Automated hierarchical classification of protein domain subfamilies based on functionallydivergent residue signatures
 Andrew F Neuwald^{1}Email author,
 Christopher J Lanczycki^{2} and
 Aron MarchlerBauer^{2}
DOI: 10.1186/1471210513144
© Neuwald et al.; licensee BioMed Central Ltd. 2012
Received: 6 February 2012
Accepted: 9 June 2012
Published: 22 June 2012
Abstract
Background
The NCBI Conserved Domain Database (CDD) consists of a collection of multiple sequence alignments of protein domains that are at various stages of being manually curated into evolutionary hierarchies based on conserved and divergent sequence and structural features. These domain models are annotated to provide insights into the relationships between sequence, structure and function via webbased BLAST searches.
Results
Here we automate the generation of conserved domain (CD) hierarchies using a combination of heuristic and Markov chain Monte Carlo (MCMC) sampling procedures and starting from a (typically very large) multiple sequence alignment. This procedure relies on statistical criteria to define each hierarchy based on the conserved and divergent sequence patterns associated with protein functionalspecialization. At the same time this facilitates the sequence and structural annotation of residues that are functionally important. These statistical criteria also provide a means to objectively assess the quality of CD hierarchies, a nontrivial task considering that the protein subgroups are often very distantly related—a situation in which standard phylogenetic methods can be unreliable. Our aim here is to automatically generate (typically suboptimal) hierarchies that, based on statistical criteria and visual comparisons, are comparable to manually curated hierarchies; this serves as the first step toward the ultimate goal of obtaining optimal hierarchical classifications. A plot of runtimes for the most timeintensive (nonparallelizable) part of the algorithm indicates a nearly linear time complexity so that, even for the extremely large Rossmann fold protein class, results were obtained in about a day.
Conclusions
This approach automates the rapid creation of protein domain hierarchies and thus will eliminate one of the most time consuming aspects of conserved domain database curation. At the same time, it also facilitates protein domain annotation by identifying those pattern residues that most distinguish each protein domain subgroup from other related subgroups.
Background
In order to provide rapid and sensitive annotation for protein sequences, including direct links to structural and functional information, the National Center for Biotechnology Information (NCBI) initiated the Conserved Domain Database (CDD) [1] —a collection of positionspecific scoring matrices (PSSMs) (essentially HMM profiles [2]) that are derived from protein multiple sequence alignments. As a result, webbased BLAST searches now include a search of the CDD, which allows users to visualize multiple sequence alignments and (via the NCBI Cn3D viewer [3]) structures of proteins sharing significant homology to the query and, within those alignments, key catalytic and ligandbinding residues. Thus BLAST searches linked to the CDD provide additional clues to the function and underlying mechanism of the query protein and are thereby often more informative, faster and more sensitive than searching against millions of individual protein sequences.
The CDD is comprised of domain models either manually curated at the NCBI or imported from other alignment collections such as PFam [4], SMART [5], and TIGRFAM [6]. A central and unique feature of the CDD is that related domains are organized into hierarchies when evidence exists to support that tree as a representation of the molecular evolution of the protein class. A significant bottleneck in the CDD pipeline is the curation of these hierarchies and the manual annotation of the corresponding profiles for functionally important residues (as gleaned from the biochemical literature). In order to begin automating this process, here we describe statisticallyrigorous procedures for automated creation of conserved domain (CD) hierarchies and for annotation of the corresponding profile alignments. These procedures do automatically, based on objective, empirical criteria, what the CDD resource group and similar groups currently do manually, based on classifications that have been established in the published literature, on phylogenetic and structural analysis and, to some degree, on subjective judgments. Our focus here is to obtain heuristically an initial (presumably suboptimal) CD hierarchy starting from a typically very large multiple sequence alignment for an entire protein class whose domain boundaries remain fixed. To do this we utilize procedures that obtain both subgroup assignments for aligned sequences and corresponding discriminating patterns associated with protein functionaldivergence.
The automated annotation of functionally critical residues is an important outcome of these proposed procedures: Just as a large enzyme class conserves residues directly involved in catalysis, protein subgroups conserve residues likely involved in subgroupspecific biochemical properties and mechanisms. Our procedures use statistical criteria to glean this biochemical information from patterns of divergent residues among related sequences in a manner similar to the use, by classical geneticists, of statistical criteria to glean information from patterns of divergent traits among related individuals. (To ensure that pattern residues are functionally important, we focus on residues that are conserved across distinct phyla and thus for more than a billion years of evolutionary time). By mapping various categories of pattern residues to corresponding PSSMs, BLAST searches against these improved CD profiles can reveal those residues most likely responsible for the specific biochemical and biophysical properties of a query protein. This can accelerate the pace of biological discovery by enabling researchers to obtain valuable clues regarding asyetunidentified protein properties through routine webbased BLAST searches.
Other methods that may be similarly described as addressing the protein subfamily classification problem find sequence clusters either based on pairwise similarity [7–11] or by cutting phylogenetic trees [12–16]. (Phylogenetic trees are, of course, likewise constructed based on sequence and profile similarity scores). Here we take a different approach, namely the hierarchical classification of a protein (domain) class based on functionallydivergent residue signatures. Unlike our approach, many existing methods, though not all (e.g., [15]), generally focus on the narrower problem of identifying orthologs or on the broader problem of clustering a database into unrelated protein classes rather than on constructing a hierarchy of domain profiles for a specific protein class. An approach, which is, in certain respects, similar to the one described here (along with some substantial differences), is the statistical coupling analysis method of Lockless and Ranganathan [17] for detecting sets of correlated residues in protein sequences [18].
