Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Using Shifts in Amino Acid Frequency and Substitution Rate to Identify Latent Structural Characters in Base-Excision Repair Enzymes

  • Ramiro Barrantes-Reynolds,

    Affiliation Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America

  • Susan S. Wallace,

    Affiliation Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America

  • Jeffrey P. Bond

    Affiliation Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America

Using Shifts in Amino Acid Frequency and Substitution Rate to Identify Latent Structural Characters in Base-Excision Repair Enzymes

  • Ramiro Barrantes-Reynolds, 
  • Susan S. Wallace, 
  • Jeffrey P. Bond


Protein evolution includes the birth and death of structural motifs. For example, a zinc finger or a salt bridge may be present in some, but not all, members of a protein family. We propose that such transitions are manifest in sequence phylogenies as concerted shifts in substitution rates of amino acids that are neighbors in a representative structure. First, we identified rate shifts in a quartet from the Fpg/Nei family of base excision repair enzymes using a method developed by Xun Gu and coworkers. We found the shifts to be spatially correlated, more precisely, associated with a flexible loop involved in bacterial Fpg substrate specificity. Consistent with our result, sequences and structures provide convincing evidence that this loop plays a very different role in other family members. Second, then, we developed a method for identifying latent protein structural characters (LSC) given a set of homologous sequences based on Gu's method and proximity in a high-resolution structure. Third, we identified LSC and assigned states of LSC to clades within the Fpg/Nei family of base excision repair enzymes. We describe seven LSC; an accompanying Proteopedia page ( describes these in greater detail and facilitates 3D viewing. The LSC we found provided a surprisingly complete picture of the interaction of the protein with the DNA capturing familiar examples, such as a Zn finger, as well as more subtle interactions. Their preponderance is consistent with an important role as phylogenetic characters. Phylogenetic inference based on LSC provided convincing evidence of independent losses of Zn fingers. Structural motifs may serve as important phylogenetic characters and modeling transitions involving structural motifs may provide a much deeper understanding of protein evolution.


For many protein families the sequences of hundreds of members, as well as the high-resolution structure of at least one member, are available. It is clear from inspection of those data sets that structural features vary during protein family evolution even when the overall fold is conserved. Such variation is reflected in amino acid substitutions that are correlated as well as substitution rates that are inhomogeneous, that is, sites exhibit heterotachy [1]. However, the most commonly applied models of protein sequence evolution do not capture these features. Our goal is to use explicit identification of transitions between states of structural characters for studying the evolution of protein sequence, structure, and function.

Amino acid substitutions reflect constraints imposed by the structural context of amino acids along reaction paths. In the case of base-excision repair proteins, first-shell residues interact directly with DNA backbone, to functional groups specific to a damage, or the opposite base. Substitutions of such amino acids are rare. Multiple second or third-shell amino acids may interact directly with first or second-shell residues respectively [2]. Amino acids in the second or third shell typically vary more quickly than first shell amino acids and might vary in concert, perhaps reflecting the existence of multiple ways to position a first shell residue. Concerted transitions involving such residues are nonetheless more rare than most other sequence substitutions. As such they may serve as important phylogenetic characters that vary more slowly than nucleotide or amino acid characters but more rapidly than catalytic residues or domain structure and thus aid phylogenetic inference related to distant times.

Many computational approaches [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] try to identify amino acid sequence positions that are under functional evolutionary constraints (functional sites). These methods differ in that they may use structural information, employ models based on information theory, phylogenetic trees, energetic calculations, or incorporate information about amino acid chemical properties.

In addition, other methods identify shifts between conservation patterns that result in subfamily-specific sites. These are functional sites within a group of homologous protein subfamilies that confer the corresponding difference in function or structure. Most of these methods take advantage of the fact that the subfamily-specific sites will vary in their rate of evolution and/or tolerated amino acid frequencies between the paralogs. Heterotachous sites need not be related to subfamily-specific functions [1]. Gu and coworkers [21], [22], [23], [24], [25] developed a method [26] for finding subfamily-specific sites based on the idea that functional sites can be identified by changes in the evolutionary rate or the biochemical nature of the amino acids (type I and type II changes respectively). Other methods [3], [15], [27], [28], [29], [30], [31], [32], [33], [34], [35] have also been developed to find the subfamily specific sites and in some cases the subfamilies themselves.

