Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains. Proteins comprise domains which are evolutionary conserved sequence segments with the ability to fold and be functional. An important class of domains mediates protein-protein interactions (PPIs); although not all interactions between proteins can be attributed to interactions between domains, and not all domains in multi-domain proteins play a direct role in protein interaction. Nonetheless, many computational methods which seek to predict PPIs with high accuracy rely on computationally inferred domain-domain interactions (DDIs), e.g. Ideally, the inferred DDIs used to support the predicted PPIs are highly reliable themselves, that is there is a large overlap between the set of inferred DDIs and the set of DDIs confirmed to interact by 3D crystal structures of proteins. This latter set is referred to in this paper as the gold standard domain-domain interactions (GDDIs). This ideal is desirable not only to tease out specific interactions in a protein complex, but also to give predictive power to protein-protein interaction prediction methods (more on this point later in this section).promiscuous domains. Promiscuous domains can occur in many domain architectures However, using GDDIs to predict PPIs generates a large number of false positives (non-interacting protein-pairs predicted as interacting) and thus reduces the accuracy of the prediction method. The large number of false positives stem from the fact that GDDIs are enriched with It is parsimonious to re-use domain-pairs that can interact to facilitate PPIs. Indeed, many DDIs are conserved across organisms by evolution not GDDIs. Promiscuous domains are observed to be heavily involved in PPI mediation promiscuity prevents GDDIs from being highly ranked in computational methods to infer DDIs. Recognizing this domain promiscuity problem, additional measures have been taken to counteract its effects when inferring DDIs from PPIs, e.g. The ‘drift towards rare domain-pairs’ phenomenon in PPI prediction methods has been noted piggy-backing potential of promiscuous domain-pairs. Piggy-backing occurs when a domain-pair improves its score because either one or both of its domain partners happen to occupy the same attribute-label set as one or more rarer domains. Piggy-backing potential is possible only in concept lattices that are not attribute-reduced, and is enhanced by the presence of many mixed architecture proteins. One of the challenges of doing bioinformatics research is the volatility and variety of bioinformatics datasets which exerts a high validation cost for any new method. Therefore, it is useful to understand why a bioinformatics method works and under what conditions. Identifying such conditions also automatically suggests the limits of a method. Towards this end, effort is made to investigate conditions favourable to the proposed concept-based domain-pair ranking method.In this paper, I propose that Formal Concept Analysis (FCA) P be the set of proteins and D the set of domains. Every protein in P comprises one or more domains in D. D(x) denotes the finite set of domains for protein x. If | D(x) | = 1, x is a single-domain protein; if | D(x) | > 1, x is a multi-domain protein. The set of proteins which contains domain a is P(a) = { x ∈ P | a ∈ D(x) }. The frequency of domain a in P is N(a) = | P(a) |. x = D(x) = {a, b, c} where x ∈ P and {a, b, c} ⊂ D denotes protein x is its set of domains D(x) which in turn comprises domains a, b and c.Let P. This relation is symmetric, i.e. if  is an interacting protein-pair, then so is . It is possible for proteins to self-interact. Let 1 denote  ∈ the set of PPIs. 1 implies proteins x and y come from the same organism, i.e.: O(x) = O(y). The set of non-PPIs is also a symmetric relation on 3, and a non-interacting protein-pair 0 also implies O(x) = O(y). O(x) = O(y) implies either 1 or 0. There may be pairs in P × P which are neither interacting nor non-interacting because they do not satisfy the same organism condition.The set of PPIs is a relation on D, and domain self-interaction is possible. A protein-pair  generates domain-pairs, each of which may or may not be reliable, through the cross-product of their domains, i.e. D(x) × D(y). A domain-pair  generates a set of protein-pairs, each of which may or may not be interacting, through the cross-product of their respective protein sets, i.e. P(a) × P(b). For a domain-pair  to be a DDI, it must generate at least one interacting protein-pair.The set of DDIs is a symmetric relation on The Riley dataset x and y implies interactions between the domains of x and the domains of y. The Riley set of proteins, domains and PPIs generate 177,233 putative domain-domain interactions (DDIs). Amongst these possible DDIs are 783 gold standard domain-domain interactions (GDDIs). These GDDIs were obtained from iPfam The proteins are associated with 12,455 Pfam-A and Pfam-B domains. Amongst the set of proteins are 26,032 protein-protein interactions (PPIs). Interactions and non-interactions between protein-pairs are restricted to proteins from the same organism In the following sub-sections, the Riley dataset is examined to support assertions made in this paper. Specifically, the data characteristics of interest are:Highly reliable domain-domain interactions (GDDIs) are enriched with promiscuous domains.GDDIs generate significantly more true positive PPIs and also more false positive PPIs than non-GDDIS. More true positive PPIs agrees with the parsimony or the re-use principle for GDDIs, and more false positive PPIs accords with the promiscuity of gold domains.Protein domain architectures are mostly a mix of rare and promiscuous domains.gold domain is a domain that participates in at least one gold standard domain-domain interaction (GDDI). There are 642 gold domains in the Riley dataset. A domain is more promiscuous if it occurs more frequently in a given set of proteins, i.e. given P and {a, b} ⊂ D, N(a)>N(b) implies a is more promiscuous than b.All domains that participate in at least one GDDI were pooled and their frequencies were compared against the frequency of occurrence of all 12,455 domains. In agreement with previous observation in y for a domain occurrence value of x means y fraction of all domains that occurs x times are gold domains. The tendency is for Gold/All to equal 1 as domain occurrence increases. This supports the notion that GDDIs are significantly enriched with promiscuous domains. This conclusion is not surprising given that the GDDIs are sourced from the iPfam database The bar chart in S. pombe.Because the dataset comprises multi-organisms, there is a possibility that the heavy imbalance between rare and promiscuous domains in the set of all domains may be due to the many organisms with only a handful of proteins  in the da, b) as [N(a)+N(b)]/2. The promiscuity of GDDIs is then compared against the promiscuity of all non-GDDIs. R’s Wilcox.test confirms that GDDIs have significantly larger promiscuity scores than non-GDDIs.The previous test examined the promiscuity of single domains. In the following test, the promiscuity of domain-pairs are examined by quantifying the promiscuity of a domain pair  generates a set of protein-pairs, each of which may or may not be interacting, through the cross-product of their protein sets, i.e. P(a) × P(b). A DDI is either a GDDI or a non-GDDI. The set of DDIs comprise 783 GDDIs and 176,450 non-GDDIs. A PPI prediction for a protein-pair  predicts that protein x interacts with protein y. This prediction is a true positive if 1 can be found in the given set of PPIs and a false positive otherwise.The set of all interacting protein-pairs (PPIs) and the set of all non-interacting protein-pairs (non-PPIs) were generated for each of the 177,233 putative domain-domain interactions (DDIs). A domain-pair (R’s t.test (non-homogeneous variance) and Wilcox.test were used to compare the sizes of the PPI sets generated by GDDIs and by non-GDDIs, and the sizes of the non-PPIs sets produced by GDDIs and by non-GDDIs. The statistical tests confirm that GDDIs generate significantly larger sets of PPIs than non-GDDIs and that GDDIs generate significantly larger sets of non-PPIs than non-GDDIs. Thus, GDDIs generate significantly more true positive and more false positive PPIs than non-GDDIs. More true positive PPIs agrees with the parsimony or the re-use principle for GDDIs, and more false positive PPIs accords with the promiscuity of gold domains.rare domain threshold x, domains which occur ≤ x times in the protein set are classified as rare. While a rare domain may occur in only a handful of proteins, collectively the set of rare domains occur in many proteins. When the rare domain threshold is 2, 54.8% of the proteins in the Riley dataset have at least one rare domain.Promiscuous domains are those which occur frequently in proteins. Rare domains are those which occur infrequently in proteins. The ratio of promiscuous to rare domains varies with the rare domain threshold . At rareonly-rare proteins, i.e. proteins comprising only rare domains, increases, while the proportion of only-promiscuous proteins, i.e. proteins comprising only promiscuous domains, decreases. Both only-rare and only-promiscuous proteins may be single- or multi-domain proteins. Mixed architecture proteins comprise both rare and promiscuous domains and are thus necessarily multi-domain proteins. The proportion of mixed architecture proteins is influenced by the proportions of rare and promiscuous domains. When the rare domain threshold is between 2 and 16, mixed architecture proteins make up the largest proportion (at least a third) of the protein population in the Riley dataset. The peak occurs at rare domain threshold 4 where 37.8% of the proteins have mixed architecture.The concept-based scoring method proposed in this paper does not require or depend on the specification of a rare domain threshold. However, since mixed architecture proteins are the workhorse proteins of the proposed method (Results section), it is reassuring to know that mixed architecture proteins are not rare phenomena in the Riley dataset and that there exists a range of rare domain threshold values where at least a third of the proteins have mixed domain architecture. However for the concept-based scoring method to be feasible at least in principle, the mixed architecture proteins also need to cover gold domains.Up to and including rare domain threshold 11, proteins with mixed architecture provide the largest coverage of all domains  top. HowG by their common attributes and dually a (finite) set of attributes M by their common objects into a (finite) set of partially ordered pairs of sets called  concepts. Implicit is a binary relation I ⊆ G x M which associates objects with attributes.  ∈ I or equivalently g I m denotes object g has attribute m. The triplet  forms the  context within which a FCA is carried out. For small finite examples, a context can be specified completely with a cross-table. The resulting set of concepts, denoted B, forms a concept lattice.Formal Concept Analysis (FCA) G = P, the set of attributes is the set of domains, i.e. M = D, and g I m denotes protein g has domain m, i.e. m ∈ D(g) and dually g ∈ P(m). S. pombe in the Riley dataset. The (fully-labeled) concept lattice depicting this context is given in For the application in this paper, the set of objects is the set of proteins, i.e. This section describes FCA for those unfamiliar with the theory and in enough detail to support the discussion in this paper. The more mathematically inclined are referred to c ∈ B is an ordered pair of sets  such that O ⊆ G, A ⊆ M and the set of all attributes common to all objects in O under relation I is A and the set of all objects with attributes in A under relation I is O. More formally, the last two conditions are A = O′ = {m ∈ M | (µg ∈ O) g I m} and O = A′ = {g ∈ G | (µm ∈ A) g I m} respectively. If this seems a bit chicken-and-egg, these last two conditions can be satisfied by working from the power-set of objects or alternatively from the power-set of attributes; but there exist more efficient FCA algorithms such as Lindig’s C implementation called Colibri-conceptsA concept O is called the extent of concept c, and the set of attributes A is known as the intent of concept c. A concept’s extent is denoted O(c) and its intent as A(c). The prime symbol ′ denotes the mapping from an extent to its intent and vice versa, i.e. let a concept c = , then O′ = A, A′ = O, O = O″, A = O″′, and so on. This pair of maps between the set of extents and the set of intents forms a Galois connection between the two partially ordered sets. The statements O = O″ and A = O″′ are true due to the maximal condition for extents and intents. This implies that if a set of objects (attributes) forms the extent (intent) of a concept, then the set of objects (attributes) uniquely identifies the concept, and conversely a concept unambiguously identifies its extent and its intent.The set of objects B; ≤〉. For two distinct concepts in B ,  ≤  implies O1 ⊂ O2 and dually A1 ⊃ A2. The join (least upper bound) and meet (greatest lower bound) are defined for every pair of non-comparable concepts in 〈B; ≤〉. 〈B; ≤〉 forms a concept lattice. Intuitively, a concept lattice is a two-in-one lattice with a right-side up lattice for the set of extents and an upside down lattice for the set of intents. More formally, a concept lattice is a complete lattice with a top element  and a bottom element . A complete lattice defined on a subset of a power-set is closed under arbitrary joins (in the form of unions) and meets (in the form of intersections) c1 ∨ c2 =  ((O(c1) ∪ O(c2))″, A(c1) ∩ A(c2) ), and the meet (infimum) of two arbitrary concepts c1 ∧ c2 =  ((O(c1) ∩ O(c2), (A(c1) ∪ A(c2))″ ). c1 ∨ c2 is a concept since A(c1) ∩ A(c2) =  (O(c1) ∪ O(c2))′ and  is always a concept. Similarly, c1 ∧ c2 is a concept since O(c1) ∩ O(c2) = (A(c1) ∪ A(c2))′ and  is always a concept. The intersection of any number of extents (intents) always results in an extent (intent). The same is not generally true for unions of extents (intents) c1) ∪ O(c2)) ⊆ (O(c1) ∪ O(c2))″ and (A(c1) ∪ A(c2)) ⊆ (A(c1) ∪ A(c2))″ hold.The set of concepts is ordered by set inclusion 〈c represented as ↓{c} = {x ∈ 〈B; ≤〉 | x ≤ c}. The up-set of c represented as ↑{c} = {x ∈ 〈B; ≤〉 | c ≤ x}. The extent of a concept c is the union of the extent of each concept ∈ ↓{c}. The intent of a concept c is the union of the intent of each concept ∈ ↑{c}. This relationship between concepts makes it possible to reduce the labeling of concepts to objects and attributes specific to a concept (reduced concept lattice.The down-set of  concept . ChanginOA); (ii) with reduced object labels and reduced attribute labels (oa); (iii) with complete object labels and reduced attribute labels (Oa); and (iv) with reduced object labels and complete attribute labels (oA). The oa and oA combinations produce object-reduced concept lattices. The oa and Oa combinations produce attribute-reduced concept lattices. In an object-reduced concept lattice, object labels appear exactly once and the set of object labels (proteins) is finite. Similarly in an attribute-reduced concept lattice, attribute labels appear exactly once and the set of attribute labels (domains) is finite. Each combination is explored in this paper for the proteins and their domains in the Riley dataset. It is possible for two different combinations to produce two different outcomes because the labels of objects and attributes are used instead of the objects and attributes themselves. OL(c) refers to the set of object-labels for O(c), and similarly AL(c) refers to the set of attribute-labels for A(c).There are then four possible ways to label a concept lattice: (i) with complete object labels and complete attribute labels  >1 are Pkinase (5), SAM_2 (2) and Cyclin_N(2)  means a larger extent, and as one goes up in a concept lattice extents increase in size, culminating in the top element whose extent is the entire object set. For lin_N(2)  and theylin_N(2) . In contS. pombe have promiscuous domains (N(d) >1), and these single-domain proteins  reside in concepts one step away from the top element in The position of a protein in an object-reduced concept lattice depends on the promiscuity of its domain(s). Three of the four single-domain proteins for L(c) = ∅ is more likely towards the top of an object-reduced concept lattice. Concepts with multi-domain protein(s) in their extents will tend to have empty attribute-label sets in an attribute-reduced concept lattice unless the multi-domain protein(s) introduces “new” domains. E.g. concept 7 in L(c) = ∅ is more likely towards the bottom of an attribute-reduced concept lattice. The presence of empty object-label sets and empty attribute-label sets influences the number of concept-pairs available to evaluate domain-pairs, and the diversity of CB and PG values produced by the concept-based scoring method.In a reduced concept lattice, there will be concepts with an empty object-label set or an empty attribute-label set. At the very least the object-label sets and attribute-label sets of the top and bottom elements will be, by definition, empty. The more numerous rare domains will “consume” proteins and leave fewer proteins available to fill the object-label sets of concepts with promiscuous domains. Thus an empty object-label set OOne of the earliest methods for detecting over-represented ‘correlated sequence-signatures’ e.g. domain-pairs, in a database of protein-protein interactions used the log-odds of the ratio between observed and expected frequencies to score pairs of sequence-signatures a, b) with the Association method is AM =  log2 [M/(N(a) × N(b))]. M = | {1 | a ∈ D(x) and b ∈ D(y)} | is the number of interacting protein-pairs in the database such that a is a domain of protein x and b is a domain of protein y. N(d) is the number of proteins in the database that has domain d in its domain architecture. The scores are negative in value with a maximum of log2(1) = 0 which is the score for domain-pairs that occur only between interacting protein-pairs, and an undefined minimum of log2(0) which is the score for domain-pairs that occur only between non-interacting protein-pairs. Domain-pairs with larger scores are ranked more highly.Specifically, the score of a domain-pair  = log2 [M/+Z)]. M is defined previously. Z = | {0 | a ∈ D(x) and b ∈ D(y)} | is the number of non-interacting protein-pairs in the database such that a is a domain of protein x and b is a domain of protein y.The Riley dataset comprises proteins from multiple organisms, and both interacting and non-interacting proteins are restricted to those from the same organism. To handle this situation, AM described before, but the scoring is done using pairs of concepts. The concepts used exclude the top and the bottom concepts, and any other concepts with an empty object-label set or an empty attribute-label set. Protein-pairs generated by the object-label sets of a concept-pair, i.e. OL(c1) × OL(c2), are used to compute the log-odds ratio which is then used to evaluate the domain-pairs generated by the attribute-label sets of said concept-pair, i.e. AL(c1) × AL(c2). Essentially, each concept-pair  provides a different context (in the form of protein-pairs) to evaluate a domain-pair. The set of concept pairs {} used to evaluate a domain-pair  is determined by the attribute-label sets, and comprises all distinct concept-pairs where a ∈ AL(ci) and b ∈ AL(cj). Concept-based scoring and ranking is demonstrated in Figures S6, S7, S8 and S9, and Table S2 in The is paper  also useoA and OA), a domain-pair can have more than one score. CB is the largest score found for domain-pair , while PG is the number of unique scores for  which are strictly smaller than CB. Domain-pairs with larger CB scores are ranked more highly. The PG scores help to break ties between domain-pairs with identical CB scores. Given identical CB scores, domain-pairs with larger PG scores are ranked more highly.With concept lattices that are not attribute-reduced (oa concept lattice when many concepts are excluded because of empty object-label or empty attribute label sets), or if its evaluation results in an undefined score, i.e. log2(0). Scoreless domain-pairs are given a negative PG value, and are placed in random order below domain-pairs with CB scores. The Oa scores are identical to those obtained with the Associative method since every domain appears exactly once in an Oa concept lattice and all proteins containing a domain appears in the object-label set of a concept whose attribute-label set has the domain.A domain-pair may be left scoreless if it was not evaluated at all , domains that are more promiscuous have more opportunity to appear in different attribute-label sets. The question then becomes is it better for a promiscuous domain to appear with other promiscuous domains or with rare domains. The results suggest