Because our approach identifies residues associated with protein functional divergence, it is also related to "functional subtype" prediction (FSP) methods [19–33], but is distinct inasmuch as these related methods typically predict specific residue functions (such as catalytic activity or substrate specificity) that are sufficiently wellunderstood to allow benchmarking [34, 35]. Instead, our approach lets the data itself reveal its most statistically striking properties without making assumptions about the types of residues to be identified. It is further distinguished from each of these related methods in at least several of the following respects: (i) It does not require that the input alignment be partitioned into divergent subsets beforehand; this is unlike many [20–27], though not all [28–31] FSP methods. (ii) It has a rigorous statistical basis. (Though at least two other methods are Bayesian based [36, 37]). (iii) It is designed for very large input alignments. (iv) For optimization it relies on Bayesian sampling, which has a solid scientific basis [38]. (We are aware of only one other method [37] with a MCMC sampling component). (v) It separates out unrelated and aberrant sequences automatically. (vi) It can identify multiple categories of coconserved residues within a given protein. (vii) It addresses concurrently the problems of protein subfamily classification and of identifying residues associated with protein functional divergence. And (viii) it can accomplish all of this automatically starting from a single multiple sequence alignment.
Problem definition and solution strategy
Here we address the following biological and algorithmic problem: We are given as input a (typically very large) multiple sequence alignment corresponding to a particular protein class. Our objective is to partition this alignment into a tree of subalignments, termed a CD hierarchy, each subtree of which corresponds to a subalignment of sequences sharing a certain pattern that most distinguishes them from those sequences associated with the parent node of the subtree and with any other subtrees attached to that parent node. We interpret these distinguishing residue signatures as associated with functional divergence of the protein class. As mentioned above, our focus is on obtaining an initial, suboptimal hierarchy that is comparable to current CDD curated hierarchies and that can serve as a starting point for further optimization using either manual or automated methods. Here we describe statisticallybased heuristic procedures that, in conjunction with Bayesian sampling, can obtain such an initial hierarchy from a multiple sequence alignment.
Bayesian sampling over contrast alignment models
Multiple category functional divergence models
More recently, a multiple category (mc)BPPS sampler was developed [42] with a view to optimally assigning aligned sequences to various nodes within a predefined protein domain hierarchy based on functionallydivergent residue signatures. Thus the mcBPPS sampler aims to precisely define both the sequences belonging to each subgroup and the patterns most distinctive of each subgroup within a specific protein class. However, because the mcBPPS sampler does not define the hierarchy of contrast alignments, it requires that the user provide (as input) both a functional divergence (FD)table (formally termed a “hyperpartition”) and seed sequences for each divergent subgroup. (Seed sequences serve as Bayesian priors or—if viewed as a missing data problem [43]—as labeled sequences that are required to remain in their preassigned subgroups during sampling and that thus help define each subgroup. The remaining (unlabeled) sequences are assigned to subgroups through Bayesian inference).
Here we describe and apply an automated multiple category (amc)BPPS program that generates its own FDtable and seed sequences automatically and therefore merely requires a multiple sequence alignment as input. The number and nature of the partitions and the patterns is completely determined by the program. When used in conjunction with procedures for viewing structural interactions involving pattern residues, the amcBPPS sampler automates and enhances the creation and annotation of CDD hierarchical alignments. And, when linked into webbased BLAST searches, this can make previously inaccessible molecular information widely available.
Results and discussion
In this section, we lay out the basic amcBPPS algorithm, illustrate an implementation of the algorithm as applied to Ploop GTPases, compare its performance against various manuallycurated CD hierarchies, further evaluate its performance using both deletehalf jackknifing and simulations, and apply it to several large protein classes for which existing hierarchies or alignments are currently unavailable.
Algorithm
Identifying simple subgroups (Step 1)
Step 1 of the amcBPPS algorithm (represented by the arrows labeled ‘a’ and ‘b’ in Figure 3) first generates a forest of simple (rooted and branchless) trees, the leaves of which correspond both to functionallydivergent subgroups within the protein domain class and to subalignments of the input alignment. This is accomplished by obtaining seed sequences and using them to create these trees, as follows: (i) All closelyrelated sequences (by default, those sharing ≥ 95% identity) from the same phylum are clustered into a common set (that are thus disjoint from other such sets). (ii) All pairs of moderately related sequences (by default, those sharing ≥ 40% identity) from distinct phyla are stored on a heap (also called a priority queue) using their pairwise scores as the key. (iii) Iteratively remove the topscoring crossphylum pair from the heap and merge their two disjoint sets into one set. (Merging is done using an efficient algorithm described by Tarjan [45]). (iv) Once a disjoint set contains sequences from a predefined minimum number of distinct phyla (four, by default), the sequences of highest rank from each phylum are used to seed a new subgroup; the disjoint set is then labeled to avoid picking this subgroup repeatedly. (v) Keep generating subgroups in this way until a predefined number of seed sequence sets are obtained (typically 1–10), at which point a simple FDtable is constructed where each subgroup node is a direct descendent of the root node. (For the correspondence between a FDtable and the nodes of a tree, see Figure 2). And (vi) repeat substeps iiiv until all sequence pairs have been removed from the heap. To ensure that the subgroups are sufficiently diverse, we require that each seed set consensus sequence share less than a specified level of sequence identity with other seed set consensus sequences (< 40% identity, by default). Taken together, these substeps favor the identification of the most conserved and phylogenetically diverse subgroups.
For each of these FDtables (and the corresponding seed sequences) the mcBPPS sampler assigns each of the multiply aligned input sequences to a subgroup (as specified by the rows in the table) and determines the differentiating conserved pattern for each contrast alignment (as specified by the columns in the table). To ensure that subgroups at different levels of the hierarchy are identified, the algorithm performs multiple runs using various numbers of leaf nodes and various prior probability settings for P(R), P(α) and P(C) (which are defined in Figure 1). Convergence on protein subfamilies is favored by specifying a high number of leaf nodes (by default 10), by lowering the (Bernoulli distributed) prior probability for assigning a sequence to a leaf node, P(R), (where by default, r_{ l } = 0.01), by setting the (beta distributed) prior probability, P(α), to favor a lower degree of background contamination by assigning more pseudoobservations to pattern matching residues and fewer pseudoobservations to contaminating residues (by default, a_{ 0 } = 9 and b_{ 0 } = 1) and by raising the (beta distributed) prior probability that a column corresponds to a pattern position, P(C) (by default, ρ_{ j } = 0.01). The rationale for choosing these settings is that, for subfamilies, membership is more exclusive, sequences are more highly conserved and, consequently, conserved patterns more extensive. (Note, however, that, in the absence of such a rationale, noninformative priors are used by default (e.g., uniform beta and Dirichlet distributions) in order to maximize the influence of the data on model optimization.) Convergence on a superfamily is favored by specifying a single subgroup and by altering these prior parameter settings accordingly (where by default, r_{ l } = 0.2, a_{ 0 } = 1, b_{ 0 } = 1 and, ρ_{ j } = 0.0001). Default settings are based on applications to actual protein sequences, though it should be noted that the influence of these prior settings is minor. Hence these priors primarily function as tuning parameters to help gently guide the sampler into finding a variety of functionally divergent subgroups. To avoid finding the same subgroup repeatedly, sequences assigned to a subgroup in a previous run are prohibited from being used as seeds in subsequent runs. Subfamilies can also be identified recursively; that is, by rerunning the program on a single subgroup in order to find subgroups within subgroups (though this approach is not used here). The pseudocode for this step of the amcBPPS algorithm is given in Methods.