It is understood that residues in a protein do not work in isolation but rather within a context as part of a cooperating system [36] that positions the substrate and residues required for binding and catalysis. Salt-bridges, zinc-fingers and catalytic units work together in order for the protein to function. Structural information can be used to identify conserved regions of the protein [37], and several authors have taken advantage of that. Panchenko et al. [38] identified functional sites by taking into account the conservation between structurally neighboring amino acids; Landgraf et al. [39] devised a method to identify functional clusters: groups of amino acids with some degree of conservation relative to the rest of the protein.

Studies have found evidence that stabilizing residues are more likely to be conserved and cluster structurally [40]. Approaches to establish statistical significance of clustering of functional divergence sites have been done on evolutionary trace sites [41], finding that they do form structural clusters. The sites found by the evolutionary trace method are sites that are highly conserved within each subfamily, but different between them. Other approaches have also recognized the structural cooperativity of important amino acids, and focused on conserved structural clusters among protein binding sites [42], on protein-protein interaction interfaces and their evolution [43], [44], and on conserved clusters of a single subfamily [45], [46], [47]. No approach has looked at the formation of structural clusters of rate-shift sites, nor has any study investigated using those clusters as characters phylogenetic inference.

The cooperating nature of amino acids is latent, or not immediately apparent, in the multiple sequence alignment. Therefore, we would like to extend the notion of functional and subfamily specific sites to latent structural characters (LSCs). We reasoned that subfamily-specific sequence/structure motifs could be identified by combining sequence-based identification of changes in selection pressure with information about proximity of amino acids in space. An LSC is then informally defined as a set of amino acids that are near each other in the protein structure and exhibit concerted changes in selection pressure. Our work is distinct from that of Gu and coworkers in that we calculate changes in amino acid frequency and substitution rate to explicitly infer shifts in the selection pressure on sets of amino acids based on a high-resolution structure. Our emphasis on detection of groups of neighboring amino acids that change concertedly in selection pressure lies at the heart of the novelty of our approach and reflects the assumption that amino acids do not work in isolation but rather cooperate in function. Our results can be easily viewed in three dimensions on the accompanying website (, which intends to serve as a repository of information on LSCs in the Fpg/Nei protein family.

Genome integrity affects survival of cells and the organism and several pathways have evolved for protection against damages [48], [49], [50], [51]. The main protection against endogenous oxidative DNA damage (such as reactive oxygen species produced by metabolism) is the base excision repair system [52], [53], [54], [55], [56]. Its association with human disease and aging (for review see [57] and 47–49) is consistent with its importance. The Fpg/Nei base-excision repair family recognizes a wide range of DNA damages [58], [59], [60], [61], [62], [63], [64], [65], [66]. Its phylogeny is not well understood, presumably because their ancient origin makes phylogenetic inference difficult [58], [65], furthermore, its core domain is not related to any other known protein family. Organisms vary not only in the kinds of damages recognized by their Fpg/Nei enzymes, but also by their number of homologs: the actinomycetes phyla has four different paralogs, the eukaryotes have three, and proteobacteria two. With respect to substrate discrimination, we have some understanding on bacterial Fpg [67], [68], [69] and Escherichia coli Nei [70] but none on the other orthologous clades (see proteopedia page for substrate specificity information). Therefore, given the functional importance, unknown phylogeny, and the fact that the structural conservation amongst its members is high [71] we decided to apply our methods to this family.

We first showed how a group of rate-shift sites cluster structurally in the enzyme and how these sites cooperate to perform an important functional role. We then identified seven LSCs in this protein family, including both familiar structural features and those that have not previously been discussed. We propose how the amino acid roles between states of these characters relate to each other (for example, compensation) and how they are distributed in the phylogeny. We have also used them to resolve previously unresolved deep branches of the family. We found that the majority of amino acids exhibit a statistically significant change in amino acid propensities or substitution rates, presumably reflecting surprising fluidity of the interactions that stabilize protein structures. Finally, we found substantial variation in overall evolutionary substitution rates among homologous subfamilies, presumably a result of changing functional roles. We found that studying LSC, rather than individual amino acids, as well as focusing on events such as changes in rate or amino acid frequency rather than individual substitutions, can shed new light on evolution and function.


