attack predominantly cysteine, histidine and lysine side chains.
despite the commonplace occurrence of these side chains in most proteins, it is well established that protein adduction in living cells is remarkably selective, with some abundant proteins experiencing little adduction while some low-abundance proteins experience high levels of adduction  <cit> .
nevertheless, the protein features that determine susceptibility to adduction are almost completely unknown  <cit> .
since many protein features can now be calculated or predicted using software programs, the analysis of target proteins using feature selection algorithms could potentially shed light on this important question  <cit> .
feature selection algorithms operate differently from conventional statistical  studies of individual features considered independently from each other.
such algorithms can identify which features among many contribute the most to determining a complex behavior such as relative susceptibility to adduction by electrophiles.
in this paper we report our efforts to use bioinformatics approaches to elucidate the interactions between reactive metabolites, their cellular target proteins, and the other proteins that interact directly with the target proteins.
in brief, we first analyzed  <dig> proteins targeted by reactive metabolites from one or more of  <dig> different protoxins and found that a number of go categories and kegg pathways were significantly enriched with some of these target proteins.
we then selected  <dig> proteins known to be adducted by reactive metabolites of at least  <dig> different protoxins and found that  <dig> of them had a total of  <dig> directly interacting partners.
go and kegg pathway analysis of the combined  <dig> proteins revealed several categories to be highly significantly enriched by target proteins and/or their directly interacting partner proteins.
finally, we applied machine learning methods to analyze the properties of  <dig> rat liver proteins targeted by reactive metabolites of thiobenzamide and  <dig> rat liver proteins targeted by reactive metabolites of bromobenzene, in an effort to identify properties that help to distinguish whether a protein is likely to be a target of reactive metabolites.
methods
the identities of the target proteins used in this study, and the structures and names of the protoxins whose metabolites bind to them, are freely available from the reactive metabolite target protein database  <cit> .
for the gene ontology and kegg pathway analyses we used all  <dig> proteins listed in the tpdb as of february  <dig>  for the protein-protein interaction analysis we used the "rank by number of hits" function within the tpdb to select the  <dig> proteins most commonly targeted by different reactive metabolites.
for the machine learning study we used  <dig> proteins targeted by thiobenzamide metabolites,  <dig> proteins targeted by bromobenzene metabolites, and  <dig> proteins targeted by both .
to create a negative learning dataset, all rat proteins in uniprotkb  <cit>  were downloaded on may  <dig>   <dig>  a software program cd-hit  <cit>  was used to filter redundant sequences in the dataset at the level of 80% identity.
we also eliminated all sequences with lengths less than  <dig> residues and subtracted all known target proteins to arrive at a set of  <dig> proteins in the negative  learning dataset.
although the list of target proteins is incomplete, the percentage of all proteins in living cells that actually become detectably adducted by reactive metabolites is relatively small .
thus the vast majority of proteins in the negative dataset are considered as non-target proteins.
in addition, the random forest algorithm used to analyze target vs. non-target proteins  is relatively tolerant of small amounts of "noise" in the data.
software programs and databases used in the study
the gene ontology project  <cit>  is a collaborative effort to develop standard vocabularies  that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner.
the controlled vocabularies are hierarchically structured  so that they can be queried at different levels.
kegg  <cit>  is a widely used database of biological pathways.
the january  <dig> version of the human protein reference database  was downloaded from the hprd website  <cit> .
the binary protein-protein interactions  were then imported into cytoscape, an open source bioinformatics software platform for visualizing and analyzing biological interaction networks .
the cytoscape plugin bingo was used to determine and visualize statistically overpopulated go categories in a set of genes  <cit> .
random forest models and feature extraction
we used a random forest package  <cit>  implemented in the "r" environment  <cit>  for this study.
since random forest models are usually insensitive to the model parameters, we used the default parameters.
values for a set of  <dig> protein features were calculated using various software programs or in-house scripts .
these features include protein composition in terms of amino acid residues, predicted secondary structures, solvent accessibility, and others.
although the predictive model might have benefited from incorporating elements of protein three-dimensional structure, such information is not available for the vast majority of target proteins.
therefore we used only sequence information and predicted secondary structures.
since the sequence of a protein is a major determinant of its structure, it is expected that features calculated from sequence information alone may be sufficient to distinguish target proteins from other proteins.
software programs for predicting a broad spectrum of other protein properties such as secondary structure, solvent exposure, instability index and others are mature and have found many applications  <cit> .
tripeptide features
the dipeptide or tripeptide composition of proteins has been found to correlate to a number of protein properties.
for example, guruprasad et al.
found a correlation between the dipeptide composition and the stability of proteins  <cit> .
more recently, tripeptide composition was applied in a machine learning model to predict protein-protein interactions  <cit> .
in this study, the  <dig> amino acids were sorted into just five groups according to their physicochemical properties: hydrophobic, gavlimpfw; polar, ytsnq; positive, rkh; negative, de; the fifth group contained only cysteine because of its unique ability to form disulfide bonds.
therefore the number of possible tripeptide features according to this classification scheme is 5*5* <dig> or  <dig>
ranking feature importance
the random forest approach offers several methods to assess the importance of features based on their contributions to the correctness of the resulting classification.
these methods include i) the mean decrease in accuracy for each class, ii) the mean decrease in accuracy over all classes, and iii) the mean decrease in the gini impurity criterion  <cit> .
in this study we used the mean decease in accuracy for the minor class  because in previous work we found it to give more accurate results than other measures for ranking feature importance  <cit> .
performance evaluation
for imbalanced data , the overall classification accuracy is not an appropriate measure of performance because very high accuracy can be achieved simply by predicting every case as the majority class  as opposed to predicting the minority class .
in this study, we used three indicators of performance: sensitivity, specificity, and area under the curve  for the receiver operating characteristic  curve.
we ranked all cases in the dataset according to the predicted likelihood of positive status and then employed that rank order to identify correctly classified true positives.
we then used these results to generate an roc curve  against the false positive rate ).
the area under the roc curve represents the trade-off between sensitivity and specificity over the whole range of data.
an auc of  <dig> represents a perfect prediction model while an auc ≥  <dig>  is considered excellent, an auc between  <dig>  and  <dig>  is considered good, and an auc in the range of  <dig> – <dig>  is fair.