Defining a hierarchy for the protein class (Step 2)
Once individual subgroup sets are identified in Step 1 (see arrow labeled ‘b’ in Figure 3), the program hierarchically arranges these into a more complex tree, from which a FDtable is obtained. It does this using efficient bitwise set operations [46], standard network algorithms [45] and patternbased statistical criteria, namely the contrast alignment loglikelihood ratio (LLR) used by the mcBPPS sampler [40, 42] (the basic component of which is given in Figure 1). These hierarchicallyarranged subgroup sets are ‘fuzzy’ due to the uncertainty associated with set membership (being based, as it is, on imperfectly conserved discriminating patterns). Thus Step 2 of the algorithm (which corresponds to the arrows labeled ‘c’ through ‘e’ in Figure 3) determines which of the input sets (from Step 1): (i) are the same set; (ii) are distinct sets; or (iii) are supersets of another set or sets. Step 2 is subdivided into three substeps: (2a) merge each collection of subgroup sets deemed to be identical into a single set; (2b) cluster related subgroup sets into common supersets; and (2c) create a tree representation of the subgroup hierarchy, which is done using a breadthfirst scanning algorithm [47] to find a shortest path tree [45]. Step 2c also refines the tree to eliminate inappropriate overlap between sets while also eliminating nodes from the tree that, as a result of this refinement process, are no longer statistically significant. The pseudocode for Step 2 is given in Methods. From this tree a FDtable is then generated as input to Step 3.
The mcBPPS sampler (Step 3) and further refinements
The output from Step 2 provides a starting point for mcBPPS sampling, which optimizes the patterns and partitions corresponding to the FDtable. The basic statistical and algorithmic aspects of the mcBPPS sampler were previously described [42]. To further expand a CD hierarchy the output files obtained from an initial amcBPPS analysis can also be used to recursively analyze, in the same way, several of the larger subgroups. To do this, the output alignment file for a major subgroup is used as an input file for the amcBPPS program. Likewise, a CD hierarchy can also be refined by editing the FDtable manually and then applying the mcBPPS sampler, as was previously described [42]. To speed up analysis of a given subtree, such manually edited FDtables (guided by the tables obtained automatically) may be designed to expand subgroups within that subtree, while modeling the other branches of the hierarchy only at the highest levels (e.g., by modeling other subtrees off the main root as single nodes). In keeping with the automated theme of this article, however, we will not describe in detail nor apply these approaches here.
Implementation and testing
The amcBPPS algorithm was implemented in C++ (executables are available from the corresponding author), applied to various protein classes and the output compared to manuallycurated CDD alignment hierarchies (when available). A wide range of CDD hierarchies—from preliminary to welldeveloped releases (as well as some outofdate versions)—were examined in this way. Input multiple alignments were obtained by using the NCBI hierarchy of CD alignments as input to the MAPGAPS program [48], which detected and aligned related protein sequences within the NCBI nr, env_nr and translated EST protein databases. To obtain input alignments corresponding to large protein classes for which CDD hierarchies are not yet available we used alternative procedures, as described in Methods.
Comparison of curated and automaticallygenerated domain hierarchies
CDD  Protein superfamily  number  length  Manually curated  Automatically generated  

Ident.  seqs^{‡}  nodes^{*}  LLR^{†}  nodes^{*}  LLR^{†}  time^{§}  
cd00030  C2  23,452  102  106 (103)  236574  78(73)  223857  19.4 
cd00138  PLDc_SF  16,765  119  105 (102)  241766  36(34)  192876  10.0 
cd00142  PI3Kc_like  2,409  219  22  34129  16  34563  4.5 
cd00159  RhoGAP  4,815  169  39(38)  55604  32  53540  7.97 
cd00173  SH2  5,917  79  111 (101)  49274  39  40075  3.5 
cd00180  Protein kinases  104,912  215  280(260)  1378273  107(104)  1536991  241.0 
cd00229  SG NH_hydrolase  14,635  187  30  180667  29  183822  14.95 
cd00306  S8/S53 peptidase  10,960  241  36  161685  45(44)  173693  30.90 
cd00368  MolybdopterinBinding  9,540  374  26  177569  44  209704  39.3 
cd00397  DNA_BRE_C  25,824  164  27 (26)  187382  39(37)  211739  16.9 
cd00761  Glycosyltransferase A (GTA)  66,260  156  71 (70)  944727  123(110)  1048396  193.8 
cd00768  Class II aaRSlike core  37,160  211  17  674454  31  833691  54.3 
cd00838  MPP_superfamily  33,753  131  61  402297  55(54)  399553  65.1 
cd00900  PHlike  22,593  99  81  211812  99(98)  274945  52.3 
cd01067  Globin_like  9,933  117  4 (1)  11133  26 (25)  73808  4.3 
cd01391  Periplasmic_Binding_Protein_1  36,330  269  142(140)  619713  68(65)  580753  169.1 
cd01494  AAT_I (PyrodoxalPO4binding)  114,781  170  16  1086328  92(84)  2027660  249.67 
cd01635  Glycosyltransferase GTB  44,366  229  45  723443  95(93)  881414  232.7 
cd02156  Class I aaRSlike core √  53,605  105  34  522962  61(57)  698273  41.4 
cd02883  Nudix_Hydrolase  32,046  123  55 (54)  321636  61(60)  367819  43.2 
cd03128  GAT1 (mcBPPS vs pmcBPPS)  46,514  92  34(32)  319515  64(62)  388621  42.2 
cd03440  hot_dog  30,162  100  22(18)  141990  70 (69)  345298  39.1 
cd03873  Zinc peptidases  24,455  237  81  596408  69(66)  590521  43.9 
cd05466  Periplasmic_Binding_Protein_2  45,287  197  76(73)  523941  49(41)  411445  31.7 
cd06587  Glo_EDI_BRP_like  36,165  112  60 (58)  335848  94(91)  479522  54.8 
cd06663  Biotinyllipoyl  25,013  73  4  53038  25(18)  66571  4.53 
cd06846  Adenylation_DNA_ligase_like  3,833  182  14  43276  20  48,475  4.8 
cd08555  PIPLCc_GDPD_SF  8,707  179  74 (73)  143201  37(32)  123075  6.9 
cd08772  GH43_62_32_68 (β propellers)  6,760  286  28  111336  51(50)  176701  30.0 
cl09931  Rossmann fold proteins  424,764  93  361 (347)  4110907  145(130)  4029120  757.2 
Average  44,057  167.7  66.4  486696  56.9  556884  83.6 
Criteria for comparing hierarchies
To assess how well the amcBPPS program performs relative to curated CD hierarchies, we compared its output against 30 manually curated CDD hierarchies (Table 1). Before considering this analysis, however, we first need to discuss the criteria used to evaluate and compare hierarchies.