that a promiscuous domain-pair is more likely to improve its score if either one or both of its partner domains appear with rare domains. This is because rare domains tend to produce fewer non-interacting protein-pairs. The Thus, several conditions favourable to the concept-based scoring method arise:The domain-pairs that need to be ranked highly are promiscuous. The GDDIs are promiscuous.not attribute-reduced, i.e. OA and oA. Strictly speaking then, neither oa nor Oa can produce concept-based scores since their PG values are always ≤ 0. The Oa scores are identical to those of the Association method.It must be possible to evaluate a domain-pair with different protein-pairs. This is possible only with concept lattices that are To improve the score of promiscuous domain-pairs through piggy-backing, it must be possible for promiscuous domains to reside with rare domains in one or more attribute-label sets. This is made possible by multi-domain proteins, in particular those which have a mix of promiscuous and rare domains in their architecture, i.e. mixed architecture proteins. Within a sensible range of rare domain thresholds, a substantial proportion of proteins in the Riley dataset are proteins with mixed architecture  and thesThe concept-based scoring method is applied to the Riley dataset using all four types of concept lattices. The algorithm in A, 0.60% in B, 0.21% in C and 0.11% in D. Median promiscuity of GDDIs to DDIs is 7.0∶5.0 in A, 8.5∶5.5 in B, 9.5∶5.5 in C and 15.0∶4.0 in D. With a smaller GDDI to DDI percentage and a much larger GDDI to DDI promiscuity ratio, scenario D is the most difficult of all. The resultant number of PPIs, DDIs, GDDIs and GPPIs for each scenario is summarized in Table S3 in The concept-based scoring method is evaluated under the following four circumstances, all of which are related to input data characteristics. In all scenarios, R’s Wilcox.test confirms that the set of GDDIs is still significantly more promiscuous than the set of non-GDDIs. More promiscuous GDDIs are more likely to survive the changes. GDDIs make up 0.44% of DDIs in Under the default or original circumstance, the complete Riley dataset is used without modification. Since all PPIs in the given input data are used, the probability of including a PPI, Pe is 1.0.PPI data obtained via high-throughput methods are error-prone. To account for the inaccuracies in PPI data, the robustness of computational methods when dealing with PPIs is commonly tested by using Pe <1.0. Ref. The log-odds ratio depends on the set of protein-pairs used and also on the PPI network. To test the robustness of the results against changes in PPIs, the nodes of each organism’s PPI network were shuffled amongst themselves. Node shuffling generates a new set of PPIs, but the number of PPIs per organism and the original PPI network structure  remains unchanged. The new set of DDIs overlaps but is no longer a subset of the original DDIs.Finally, to test the influence of domain architecture on the results, the domains of proteins are shuffled. For this scenario domain repetition within a protein is allowed and a protein becomes a multi-set of domains. The shuffle changes the frequency of domains in a small but still statistically significant way. The new set of DDIs overlaps but is no longer a subset of the original DDIs. Domain shuffling was accomplished with the following steps:Place every instance of a domain in the input data into a sequence, sorted by frequency of occurrence. Domain instances with identical frequency are shuffled amongst themselves.Sort the proteins by their size, i.e. number of domains they contain.Starting from the largest to the smallest protein, assign domains to proteins starting from the least to the most frequently occurring domains. The reason for this is to reduce domain repetition within a protein.The impact of shuffling on domain architecture is shown in a, b) = [N(a)+N(b)]/2 where N(d) is the number of times domain d occurs in a protein set. The GDDI rankings were extracted from the rankings of all DDIs (ranking domain-pairs is described in step 6 of OA and oA ones exhibit a negative correlation between GDDI promiscuity and rank. A negative correlation implies that higher ranking GDDIs tend to be more promiscuous. The relationship is strongly positive (Spearman’s rank correlation rho = 0.88) when the Oa rankings are used. Oa results are identical to the Associative method which is known to penalize promiscuous domain-pairs.Of the four plots in oa to rank promiscuous GDDIs less highly (Spearman’s rank correlation rho = 0.49), but this positive relationship is not as strong as Oa’s because scoreless GDDIs are included in the plot . When the oa concept lattice is used, only 350 of the 783 GDDIs have CB scores. The remaining 55.3% of GDDIs which are scoreless are ranked in random order below the GDDIs with CB scores. But R’s Wilcox.test confirms that the domain-pairs (including non-GDDIs) with CB scores are significantly less promiscuous than the scoreless domain-pairs. Hence, the oa concept lattice tends to ranks more promiscuous domain-pairs less highly.There is also a tendency for oa) concept lattice is the reason many domain-pairs are scoreless. Concepts with an empty object-label set or an empty attribute-label set are ignored when computing scores for domain-pairs (oa concept lattice. The many scoreless domain-pairs means the oa results are unsuitable or too weak for identifying strong interactions in protein complexes. oa performs poorly in the Nye test (Results section). Although oA has empty object-label sets and Oa has empty attribute-label sets, they do not produce any scoreless domain-pairs for the Riley dataset. The number of concepts used by each type of concept lattice for the Riley dataset is given in Table S4 in The increased presence of empty object-label sets and empty attribute-label sets in a reduced , both OA and oA GDDI rankings become positively related with promiscuity, i.e. less promiscuous GDDIs are ranked more highly  and domain frequency (the number of times a domain appears in a protein in a set of input proteins) (oA and OA produce similar results to Oa in Domain shuffling in scenario proteins  and chanroteins) . InsteadD, only 1.69% of the domain-pairs evaluated with oA have PG >0  to evaluate promiscuous domain-pairs, and a decrease in piggy-backing opportunities. ve PG >0  bottom. e highly .GDDI recovery is concerned with finding as many GDDIs as possible while making as few mistakes as possible. The GDDIs are sourced from the iPfam database reliable DDIs, it is preferable to keep the FPR low. Even at FPR = 0.2, there are already 35,290 false positives to eliminate either experimentally or through literature search in scenario A. Further, errors in predicting DDIs can be propagated to other areas that rely on DDI data, such as prediction of protein-protein interactions and identification of protein binding surfaces. Hence, the argument for low FPR or high Specificity. A cost-efficient high-throughput method to eliminate non-interacting domain-pairs with high confidence would render computational efforts to predict reliable DDIs obsolete.For the problem of identifying D, when FPR ≤ 0.2, rankings produced by the non-attribute-reduced concept lattices (OA and oA) have larger TPR or higher Sensitivity than the attribute-reduced concept lattices (Oa and oa). This outcome supports the hypothesis that using concept lattices that are not attribute-reduced is necessary to create a favourable condition for the concept-based scoring method to identify GDDIs. This outcome is robust to changes in the PPI data as evidenced by the results from scenarios B and C. The oa rankings become slightly more competitive when FPR ≥0.25, but the rankings quickly become random as oa is unable to score many domain-pairs due to empty object-label and empty attribute-label sets in the oa concept lattice. For instance, 72.14% of the possible domain-pairs and 55.3% of GDDIs are left scoreless by oa in scenario A. The Oa rankings or Associative method gave the worst GDDI recovery performance.The TPR vs. FPR or ROC graphs are shown in D, up to an FPR as large as 0.5, oA’s GDDI recovery dominates that of OA. A closer look at the top 100 domain-pairs . However, high ranking non-GDDIs do at times interfere with OA’s Nye test results (Results section).Except for the concocted scenario in-pairs  reveals oA domain-pair rankings dominates that of OA is given in oA’s TPR dominates the other three rankings. Of the four rankings , oA contains the most number of GDDIs, DOMINE domain-pairs and 3did domain-pairs in the top 100 domain-pairs  which is not a GDDI and whose interaction is neither recorded in DOMINE nor predicted by 3did. However, a possible 3D structure for the obesity-related protein adipose (adp) involves interaction between TPR and WD40 domains Further evidence that the in-pairs . AmongstoA producing better GDDI recovery results than OA. However, oA is more vulnerable to changes in protein domain architecture than OA. When the domains are shuffled to reduce the number of mixed architecture proteins (scenario D), oA’s TPR which previously has been above 0.2 when FPR ≤ 0.2 drops to less than 0.2. This decline in oA’s GDDI recovery performance supports the notion that mixed architecture proteins play an important role in the success of concept-based scoring.The discussion so far all point to OA’s prioritization of promiscuous domain-pairs, but the effect on GDDI recovery is dampened by OA’s ability to provide many more contexts to evaluate domain-pairs. In scenario D, the oA rankings are produced with only 53.71% of the concepts. In contrast, oA uses 82.38% of the concepts in the other three scenarios . While domain shuffling reduces the range of PG values for OA, the proportion of domain-pairs still able to piggy-back in OA remains above 62%. In contrast, only 1.69% of the domain-pairs evaluated with oA have PG >0  are left scoreless by the highest ranking DDI for a GPPI is a GDDI. It was first performed by Nye et al. This test is concerned with whether OA and oA) produce much higher pass rates than the attribute-reduced concept lattices (Oa and oa). OA’s higher pass rate is attributed to its greater piggy-backing potential  become less able to pass the Nye test. None of the GPPIs that generate more than 50 DDIs pass the Nye test when oa’s ranking is used. For the Oa concept lattice (the Associative method), the cut-off point is even earlier, at 16 DDIs. In