RESULTS
bioinformatic analysis of known target proteins
to identify go terms significantly enriched with target proteins we used bingo  <cit> , a plugin of the cytoscape software suite  <cit> , to map target proteins to go categories.
bingo uses a hypergeometric test to search for predominant categories in terms of p-values and go diagrams.
for this experiment we used all  <dig> target proteins in the tpdb  and found that  <dig>   <dig> and  <dig> of them, respectively, were represented  in the molecular function, biological process, and cellular component categories of the gene ontology classification system .
a complete listing of terms having a false discovery rate  <  <dig> e- <dig> is presented in additional file  <dig>  while graphical representations of enriched go categories and their hierarchical structures are presented in additional files  <dig> and  <dig>
a go analysis was performed using bingo in cytoscape.
the rat proteome was used as the background for comparison.
a complete listing of terms having a false discovery rate  <  <dig> e- <dig> is presented in table s <dig> .
also available in the additional files are graphic representations of enriched go categories and their hierarchical structures .
of the  <dig> known targets,  <dig> sort into the broad molecular function subcategory of catalytic activity, while only  <dig> sort into the much more specifically-defined subcategory of antioxidant activity.
nevertheless, both results are highly significant statistically.
likewise,  <dig> of the  <dig> target proteins sort into the broad biological process subcategory of "metabolic process" while only  <dig> sort into the subcategory of "xenobiotic metabolic process."
again, both of these results are highly significant statistically.
however, the results of sorting target proteins into various cellular component subcategories must be viewed with some caution for several reasons.
first, some target protein identification studies analyzed whole tissue samples whereas others analyzed only selected subcellular fractions of tissue homogenates.
second, a majority of the enzymes associated with bioactivation of xenobiotics to reactive metabolites are located in the endoplasmic reticulum  of the cell.
for certain metabolites , the reactivity of the metabolite may limit its ability to diffuse from the site of formation to other sites within the cell.
to map target proteins to kegg pathways we used david bioinformatics resources, an online database for annotation, visualization and integrated discovery developed by niaid nih  <cit> .
we found that of the  <dig> known target proteins,  <dig> are associated with one or more kegg pathways,  <dig> of which are specifically enriched  compared to statistical expectations .
pathways involved in glycolysis, gluconeogenesis and glutathione metabolism pathways are among the most enriched.
apathway mapping was performed using david, and  <dig> of  <dig> target proteins could be mapped to one or more kegg pathways.
the subset of rat proteins that are allocable to kegg pathways includes  <dig> proteins.
analyses such as those presented above are based on the principles of statistics; thus the observed enrichments of target proteins in certain go categories and kegg pathways are a priori significant, at least in the statistical sense.
for example,  <dig> of the  <dig> target proteins in the molecular function go category sorted into the catalytic activity subcategory .
although this result is highly significant statistically , these  <dig> targets represent only 2% of all proteins in this broad subcategory.
thus, regardless of its statistical significance, this finding can at best hint at a biologically significant mechanistic connection to toxicology.
in contrast, only  <dig> of the  <dig> target proteins sorted into the peroxiredoxin activity subcategory.
this result too is highly significant statistically , but since this entire subcategory contains only  <dig> proteins, the finding that five of them  are targeted by reactive metabolites suggests that perturbation of this molecular function by protein adduction could perhaps be significant biologically.
a number of other molecular function and biological process go terms that are overpopulated with target proteins have been individually implicated in the context of reactive metabolite toxicity .
likewise  <dig> of the <dig> target proteins  were also enriched in certain kegg pathways , some of which  have also been linked to potential mechanisms of chemical cytotoxicity.
collectively, the highlighting of certain go terms and kegg pathways via the more independent and more comprehensive bioinformatics approach taken here is intriguing and encouraging.
while findings such as this can help to formulate or reinforce hypotheses about mechanisms of reactive metabolite cytotoxicity, each such hypothesis will need more extensive testing before it can be taken seriously as a significant contributor to the overall mechanism of cytotoxicity.
in the end, however, it must be conceded that the systematic global analyses of all known target proteins affords only a modest advance toward elucidation of mechanisms beyond that afforded by gazing at lists of arbitrarily-grouped target proteins of single protoxicants  <cit> .
to go beyond this limitation, we searched for and analyzed non-target proteins that interact directly with known target proteins, as described below.
bioinformatic analysis of common target proteins and their interacting partners
protein-protein interaction data analysis
from the total set of  <dig> target proteins in the tpdb we selected  <dig> rat or mouse proteins that are common targets for multiple reactive metabolites .
because published ppi data for rat and mouse are quite sparse, whereas extensive ppi data are available for human proteins, we first found the human orthologs of these  <dig> rat or mouse target proteins by searching against human proteins in the ncbi non-redundant protein database  using the online ncbi blast server.
the search results were manually inspected and the most significant matches were selected .
the minimum and average percent identity of the target proteins and their human homologs is 69% and 88%, respectively.
the maximum difference in sequence lengths for these protein pairs is only  <dig> %.
thus, the selected human proteins are very likely to be orthologs of the target proteins.
we then searched for the respective interacting partners of the target protein orthologs in the human protein reference database  <cit> ; this approach is based on the common assumption that ppis are conserved across species  <cit> .
we found a total of  <dig> proteins to be directly interacting partners to  <dig> of the  <dig> target protein orthologs.
a full list of these interacting proteins is available in the additional materials .
a no interacting partners were found for  <dig> of the  <dig> proteins.
see additional file  <dig> for full listing of all the interacting partners found.
go and kegg pathway analysis of common targets and their interacting partners
we used bingo to identify go categories significantly overpopulated with target proteins or their first interacting partners.
of the  <dig> target proteins and direct partners analyzed,  <dig> link to one or more go terms.
as summarized in table  <dig>  highly significant enrichment was observed in the subcategories of protein folding, unfolded protein binding, response to unfolded protein, and apoptosis.
in addition to the statistical significance of the sorting results, the fact that adduction affected a relatively large fraction of the proteins in these categories suggests that these results may also be biologically significant in terms of the degree to which important processes involving these proteins may be impaired or altered by target protein adduction.
of the  <dig> target proteins plus partners, we found  <dig> to be involved in one or more kegg pathways, eight of which are significantly overpopulated compared to statistical expectations .
among them, the map kinase signaling pathway had the most significant enrichment.
this result is particularly intriguing, since this association was not found when we analyzed only the reactive metabolite target proteins themselves without including their interacting partners.
apparently, looking beyond just target proteins and considering their first interacting partners may provide a deeper look into potential mechanisms of cytotoxicity.
the potential indirectness of analyzing human orthologs of rat and mouse target proteins  was offset by focusing on just  <dig> proteins known to be targeted by multiple different reactive metabolites.
comparing the results of table  <dig> to those of table  <dig> shows that the go molecular function subcategory "unfolded protein binding" was again highlighted, but this time with much higher statistical significance  and much higher fractional coverage of the class .
the significance and coverage of the biological process "protein folding" are greatly increased in table  <dig> vs. table  <dig>  in addition, table  <dig> also lists two terms having both high statistical significance and high fractional coverage that are absent from table  <dig>  namely, "apoptosis" and "response to unfolded protein," both of which have high relevance to cytotoxicity.
in terms of kegg pathway analysis of targets and their partners , the flagging of the map kinase pathway is particularly noteworthy.
this finding is consistent with independent experimental evidence that also strongly supports a role for this pathway in apoptosis and other pathological cellular responses to toxic chemicals  <cit> .
there is also considerable interest in the involvement of the immune system in some forms of reactive metabolite-mediated toxicity in vivo  <cit> .
the flagging of four pathways related to the nervous system was unexpected, and will require further analysis to evaluate.
nevertheless, it appears that our approach of considering not just target proteins but also the proteins with which they interact in the cell may provide clues to downstream events important to cytotoxicity and thereby help in elucidating mechanisms of cytotoxicity.
stated another way, it is perhaps essential ppis rather than proteins per se that are the important targets of reactive metabolites in living cells.