Lack of gold standards
CDD hierarchies have been carefully constructed by expert curators and therefore come the closest to a benchmark set for evaluating the amcBPPS sampler. However, as this study reveals, certain aspects of CDD hierarchies lack statistical support or are incomplete or incorrect for various reasons: For example, CDD hierarchies are typically at different stages in an ongoing refinement process, and, for protein domain classes consisting of tens or hundreds of thousands of sequences, the number of possible hierarchies to consider is astronomical, which makes optimization through manual curation extremely difficult. Furthermore, due to the stochastic nature of and the inability to directly observe evolutionary divergence, it is impossible to eliminate the inherent uncertainties associated with protein classification. Hence, for the present study our aim is merely to replicate the current manual curation process by generating hierarchies of comparable quality automatically, thereby dramatically speeding up the current laborintensive curation process.
Comparison criteria for this analysis
Evaluation of the amcBPPS program
To evaluate the amcBPPS program over a wide variety of input, we chose the 30 conserved domains given in Table 1. These domains vary in the numbers of members detected in the protein databases (from a few thousands to hundreds of thousands of sequences as indicated in column 3), in the lengths of their conserved core (from 73 to 374 residues; column 4), and in the size (column 5) and complexity (Figure 5) of their curated hierarchies.
Comparisons with manually curated CDD hierarchies
Based on the LLR statistic the automaticallygenerated hierarchies (column 8) are comparable to the corresponding manuallycurated hierarchies (column 6) and are, in fact, slightly better on average (556,884 nats versus 486,696 nats for the curated hierarchies). Manual and amcBPPS hierarchies (Figure 5) were also compared qualitatively by determining the degree to which the sequence sets corresponding to the nodes in one hierarchy overlap with those of the other hierarchy; in Additional File 1: Figure S3 illustrates such a comparison for the PI3Kc_like domain hierarchy (cd00142). This provides a detailed comparison of two hierarchies by computing how the sequences at each level of one hierarchy are assigned to each level of the other hierarchy and vice versa. Such comparisons indicate that the differences between the curated and amcBPPS hierarchies are mainly due to the following: (i) A node in one hierarchy being modeled as a subtree in the other. (ii) Additional child nodes being added to parent nodes in one hierarchy but not the other. (iii) A subtree in one hierarchy being split into unrelated subtrees (or nodes) in the other due to a failure to join these to a common internal (parent) node. And (iv) inherently ambiguous sequences that can’t be clearly assigned to a specific node in the hierarchy; such sequences may correspond to pseudogene products or functionally defective members of a protein class that are difficult to categorize because they harbor degenerate subgroup patterns. Of course, the larger and more functionally divergent hierarchies are more challenging.
Most of the differences between the manual and automated hierarchies are due to fundamental differences between the two approaches (as revealed by examination of comparative analyses like the one shown in Additional File 1: Figure S3). Curated hierarchies may rely, in part, on information that is (currently) ignored by the amcBPPS program, such as subfamilyconserved inserts and 3D structures—though, on the other hand, the amcBPPS program utilizes a far greater amount of sequence data that is also uptodate. In contrast, some of the CDD heirarchies may be outofdate or still incomplete. The amcBPPS program also requires each node to (initially) correspond to at least 30 sequences (by default) in order to avoid statistical biases due to small sample size. CDD curators, however, may construct subgroups containing fewer sequences. Likewise, the amcBPPS program selects seed sequences from at least three or four distinct phyla in order to avoid sampling biases introduced by orthologous sequences from closely related organisms. Hence, it will fail to identify subgroups that only occur in vertebrates, for instance. (Of course, this restriction could be relaxed somewhat by using less conservative, yet still valid criteria.) In contrast, CDD curators may choose representative sequences (which were used as seeds for the analyses in Table 1) from more closely related taxa. Due to such restrictions, an amcBPPSgenerated hierarchy tends to have fewer nodes (i.e., rows in the FDtable) because it is prohibited from identifying certain CDDdefined subgroups. This also tends to lower the LLR, which (other things being equal) increases with the number of nodes in the hierarchy. (This increase occurs at a slower rate as the number of nodes increases, however, inasmuch as the most strikingly divergent subgroups are typically modeled first.) Despite these differences, after taking these considerations into account, we found the amcBPPS hierarchies to be persuasively consistent with the corresponding CDD hierarchies (Table 1). A perceived “unfair” advantage of the amcBPPS algorithm might be that it utilizes the same statistical model to construct a hierarchy that is used to score that hierarchy, whereas curators do not. However, subsequent (patternpartition) optimization of both hierarchies using the mcBPPS sampler should counteract this advantage. That is, assuming that the curated hierarchy is in fact superior and that our statistical model is biologically meaningful, then optimal partitioning of the sequences and optimal pattern assignment by the sampler for both types of hierarchies should result in a superior LLR score for the curated hierarchy.