Type I transitions are associated with a change in the mechanism of substrate recognition

For one quartet we rejected (p = 0.002) the null hypothesis that the type I transitions are not spatially correlated. More precisely, we permuted edge assignments of type I transitions occurring along the BaFpg1-PFNei or AcNei1-AcNei2 edges of the quartet and employed a test statistic reflecting amino acid proximity (see the Methods section). Inspection of the type I transitions suggests that the spatial association results from the fact that many of them belong to the βF-α10 loop (Figure 1). The βF-α10 loop plays a critical role in substrate recognition of 8-oxoG in BaFpg1 by making important hydrogen bonds with O6, N1, N7 and N2 of 8-oxoG [67]. BaFpg1 discriminates between 8-oxoG and G by exploiting the difference in the protonation state of N7 caused by the extra carbonyl group in 8-oxoG [67]. FapyG recognition is accomplished by the same loop, but in a strikingly different way [68].

Figure 1. Example of structural clustering of type I sites.

Type I sites, amino acids that shift in substitution rate among two clades (BaFpg1, and PFNei), are colored in green in the B. stearothermophilus MutM structure [67] (1R2Y). Three structural clusters (LSCs) are shown, the (a) zinc finger (BaFpg1), zincless finger (PFNei); b) two highly conserved glycines on Fpg which mark the beginning and end of the recognition loop, and which have a higher rate on PFNei, suggesting that the loop does not perform the same role in recognition and c) a triad that stabilizes the DNA and the opposite base which allows for more variability on PFNei.

The role of this loop is unknown in every clade except BaFpg1. It corresponds to a helix in the human Neil1 crystal structure [65], a much shorter loop in Mimivirus [72], and a disordered region in the bound EcoNei structure [70]. In particular, aligned regions in other clades contain gaps. This observation would appear to validate our result, that is, that the βF-α10 loop plays an important role in substrate specificity in BaFpg1 that it does not play in other clades.

Our observations suggest treating the βF-α10 loop as one structural character. Just as type I amino acids are identified after quantifying substitution counts at one aligned position relative to the number of substitutions in the remainder of the protein, a change in the summary substitution rate of a region relative to the rest of the protein may indicate a change in its role. We rejected the null hypothesis (Table 1) that the mean substitution rate in the BaFpg1 recognition loop is the same as that of the remainder of the enzyme (p<0.05; it evolves more slowly) and that the distribution of mutation rates in this loop is equal to the distribution of rates in the remainder of the protein (p<0.004). Notably the aligned region appears to evolve more rapidly than the remainder of the protein in other clades. Based on a likelihood ratio test the substitutions in the βF-α10 recognition loop vary among the nine clades (p<10−15; the BaFpg1 substitution rate is smaller than all but the PFNei). Again, these results suggest a concerted change that involves many amino acids in the βF-α10 loop that is reflected in their substitution rates.

Table 1. Comparison of tree lengths and hypothesis testing of randomness of the loop on all subclades.

However, we note that the βF-α10 loop contains gaps in other clades. It is our expectation that that our combination of PAML and Gu's method provides for identification of substantial changes in selection pressure in regions that contain gaps. In particular, it is clear that our method does not indiscriminately call gapped regions as type I sites. We cannot be sure that in this one case we did not get the right answer for the wrong reason and note that the use of PAML substitution counts and Gu's method for regions containing gaps merits further study.

Seven LSCs were found in the Fpg/Nei protein family

Seven LSCs were identified in the Fpg/Nei family (Table 2). These include the well-known zinc finger, LSC6, which has four perfectly conserved cysteines in the BaFpg1, BaFpg2, actinomycetes (AcNei1 and AcNei2), PrFPG and MeNeil3 clades that coordinate the zinc (MeNeil2 possesses a CHCC zinc finger). However, the aligned positions are highly variable in the PFNei and MeNeil1 subfamilies and therefore easily identified as Type I sites. Since the changes in rates for these four sites have the same direction (the pair of clades ordered by substitution rate) it is not immediately clear whether the evolutionary transition between proteins with or without a zinc finger involves the loss of the structural role provided by the zinc finger or whether the role is filled by some alternative structure. In fact, the PFNei and MeNeil1 subfamilies, as well as the Mimivirus enzymes have a zincless finger that does not bind zinc but plays the same role [65], [66], [72].