machine learning approaches to elucidate factors that differentiate target from non-target proteins
in addition to identifying the target proteins whose modification appears to initiate cytotoxic responses, we also examined them more specifically to elucidate protein features that predict susceptibility to reactive metabolites.
because the reactive metabolites of the  <dig> protoxins included in the tpdb span a wide range of chemical reactivity and hydrophobicity, and because for many of them only a few target proteins are known, we decided to use the set of  <dig> proteins that are targeted in vivo by thiobenzamide metabolites , bromobenzene metabolites  or both  for this study.
thiobenzamide forms a reactive acylating metabolite with a very strong preference for reaction with amine side chains on lysine  <cit>  or phosphatidyl-ethanolamine  lipids  <cit> .
on the other hand, prior analyses of proteins adducted by bromobenzene metabolites indicates a strong preference for adduction on cysteine sulfhydryls  <cit>  as opposed to lysine or histidine  <cit>  or pe lipids  <cit> .
thus, one might expect the composition of the proteins targeted by these two metabolites to reflect their differing chemical selectivities to at least some extent.
to investigate this we used the random forest algorithm developed by breiman  <cit> .
random forest is an ensemble approach that combines many individual classifiers to formulate a robust composite classifier.
it is particularly suitable in classifying high-dimensional and noisy data, and it can handle a mixture of both categorical and continuous predictors such as those in table  <dig>  each of the classifiers is built on a bootstrap sample of the data and utilizes a random subset of the available variables  without pruning to obtain low-bias trees.
the random forest algorithm has been applied to a broad range of classification tasks and has demonstrated superior performance compared to other classification algorithms  <cit> .
we used five-fold cross validation to estimate the performance of the classification models.
in brief,  <dig> target proteins and the  <dig> rat proteins taken as a negative control set  were randomly split into  <dig> equal portions.
one portion was reserved as a test data set while the other four were pooled and used as a training set.
a random forest classifier with  <dig>  trees was built using  <dig> protein features  and the performance of the model was then evaluated using the reserved dataset.
this process was repeated four times, each time starting with a different one of the five subsets of proteins as the test set.
the results from all five repeats were then combined to afford an overall performance estimation.
for our model the roc curve in figure  <dig> has an auc of  <dig> , while the specificity and sensitivity of the model are  <dig>  and  <dig> , respectively.
thus this simple model appears to have fairly good predictive power.
the relative importance of the  <dig> protein features used in the model was ranked using the random forest algorithm and the top  <dig> are displayed in figure  <dig>  we also estimated the performance of models built using only these top  <dig> features.
the roc curve of this truncated model has an auc of  <dig> , somewhat inferior to the auc of  <dig>  for the model using all features, suggesting, not surprisingly, that protein targeting by reactive metabolites is a complicated process that depends on many factors.
interestingly, the most decisive feature of the full model is the percentage of lysine in the proteins.
this is consistent with the fact that the reactive iminosulfinic acid metabolite of thiobenzamide reacts preferentially with amine groups such as those on lysine side chains.
figure 3a shows a plot of the cumulative distribution of lysine in thiobenzamide target vs. non-target proteins.
this plot indicates, as might be expected, that lysine residues are more common among thiobenzamide targets than non-target proteins, but surprisingly, the same is true for bromobenzene target proteins.
figure 3b shows a similar plot for the distribution of cysteine among target vs. non-target proteins.
surprisingly, both sets of target proteins actually show somewhat lower cysteine content than the non-target proteins, and again there is no apparent difference between thiobenzamide vs. bromobenzene targets despite the extremely different chemical reactivities of their respective reactive metabolites toward specific protein nucleophiles.
consistent with the results of figure 3b, figure  <dig> also indicates that cysteine content is a relatively unimportant factor for distinguishing target from non-target proteins in vivo, at least for reactive metabolites of bromobenzene and thiobenzamide.
dennehy et al.
investigated the reaction of two model cysteine-reactive electrophiles  with proteins from the nuclear and cytosolic fractions of hek <dig> cells  <cit> .
they found that 89% and 85%, respectively, of  <dig> proteins that became adducted were selectively adducted at only one or two cysteines per protein.
the overlap of target proteins hit by iab vs. bmcc is only about 20%.
in an attempt to explain this selectivity they analyzed the frequency of occurrence of lysine, arginine, histidine or a second cysteine within the first five residues on either side of the adducted cysteine.
some differences in the frequencies were noted for target vs. non-target proteins, but unfortunately, the most likely reasons for the selective reactivity of only a few cysteines among many, namely, protection by intramolecular disulfide formation and/or steric occlusion within the protein, could not adequately be evaluated because such structural information is generally not available for a majority of proteins.
in our analysis of features of target proteins we used only two large sets of targets, one from a lysine-selective metabolite and one from a cysteine-selective metabolite.
the comparative unimportance of cysteine  in differentiating target proteins from non-target proteins probably indicates that while a given reactive metabolite may require a specific type of target nucleophile, the target site is only one factor, and a possibly not a sufficient factor overall, for differentiating target from non-target proteins.
the second most important feature of target proteins is the percentage of aspartic acid.
aspartic acid residues are generally not considered to be nucleophilic target sites for most reactive electrophilic metabolites.
it is possible that the higher content of aspartic acid provides negative charges to balance the positive charges resulting from a higher percentage of lysine in target proteins; however, the equally negatively charged glutamic acid did not play a significant role in distinguishing target proteins .
moreover, we find that the correlation between the content of lysine vs. aspartic or glutamic acid in the non-target set is insignificant , in agreement with the wide range of pi values that proteins span.
clearly more work will be required to understand why some abundant proteins receive little adduction while some low-abundance proteins become more heavily adducted by a given reactive metabolite.
the third most important feature of target proteins is their predicted index of instability .
if we consider proteins with a calculated instability index ≥  <dig> as unstable, and those with an index <  <dig> as stable  <cit> , then  <dig> out of the  <dig> target proteins  are classified as stable while only 26% of the proteins in the negative learning set  are classified as stable .
the relative instability of a covalent metabolite-protein adduct can arise from 1) chemical instability of the covalent linkage, 2) a high intrinsic rate of turnover of the protein itself, or 3) an enhancement of protein turnover induced by adduction.
a priori, the stability of a protein can influence both the detection and analysis of its adducted forms and the cellular consequences of its adduction .
surveys of target proteins are usually conducted at just a single time after dosing, chosen to maximize the total amount of covalent binding.
proteins whose turnover is rapid relative to the endpoint time will thus experience a lower exposure to reactive metabolite and a greater likelihood of removal by degradation, resulting in a lower apparent rate of adduction compared to proteins that turnover slowly.
if adduction flags a protein for accelerated destruction, the result will be the same.
this may be one reason that adduct densities vary so widely across a target proteome .
if adduct density is lowered sufficiently , the protein may then be missed altogether as a target.
on the other hand, since a relatively high proportion of target proteins turn over less rapidly than non-target proteins, their adduction may result in a more persistent perturbation to endogenous ppis and signaling mechanisms and may therefore contribute more to cytotoxicity.
interestingly, lin et al.