Deletehalf jackknife analyses
A bootstrap or jackknife [49] procedure can be used to estimate confidence levels for evolutionary trees [50]. However, applying this approach to a CD hierarchy is complicated by the potential runtorun variability both in the number of the leaf nodes and in the associated sequence sets. Thus existing evolutionary tree bootstrap and jackknife procedures, which require that each of the sampled trees use the same set of leaf nodes, cannot be used. Instead, we implemented a deletehalf jackknife procedure that—though unable to provide quantitative confidence levels for specific features of a hierarchy—can nevertheless provide a qualitative assessment of the runtorun variability of amcBPPSgenerated hierarchies. This involved running the amcBPPS program on each of 24 different input alignments (the domain identifiers of which are given in Methods) after randomly removing half of the sequences. Given ten such runs for each domain, we compared the consistency of the resultant hierarchies from run to run as follows: For each pair of runs (i.e., 45 pairs for each domain tested) we determined how those input sequences shared by both hierarchies (i.e., about onefourth of the sequences in the input alignment) were partitioned among the nodes of one hierarchy relative to the other hierarchy. An example output file is shown in (Additional File 1: Figure S4).
For these analyses we found that, among the leaf node sets in one tree that share at least one sequence in common with a leaf node set in the other tree, on average 47% share precisely the same set of sequences (i.e., among those sequences present in both trees) and 74% share more than 90% of their sequences in common. Moreover, in most cases where an identical sequence set is not found, the missing sequences were typically assigned, not to unrelated leaf nodes, but either to a parent node further up the tree or to the rejected sequence set. Among the remaining cases, a node in one hierarchy is either split into multiple nodes or (in the worst case) split between nodes in the other hierarchy. At times a hierarchy could end up omitting certain nodes due to the deletehalf jackknife procedure removing sequences belonging to certain phyla resulting in insufficient phylogenetic diversity to seed the formation of a subgroup. Of course the topologies (shapes) of the jackknife trees found by the sampler also differ, which is a common problem associated with evolutionary trees consisting of large numbers of distantly related sequences. This is presumably due in large part to the amcBPPS algorithm failing to find the optimal topology—an issue that, in the future, we will address by sampling over alternative topologies. Of course, both this future sampler and the jackknife procedure applied here will be useful for identifying the most reliable features of a hierarchy. Taken together, these results confirm the observation we made in the previous section, namely that the amcBPPS program generally finds a suboptimal hierarchy that, nevertheless, provides a good starting point both for curation and further automation. Output from these jackknife analyses are available at http://www.chain.umaryland.edu/amcbpps/jackknife.txt.
Simulations
As an additional check, we implemented a procedure to generate simulated sequences from profile HMMs where each such profile corresponds to a node from one of the 24 domain hierarchies used in the jackknife analysis. The rationale for doing this was to determine how well the amcBPPS program identifies sequences corresponding to predefined subgroups. Note that this procedure captures sequence features of each subgroup, but not how those subgroups are hierarchically arranged. For each node of each hierarchy we generated the same number of aligned sequences as were assigned to that node in the original hierarchy. After running the amcBPPS program on each of these simulated alignments, we determined the degree to which each set of related simulated sequences were correctly modeled as belonging to a single subgroup. An example output file in (Additional File 1: Figure S5) illustrates how the amcBPPS program correctly categorized nearly all of these sequences given the structure of the inferred hierarchy (on average 69% of the sampled simulated sets correspond exactly to the HMMgenerated sequence sets). Output from simulations for the 24 domains is available at http://www.chain.umaryland.edu/amcbpps/simulate.txt.
Time complexity
The computationally most intensive routine in Step 1 of the amcBPPS program is an allversusall pairwise comparison of prealigned sequences (with indels ignored). This has a time complexity of O(k·m^{2}) = O(n·m) where k is the number of aligned columns, m is the number of sequences and n = k·m is the effective size of the input alignment. In addition, Step 1 involves a simpler version of the Step 3 algorithm that, of course, exhibits the same time complexity as Step 3 (see below) as well as other operations that perform better than O(n·m) (e.g., heap and disjoint set operations on m sequences) [45]. Hence the time complexity for Step 1 is O(n·m).
Because the run times for Steps 2–3 are also likely to depend on the size of the hierarchy generated by the program in Step 2, Figure 7B plots the run times versus the number of aligned residues times the number of nodes in the hierarchy (i.e., rows in the FDtable). This yields a slightly improved, essentially linear dependency. This observed time complexity is largely due to Step 3 being more or less independent of the number of nodes, which is achieved by computing conditional posterior probabilities (the most time consuming routine) for each column of the FDtable only when considering the assignment of a sequence to one of two possible new partitions rather than to one of a typically much larger number of rows. Thus the amcBPPS program can be applied to very large multiple sequence alignments, which is important given the current rapid increase in sequence data.