Other LSCs include Type I sites having different directions, which allows us, in addition, to identify alternative states of the character. LSC1 provides stability to N174 (Figure 2A) [67], part of the helix-two turns-helix motif. This residue orients and stabilizes the damaged base, along with R264 and K60, by hydrogen-bonding to P0, P-1 and P-2. BaFpg1, BaFpg2 and PFNei exhibit conservation of LSC1 amino acids D178 and K160 (Figure 2B), which form a salt bridge and stabilize N174 via a hydrogen bond (Figure 2A). However, in the other clades the corresponding amino acid substitution rates are significantly higher as a result of a change in the state of LSC1 (Figure 2C). Stabilization of other subfamilies is enhanced by a highly conserved arginine aligning with position 177 (Figure 2B,C; R171 in the E. coli Nei sequence), which is part of a network of hydrogen bonds that stabilize the critical asparagine (Figure 2C). The direction of the 171 Type I (Figure 2C) is opposite to that of D178 and K160 (Figure 2A), consistent with coupled compensatory changes. Both K160 and R171 have been mutated, resulting in significant loss of stability and activity [3], [12], [18], [19].

Figure 2. Multiple States of an LSC: Two solutions to the same problem.

An LSC can have multiple states. A) State of LSC1 in the B. stearothermophilus MutM structure [67]. N174 (in pink), part of the helix-two-turn-helix (H2TH) motif along with two other amino acids (including the key amino acid R264, in blue) functions in the orientation and kinking of the DNA [70]. K160 (blue) helps keep the proper arrangement between the zinc finger and the H2TH [69]. B) Sequence logos for the each of the nine LSC1 amino acids in each of the three clades as well as MvNei1. Column headings indicate the aligned position in both the B. stearothermophilus MutM and E. coli Nei sequences. The sequence logos associated with 1R2Y K160 suggest that in three of the nine clades (BaFpg1, BaFpg2 and PFNei) the arrangement between the zinc finger and the H2TH is stabilized by a lysine in the same manner as in the B. stearothermophilus MutM protein. C) State of LSC1 in the E. coli Nei structure (62, PDB 1K3W). R171 hydrogen bonds to the other beta-sheet of the zinc-finger, presumably playing a role analogous to 1R2Y K160, which originates on a different helix. The sequence logos associated with R171 suggests that in six subfamilies (AcNei1 and AcNei2, PrNei and all vertebrate subfamilies), the arrangement between the zinc finger and the H2TH is maintained by an arginine or lysine in the same manner as in the E. coli Nei protein. For the subfamilies of BaFpg1 and PrNei, sites 160 and 266 are a type I, 174 and 264 are a type 0, and the rest are type II.

Since LSC admit some variation in amino acids their evolution is necessarily no faster than for single sites and they might be helpful as characters in a phylogeny of distant homologous subfamilies. We used maximum parsimony to construct a phylogeny of the nine clades using the LSCs as characters (Figure 3), reasoning that these higher-order structures would provide useful phylogenetic characters over long evolutionary time scales.

Figure 3. The states of LSCs can be used to infer the Fpg/Nei family phylogeny.

The most parsimonious protein phylogeny consistent with the states of the six LSCs is shown with the changes in LSCs annotated as red bars. The choice of the root results in one of its children (BaFpg1, BaFpg2) represents well the diversity of bacteria while the other represents plants, fungi, and metazoans.

Fpg/Nei Paralogous Clades Evolve at Different Rates

Fpg/Nei has four actinomycetes subtrees, each composed of orthologous proteins from the same genomes [73], three subtrees from eukaryotes and two from proteobacteria. For each of these three sets of ortholog subtrees we examined variation in the protein-wide rate of evolution between member subtrees at two levels (Figure 4). First, we compared the number of substitutions in each subtree (since each subtree contains the same organisms the estimated number of substitutions represents the overall variation rate). Secondly, we estimated the branch length from the most last common ancestor of all subtrees to the root of each subtree.

Figure 4. Rate variation does not differ dramatically between replicate Proteobacterium, Actinomycete, or Eukaryote organism tree topologies.

Each column corresponds to one of the three organismal phylogenies. Each entry in a column (paired blue and green bars) represents an instance of the organismal phylogeny in the Fpg/Nei family protein phylogeny. The blue bars correspond to the number of substitutions from the last common ancestor (LCA) of each replicate tree to the present while the green bars correspond to the number of substitutions from the LCA of the phylogeny of replicate trees to the LCA of the each replicate tree.