<cit>  recently found a major difference between iab and bmcc in terms of adduct stability in vivo .
both agents form covalent adducts that do not dissociate chemically or in cell-free fractions, but in living cells, the extent of protein adduction by iab increases continuously over  <dig> hours, whereas adducts formed by bmcc peak at around  <dig> minutes after exposure and diminish rapidly thereafter.
their disappearance is strongly retarded at 4°c, suggesting that the disappearance may be enzyme mediated.
two other acetamide/maleimide pairs showed similar differences in protein adduct stability.
lin et al.
noted that the rapid clearance of maleimide adducts coincided with their relative lack of toxicity and suggested that protein adduct stability is a critical requirement for the induction of cellular responses.
their observation of declining adduct levels over time can be explained by reversal of the adduction process, and/or by the degradation of protein molecules bearing adducts.
further work will clearly be required to address these possibilities, but it is encouraging that results of both bioinformatic and laboratory analysis of target proteins are pointing in the same direction with respect to the role and importance of protein turnover and adduct persistence in eliciting cytotoxicity.
CONCLUSIONS
the covalent binding of reactive metabolites to cellular proteins has long been associated with the production of acutely cytotoxic effects.
the past several years have witnessed much progress toward identifying their reactive metabolites and the specific intracellular proteins that become adducted, often highly selectively, by reactive metabolites from a number of different protoxicants.
for the better-studied protoxicants, much is known about the structures and reactivities of their reactive metabolite, the structures of the adducts they form on proteins, and the identities of many of their protein targets  <cit> .
for technical reasons it has proven challenging to elucidate which specific residues on a given target protein become modified  <cit> , but progress is being made in this area as well  <cit> .
the challenge has been to use the available information to discover factors that govern the selectivity of adduction , and to discover mechanisms that link protein adduction to cytotoxicity.
as of this writing, the tpdb lists  <dig> compounds whose reactive metabolites present widely varying chemical reactivities and hydrophobicities, and a total of  <dig> proteins that are modified by reactive in animals or living cells in vitro  <cit> .
however, for only  <dig> of these  <dig> compounds, one of which is not cytotoxic, are  <dig> or more target proteins known, while for  <dig> other compounds fewer than  <dig> target proteins are known.
thus, considering the breadth of the phenomenon, the descriptive data are still rather sparse.
little commonality of targets among different protoxins is apparent, and analyses of target proteins per se have failed to illuminate mechanisms linking covalent binding to toxic outcomes.
because proteins in cells interact specifically and extensively with other proteins, we hypothesized that xenobiotic adduction might disrupt endogenous ppis and signaling pathways vital to cellular homeostasis and survival.
in the current work we found that the human orthologs of  <dig> common rat or mouse target proteins have  <dig> direct-interacting partners that participate in a total of  <dig> ppis.
these  <dig> proteins are significantly concentrated in several go categories and kegg pathways that experimental studies by others have shown to be highly relevant to cell signaling and cell survival.
this suggests that compared to direct analysis of target proteins, further bioinformatic analysis of proteins that interact with greater numbers of target proteins may be able to point toward more fruitful areas for generating and testing hypotheses about mechanisms of toxicity.
finaly, given the chemical and structural diversity of cellular proteins, it seems unlikely that simple principles of chemical reactivity will in themselves play an important role in differentiating target from non-target proteins.
as shown by studies with bromophenol, m-hydroxyacetanilide, and mycophenolic acid, not all protein adduction has toxic concequences.
it seems equally likely that among adducts generated from toxic compounds, only some but not all will have toxic consequences.
it will take time and much more detailed information about protein adduction in living cells to sort this out, and the outcome is likely to point to a number of mechanistic paths from protein modification to cellular impairment or death.
in the meantime, one aspect of adduct chemistry that experiments and bioinformatic analysis both suggest may be important is the persistence of the adducted protein in the cell.
unstable adducts that dissociate, and adducted proteins that turnover rapidly, may be less effective at perturbing cellular homeostasis and injuring the cell than more durable adducts.
authors' contributions
all authors contributed to the design of the three sub-projects, the interpretation of the results, and the drafting and production of the manuscript.
bioinformatic analyses were conducted by jf.
all authors read and approved the final manuscript.
supplementary material
additional file 1
figure s <dig>  graphic representation of enriched go biological process categories and their hierarchical structure of the  <dig> target proteins.
heirarchial tree diagram showing the names and relationships of the go biological process categories in which members of a set of  <dig> reactive metabolite target proteins are statistically over-represented.
click here for file
additional file 2
figure s <dig>  graphic representation of enriched go molecular function categories and their hierarchical structure of the  <dig> target proteins.
heirarchial tree diagram showing the names and relationships of the go molecular function categories in which members of a set of  <dig> reactive metabolite target proteins are statistically over-represented.
click here for file
additional file 3
table s <dig>  human orthologs of  <dig> common rat/mouse target proteins.
accession numbers of  <dig> common rat/mouse target proteins and their human orthologs, and their degree of similarity.
click here for file
additional file 4
table s <dig>  first-partners of  <dig> common target proteins.
accession numbers of  <dig> common rat/mouse target proteins and their human orthologs, and their degree of similarity.
accession numbers and gene symbols of the  <dig> directly-interacting partners found for  <dig> common rat and mouse target proteins.
click here for file
additional file 5
table s <dig>  significantly over-populated go terms.
go id numbers and names of molecular function, biological process and cellular component categories in which members of a set of  <dig> reactive metabolite target proteins are statistically over-represented.
click here for file
acknowledgements
this work was supported by nih grant gm- <dig>  and the kansas-inbre .