Analysis of protein domains lacking CD hierarchies
Protein domain hierarchies generated automatically either from a single curated alignment or from nonaligned sequences
identifier  Protein superfamily name  # seqs  # nodes amcBPPS  LLR  Run time^{§} 

Started from curated alignments:  
cd00075  Histidine kinaselike ATPase c  87,258  95(62)  518062  119.27 
cd00130  PAS  50,200  117(115)  416375  103.95 
cd00174  SH3  13,890  44(35)  26971  3.83 
cd00590  RRM  107,488  63(56)  557782  63.75 
cd01427  HADlike hydrolases  41,818  85(73)  324699  59.77 
cd02440  AdoMet_MTases  150,872  112(99)  1417985  250.27 
cd04301  NATSF  43,486  71  244420  23.30 
cl02566  SET (pfam00856)  8,946  21  54230  2.58 
cl10444  Ploop GTPases^{ ‡ }  198,624  115 (109)  3826672  464.67 
none  AAA + ATPases^{ ‡ }  84,695  86(85)  1779227  173.73 
Started from unaligned sequences:  
none  α,β hydrolase fold  50,811  109(104)  752259  139.82 
none  Helicases  86,287  117 (111)  1935380  342.10 
Conclusions
Currently the construction and annotation of CD hierarchies relies on the labor intensive process of manual curation. This has created a bottleneck hindering the CDD [53] from achieving the goal of comprehensive coverage of the protein domain universe. The incorporation of the amcBPPS program into the CDD curation pipeline can help automate this process while also providing a statistical measure of the quality of CD hierarchies. Likewise, the deletehalf jackknife procedure applied here can provide qualitative estimates of the reliability of various features of a given hierarchy. And, because the amcBPPS program models protein domains based on those residue signatures that most distinguish each functionally divergent subgroup within a protein class from other subgroups, it can also accelerate the annotation of domain profiles. By linking these profiles to the Cn3D viewer [3, 54] structural features associated with likely functionally critical residues can be identified within webbased BLAST searches. In previous studies (e.g., [55–59]) we have mapped key residues identified using the mcBPPS sampler to available crystal structures in this way, thereby obtaining insights into biological functions and mechanisms. Such information also facilitates structural evaluation of sequence alignments.
Of course, starting from the procedures described here, the CDD pipeline can be further automated and improved in various ways along similar lines. For example, we have demonstrated that our Bayesian alignment methods can be used to generate, for major protein classes (such as the AAA + ATPases, α,βhydrolase fold enzymes and helicases in Table 2), large multiple alignments in the aligned blockbased format required by the CDD. These, in turn, can serve as input alignments for generating protein domain hierarchies. Moreover, these alignment procedures could be refined to utilize information regarding pattern residue 3D structural interactions to identify and correct misaligned regions automatically (via iterative application of multiple alignment and BPPS procedures). Likewise, protein domain hierarchies generated by the amcBPPS program could be further optimized by implementing sampling operations to add or remove leaves and branches. More sophisticated taxonomic schemes could be devised for distinguishing conserved patterns due to functional constraints rather than to recent common descent. Taken together, these enhancements will accelerate the construction of an optimal, comprehensive set of hierarchically arranged CD profiles. This will free up curators to focus less on the tedious and labor intensive aspects of database construction and more on biological interpretation, a task that computational and statistical procedures cannot perform.
Having such a comprehensive set of well annotated, high quality CD profiles will summarize what is known about each type of domain. Through application of the MAPGAPS program, these CD hierarchies could be used to obtain uptodate, very large and highly accurate multiple sequence alignments of an entire protein class for indepth computational analyses. And by mapping various categories of pattern residues to corresponding structures, BLAST searches against these improved CD profiles can reveal those residues most likely responsible for the specific biochemical properties of a query protein. This can accelerate the pace of biological discovery by enabling researchers to obtain valuable clues regarding asyetunidentified protein biochemical and biophysical properties.
Methods
Protein sequences were obtained from the NCBI nr and env_nr databases and from translated EST sequences within the NCBI est_others database (for which only open reading frames of at least 100 residues in length were retained). The phylum and kingdom to which each of these sequences belonged were determined using the NCBI taxonomy database dump. For those protein classes in Table 2 that lacked an existing curated alignment, sequences were identified through iterative PSIBLAST [60] and PROBE [52] searches and then multiply aligned using a Bayesian MCMC multiple alignment method [51]. The MAPGAPS program [48] was used to obtain accurate multiple alignments containing vast numbers of sequences starting from a curated alignment. The mcBPPS sampling procedure is described in [42]. Routines to generate contrast alignments are described in [41].
Evaluation procedures
The amcBPPS program was evaluated (see Table 1) as follows: First, an input multiple alignment for each domain was obtained using the alignments corresponding to the CDD hierarchies, as input to the MAPGAPS program [48]; this identified and aligned related sequences within the protein databases. (MAPGAPS aligns the sequences comparable to the accuracy of the curated alignments, which serve as templates.) The alignments obtained in this way were then used as input to the amcBPPS program to generate domain hierarchies. Each of these alignments was also used—along with the corresponding CDD seed alignments and FDtable (obtained from the tree, as shown in Figure 2)—as input to the mcBPPS sampler; this generates the same sort of hierarchy as is generated by the amcBPPS program. We then compared, for each domain, the consistency between the two output hierarchies—that is, we check whether the curated and automaticallygenerated FDtables and seed alignments converged on more or less the same sequence sets (as illustrated in of Additional File 1: Figure S3). For the jackknife and simulation procedures the following domains (listed in Table 1) were used: cd00030, cd00138, cd00142, cd00159, cd00173, cd00229, cd00306, cd00368, cd00397, cd00768, cd00838, cd00900, cd01067, cd02156, cd02883, cd03128,cd03440, cd03873, cd05466, cd06587, cd06663, cd06846, cd08555, cd08772.
Pseudocode for Step 1
The following pseudocode, which focuses on Step 1, corresponds to the main amcBPPS function, after which routines implementing Steps 2 and 3 are called. The output from Step 1 is used to create (in Step 2) a FDtable and a set of seed sequences for mcBPPS sampling (in Step 3). Note that this Step 1 pseudocode creates single category FDtables, but it can be easily modified to create multiple category FDtables.
function amcBPPS(SeqAln)//creates a CD hierarchical alignment.
input: a multiple alignment of protein sequences (SeqAln).
output: a hierarchy (tree) and corresponding contrast alignments (CHA).