For each of these six tests (one comparison for each of the 3 sets of subtrees, and one for the each of the three sets of ancestral edges leading to the subtrees, Figure 4) we were able to reject the null hypothesis that the summary substitution rate is uniform at p<0.03. BaFpg1 exhibits significantly fewer substitutions than the other paralogs in both actinomycetes (AcFpg1, p<10−15) and proteobacteria (PrFpg1, p<10−15). Likewise, MeNeil1 has significantly fewer substitutions than MeNeil2 and MeNeil3 (p<10−15). Interestingly, the edge leading to the MeNeil1 subtree is longer than for either MeNeil2 or MeNeil3 (p<0.003). While it is clear that the difference in substitution rates is statistically significant, the biological significance of differences of this magnitude is not clear. The rate variation shown in Figure 5 is nonetheless small compared with the substitution rates exhibited by other pairs of proteins and it seems unlikely that this variation is anomalous even among clades of paralogous proteins. It appears, therefore, that these proteins are all under significant selection pressure, even though some actinomycetes appear to have lost one member.

Figure 5. Substitution rates of individual aligned amino acid positions can differ between clades of orthologs.

Substitution rates of individual aligned amino acid positions can differ between clades of orthologs from actinomycetes (left, Pearson correlation 0.47) or eukaryotes (right, 0.19). Each axis reflects amino acid variation rate in one of the replicate organism trees described in the legend to Figure 4. Each point is an aligned amino acid sequence position. Sites that have experienced a rate-shift (Type I) are green while those that exhibit an amino acid frequency-shift (Type II) are red.

Most amino acids exhibit changes in their structural or functional roles between paralogous clades

We can estimate the proportion of amino acids that have undergone functional divergence by calculating the type I and type II coefficients of functional divergence between the different subgroups [21], [25]. Type I coefficients refer to the proportion of sites that have undergone a change in rate between two clades (e.g. conserved in one clade, variable in another, a rate-shift [74]). Type II coefficients refer to the proportion of sites that have undergone a change in amino acid frequency (e.g. a conserved arginine in one clade vs. a conserved leucine in the other [25]). We found that all comparisons exhibit a coefficient of functional divergence between 0.4 and 0.8, implying that a large number of sites have changed in rate or constraint throughout evolution (Table 3).

Table 3. Coefficient of Type I (above diagonal) and Type II (below diagonal) functional divergence for Fpg/Nei clades.


Clustering of type I sites

We found with high statistical significance (p<0.002) that highly ranked type I sites in a quartet cluster in a loop that plays a role in function in one clade that it does not play in other clades. This suggests that the study of the function and evolution of the enzyme family might benefit from employing explicit random variables that represent concerted transitions because doing so should allow us to 1) identify groups of amino acids that cooperate in structure or function, and 2) derive phylogenetic characters that change in state more slowly than characters associated with individual sequence positions. Such phylogenetic characters might be suitable for resolving the deep branches of protein superfamilies. In this paper we address these two goals, opening the door for further development of methods for automatically finding LSCs. An important question is whether this clustering of rate-shift sites is a general aspect of protein families, a reasonable assumption that should be explored in future studies.

LSCs Provide a Surprisingly Comprehensive Description of the Substrate Binding Site

Amino acids in Fpg/Nei family members directly contact the DNA substrate through interactions with the damaged base, opposite base, adjacent bases or phosphate backbone (Figure 6). Identification of highly conserved amino acids usually serves to find these first-shell amino acids [70], [75], [76], [77], [78], [79], [80]. Collections of second- and third-shell amino acids stabilize first-shell amino acids. We find that our methods for identifying LSC, which are based on differential conservation/variation rather than simply conservation, serve to find collections of second- and third-shell amino acids. In principle, these residues need not appear in LSC, that is, they might either be uniformly conserved or highly variable. However, seven LSC provide a surprisingly complete description of enzyme-substrate interactions.

Figure 6. LSCs supply or stabilize residues that participate in enzyme-DNA interactions.