 more than half a century has passed since the discovery that reactive electrophilic metabolites derived from xenobiotic agents covalently modify endogenous cellular proteins. since then such covalent binding by reactive metabolites has been strongly correlated with, and is widely believed to be responsible for, the acute organ-damaging effects of a wide range of xenobiotic agents including drugs and natural products. tissue injury is a complex phenomenon. most tissues are comprised of more than one cell type, and macroscopic tissue injury often involves various secreted chemical mediators such as cytokines, tumor necrosis factor, nitric oxide and pro-inflammatory cells such as leukocytes and macrophages or kupffer cells. thus it is important to note that the same compounds that can cause organ damage in vivo can also cause acute cytotoxicity, correlated to protein covalent binding, in isolated metabolically-competent cells in vitro. it has generally been presumed that protein adduction impairs protein function, leading to disruption of metabolic or signaling pathways, organelle failure, loss of cellular homeostasis, aberrant cell-cell interactions, macroscopic tissue damage through necrosis and/or apoptosis, and in the extreme, organ failure and death. we know that some compounds that give rise to covalently bound residues do not cause toxicity, i.e., none of their adducts trigger events leading to cytotoxicity. likewise some, perhaps many, of the adducts of toxic compounds are ineffective at causing toxicity, but some of the adducts of toxic compounds do trigger events leading to cytotoxicity. the challenge is to identify which adduct structure on which amino acid residue of which protein are important for toxicity, and the mechanism by which their appearance triggers toxicity while others do not. despite extensive investigation, the mechanisms by which covalent binding events trigger cytotoxic outcomes remain largely unclear. a major reason for this gap is that only recently has it become technically feasible to identify numbers of individual proteins targeted by xenobiotic reactive metabolites. early target protein identifications were based on isolating individual adduct-bearing proteins, one at a time, using traditional protein separation methods. by only proteins targeted by xenobiotic reactive metabolites had been isolated and identified, largely by n-terminal sequencing. in however, the coupling of 2d gel electrophoresis with mass spectrometric methods of protein identification literally revolutionized the field. since then both the number of known target proteins and the number of small-molecule adduct-forming xenobiotics studied have increased nearly ten-fold. to help keep track of this information and to facilitate its analysis, we recently built the reactive metabolite target protein database. all proteins listed in this database came from studies using live animals or intact living cells. as the number of known target proteins grew, so did the hope of elucidating mechanistic pathways connecting covalent adduction events to the observed cytotoxic outcomes. for example, we and others have attempted to make sense of lists of target proteins by arbitrarily grouping them into categories according to function, but this approach has done little to reveal to a unifying mechanism of toxicity caused by a variety of reactive metabolites. another way to analyze target proteins is to sort them into gene ontology categories and determine whether any show an over-abundance of target proteins relative to statistical expectations. similarly, kegg biological pathway analysis can identify metabolic pathways in which target proteins are over-represented. such analyses could potentially indicate a functional connection between target protein adduction and biological consequence in the context of systems biology. in living cells, proteins interact extensively with other proteins, forming protein-protein interaction networks that sense and respond to the abundance or status of other network proteins. since endogenous post-translational modifications of proteins are well-known to perturb ppi networks comprising intracellular signaling cascades, it is plausible to hypothesize that protein modification through adduction by xenobiotic reactive metabolites could constitute an aberrant form of signaling leading to cytotoxic consequences. thus, inspection of the interacting partners of reactive metabolite target proteins might shed new light on the path from protein adduction to cytotoxicity. in addition to the question of how protein adduction leads to cytotoxicity, it is also of interest to know what features of a protein, beyond simple abundance, determine whether or not it is likely to be a target for reactive metabolites. electrophilic xenobiotic metabolites can be classified broadly as having acylating or alkylating activity. the former tend to attack lysine side chains, while the latter tend to attack predominantly cysteine, histidine and lysine side chains. despite the commonplace occurrence of these side chains in most proteins, it is well established that protein adduction in living cells is remarkably selective, with some abundant proteins experiencing little adduction while some low-abundance proteins experience high levels of adduction. nevertheless, the protein features that determine susceptibility to adduction are almost completely unknown. since many protein features can now be calculated or predicted using software programs, the analysis of target proteins using feature selection algorithms could potentially shed light on this important question. feature selection algorithms operate differently from conventional statistical studies of individual features considered independently from each other. such algorithms can identify which features among many contribute the most to determining a complex behavior such as relative susceptibility to adduction by electrophiles. in this paper we report our efforts to use bioinformatics approaches to elucidate the interactions between reactive metabolites, their cellular target proteins, and the other proteins that interact directly with the target proteins. in brief, we first analyzed proteins targeted by reactive metabolites from one or more of different protoxins and found that a number of go categories and kegg pathways were significantly enriched with some of these target proteins. we then selected proteins known to be adducted by reactive metabolites of at least different protoxins and found that of them had a total of directly interacting partners. go and kegg pathway analysis of the combined proteins revealed several categories to be highly significantly enriched by target proteins and/or their directly interacting partner proteins. finally, we applied machine learning methods to analyze the properties of rat liver proteins targeted by reactive metabolites of thiobenzamide and rat liver proteins targeted by reactive metabolites of bromobenzene, in an effort to identify properties that help to distinguish whether a protein is likely to be a target of reactive metabolites. methods the identities of the target proteins used in this study, and the structures and names of the protoxins whose metabolites bind to them, are freely available from the reactive metabolite target protein database. for the gene ontology and kegg pathway analyses we used all proteins listed in the tpdb as of february for the protein-protein interaction analysis we used the "rank by number of hits" function within the tpdb to select the proteins most commonly targeted by different reactive metabolites. for the machine learning study we used proteins targeted by thiobenzamide metabolites, proteins targeted by bromobenzene metabolites, and proteins targeted by both. to create a negative learning dataset, all rat proteins in uniprotkb were downloaded on may a software program cd-hit was used to filter redundant sequences in the dataset at the level of 80% identity. we also eliminated all sequences with lengths less than residues and subtracted all known target proteins to arrive at a set of proteins in the negative learning dataset. although the list of target proteins is incomplete, the percentage of all proteins in living cells that actually become detectably adducted by reactive metabolites is relatively small. thus the vast majority of proteins in the negative dataset are considered as non-target proteins. in addition, the random forest algorithm used to analyze target vs. non-target proteins is relatively tolerant of small amounts of "noise" in the data. software programs and databases used in the study the gene ontology project is a collaborative effort to develop standard vocabularies that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. the controlled vocabularies are hierarchically structured so that they can be queried at different levels. kegg is a widely used database of biological pathways. the january version of the human protein reference database was downloaded from the hprd website. the binary protein-protein interactions were then imported into cytoscape, an open source bioinformatics software platform for visualizing and analyzing biological interaction networks. the cytoscape plugin bingo was used to determine and visualize statistically overpopulated go categories in a set of genes. random forest models and feature extraction we used a random forest package implemented in the "r" environment for this study. since random forest models are usually insensitive to the model parameters, we used the default parameters. values for a set of protein features were calculated using various software programs or in-house scripts. these features include protein composition in terms of amino acid residues, predicted secondary structures, solvent accessibility, and others. although the predictive model might have benefited from incorporating elements of protein three-dimensional structure, such information is not available for the vast majority of target proteins. therefore we used only sequence information and predicted secondary structures. since the sequence of a protein is a major determinant of its structure, it is expected that features calculated from sequence information alone may be sufficient