contrast, several GPPIs with more than 50 DDIs could still pass the Nye test when ranking is based on concept-based scores computed with the non-attributed-reduced concept lattices (OA and oA).The Nye test results for scenario A is broken down by difficulty in OA ranking than with the oA ranking . This GPPI is supported by only one GDDI, , which has a promiscuity of 100. oA ranks  as the highest domain-pair for GPPI  and so the Nye test is passed. OA ranks  as the second highest, below  which has a promiscuity of 189.5 but is not a GDDI, and so the Nye test is failed. The Nye test is also passed by oa since  is the only domain-pair with a score for GPPI . However, oa’s Nye test performance on a GPPI with this high level of difficulty is more the exception than the norm.With a few exceptions, regardless of difficulty, more GPPIs pass the Nye test with the  ranking . This isy-backed . HoweverOA. These promiscuous Pfam-B domains also suppress GDDI recovery for OA. All domain-pairs in OA’s top 100 that do not involve a Pfam-B domain is either a GDDI, or identified in DOMINE or 3did (Oa). Therefore, there is a need to evaluate the effectiveness of the concept-based scoring method in the absence of Pfam-B domains and on a more current dataset for single organisms. The organisms tested here are yeast (NCBI taxid 559292) and human (NCBI taxid 9606), and the data come from Biogrid (version 3.2.107) A conditions.The previous case with GPPI  illustrates how the presence of promiscuous Pfam-B domains can cloud the Nye test results for  or 3did . HoweverOa is no longer below the y = x line as it was in OA and oA) still dominate that of the attribute-reduced concept lattices (Oa and oa) although now it is no longer as clear as it was in oA’s TPR dominates OA’s TPR , but between attribute-reduced where piggy-backs are impossible and non-attribute-reduced where piggy-backs are possible. The results in OA and oA can still outperform both Oa and oa. The Nye test for yeast and for human is also more successfully passed by both OA and oA than either Oa or oa Click here for additional data file.File S2(XLS)Click here for additional data file.


Vertebrate DNA can be chemically modified by methylation of the 5 position of the cytosine base in the context of CpG dinucleotides. This modification creates a binding site for MBD (methyl-CpG-binding domain) proteins which target chromatin-modifying activities that are thought to contribute to transcriptional repression and maintain heterochromatic regions of the genome. In contrast with DNA methylation, which is found broadly across vertebrate genomes, non-methylated DNA is concentrated in regions known as CGIs (CpG islands). Recently, a family of proteins which encode a ZF-CxxC (zinc finger-CxxC) domain have been shown to specifically recognize non-methylated DNA and recruit chromatin-modifying activities to CGI elements. For example, CFP1 (CxxC finger protein 1), MLL (mixed lineage leukaemia protein), KDM (lysine demethylase) 2A and KDM2B regulate lysine methylation on histone tails, whereas TET (ten-eleven translocation) 1 and TET3 hydroxylate methylated cytosine bases. In the present review, we discuss the most recent advances in our understanding of how ZF-CxxC domain-containing proteins recognize non-methylated DNA and describe their role in chromatin modification at CGIs. The vast majority of cytosine methylation in vertebrates is found within the context of cytosine guanine dinucleotides (CpGs), occurring in up to 80% of CpGs in the genome ,2. MethyDespite the prevalence of CpG methylation, short ~1–2 kb) contiguous CpG-rich stretches of the genome exist which are generally refractory to DNA methylation ,9. These kb contiWith the knowledge that methylated CpG dinucleotides are recognized by MBD proteins, it was proposed that non-methylated CpG dinucleotides may also act as a protein-binding site. To explore this possibility, Skalnik and colleagues conducted a phage-based ligand screen to discover protein factors that have the capacity to bind non-methylated CpGs . From th2+ ions intervened by a seemingly divergent sequence that effectively segregates the ZF-CxxC proteins into three distinct subtypes  primary sequence of the ZF-CxxC domain and its conspicuous arrangement of ion-co-ordinating cysteine residues suggested, even without atomic resolution information, that the ZF-CxxC domain would form a compact DNA-binding module A. It was4/5CGxCxxC and CxxRxC motifs  3 type-3 ZF-CxxC domain revealed a more flexible mode of DNA binding that permits recognition of non-methylated cytosine bases in either a CpG or a non-CpG context. Similar to the type-1 domains described above, the type-3 ZF-CxxC domain of TET3 forms a crescent-like structure with a positively charged DNA-binding surface that wedges into the DNA major groove [CGT context (CmCGG) revealed a unique capacity for the type-3 ZF-CxxC domain to interact with unmodified cytosine in a non-CpG context. In this sequence, the ZF-CxxC domain shifts one nucleotide along to interact with the non-methylated cytosine , the DNA-binding interface of the TET3 ZF-CxxC domain is not as rigid as those found in type-1 ZF-CxxC domains. The increased flexibility that this confers allows TET3 to seemingly recognize non-methylated cytosine bases in a broader range of sequence contexts, albeit with a slight preference for CpG [Interestingly, a recent structural study of the r groove   to convincingly demonstrate at the genome-scale that the ZF-CxxC domain can function as a CGI-targeting module [in vivo.g module ,14. In t36 with a preference for the dimethyl modification state (H3K36me2) [in vitro [KDM2A is a JmjC (Jumonji C) domain-containing histone lysine demethylase enzyme which catalyses removal of methylation from histone H3 Lys3K36me2) . In addiin vitro . KDM2A iin vitro   or tudor domain effector proteins [CFP1 encodes a type-1 ZF-CxxC domain and is essential for early mouse development . The faigulation . The CFPgulation –44. The gulation ,43. Thesgulation ,45–47, cgulation ,48. Althproteins –53.Genome-wide binding studies in mouse brain tissue demonstrated that CFP1 associates with more than 80% of CGIs, and almost all CFP1-bound CGIs exhibit significant enrichment of H3K4me3  Figure . SimilarIn keeping with a role for CFP1 in targeting H3K4 methylation, mouse ESCs with constitutively deleted CFP1 exhibit a loss of H3K4me3 at up to half of CGIs in the mouse genome . SomewhaIn addition to H3K4me3 loss at CGI promoters, CFP1-null ESCs also appear to mistarget H3K4me3 methylation. The resulting ‘ectopic’ H3K4me3 peaks appear at numerous intergenic regions of the genome, and genes within the vicinity of these new H3K4me3 sites often displayed increased transcription . Reintroin vitro [in vivo is not fully understood.In addition to CFP1-containing SETD1 complexes, links between the mammalian H3K4 methylation systems and recognition of non-methylated DNA via ZF-CxxC domains extends to the MLL family. The MLL H3K4 methyltransferase family comprises four large proteins (MLL1–MLL4) that form independent multisubunit complexes that share a set of interaction partners with the SETD1 complexes, including ASH2L, WDR5, RbBP5 and DPY-30 . MLL1 a, whereasin vitro ,60, but HoxA cluster, a GC-rich genomic region exhibiting numerous CGIs [in vivo.MLL1 plays an essential role in early mammalian development and in definitive haemopoiesis ,62. At aous CGIs ,64  . Despitein vitro ,60  that recognizes hemimethylated CpGs and is essential for correct targeting of DNMT1 [DNMT1 is a large modular protein composed of a RFTS (replication foci-targeting sequence), a type-1 ZF-CxxC domain, a pair of BAH (bromo-adjacent homology) domains (BAH1 and BAH2), and a C-terminal catalytic domain . DNMT1 aof DNMT1 –90.The presence of a functional type-1 ZF-CxxC domain in DNMT1  is perhaThe ZF-CxxC-dependent autoinhibitory model was based on the study of a truncated form of DNMT1 that does not include the N-terminal RFTS domain. A subsequent analysis of full-length DNMT1 revealed that the ZF-CxxC domain did not influence its preference for hemimethylated over unmodified DNA substrates . This obin vitro [in vitro did not affect the recruitment of MBD1, suggesting that functional MBD1 targeting can be achieved in the absence of the ZF-CxxC domain [The transcriptional repressor MBD1 encodes an MBD capable of recognizing methylated CpGs –97 and tin vitro ,96. The in vitro . HoweverRecently, it has become apparent that vertebrate genomes contain small yet significant levels of 5hmC (5-hydroxymethylcytosine) –103. 5hmThe three TET enzymes have distinct expression patterns and exhibit different phenotypes upon genetic perturbation, suggesting that they may have unique functions during development and in specific cell types. TET1 is highly expressed in mouse ESCs  where itin vivo evidence suggests that, at least in some instances, TET ZF-CxxC domains may constitute CGI-targeting modules. A number of independent studies have profiled TET1 localization genome-wide in mouse ESCs [in vitro pull-down assay [The type-3 ZF-CxxC domains found in TET1 and TET3 differ from type-1 domains, as they exhibit a truncated linker region and a divergent DNA-binding loop A. The couse ESCs ,117,119 cis-regulatory elements [On the basis of the enzymatic activity of TET proteins, there has been intense focus on determining where 5hmC is found in the genome. Genome-wide mapping studies using a variety of approaches have suggested that 5hmC is enriched at gene promoters with intermediate to high CpG density ,117,119,elements ,120,121.elements  (Figure in vivo are essential in achieving this goal.In order to fully understand the contribution of CGIs to gene expression, an important future challenge is to elucidate the influence that ZF-CxxC proteins have on CGI function. Although there has been a significant amount of progress in this area over the last few years, clearly a more defined grasp of ZF-CxxC DNA-binding specificity and detailed understanding of ZF-CxxC domain-containing protein localization and function