//assign each sequence to its own disjoint set (see Tarjan[45]).
for each sequence s ∈ SeqAln do s.labeled := false; s.rank := ∞; Set(s) := {s}; end for
dheap H;//Priority queue; for the data structure and algorithm see[45].
for each sequence pair < s_{ 1 }, s_{ 2 } > do:
if the sequences are from the same phylum then
if sequences ≥ 95% identical then merge their disjoint sets end if
else if sequences ≥ 40% identical then
key := PercentIdentity(s_{ 1 }, s_{ 2 });//Using the pairwise sequence identity as the key…
Insert(key, < s_{ 1 }, s_{ 2 } >, H);// store crossphyla sequence pairs on priority queue.
end if
end for
//Obtain an array of simple contrast alignments (CA) for distinct subgroups.
r := 0; g := 0;
while < s_{ 1 }, s_{ 2 } > := deleteMax(H) ≠ Ø do
r++; s_{ 1 }.rank := min(r, s_{ 1 }.rank); s_{ 2 }.rank := min(r, s_{ 2 }.rank);
if Â¬ s_{ 1 }.labeled ⋀ Â¬ s_{ 2 }.labeled ⋀ Set(s_{ 1 }) ≠ Set(s_{ 2 }) then
Set(s_{ 1 }) := Set(s_{ 2 }) := Set(s_{ 1 }) ∩ Set(s_{ 2 });//merge their sets.
if NumPhyla(Set(s_{ 1 })) ≥ N_{ min } then//(by default, N_{ min } = 4).
for each s ∈ Set(s_{ 1 }) do s.labeled := true; end for
g++; Seed[g] := {};//Seed set for group g.
for each p ∈ {p: p = s.phylum ^ s ∈ Set(s_{ 1 })} do:
//Add to seed set the lowest ranked seq. from each phylum in merged set.
Seed[g] := Seed[g] ∩ {s’: s’.rank = min(s’.rank: s’ ∈ Set(s_{ 1 })⋀ s’.phylum = p)};
end for
FDtables[g] := $\left[\begin{array}{c}\hfill +\hfill \\ \hfill +\hfill \\ \hfill \hfill \end{array}\begin{array}{c}\hfill \hfill \\ \hfill +\hfill \\ \hfill 0\hfill \end{array}\right]$; //column 2: subgroup g vs other proteins in class.
//call mcBPPS sampler[42]to identify a contrast alignment for subgroup g.
CHA[g] := mcBPPS(FDtables[g], Seed[g], SeqAln);
end if
end if
end while
mc := CreateFullHierarchy (FDtables,CHA, g);//Step 2: create mcBPPS input.
return mcBPPS(mc. FDtable, mc.Seed, SeqAln)//Step 3: optimize CD hierarchy.
end function
Pseudocode for Step 2. Step 2 (i.e., the CreateFullHierarchy() routine) is subdivided into three substeps. For Step 2a, the MergeSimilarSets() function finds cliques of similar sequence sets by applying the BronKerbosch algorithm [61] and then combines the sets within each clique:
function MergeSimilarSets(SqSets)
input: sequence sets (SqSets) from Step 2.
output: a reduced, nonredundant collection of sets and associated patterns.
//obtain an undirected graph of similar sequence sets.
Create a node for each input set
for each pair of sets I, J within SqSets do
if the smaller set intersects with < 80% of the larger set then continue;
Find pattern optimally discriminating sequences in sets I and J from other sequences;
//The optimum pattern is defined based on the mcBPPS statistical model.
if the two patterns intersect by < 33% or by < 5 pattern positions then continue;
LLR_{i,j} := LLR with foreground = set I, background = , Â¬(set J ∩ set I) & set J pattern.
LLR_{j,i} := LLR with foreground = set J, background =, Â¬(set J ∩ set I) & set I pattern.
if LLR_{i,j} ≥ 80% of LLR_{j,i} ⋀ LLR_{j,i} ≥ 80% of LLR_{i,j}then AddEdge(I,J) end if
end for
Find the cliques in the graph using the BronKerbosch algorithm [61].
for each clique do
Create a consensus set of those sequences present in ≥ 50% of the clique sets.
Compute pattern optimally discriminating consensus set from other sequences.
Replace the sets belonging to the clique with the consensus set and pattern.
end for
end function
By determining whether the sets substantially overlap, are roughly equal in size, and have similar discriminating patterns, the first two ‘if’ statement within MergeSimilarSets() merely prune the search by skipping over sets that are unlikely to correspond to the same protein subgroup. (Note that, if missed, sets corresponding to the same subgroup are likely to be detected in subsequent steps). To determine whether two different yet overlapping sets correspond to the same functionallydivergent subgroup, the procedure computes the BPPS loglikelihood using the pattern from one set with the partition defined by the other set and vice versa. If the patterns are more or less interchangeable between sets then an edge is added between the nodes corresponding to these sets. Next the BronKerbosch algorithm is used to identify set cliques, each of which is then merged into a single (consensus) set. MergeSimilarSets() is applied iteratively to the modified sets from the previous iteration until it fails to identify and combine any additional similar sets.
Step 2b combines subgroup sets into larger supersets using the following FindSuperSets() function:
function FindSuperSets(SqSets)//Obtain supersets from overlapping existing sets.
input: sequence sets (SqSets) from Step 2a.
for each Set do Assign it to a unique disjoint set end for // (see Tarjan[45]).
for each pair of sets I, J do//find candidate supersets
if the intersection of the smaller set ≥ 66% of the larger set then
Assign both sets to the same disjoint set;
endif
end for
for each Disjoint set ‘dset’ containing at least 2 subsets do
Superset := the union of the subsets;
Superpattern := the pattern optimally discriminating the Superset from Â¬ Superset;
if Any subsets in dset fail to contribute their ‘fair share’ to the superset LLR then
Remove these subsets from dset and repeat from the start of this ‘for’ loop
else Save the superset and superpattern endif
end for
return: The saved supersets and superpatterns.
end function
FindSuperSets() first identifies collections of (possibly minimally) overlapping sequence sets as possible candidates for merging into supersets. Next, it combines into a superset those sets that contribute their ‘fair share’ to the optimum LLR for the proposed superset—where the ‘fair share’ is defined as contributing at least 80% of the estimated average contribution of each sequence to the LLR times the number of sequences in the subset. (Based on the statistical formulation [40, 42], each sequence will contribute equally, on average, to the loglikelihood. For such calculations, however, the sequences are downweighted for redundancy, as previously described [40]).