to distinguish target proteins from other proteins. software programs for predicting a broad spectrum of other protein properties such as secondary structure, solvent exposure, instability index and others are mature and have found many applications. tripeptide features the dipeptide or tripeptide composition of proteins has been found to correlate to a number of protein properties. for example, guruprasad et al. found a correlation between the dipeptide composition and the stability of proteins. more recently, tripeptide composition was applied in a machine learning model to predict protein-protein interactions. in this study, the amino acids were sorted into just five groups according to their physicochemical properties: hydrophobic, gavlimpfw; polar, ytsnq; positive, rkh; negative, de; the fifth group contained only cysteine because of its unique ability to form disulfide bonds. therefore the number of possible tripeptide features according to this classification scheme is 5*5* or ranking feature importance the random forest approach offers several methods to assess the importance of features based on their contributions to the correctness of the resulting classification. these methods include i) the mean decrease in accuracy for each class, ii) the mean decrease in accuracy over all classes, and iii) the mean decrease in the gini impurity criterion. in this study we used the mean decease in accuracy for the minor class because in previous work we found it to give more accurate results than other measures for ranking feature importance. performance evaluation for imbalanced data, the overall classification accuracy is not an appropriate measure of performance because very high accuracy can be achieved simply by predicting every case as the majority class as opposed to predicting the minority class. in this study, we used three indicators of performance: sensitivity, specificity, and area under the curve for the receiver operating characteristic curve. we ranked all cases in the dataset according to the predicted likelihood of positive status and then employed that rank order to identify correctly classified true positives. we then used these results to generate an roc curve against the false positive rate ). the area under the roc curve represents the trade-off between sensitivity and specificity over the whole range of data. an auc of represents a perfect prediction model while an auc ≥ is considered excellent, an auc between and is considered good, and an auc in the range of – is fair.  bioinformatic analysis of known target proteins to identify go terms significantly enriched with target proteins we used bingo, a plugin of the cytoscape software suite, to map target proteins to go categories. bingo uses a hypergeometric test to search for predominant categories in terms of p-values and go diagrams. for this experiment we used all target proteins in the tpdb and found that and of them, respectively, were represented in the molecular function, biological process, and cellular component categories of the gene ontology classification system. a complete listing of terms having a false discovery rate < e- is presented in additional file while graphical representations of enriched go categories and their hierarchical structures are presented in additional files and a go analysis was performed using bingo in cytoscape. the rat proteome was used as the background for comparison. a complete listing of terms having a false discovery rate < e- is presented in table s. also available in the additional files are graphic representations of enriched go categories and their hierarchical structures. of the known targets, sort into the broad molecular function subcategory of catalytic activity, while only sort into the much more specifically-defined subcategory of antioxidant activity. nevertheless, both results are highly significant statistically. likewise, of the target proteins sort into the broad biological process subcategory of "metabolic process" while only sort into the subcategory of "xenobiotic metabolic process." again, both of these results are highly significant statistically. however, the results of sorting target proteins into various cellular component subcategories must be viewed with some caution for several reasons. first, some target protein identification studies analyzed whole tissue samples whereas others analyzed only selected subcellular fractions of tissue homogenates. second, a majority of the enzymes associated with bioactivation of xenobiotics to reactive metabolites are located in the endoplasmic reticulum of the cell. for certain metabolites, the reactivity of the metabolite may limit its ability to diffuse from the site of formation to other sites within the cell. to map target proteins to kegg pathways we used david bioinformatics resources, an online database for annotation, visualization and integrated discovery developed by niaid nih. we found that of the known target proteins, are associated with one or more kegg pathways, of which are specifically enriched compared to statistical expectations. pathways involved in glycolysis, gluconeogenesis and glutathione metabolism pathways are among the most enriched. apathway mapping was performed using david, and of target proteins could be mapped to one or more kegg pathways. the subset of rat proteins that are allocable to kegg pathways includes proteins. analyses such as those presented above are based on the principles of statistics; thus the observed enrichments of target proteins in certain go categories and kegg pathways are a priori significant, at least in the statistical sense. for example, of the target proteins in the molecular function go category sorted into the catalytic activity subcategory. although this result is highly significant statistically, these targets represent only 2% of all proteins in this broad subcategory. thus, regardless of its statistical significance, this finding can at best hint at a biologically significant mechanistic connection to toxicology. in contrast, only of the target proteins sorted into the peroxiredoxin activity subcategory. this result too is highly significant statistically, but since this entire subcategory contains only proteins, the finding that five of them are targeted by reactive metabolites suggests that perturbation of this molecular function by protein adduction could perhaps be significant biologically. a number of other molecular function and biological process go terms that are overpopulated with target proteins have been individually implicated in the context of reactive metabolite toxicity. likewise of the target proteins were also enriched in certain kegg pathways, some of which have also been linked to potential mechanisms of chemical cytotoxicity. collectively, the highlighting of certain go terms and kegg pathways via the more independent and more comprehensive bioinformatics approach taken here is intriguing and encouraging. while findings such as this can help to formulate or reinforce hypotheses about mechanisms of reactive metabolite cytotoxicity, each such hypothesis will need more extensive testing before it can be taken seriously as a significant contributor to the overall mechanism of cytotoxicity. in the end, however, it must be conceded that the systematic global analyses of all known target proteins affords only a modest advance toward elucidation of mechanisms beyond that afforded by gazing at lists of arbitrarily-grouped target proteins of single protoxicants. to go beyond this limitation, we searched for and analyzed non-target proteins that interact directly with known target proteins, as described below. bioinformatic analysis of common target proteins and their interacting partners protein-protein interaction data analysis from the total set of target proteins in the tpdb we selected rat or mouse proteins that are common targets for multiple reactive metabolites. because published ppi data for rat and mouse are quite sparse, whereas extensive ppi data are available for human proteins, we first found the human orthologs of these rat or mouse target proteins by searching against human proteins in the ncbi non-redundant protein database using the online ncbi blast server. the search results were manually inspected and the most significant matches were selected. the minimum and average percent identity of the target proteins and their human homologs is 69% and 88%, respectively. the maximum difference in sequence lengths for these protein pairs is only %. thus, the selected human proteins are very likely to be orthologs of the target proteins. we then searched for the respective interacting partners of the target protein orthologs in the human protein reference database; this approach is based on the common assumption that ppis are conserved across species. we found a total of proteins to be directly interacting partners to of the target protein orthologs. a full list of these interacting proteins is available in the additional materials. a no interacting partners were found for of the proteins. see additional file for full listing of all the interacting partners found. go and kegg pathway analysis of common targets and their interacting partners we used bingo to identify go categories significantly overpopulated with target proteins or their first interacting partners. of the target proteins and direct partners analyzed, link to one or more go terms. as summarized in table highly significant enrichment was observed in the subcategories of protein folding, unfolded protein binding, response to unfolded protein, and apoptosis. in addition to the statistical significance of the sorting results, the fact that adduction affected a relatively large fraction of the proteins in these categories suggests that these results may also be biologically significant in terms of the degree to which important processes involving these proteins may be impaired or altered by target protein adduction. of the target proteins plus partners, we found to be involved in one or more kegg pathways, eight of which are significantly overpopulated compared to statistical expectations. among them, the map kinase signaling pathway had the most significant enrichment. this result is particularly intriguing, since