In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life. Enzymes, as biological catalysts, are crucial to life. Understanding how enzymes have evolved to perform the wide variety of reactions found across all kingdoms of life is fundamental to a broad range of biological studies, especially those leading to new therapeutics. To unravel the evolution of novel enzyme function requires combining information on protein structure, sequence, phylogeny and chemistry . We have developed a protocol for integrating this wide range of data, which we have applied to a relatively large number of families comprising some very diverse relatives. This has permitted us to present an initial overview of the evolution of novel enzyme functions, in which we observe that some changes in function between relatives are more common than others, with most of the functionality observed in nature confined to relatively few families. Moreover, we are able to identify the evolutionary route taken within a superfamily to change the enzyme function from one reaction to another. This information may help in predicting the function of an enzyme that has yet to be experimentally characterised as well as in designing new enzymes for industrial and medical purposes. Enzymes, as biological catalysts, are critical for life, with a significant proportion (approximately 45%) of gene products annotated as having an enzyme function An enzyme's function, and in particular the reaction chemistry it catalyses, is encapsulated by a hierarchical classification system developed and maintained by the Enzyme Commission (E.C.) In general, it is possible to organise and classify proteins into families and superfamilies based on similarities between sequence and/or structure. Very distant relationships between proteins can usually be more successfully detected through analysis of their three-dimensional atomic structures rather than by sequence alone http://www.ebi.ac.uk/thornton-srv/databases/FunTree/).A number of studies have been undertaken on collections of superfamilies whose membership predominantly consists of enzyme structures and sequences We apply the pipeline to analyse enzyme superfamilies in CATH, using robust structurally-informed multiple sequence alignments to build phylogenetic trees, which are then annotated with structural and functional data. Relationships between metabolites, obtained by exploiting tools for comparing small molecules, are displayed on the phylogenetic tree. We have chosen two specific superfamilies to illustrate the value of combining structural and functional data to explore evolutionary changes. Analyses of these functional changes in 276 well-defined enzyme superfamilies has allowed us to present a preliminary overview of the evolution of novel enzyme functions in order to begin to gather, catalogue and classify the emergence of the catalytic reactions necessary for life.In order to understand the phylogenetic relationships and divergence of functions between protein domains, the first step is to identify related domains using both three dimensional data, based on CATH definitions, and sequence data. However, some of the structural superfamilies in CATH are highly diverse, containing very distant relatives with pairwise sequence similarities less than 10%. Whilst the core of the structural domain is generally well conserved, in some superfamilies distant relatives may exhibit different structural embellishments to this core Therefore, we have developed a protocol for identifying groups of structurally similar relatives within a superfamily that can be well aligned and superimposed in 3D. These are termed ‘structurally similar groups’ (SSG). Sequence relatives were added to each SSG and then all sequences were multiply aligned and used to derive the phylogenetic tree for that SSG  of a superfamily of enzymes with their structures and evolutionary history. We use two superfamilies to illustrate how FunTree captures and describes changes in function. These two superfamilies exemplify two different paradigms of enzyme evolution. We then integrate the functional changes of all 276 superfamilies, giving for the first time an overview of our current knowledge of the scope and evolution of the ‘reactions of life’ as known today.The phosphatidylinositol (PI) phosphodiesterase superfamily (CATH id 3.20.20.190) is relatively structurally conserved with all domain structure representatives in one structurally similar group (SSG). There are only four different MDAs, with only one change in domain partner having a corresponding change in function see . HoweverThe phylogenetic tree for this SSG has three distinct clades. The first clade (C1) contains a variety of general and specific phosphodiesterases see  from bacIn the second clade (C2) in the phylogenetic tree the enzymes become lyases, which are found in bacteria and protozoan trypanosomes rather than mammals, changing between a hydrolase (E.C. 3.1.4.11) and a lyase (E.C. 4.6.1.13/4.6.1.14). The mammalian and bacterial phosphodiesterases all follow the same initial mechanism, the intramolecular addition of a hydroxyl group adjacent to the phosphate with elimination of the first alcohol substrate; however, for the bacterial enzymes in the second clade the metal cofactor is not present. This results in the cyclic intermediate leaving the active site prior to hydrolysis (thus defining a lyase rather than a hydrolase) whereas in the mammalian case, the intermediate is strongly bound and hydrolysis occurs within the enzyme. In both cases a pair of histidine residues act as general acid/base catalysts in the mechanism. The structure-informed sequence alignment reveals that none of the three metal chelating residues are conserved between these two clades.A fourth phosphodiesterase enzyme is also found in the third clade (C3) but this acts on 2-lysophosphatidylcholine (E.C. 3.1.4.41) and is involved in the generation of venom in Sicariid spiders nd level) and substrate specificity (4th level) within a superfamily indicate transitions that have occurred since the protein diverged from its common ancestor. This superfamily has undergone a single transition between the hydrolase and lyase classes with no changes occurring at the sub-class level  is relatively structurally diverse, with three SSGs See . SSG 1 cin vivo they are part of a large multi-subunit machine.Some of the domain relatives in this superfamily form part of the proteasome, a large and important multi-subunit complex found in all major kingdoms that undertakes the vital function of eliminating proteins that are mis-folded, harmful or unnecessary A separate set of structures and sequences, which are singleton SSGs (ie they contain only one structure in the SSG), are associated with glutamate synthesis . All use glutamate but differ in the co-factor that they use which varies between NAD+, NADP+ and ferredoxin. There is another singleton with a very different function (E.C. 3.5.1.11 - penicillin amidase) associated with it. Examination of the known structures and mechanisms reveals that this function is performed by one of the other domains in the enzyme and is also found in some multi-domain enzymes where the Ntn-terminal type amide hydrolasing domain is not found. This highlights the care needed when associating function with an enzyme with multiple domains.We performed the same analysis as for the previous superfamily cataloguing changes in E.C. numbers at various levels see . This isThe 276 superfamilies processed in the current version of FunTree account for approximately 15% of all known domain assignments in CATH-Gene3D. The top 10% of superfamilies in our data (<30 superfamilies) ranked by number of SSGs account for 1,064,627 sequences (49% of sequences in our data) with on average 5 SSGs per superfamily. The rest of the superfamilies have on average only one SSG per superfamily. Whilst the majority (∼75%) of the superfamilies contain only one structurally similar group (SSG), indicating that most superfamilies show limited structural divergence see  a few suThere is some correlation between the number of SSGs and MDAs . This is expected since structural modifications and decorations to the central core facilitate new interactions with domain partners, as in the Ntn-terminal type amide hydrolasing superfamily. However, the number of unique multi-domain architectures in each superfamily correlates poorly with the number of unique E.C. numbers . This indicates that, although in some families a domain partner brings an increase in functional diversity, surprisingly there are a number of families where most of the functionality is present in the single domain, for example the terpene synthases/cyclases and the phosphatidylinositol-phosphodiesterases.th level in each superfamily. Although a significant number of our superfamilies have both general chemistry and substrate diversity present in the superfamily e.g. the NADH binding domain superfamily, most functional diversity comes from utilising multiple substrates with 177 (67%) superfamilies where changes in E.C. occur only at level 4 i.e. change in substrate specificity. This builds upon our previous smaller scale studies The distribution of the number of associated functions for each superfamily, as defined by the E.C. number and our general observations indicate that, although the majority of members of an enzymatic superfamily share a common function, some superfamilies have the ability to accommodate many diverse functions see . The topE.C. numbers attributed to these 276 superfamilies (including relatives where the domain is in different MDA contexts) account for 71% of the 2,676 E.C. numbers assigned to known enzymes, with the E.C. numbers associated with single domain enzymes accounting for approximately 36%. The high coverage of enzyme functionality from just 276 superfamilies, given that this represents only 15% of known domains, is surprising. Moreover, just 45 superfamilies account for 50%, of all sequences assigned E.C. numbers with 31 superfamilies in which the single domain accounts for 25%.From this we can postulate that a limited repertoire of structural frameworks has evolved to carry out a large proportion of reactions required for all of life. Moreover, it is clear that generating new chemistry does not necessarily require large leaps, such as the evolution of novel protein structures or large structural re-arrangements, but can be made by small local changes e.g. residue substitutions or small insertions or deletions. Functional changes can also arise from changes in MDA and less frequently insertion/deletion of unstructured regions. This is perhaps not surprising since residue changes in the active site can easily induce changes in chemistry. Superfamilies supporting a wide range of enzyme functions predominantly adopt one of a few relatively highly populated superfamilies, such as the TIM barrel or Rossmann-like fold, which both possess large surface