Next the function CreateSuperSets() is called to create additional supersets from the current sets that fail to overlap or that overlap only moderately. As long as new supersets are created, this function is called repeatedly (this merges subsets into supersets that might otherwise have been overlooked).
function CreateSuperSets(SqSets)//Create supersets by combining (possibly distinct) sets.
input: sequence sets (SqSets).
output: new supersets.
for each set I do
SuperSet := set I; SuperPattern := Ø;
for each set J that at least slightly overlaps with set I do
Set X := SuperSet ∩ set J;
Pattern X := the pattern optimally discriminating set X from Â¬ X;
if both SuperSet & set J contribute their ‘fair share’ a significant LLR then
Superset := Set X; SuperPattern := pattern;
endif
end for
if set I ⊂ SuperSet then save the current Superset endif
end for
end function
Step 2c uses the sets obtained in the previous steps to construct a tree hierarchy, from which a FDtable is then obtained—, along with corresponding seed alignments and initial partitions—as follows:
function CreateTree(SqSets)//obtain an optimized tree.
input: sequence sets (SqSets) and corresponding patterns from steps 1–2 above.
output: a FDtable + corresponding starting subgroup sets, patterns, and seed alignments
wdiGrph := RtnDiGraph(SqSets);//returns a weighted directed graph of set relationships
Tree := ShortestPathTree(wdiGrph);//as defined by Tarjan[45]
Tree := RefineTree(Tree);//eliminates insignificant nodes and overlap between sets.
FDTable := TreeToFDtable(Tree);
sma := CreateSeedAlignments(Tree); // characteristic, crossphyla seqs for each set.
end function
where the RtnDiGraph() functions is defined as:
function RtnDiGraph (SqSets)
input: sequence sets (SqSets) from Steps 2a and 2b.
output: a weighted directed acyclic graph representing the set relationships.
Create a weighted directed graph where each set is a node
for each pair of sets I, J do//find pairs of sets where set I ⊂ set J.
//simple heuristics for speed.
if setI is smaller than setJ then continue
else if setI ∩ setJ < 50% of setI then continue
else if setJ < 33% larger than setI ∩ setJ then continue endif
//identify those pairs where set I is a (typically fuzzy) subset of set J
Compute the optimum pattern and LLR for set I versus set J – set I;
if LLR is not significant then continue;//significance defined as for FindSuperSets ()
if Set I fails to contribute its ‘fair share’ to the superset LRR then continue;
Add an arc pointing from node J to node I & weighted by –LLR;
end for
for each set that lacks a Superset do
Compute the optimum pattern and LLR for the set versus the complementary set;
Add an arc pointing from the root to the corresponding node & weighted by –LLR
end for
end function
Note that the RtnDiGraph () function returns a directed acyclic graph (DAG), for which the ShortestPathTree() algorithm [45] finds a minimum spanning tree emanating from the root node. Because the distances assigned to the arcs in the graph correspond to the negatives of the LLRs, this tree maximizes the total LLR as defined for the corresponding FDtable (see [42]). Incidentally, in this sense, this approach is akin to using the data to infer the DAG and parameters corresponding to a Bayesian network [62] and then determining the most likely paths through the DAG from a predefined root node. This approach avoids the computational expense of using MCMC sampling to optimally define both the FDtable and the corresponding patternpartition pairs concurrently by using an heuristic approach that is substantially faster yet still based on statistical criteria.
The sequence sets corresponding to the tree returned by the ShortestPathTree() algorithm are still fuzzily defined and thus typically contain sequences that belong to one or more distinct protein subgroups and thus that are not proper subsets of their respective supersets. The following RefineTree() function eliminates inappropriate overlap between sets while also eliminating nodes from the tree that, as a result of the refinement process, are no longer statistically significant:
function RefineTree (Tree)//return a refined tree representing subgroup relationships.
input: a tree where each node corresponds to a sequence set
output: refined tree
do
do//eliminate insignificant nodes from the tree…
Find the arc with the lowest weight (i.e., with the lowest subsettosuperset LLR);
if this LLR is not significant then
Remove the arc and the child (subset) node from the tree;
Connect the children of the removed node to the parent of that node;
Merge the set corresponding to the removed node into the parent set;
end if
while an arc has been removed;
do //eliminate overlap between the sequence sets…
Label the leaf nodes as ‘candidates’ and leave other nodes unlabeled.
for each pair of nodes do
if both nodes are labeled as ‘fixed’ then continue;
else if one node is the root then continue;
else if one node is ‘fixed’ and the other is a ‘candidate’ then
remove all overlapping sequences from ‘candidate’ node;
else if both nodes are candidates then
for each sequence S present in both node sets do
remove S from the set with the poorer optimal pattern match;
end for
end if
end for
Label all current ‘candidate’ nodes as ‘fixed’;
Label as ‘candidates’ all nodes whose subtree consists entirely of labeled nodes;
while some nodes were newly labeled as candidates;
Define the root node set as containing all sequences absent from the other node sets;
Merge each leaf node with only a few sequences into its parent node;
Merge nodes with a single child into their parent nodes; //this step is optional
Relocate nodes that, due to previous step, are no longer properly placed in the tree.
while the tree has been changed in any way;
end function
The tree returned by the RefineTree() function is output as a Newickformat character string (a formal language specification for trees), which is then parsed and translated into a FDtable within the CreateTree() routine. This routine also creates a seed alignment for each row in the FDtable using a few of the most characteristic sequences in each set. These, along with the corresponding patterns (one for each column), are then used as input to the mcBPPS procedure (Step 3).
Abbreviations
 CD :

conserved domain
 CDD :

Conserved Domain Database
 DAG :

directed acyclic graph
 FD :

functional divergence
 LLR :

loglikelihood ratio
 mcBPPS :

multiple category Bayesian Partitioning with Pattern Selection
 amcBPPS :

automated mcBPPS
 MCMC :

Markov chain Monte Carlo.
Declarations
Acknowledgements
We thank Art Delcher for critical reading of the manuscript. Funding for AFN provided by the University of Maryland and the NIH Division of General Medicine Grant GM078541. Funding for CL and AMB provided by the Intramural Research Program of the National Library of Medicine at National Institutes of Health/DHHS. Funding to pay the Open Access publication charges for this article was provided, in part, by the Intramural Research Program of the National Library of Medicine at the National Institutes of Health/DHHS.
Authors’ Affiliations
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