this association was not found when we analyzed only the reactive metabolite target proteins themselves without including their interacting partners. apparently, looking beyond just target proteins and considering their first interacting partners may provide a deeper look into potential mechanisms of cytotoxicity. the potential indirectness of analyzing human orthologs of rat and mouse target proteins was offset by focusing on just proteins known to be targeted by multiple different reactive metabolites. comparing the results of table to those of table shows that the go molecular function subcategory "unfolded protein binding" was again highlighted, but this time with much higher statistical significance and much higher fractional coverage of the class. the significance and coverage of the biological process "protein folding" are greatly increased in table vs. table in addition, table also lists two terms having both high statistical significance and high fractional coverage that are absent from table namely, "apoptosis" and "response to unfolded protein," both of which have high relevance to cytotoxicity. in terms of kegg pathway analysis of targets and their partners, the flagging of the map kinase pathway is particularly noteworthy. this finding is consistent with independent experimental evidence that also strongly supports a role for this pathway in apoptosis and other pathological cellular responses to toxic chemicals. there is also considerable interest in the involvement of the immune system in some forms of reactive metabolite-mediated toxicity in vivo. the flagging of four pathways related to the nervous system was unexpected, and will require further analysis to evaluate. nevertheless, it appears that our approach of considering not just target proteins but also the proteins with which they interact in the cell may provide clues to downstream events important to cytotoxicity and thereby help in elucidating mechanisms of cytotoxicity. stated another way, it is perhaps essential ppis rather than proteins per se that are the important targets of reactive metabolites in living cells. machine learning approaches to elucidate factors that differentiate target from non-target proteins in addition to identifying the target proteins whose modification appears to initiate cytotoxic responses, we also examined them more specifically to elucidate protein features that predict susceptibility to reactive metabolites. because the reactive metabolites of the protoxins included in the tpdb span a wide range of chemical reactivity and hydrophobicity, and because for many of them only a few target proteins are known, we decided to use the set of proteins that are targeted in vivo by thiobenzamide metabolites, bromobenzene metabolites or both for this study. thiobenzamide forms a reactive acylating metabolite with a very strong preference for reaction with amine side chains on lysine or phosphatidyl-ethanolamine lipids. on the other hand, prior analyses of proteins adducted by bromobenzene metabolites indicates a strong preference for adduction on cysteine sulfhydryls as opposed to lysine or histidine or pe lipids. thus, one might expect the composition of the proteins targeted by these two metabolites to reflect their differing chemical selectivities to at least some extent. to investigate this we used the random forest algorithm developed by breiman. random forest is an ensemble approach that combines many individual classifiers to formulate a robust composite classifier. it is particularly suitable in classifying high-dimensional and noisy data, and it can handle a mixture of both categorical and continuous predictors such as those in table each of the classifiers is built on a bootstrap sample of the data and utilizes a random subset of the available variables without pruning to obtain low-bias trees. the random forest algorithm has been applied to a broad range of classification tasks and has demonstrated superior performance compared to other classification algorithms. we used five-fold cross validation to estimate the performance of the classification models. in brief, target proteins and the rat proteins taken as a negative control set were randomly split into equal portions. one portion was reserved as a test data set while the other four were pooled and used as a training set. a random forest classifier with trees was built using protein features and the performance of the model was then evaluated using the reserved dataset. this process was repeated four times, each time starting with a different one of the five subsets of proteins as the test set. the results from all five repeats were then combined to afford an overall performance estimation. for our model the roc curve in figure has an auc of, while the specificity and sensitivity of the model are and, respectively. thus this simple model appears to have fairly good predictive power. the relative importance of the protein features used in the model was ranked using the random forest algorithm and the top are displayed in figure we also estimated the performance of models built using only these top features. the roc curve of this truncated model has an auc of, somewhat inferior to the auc of for the model using all features, suggesting, not surprisingly, that protein targeting by reactive metabolites is a complicated process that depends on many factors. interestingly, the most decisive feature of the full model is the percentage of lysine in the proteins. this is consistent with the fact that the reactive iminosulfinic acid metabolite of thiobenzamide reacts preferentially with amine groups such as those on lysine side chains. figure 3a shows a plot of the cumulative distribution of lysine in thiobenzamide target vs. non-target proteins. this plot indicates, as might be expected, that lysine residues are more common among thiobenzamide targets than non-target proteins, but surprisingly, the same is true for bromobenzene target proteins. figure 3b shows a similar plot for the distribution of cysteine among target vs. non-target proteins. surprisingly, both sets of target proteins actually show somewhat lower cysteine content than the non-target proteins, and again there is no apparent difference between thiobenzamide vs. bromobenzene targets despite the extremely different chemical reactivities of their respective reactive metabolites toward specific protein nucleophiles. consistent with the results of figure 3b, figure also indicates that cysteine content is a relatively unimportant factor for distinguishing target from non-target proteins in vivo, at least for reactive metabolites of bromobenzene and thiobenzamide. dennehy et al. investigated the reaction of two model cysteine-reactive electrophiles with proteins from the nuclear and cytosolic fractions of hek cells. they found that 89% and 85%, respectively, of proteins that became adducted were selectively adducted at only one or two cysteines per protein. the overlap of target proteins hit by iab vs. bmcc is only about 20%. in an attempt to explain this selectivity they analyzed the frequency of occurrence of lysine, arginine, histidine or a second cysteine within the first five residues on either side of the adducted cysteine. some differences in the frequencies were noted for target vs. non-target proteins, but unfortunately, the most likely reasons for the selective reactivity of only a few cysteines among many, namely, protection by intramolecular disulfide formation and/or steric occlusion within the protein, could not adequately be evaluated because such structural information is generally not available for a majority of proteins. in our analysis of features of target proteins we used only two large sets of targets, one from a lysine-selective metabolite and one from a cysteine-selective metabolite. the comparative unimportance of cysteine in differentiating target proteins from non-target proteins probably indicates that while a given reactive metabolite may require a specific type of target nucleophile, the target site is only one factor, and a possibly not a sufficient factor overall, for differentiating target from non-target proteins. the second most important feature of target proteins is the percentage of aspartic acid. aspartic acid residues are generally not considered to be nucleophilic target sites for most reactive electrophilic metabolites. it is possible that the higher content of aspartic acid provides negative charges to balance the positive charges resulting from a higher percentage of lysine in target proteins; however, the equally negatively charged glutamic acid did not play a significant role in distinguishing target proteins. moreover, we find that the correlation between the content of lysine vs. aspartic or glutamic acid in the non-target set is insignificant, in agreement with the wide range of pi values that proteins span. clearly more work will be required to understand why some abundant proteins receive little adduction while some low-abundance proteins become more heavily adducted by a given reactive metabolite. the third most important feature of target proteins is their predicted index of instability. if we consider proteins with a calculated instability index ≥ as unstable, and those with an index < as stable, then out of the target proteins are classified as stable while only 26% of the proteins in the negative learning set are classified as stable. the relative instability of a covalent metabolite-protein adduct can arise from 1) chemical instability of the covalent linkage, 2) a high intrinsic rate of turnover of the protein itself, or 3) an enhancement of protein turnover induced by adduction. a priori, the stability of a protein can influence both the detection and analysis of its adducted forms and the cellular consequences of its adduction. surveys of target proteins are usually conducted at just a single time after dosing, chosen to maximize the total amount of covalent binding. proteins whose turnover is rapid relative to the endpoint time will thus experience a lower exposure to reactive metabolite and a greater likelihood of removal by degradation, resulting in a lower apparent rate of adduction compared to proteins that turnover slowly. if adduction flags a protein