clefts likely to tolerate residue mutations We also observe that the addition of another domain or set of domains can bring a function associated solely with those domains and not with the superfamily domain see  and S10 The major reason for this work was to explore the evolution of enzyme function; therefore we examined the range of E.C. classes found within each superfamily. We did this first by using the phylogenetic tree derived by FunTree to identify the evolutionary route taken within a superfamily to exchange the enzyme function from one reaction to another. It can be seen from Using the evolutionary route we are able to determine the structural changes associated with the functional shifts, which can include modulation of functional residues or through the loss or gain of loop regions. For all changes in function in the phylogenetic tree found at a bifurcation with a single function represented on each side of the divide, we catalogued whether known catalytic site residues were aligned to gapped (with a minimum gap space of three places) region. The requirement for high quality annotations and single functional changes means that the total number of exchanges catalogued is small . Only 5% of functional shifts are associated with the addition/deletion of loops and these functional changes split equally between changes in E.C. classes and within E.C. classes.In addition, we also catalogued where these exchanges corresponded to a change in the multi-domain architecture (MDA). This showed that 27% of changes were associated with changes in the multi-domain architecture, although this is an upper estimate as it includes enzymes where acquisition of function is through the addition of a domain and not by changes in the domain under scrutiny.However, by counting E.C. exchanges using the FunTree derived phylogenetic trees E.C. exchanges occurring between SSGs will be missed see . In addiBy combining information from the 276 superfamilies we can generate an E.C. exchange matrix summing all possible changes within the superfamilies. This gives 2502 unique exchanges at all levels of the E.C. classification, with 354 exchanges  in the primary E.C. level.2 test of the two matrices shows they are significantly  different. The most striking difference is that exchanges within a class , are much more common than would be expected. These interchanges represent 85% of all changes observed, with most occurring in the first three classes as expected due to the higher number of divisions in E.C. 1, E.C. 2 and E.C. 3.In addition to counting all possible changes, the all-by-all counting permitted a random model of expected changes to be generated based on all the E.C. numbers present in the dataset, with pairs of E.C. numbers picked at random to generate the matrix. Comparison using a χwithin a class are removed from the matrices .Our analysis has reinforced the observation that enzyme evolution is incredibly complex, with many different routes being taken to obtain different reactions, mechanisms and specificities within a superfamily. Such routes involve gene duplication followed by sub-functionalisation. The basis of such sub- functionalisation can be twofold: Firstly, by alteration of the enzyme structure, either by mutations, local insertions and/or deletions within a domain, or secondly by changes in multi-domain architecture as exemplified in the Ntn-terminal type amide hydrolasing superfamily. From a chemistry perspective, these structural changes can affect the overall reaction or the substrates, as exemplified in the phosphatidylinositol phosphodiesterase superfamily.The tools we have developed in FunTree bring together all the relevant data to help understand the molecular basis for each reaction change, but still require detailed inspection of enzyme mechanisms (as captured in MACiE) and three-dimensional structures to achieve a thorough understanding much has we have already done for the phosphatidylinositol phosphodiesterase and Ntn-type amide hydrolases superfamilies. These superfamilies provide exemplars of the type of analysis that is possible using the resource. The primary level of the E.C. classification can be summarised by simple chemical reactions see . We hopeFunTree is based on domain superfamilies defined by the CATH ‘H’ level. The superfamilies used in this study were selected based on possessing active site residues, as identified from the MACiE database, located on a single CATH domain. These superfamilies were subsequently grouped according to structural similarity and according to their domain partners.Although domains in a superfamily share a common structural core, distant homologues can show considerable variation outside this core, making it hard to robustly superimpose all domains within some superfamilies. Therefore we identified structurally similar groups (SSGs) of non-redundant domains with greater than 35% sequence identity which could be superimposed with a root mean squared deviation of less than 9 Å. Multiple structure alignments were generated using CORA A domain is often part of a larger protein containing other domains that may be contributing to the protein's overall function, thus alignments of the entire protein sequence are also useful. We group together domains within a superfamily sharing the same domain partners and multi-domain architecture (MDA). For each superfamily in the FunTree dataset, protein sequences having the same MDA are aligned. CATH-Gene3D defines the MDA of each protein by initially scanning the sequence against hidden Markov models built from CATH domains. Any unassigned sequence regions large enough to constitute a domain are checked against Pfam and if a non-overlapping Pfam domain is found then it is included in the MDA. Sequences with the same multi-domain architectures are clustered using ArchSchema Some superfamilies can have an extremely large number (tens of thousands) of associated sequences. This can lead to problems in both generating the alignments and calculating the phylogenetic trees, so the sequences are first filtered to reduce redundancy and numbers. If a family contains more than a few hundred sequences, the sequences are filtered by taxonomic lineage and uniqueness of function, retaining only unique representatives of each. The alignments from the SSGs and MDAs are used to generate a phylogenetic tree built with TreeBest  PDB, CATH and MACiE references. In addition, the E.C. number is shown along with the clustering of metabolites as coloured boxes where the colour of the box indicates the relative similarity using a rainbow-colouring scheme. At the end of each leaf the multiple domain architecture is also show as coloured bars, with the phosphatidylinositol-phosphodiesterase defining domain identified in red. Three groupings can be detected in the tree: the first (G1) is the phosphodiesterases involved in glycerol metabolism and the mammalian phosphatidylinositol phosphodiesterases. The second group (G2) out-lying to G1 consists of just the phosphatidylinositol phosphodiesterases from bacteria. The third group (G3) out-lying to the rest of the phylogeny is the phosphodiesterases acting on 2-lysophosphatidylcholine in the generation of spider venom. Two sequences indicated by * are included in the tree which have structural data classified by CATH but are not included in the ArchSchema graph in (EPS)Click here for additional data file.Figure S2Inositol-1,4,5-trisphosphate Bound to a Structure of a Mammalian Phosphoinositide-specific phospholipase C. A LigPlot diagram of the residues interacting with the bound inositol-1,4,5-trisphosphate cognate ligand of phosphoinositide-specific phospholipase c-delta1 from rat (PDB ID 1djx). The residues directly hydrogen bonding (indicated by a dashed green line) with the ligand as well, as the ligand itself, are shown as ball and stick. Below are shown the residues identified in the plot mapped on the multi-domain architecture. It can be seen that the second domain does not participate in any interaction with the liagnd or active site and does not directly affect the enzymatic function.(EPS)Click here for additional data file.Figure S3The Different Mechanisms Undertaken by Phosphatidylinositol-Phosphodiesterase Superfamily. The first split is between the case where a histidine acts as a nucleophile  and the histidines acting as general acid bases with a cyclic-phospho intermediate. There is a further split in the general acid/base class where, in eukaryotes, a metal ion holds the intermediate firmly in the active site, allowing hydrolysis to occur within the enzyme whereas, in the bacterial case, the metal is no longer present and so the intermediate diffuses from the active site as the cyclic-phospho species, and undergoes hydrolysis outside of the enzyme and thus is classified as a lyases (EC 4) rather than a hydrolase (EC 3).(EPS)Click here for additional data file.Figure S4Changes in Functional Residues in the Phosphatidylinositol-Phosphodiesterase Superfamily. The alignment of two representative structures, one from each of group 1 and group 3, using the structure based multiple sequence alignment used to generate the SSG phylogenetic tree of the phosphatidylinositol-phosphodiesterase superfamily. Residue conservation is highlighted in blue, with the graduation of the colour representing the level of conservation, as measured by ScoreCons. Catalytic residues are highlighted in red.(EPS)Click here for additional data file.Figure S5Changes in Functions within Two Example Superfamilies. Heatmaps showing the cumulative changes in phosphatidylinositol-phosphodiesterase (A) and Ntn-type amide hydrolase (B) superfamilies, where a change is observed based on the differences in E.C. annotations at the class level in a superfamily. The colour intensity indicates the number of times a change in E.C. class occurs. The top right half of the matrix shows the total counts of changes in E.C. class, while the lower right half shows the counts obtained by using the phylogenetic tree to infer the order in which changes have occurred. Along the matrix diagonal the number of changes occurring at the subclass (2nd level) of the E.C. classification within that class are shown, with the number of changes occurring at the 4th level of the E.C. number shown in brackets.