Top) Amino acids side chains associated with LSC 1–6 are shown in the context of the protein backbone, DNA backbone, damaged nucleotide, opposite nucleotide, and Zn ion [67]. The green residues in both the structure (top) and the diagram (bottom) correspond to first-shell amino acids conserved in the entire family: R264 (contained in LSC6), N174 (stabilized by LSC1), and K60 (stabilized by LSC3/LSC2) stabilize the phosphate of the damaged base, and P2, E3 and are part of the catalytic residues [97]. The helix containing P2 and E3 may be stabilized by LSC2 as well. The enzyme everts the damage, and an intercalation loop (LSC4) fills the void and makes contact with the opposite base [68]. The damage itself in BaFpg1 is recognized by a recognition complex [67]. Other important residues not included here include H74 [97] and E6 [48]. A DNA binding residue not discussed in the literature corresponds to Tyr242 (part of LSC5).

More generally, we showed (Table 3) that a great percentage of amino acids change in rate and/or amino acid frequency at some point during evolution. Therefore amino acids not involved in substrate binding/catalysis are also found to exhibit changes in role and the possibility exists that LSC may provide a comprehensive description of enzyme structure and function more generally. These observations support a broadening recognition that quantitative studies of variation in selective constraints within aligned amino acid positions have the potential to add much to the picture provided by studying only structure or conservation.

LSCs can serve as phylogenetic characters

For phylogenetic inference the time scale of character variation should match the evolutionary time scale in question. Nucleotide substitutions occur more rapidly than amino acid substitutions, which in turn occur more rapidly than changes in amino acid properties [81], [82], [83], [84]. It is therefore common to evaluate differences in amino acid properties, which may reflect changes in roles in structure and function that are latent in sequence alignments and which change relatively slowly. An obvious extension of this idea is that selection can act on the collective properties of a group of cooperating amino acids (example, a catalytic unit, [85]. Furthermore, changes in amino acid roles may be manifest as changes in substitution rates, not simply frequency. The rate of LSC evolution is necessarily similar to, or slower than, the variation rate of individual amino acids. The observation that LSCs are widespread suggests that our method for identifying LSCs provides useful characters for phylogenetic inference.

The deep branches in the phylogeny of the Fpg/Nei have been very difficult to resolve. Heterotachy, or within-site rate variation, is a known property of proteins [63] and makes conventional methods very difficult to apply [86], [87], [88]. Cheng and coworkers [89] suggested removing this difficulty by disregarding all heterogeneous rate amino acids. However, if we excluded these sites from our analysis we would be left with 10–20 amino acids which have not experienced a rate-shift, too few for a reliable phylogeny. Moreover, our approach suggests that those heterogeneous amino acids can provide valuable information regarding the deep branches of the phylogeny.

Our method produced a phylogeny consistent with reliable parts of existing phylogenetic trees, and resolved an existing branch (Figure 3). An important question pertains to the homology of the zincless fingers. Both the PFNei clade and the MeNeil1 clade have a zincless finger structure [49], [65] which are not similar in sequence. However, the resulting phylogeny, and in particular, the fact that the states of LSC1 and LSC2 are shared by BaFpg1, BaFpg2 and PFNei, whereas MeNeil1 shares a alternative state with the rest of the subfamilies supports separate origins of both zinc-less fingers.

We also used LSCs for gene classification [64]. Two viral proteins contain an Fpg-like gene [66]. We can use the presence/absence of the LSCs as a classification tool, determining that MvNei1 arose sometime after the divergence of MeNeil1 and PFNei, but not within MeNeil1. This we determine by noticing that MeNeil has a unique character state (LSC3) not shared by any other group of enzymes, whereas MvNei1 has the alternative character state that is shared with the rest of the subfamilies.

Non-randomness of the Recognition Loop

The βF-∝10 loop plays a critical role in substrate recognition in the BaFpg1 subfamily [67], [68]. Even though the structure is known for some of the subfamilies: a helix in the human Neil1 structure [65], a longer loop in Mimivirus [72], but disordered in the E. coli Nei enzyme-substrate complex structure [70], its role is unknown in every clade except BaFpg1. Within BaFpg1, the conservation is not high, consistent with the observation of Fromme and Verdine [67] that damage recognition is provided by main chain amides. However, this loop is non-random in its distribution of amino acid rates.

What is the basis for substrate specificity?

One of the most puzzling problems in base-excision repair enzymology is to understand how an individual member of the Nth or Fpg/Nei families excises structurally very different damages while failing to excise normal bases, which may appear relatively similar to some of the damages. Had we found precisely one LSC that 1) is comprised of amino acids near the damaged base and 2) has one state for each substrate specificity class then the interpretation would have been straightforward. In contrast, we found that LSCs essentially cover the protein-DNA interface. Furthermore we expect that a more comprehensive analysis of Type I and Type II sites remote from the protein-DNA interface will yield additional LSCs. LSCs appear to be more the rule than the exception.