for accelerated destruction, the result will be the same. this may be one reason that adduct densities vary so widely across a target proteome. if adduct density is lowered sufficiently, the protein may then be missed altogether as a target. on the other hand, since a relatively high proportion of target proteins turn over less rapidly than non-target proteins, their adduction may result in a more persistent perturbation to endogenous ppis and signaling mechanisms and may therefore contribute more to cytotoxicity. interestingly, lin et al. recently found a major difference between iab and bmcc in terms of adduct stability in vivo. both agents form covalent adducts that do not dissociate chemically or in cell-free fractions, but in living cells, the extent of protein adduction by iab increases continuously over hours, whereas adducts formed by bmcc peak at around minutes after exposure and diminish rapidly thereafter. their disappearance is strongly retarded at 4°c, suggesting that the disappearance may be enzyme mediated. two other acetamide/maleimide pairs showed similar differences in protein adduct stability. lin et al. noted that the rapid clearance of maleimide adducts coincided with their relative lack of toxicity and suggested that protein adduct stability is a critical requirement for the induction of cellular responses. their observation of declining adduct levels over time can be explained by reversal of the adduction process, and/or by the degradation of protein molecules bearing adducts. further work will clearly be required to address these possibilities, but it is encouraging that results of both bioinformatic and laboratory analysis of target proteins are pointing in the same direction with respect to the role and importance of protein turnover and adduct persistence in eliciting cytotoxicity.  the covalent binding of reactive metabolites to cellular proteins has long been associated with the production of acutely cytotoxic effects. the past several years have witnessed much progress toward identifying their reactive metabolites and the specific intracellular proteins that become adducted, often highly selectively, by reactive metabolites from a number of different protoxicants. for the better-studied protoxicants, much is known about the structures and reactivities of their reactive metabolite, the structures of the adducts they form on proteins, and the identities of many of their protein targets. for technical reasons it has proven challenging to elucidate which specific residues on a given target protein become modified, but progress is being made in this area as well. the challenge has been to use the available information to discover factors that govern the selectivity of adduction, and to discover mechanisms that link protein adduction to cytotoxicity. as of this writing, the tpdb lists compounds whose reactive metabolites present widely varying chemical reactivities and hydrophobicities, and a total of proteins that are modified by reactive in animals or living cells in vitro. however, for only of these compounds, one of which is not cytotoxic, are or more target proteins known, while for other compounds fewer than target proteins are known. thus, considering the breadth of the phenomenon, the descriptive data are still rather sparse. little commonality of targets among different protoxins is apparent, and analyses of target proteins per se have failed to illuminate mechanisms linking covalent binding to toxic outcomes. because proteins in cells interact specifically and extensively with other proteins, we hypothesized that xenobiotic adduction might disrupt endogenous ppis and signaling pathways vital to cellular homeostasis and survival. in the current work we found that the human orthologs of common rat or mouse target proteins have direct-interacting partners that participate in a total of ppis. these proteins are significantly concentrated in several go categories and kegg pathways that experimental studies by others have shown to be highly relevant to cell signaling and cell survival. this suggests that compared to direct analysis of target proteins, further bioinformatic analysis of proteins that interact with greater numbers of target proteins may be able to point toward more fruitful areas for generating and testing hypotheses about mechanisms of toxicity. finaly, given the chemical and structural diversity of cellular proteins, it seems unlikely that simple principles of chemical reactivity will in themselves play an important role in differentiating target from non-target proteins. as shown by studies with bromophenol, m-hydroxyacetanilide, and mycophenolic acid, not all protein adduction has toxic concequences. it seems equally likely that among adducts generated from toxic compounds, only some but not all will have toxic consequences. it will take time and much more detailed information about protein adduction in living cells to sort this out, and the outcome is likely to point to a number of mechanistic paths from protein modification to cellular impairment or death. in the meantime, one aspect of adduct chemistry that experiments and bioinformatic analysis both suggest may be important is the persistence of the adducted protein in the cell. unstable adducts that dissociate, and adducted proteins that turnover rapidly, may be less effective at perturbing cellular homeostasis and injuring the cell than more durable adducts. authors' contributions all authors contributed to the design of the three sub-projects, the interpretation of the results, and the drafting and production of the manuscript. bioinformatic analyses were conducted by jf. all authors read and approved the final manuscript. supplementary material additional file 1 figure s graphic representation of enriched go biological process categories and their hierarchical structure of the target proteins. heirarchial tree diagram showing the names and relationships of the go biological process categories in which members of a set of reactive metabolite target proteins are statistically over-represented. click here for file additional file 2 figure s graphic representation of enriched go molecular function categories and their hierarchical structure of the target proteins. heirarchial tree diagram showing the names and relationships of the go molecular function categories in which members of a set of reactive metabolite target proteins are statistically over-represented. click here for file additional file 3 table s human orthologs of common rat/mouse target proteins. accession numbers of common rat/mouse target proteins and their human orthologs, and their degree of similarity. click here for file additional file 4 table s first-partners of common target proteins. accession numbers of common rat/mouse target proteins and their human orthologs, and their degree of similarity. accession numbers and gene symbols of the directly-interacting partners found for common rat and mouse target proteins. click here for file additional file 5 table s significantly over-populated go terms. go id numbers and names of molecular function, biological process and cellular component categories in which members of a set of reactive metabolite target proteins are statistically over-represented. click here for file acknowledgements this work was supported by nih grant gm- and the kansas-inbre.
sorting known reactive metabolite target proteins revealed a number of go categories and kegg pathways to be significantly enriched in targets, but in most cases the classes were too large, and the "percent coverage" too small, to allow meaningful conclusions about mechanisms of toxicity. recent rapid progress in reactive metabolite target protein identification has shown that adduction is surprisingly selective and inspired the hope that analysis of target proteins might reveal protein factors that differentiate target- vs. non-target proteins and illuminate mechanisms connecting covalent binding to cytotoxicity. as yet there is no good explanation for why some low-abundance proteins become heavily adducted while some abundant proteins become only lightly adducted in vivo. protein covalent binding by reactive metabolites of drugs, chemicals and natural products can lead to acute cytotoxicity. however, a similar analysis of the directlyinteracting partners of common targets of multiple reactive metabolites revealed highly significant enrichments in terms likely to be highly relevant to cytotoxicity. analyzing the directly interacting partners of target proteins appears to yield greater insight into mechanisms of toxicity than analyzing target proteins per se.
 protein covalent binding by reactive metabolites of drugs, chemicals and natural products can lead to acute cytotoxicity. recent rapid progress in reactive metabolite target protein identification has shown that adduction is surprisingly selective and inspired the hope that analysis of target proteins might reveal protein factors that differentiate target- vs. non-target proteins and illuminate mechanisms connecting covalent binding to cytotoxicity.  sorting known reactive metabolite target proteins revealed a number of go categories and kegg pathways to be significantly enriched in targets, but in most cases the classes were too large, and the "percent coverage" too small, to allow meaningful conclusions about mechanisms of toxicity. however, a similar analysis of the directlyinteracting partners of common targets of multiple reactive metabolites revealed highly significant enrichments in terms likely to be highly relevant to cytotoxicity. machine learning was used to rank the contribution of computed protein features to determining protein susceptibility to adduction. protein lysine content and protein instability index were among the features most important to determining susceptibility.  as yet there is no good explanation for why some low-abundance proteins become heavily adducted while some abundant proteins become only lightly adducted in vivo. analyzing the directly interacting partners of target proteins appears to yield greater insight into mechanisms of toxicity than analyzing target proteins per se. the insights provided can readily be formulated as hypotheses to test in future experimental studies. 