(EPS)Click here for additional data file.Figure S6Structurally Similar Groups and Structural Diversity for the Ntn-Type Hydrolasing Superfamily. A schematic dendrogram (top left) showing the relative similarity based on SAP scores between each of the CATH representative domains (CATH domain identifiers are given at the leaves). Below, the representatives superimposed based on the cores of the domains. The representative from each SSG is shown on the right.(EPS)Click here for additional data file.Figure S7The Common Mechanism of the Ntn-Type Amide Hydrolasing Domain. In the majority of cases the N-terminal nucleophile is cysteine (as shown here), however in the protease enzymes it is threonine and, in the case of glutaryl-7-aminocephalosporanic-acid acylase (EC 3.5.1.93) is it serine. The variability of the reactions seen in this superfamily come entirely from the transport of the ammonia product (of the glutamine hydrolysis reaction) to a second domain, which then utilises the ammonia in a second reaction. In the case of the hydrolase enzymes in this sub-superfamily, there are no second catalytic domains associated with the mechanism, and so we see limited variability in the reactions.(EPS)Click here for additional data file.Figure S8Contextualising Domain Partners with the Ntn-Type Hydrolasing Domain. Relationships between the Ntn-type hydrolasing defining domain and its various domain partners in the context of the extended multi-subunit molecular machines they form. Representatives of each of the MDAs found in the 3 SSGs are shown.(EPS)Click here for additional data file.Figure S9The Effect of Multi-Domain Architecture on Function. For each superfamily, the fraction of its E.C.s found in a single domain is shown in green and those found in combination with other domains (MDA) in blue. Clearly novel function can be brought to any protein by adding another domain or set of domains . The proportion of functions that results from this domain addition is not coloured.(EPS)Click here for additional data file.Figure S10Determining Contribution of Domains in MDA to Function. For a superfamily domain (domain C), the functions for the multi-domain architectures that contain that domain are collected. To determine if the function is associated solely with the MDA or if the function exists without the context of domain C, each of the possible linear combinations of other domains in the MDA are analysed for functional annotations. If the novel function, in this example F4, is found in a different MDA context then there is ambiguity about the contribution of domain C to the function.(EPS)Click here for additional data file.Figure S11Changes in Function within 276 Superfamilies Based on Phylogenetic Trees.A. A heatmap showing the cumulative changes in all superfamilies where a change is observed based on the differences in E.C. annotations at the class level in a superfamily using the phylogenetic tree to infer the order in which changes have occurred. The colour intensity indicates the number of times a change in E.C. class occurs. As exchanges can occur more than once in a tree the number of observations are elevated compared to a simple all-by-all comparison (shown as the superscript number). For example, in the exchanges occurring within the E.C.1 class, 651 more observations are made than the 646 made in the all-by-all matrix. In addition, exchanges that occur between SSGs and therefore phylogenetic trees, are not counted (shown as a negative subscript number). Thus for the exchanges occurring within the E.C. 1 class, 85 exchanges are not accounted for. This results in 1,212 observations being made . B. A heatmap showing the cumulative changes in all superfamilies of enzymes with a single domain where a change is observed based on the differences in E.C. annotations at the class level in a superfamily. The colour intensity indicates the number of times a change in E.C. class occurs. The top right half of the matrix shows the percentage of changes  of changes in E.C. class observed across all 276 superfamilies, while the lower right half shows the percentage of changes expected based on a random simulation of E.C. changes. Along the matrix diagonal the number of changes occurring within the E.C. class at the 4th level of the E.C. number. C. The same exchanges as described for A but concentrating on the interchanges between classes. The top right of the matrix shows the observed percentage of changes, with actual count totals in brackets, and a random model of expected interchanges in the lower left of the matrix.(EPS)Click here for additional data file.Figure S12General Reactions for Each E.C. Class. Generalised reactions for each of the main classes in the E.C. classification and the average number of steps in each class .(EPS)Click here for additional data file.Text S1Supporting Material and Methods and Results sections. The Methods section outlines the data collection of sequence, structure and functional data. It also describes the comparison of small molecule metabolites and determination of a domain's contribution to function in a multi-domain architecture. The Results section highlights the details of the changes in function within the first clade of the phosphatidylinositol-phosphodiesterases superfamily as well as details of the reaction mechanisms of the Ntn-type amide hydrolasing superfamiliy.(DOC)Click here for additional data file.


IPK is one of the endoplasmic reticulum- (ER-) localised DnaJ (ERdj) proteins which interact with the chaperone BiP, the mammalian ER ortholog of Hsp70, and are thought to contribute to the specificity and regulation of its diverse functions. P58IPK, expression of which is upregulated in response to ER stress, has been suggested to act as a co-chaperone, binding un- or misfolded proteins and delivering them to BiP. In order to give further insights into the functions of P58IPK, and the regulation of BiP by ERdj proteins, we have determined the crystal structure of human P58IPK to 3.0 Å resolution using a combination of molecular replacement and single wavelength anomalous diffraction. The structure shows the human P58IPK monomer to have a very elongated overall shape. In addition to the conserved J domain, P58IPK contains nine N-terminal tetratricopeptide repeat motifs, divided into three subdomains of three motifs each. The J domain is attached to the C-terminal end via a flexible linker, and the structure shows the conserved Hsp70-binding histidine-proline-aspartate (HPD) motif to be situated on the very edge of the elongated protein, 100 Å from the putative binding site for unfolded protein substrates. The residues that comprise the surface surrounding the HPD motif are highly conserved in P58IPK from other organisms but more varied between the human ERdj proteins, supporting the view that their regulation of different BiP functions is facilitated by differences in BiP-binding.P58 IPK (58-kDa inhibitor of protein kinase) is a member of the endoplasmic reticulum- (ER-) localised DnaJ family of proteins (ERdj proteins) which interact with BiP, an important regulator of the functions of the endoplasmic reticulum (ER)  motifs. P58IPK has several known interaction partners and, like many members of the ERdj family, it appears to have quite diverse functions in the cell; it was initially identified as a cytosolic inhibitor of PKR (RNA-activated protein kinase) in influenza infection IPK to inactivate PKR, thereby allowing viral protein synthesis to continue IPK to PKR is inhibited through interaction with another protein, P52rIPK (52-kDa repressor of the inhibitor of protein kinase) P58IPK was later shown to be induced in response to ER stress IPK is one of the proteins that are transcriptionally induced during the UPR and is known to bind PERK (PKR-like ER kinase), one of the key regulators of the response, controlling PERK-dependent phosphorylation of eIF2 in the later stages of the response The transcription of P58IPK had already been linked to the UPR by its inhibition of PERK, its direct interaction with BiP came to light after the cellular localisation of the protein was re-examined by Rutkowski and co-workers IPK is now known to contain an N-terminal ER-localisation signal, and to be found primarily in the ER lumen; it is thought that slightly inefficient translocation explains the small fraction of the protein remaining in the cytosol IPK in the ER lumen and its involvement in the UPR have been the focus of many recent studies. Its confirmed involvement with BiP suggested a co-chaperone function, a role that has been endorsed by recent findings indicating that P58IPK can also make a direct interaction with unfolded protein substrates IPK deficient cells show a reduced ability to cope with misfolded proteins IPK in a mouse model causes a mild diabetic phenotype IPK binds unfolded protein substrates, stabilises them, and then subsequently binds BiP and facilitates the transfer of the substrate to the chaperone IPK to BiP. Recently, the first structure of the TPR domain of the mouse P58IPK protein was determined While P58IPK, including the BiP-binding J domain. The crystal structure reveals P58IPK to be an elongated protein, with the J domain attached to the very end of the TPR domain via a flexible linker. From the structure we can identify the probable binding site for BiP based on the position of the conserved histidine-proline-aspartate (HPD) motif which is necessary for Hsp40-Hsp70 interactions. This motif is positioned on the very edge of the elongated protein and at a distance of 100 Å from the previously suggested site for binding of unfolded protein substrates. The large distance between the two binding sites will have implications for the mechanism by which unfolded protein is passed from the co-chaperone to BiP.In this study we present the structure of human P58IPK, lacking the N-terminal signal recognition sequence, were initially prepared and screened for solubility with varied success. No constructs including the 43 C-terminal amino acid residues, which are predicted to be unstructured, could be expressed in soluble form. The longest soluble construct (residues 35–461) therefore consisted of the TPR domain and the J domain, without these C-terminal residues, and this was used for crystallisation. Extensive screening for crystallisation conditions was performed using protein both with and without the N-terminal His-tag that was included in the expression construct, but crystals were obtained in only a single condition using protein containing the intact His-tag . Despite extensive optimisation and screening of many crystals at the synchrotron beamline, these native crystals did not diffract beyond 4–5 