Birth, death, and state transitions of LSC can be the result either of selection or drift. It may be that, like the majority of amino acid changes, LSC evolution is largely driven by drift. In this case LSC, like amino acid substitution, may ultimately prove more useful for understanding protein structure and evolution than in understanding specificity. On the other hand, the evolution of many of the LSC in the Fpg/Nei family may be the product of a highly complex selection for the ability to excise one diverse members of set of damaged while failing to remove the four normal bases. In contrast to cases in which a small number of key interactions between enzyme and substrate functional groups explains specificity, explaining specificity in the Fpg/Nei family may require the relative stabilities of damages in B-DNA as well as in metastable and transition states of the protein/DNA/solvent complex along multiple reaction paths that account for substrate diversity. For example, structural variation associated with rigid body movements of large portions of the protein suffices to give the state of an LSC an impact on specificity over long ranges. This is also consistent with the suggested plasticity of proteins that can be explained by population dynamics, and which results in different amino acid sequences giving rise to equivalent proteins in structure and function [33].

Nonetheless, some of our LSC are consistent with a link to specificity, for example, LSC5 in PFNei. LSC5 contains R/K244, GQR243 and Y242, which binds DNA. The function of Y242 is unknown but, while it is highly conserved at least in its aromatic character in most enzymes, it differs between plants and fungi. The Arabidopsis works best as an AP endonuclease and prefers oxidation products of 8-oxoguanine guanidinohydantoin (Gh), and spiroiminodihydantoin [49]. The absence of this LSC, very well conserved in the rest of the family, might give insight into its workings.

In summary, analysis of LSC provide a novel and powerful way to describe protein evolution over time scales for which amino acid substitution models weaken. We propose that improved methods for automated identification of LSCs in combination with quantitative models of the birth, death, and state transitions of LSC will improve our understanding of protein structure, function, and evolution.

Materials and Methods

Estimation of Fpg/Nei subfamily phylogenies

Fpg/Nei homologs were identified in the NCBI RefSeq database [90] using the PFAM [91], [92] domain profile pfam06831 and the CDD database [93] search software. T Coffee [94], [95] was used to identify a smaller set (415) of sequences that represent the diversity of the tree. MAFFT [96] and ASH [92] were used, with high accuracy parameters (iterative refinement incorporating local alignment information for the alignment), to align the sequences based on crystal structures [65], [67], [69], [97], [98]. PFAAT [99] was used for visualization of the sequence alignment and Seaview [100] was used to remove phylogenetically uninformative sites. Neighbor-joining phylogenetic trees were constructed using PHYLIP [57] PROTDIST, NEIGHBOR (standard parameters) and SEQBOOT (100 bootstrap replicates). Sequence alignment, genbank ids and trees are available upon request.

Based on the resulting sequence phylogeny, as well as on taxonomy and biochemical considerations, nine subfamilies were identified (Table 4). CDTree [93] was used to remove false positives and to build a position-specific scoring matrix for each subfamily. Subsequently, these models were used to subclassify RefSeq Fpg/Nei hits using RPS-BLAST [54] (see proteopedia page for the resulting table). Sequences were removed if they attached to a tree with an edge representing more than one substitution per site on average. Maximum likelihood trees, used exclusively in what follows, were constructed using RAXML [53].

Estimation of numbers of substitutions

PAML [55] was used to estimate the number of substitutions at each site in each subfamily. Briefly, PAML infers ancestral states and, for each site, counts edges associated with different end states. The advantage of this method is that it uses branch length information as well as the substitution rates between the amino acids to calculate the ancestral states [55]. The correction of Gu [101] was then applied to these counts to obtain the estimated number of substitutions. PAML assigns amino acids, not gaps, to all ancestral sites. As a result each gap in an extant sequence is associated with one inferred substitution so, for example, an aligned position having N gaps is assigned at least N substitutions.