Å. However, selenomethionine-substituted crystals, intended for phasing purposes, diffracted to a maximum of 3.2 Å and provided the dataset which was used to solve the structure in space group P312 using a combination of molecular replacement and single wavelength anomalous diffraction phasing. Finally, it was found that using this optimised condition to crystallise native protein, after enzymatic cleavage of the His-tag, yielded crystals that looked very similar but diffracted in space-group H32 . These crystals provided the dataset used to refine the structure at 3.0 Å. The two crystal forms are very closely related. Crystal form 1 (P312) has one chain in the asymmetric unit and crystal form 2 (H32) has three chains. If chain A of crystal form 2 is superimposed on the single chain in crystal form 1, chains B and C are shifted only 2–4 Å and 1–2 Å, respectively, from symmetry-generated chains in the other space-group. The crystals have estimated solvent content of approximately 72% in crystal form 1 and 71% in crystal form 2. Multiple constructs of the mature P58IPK is essentially complete and the majority of sidechains are clearly visible. In both crystal forms, the electron density in the TPR domain is of particularly high quality for the moderate resolution, while the density for the J domain is weaker. The linker between the TPR domain and the J domain (particularly residues 402–408) and a loop in the J domain (particularly residues 425–428) show highest flexibility, however even these regions are relatively well resolved in chain A of crystal form 2, which enabled model building. The final models of P58IPK both contain residues 35–455. The refinement statistics are summarised in In both structures, the mainchain density for P58IPK is an entirely α-helical protein with a very elongated monomer structure, approximately 120 Å long but only 20–50 Å wide  is unstructured.The J domain is composed of residues 393–455. The first three residues of the J domain (393–395) adopt an extended conformation, and are followed by a very short helical segment and a long loop (residues 398–408). This section, which essentially forms a linker between the TPR domain and the main body of the J domain, is then followed by two longer anti-parallel helices . After the second long helix the density gets weaker, indicating flexibility, although residues 452–454 form another single turn of helix. The last six residues of the J domain are not visible in the electron density, despite being present in the crystallised protein. This, together with our observation that protein constructs including the C-terminal amino acid residues are not soluble, would appear to confirm predictions that the extreme C-terminus of P58IPK; one between Cys248 and Cys258 linking helices 12 and 13 and one between Cys313 and Cys329 linking helices 15 and 16. This is consistent with P58IPK's primary location in the oxidative environment of the ER. Both disulfides are located in the core of the protein.There are two disulfide bonds in the structures of P58IPK was recently published, containing only the TPR domain (PDB ID: 3IEG) A partial structure of mouse P58IPK structure, we found that using the complete mouse TPR domain as search model for molecular replacement was unsuccessful; a solution could only be found after removal of the N-terminal half of the mouse structure from the model. The differences between the human and mouse crystal structures explain this observation, but also raise the question of whether the observed differences have any physiological relevance. It is possible that there could in fact be a structural difference between the two homologues, although this is unlikely considering their very high sequence identity. The isolated context of the mouse TPR domain could also account for the differences between the two structures; the absence of the J domain could potentially affect the shape of the TPR domain. A third, and perhaps more likely, possibility is that the differences arise as an effect of crystal packing. Each chain in our structure makes similar crystal packing interactions, and these are indeed different from those seen in the crystals of the mouse TPR domain. However, regardless of the reason, the observed differences in structure do emphasise an inherent flexibility of P58IPK, which is very likely to be important for its function. P58IPK binds multiple interaction partners, often two or more simultaneously, and the flexibility is likely to facilitate this.When solving the human P58IPK, share only the J domain. The structures of J domains from several different Hsp40s from various organisms have been solved, and they all contain four alpha helices; one short (I), two longer (II and III) and another short (IV). Between the second and third helices lies a conserved histidine-proline-aspartate (HPD) motif which is required for Hsp70 interaction IPK, and confirm the fold to be similar to those of other Hsp40s. A Dali Vibrio cholerae (PDB ID: 3HHO), which aligns with an RMSD of 1.5 Å over 61 amino acid residues. The structure of the two long helices, and the loop between them, is very similar to previous J domain structures, but in P58IPK the two short helices are barely present, with each represented by just one turn of a helix , and therefore the predicted BiP-interaction site, is located at the start of the loop between the two large helices of the J domain (helix II and helix III). The motif is positioned at the very edge of the elongated P58IPK structure, allowing plenty of space for interaction with the large BiP protein, and the sidechains of both the aspartate and the histidine are on the surface of the protein.Previous studies have confirmed that the conserved HPD motif of P58IPK the region encompassing the surface of helix II, the loop and the start of helix III is indeed comprised of a combination of positively charged and hydrophobic residues, and the charged residues are localised primarily to helix II, in agreement with previous data. However, P58IPK has a lower proportion of charged surface in this area than most other J domain structures, and those residues that are present  are on the N-terminal end of helix II which is furthest from the HPD motif  at the N-terminus. The sequences of the cloned constructs were verified by sequence analysis.The cDNA corresponding to the TPR repeats and the J domain (residues 35–461) of human P58IPK was expressed in Escherichia coli Rosetta (DE3) cells. Cultures in LB medium were incubated with shaking at 37°C to OD600 0.5–0.8, at which point the temperature was lowered to 18°C and recombinant protein production was induced by addition of isopropyl β-D-thiogalactopyranoside to a final concentration of 0.2 mM. The cultures were then grown for an additional 17–21 h at 18°C before harvest by centrifugation. Cell pellets were frozen and stored at −20°C until further use. Selenomethionine-substituted protein was produced by suppression of methionine biosynthesis using standard methods Recombinant human P58IPK by Ni-NTA affinity chromatography. Samples containing native and selenomethionine-derivative P58IPK were dialysed against crystallisation buffer , concentrated to 31 and 18 mg/ml, respectively, and used in subsequent crystallisation experiments. The size of the protein and the incorporation of selenomethionine were verified by mass spectrometry.Prior to purification, the cell pellets were thawed and resuspended in 5 ml/g cell weight of lysis buffer  supplied with Complete protease inhibitor cocktail (Roche) and lysed by sonication at 4°C. Cell debris was removed by centrifugation and the soluble fraction was filtered through double syringe filters (0.45 µm and 0.22 µm pore size). The protein was purified in three consecutive steps of chromatography. The cell lysate was loaded onto a HisTrap HP column  and eluted with increasing imidazole concentration, followed by anion exchange on a Q HiTrap column  against increasing concentration of NaCl. Size-exclusion chromatography was performed on a Superdex 200 column  using a gel-filtration buffer consisting of 50 mM Tris-HCl, pH 8.0, and 300 mM NaCl. When appropriate, the hexahistidine tag was removed by overnight incubation with TEV protease and separated from P58IPK was crystallised in two different crystal forms. Crystal form 1 (selenomethionine) grew at 4°C in drops comprised of 1 µl of well solution and 2 µl of protein solution (18 mg/ml). The well solution contained 0.1 M Tris, pH 8.5, 1.3 M succinic acid and 1% (w/v) PEG MME 2000. Crystal form 2  grew at 20°C in drops comprised of 1 µl of well solution and 2 µl of protein solution (31 mg/ml). The well solution contained 0.1 M HEPES, pH 8.2, 1.4 M succinic acid and 1% (w/v) PEG MME 2000. Crystals typically began to grow after 1–2 weeks. Prior to data collection, crystals were cryoprotected using artificial mother liquor containing 25% glycerol and were flash-frozen in liquid nitrogen.Initial crystallisation screening was performed by the sitting drop vapour-diffusion method using standard crystal screens . Suitable crystallisation conditions were established and optimised for native and selenomethionine-substituted protein. P58IPK crystals were collected at the European Synchrotron Radiation Facility, Grenoble, France. Anomalous data were collected from a single selenomethionine-substituted crystal of crystal form 1 to 3.2 Å resolution on beamline ID23-1, which is equipped with an ADSC Q315R CCD detector. 120 degrees of data were collected at a wavelength of 0.979 Å using a 1° oscillation angle. Higher resolution native data were collected to a resolution of 3.0 Å on beamline ID14-1 which is equipped with a ADSC Q210 CCD detector, using a single crystal of crystal form 2. 73.15 degrees of data were collected at a wavelength of 0.933 Å using a 0.55° oscillation angle. Data were processed using iMosflm X-ray diffraction data for native and selenomethionine-substituted P58IPK (PDB ID: 3IEG) C.elegans (PDB ID: 2QSA) as a template. The structure was refined with alternating cycles of restrained refinement in REFMAC5 Phases for crystal form 1 were obtained by a combination of single wavelength anomalous diffraction and molecular replacement (MR-SAD) using the PHENIX http://www.rcsb.org) with accession numbers 2Y4T and 2Y4U.Structure factors and the coordinates of the final models have been deposited in the Protein Data Bank  The TPR domain. (B) The J domain. (C) The linker between the TPR domain and the J domain.(TIF)Click here for additional data file.