Identification of Type 0, I and II sites associated with each pair of subfamilies

Classification of sites into Type 0 (highly conserved), Type I (undergo a change in substitution rate along an edge) and Type II (undergo a change in preference for amino acid properties) have been described by Gu [21]. The maximum likelihood methods of Gu et al. [21], [22], [23], [24], [25] were used to identify type I and type II sites based on a posterior probability greater than 90%. Mathematica [102] was used for the implementation of those methods and for all calculations and graphs (software available upon request). Briefly, identification of type I sites is based on application of a likelihood model to two subfamilies; each site is either related to functional divergence or not [3]. In the former case substitution rates are unequal while in the latter case substitutions rates are equal.

Based on the way PAML counts substitutions for positions containing gaps, positions having a high proportion of gaps are assigned high substitution rates. Thus, although we did not implement a formal probability model for gaps, the resulting substitution rate assignments nonetheless reflect selection pressure that is less than for ungapped positions. This gives the desirable feature that segments that are highly conserved in one subfamily but gapped in another subfamily are easily identified as segments of type I sites. In principle the possibility exists that this method assigns type I calls when part of the protein is weakly conserved in one subfamily and gapped in another. We inspected our type I calls and found that in nearly all cases the slower rate in the rate pair is below the median substitution rate.

Type II sites were identified using 1) substitution counts for each subtree, 2) PAML's ancestral reconstruction at the subtree roots, and 3) a definition of radical changes between clades. We considered a change radical if it altered membership with respect to four groups: charge positive (K, R, and H), charge negative (D and E), hydrophilic (S, T, N, Q, C, G, and P), and hydrophobic (A, I, L, M, F, W, V, and Y) [25]. We implemented both methods of Gu et al. and applied it to multiple clades. Our results were checked by comparison with Gu's program Diverge [103].

Statistical test for the structural clustering of type I sites

We designed a test to determine if the sites that change in rate (type I sites) cluster together in space. Consider the quartet ((A,B),(C,D)). Our test statistic, the number of pairs of type I transitions that occur on the same edge and within 4 angstroms of each other, is large when changes in selection pressure involving neighboring amino acids tend to be concerted. We ranked all sites based on the posterior probability that they exhibit Type I transitions along (A,B) and, separately, along (C,D). The value of the test statistic was calculated using the top N such sites for each edge. The distribution of the test statistic under the null hypothesis was determined by permuting the edge assignments of the 2N Type I transitions.

One consideration is that, even under the null hypothesis, type I sites are not expected to be distributed uniformly on the protein. Type I sites may be spatially correlated because changes in rate tend to happen on specific regions of the enzyme. We therefore condition on observed type I sites, permuting type I events among edges in the phylogeny.

A second consideration pertains to the thresholding procedure used to identify type I sites associated with different pairs of clades. The permutation test requires control over both false positive and false negative rates for both edges. We find that, with one poserterior probability threshold, it is not possible to achieve acceptable false positive and false negative rates for both edges. We handle this problem by choosing the same number, N, of type I sites for each clade (see below). We do not consider clades having a tree length so small that they are unlikely to exhibit N type I sites.

Based on the considerations above, we consider quartets such as ((BaFpg1,PFNei), (AcNei1,AcNei2)) that contain large numbers of substitions, both within and between clades. We chose the 25 type I sites for each of the two edges having the lowest posterior probability (common type I sites are discarded). The (BaFpg1, PFNei) and the (AcNei1, AcNei2) edges having 13 and 18 neighbor type I pairs, respectively, for a total of 31 for our statistic. They have 3 type I sites in common, thus we will then do a statistical permutation test in which we resample without replacement pairs of 22 sites from our pool of 44, and calculate our statistic. Other quartets exhibited a similar pattern (results not shown). A second statistic was used to determine the importance of the structure on the clustering, and consisted of the number of pairs of amino acids that are next to each other. There were 12 consecutive pairs in total for the statistic.

Identification of LSCs based on Type class assignment and structural information

Consider the set, V(A,B), of amino acid positions assigned to class Type 0, I or II with respect to a pair of subfamilies (A,B). Consider also the set E of pairs of elements of V having α-carbons within 4 angstroms. An LSC is a connected sub graph of the graph (V,E) containing at least two amino acids, at least one of which is either Type I or II. We found V associated with every pair of the 9 subfamilies. All programming was done in Mathematica 6.0 and 7.0, and the reference structure was the Bacillus stearothermophilus MutM (PDBid 1R2Y) [67]. All structure visualization was done in PyMOL [56]. In principle different structures would give different results, but for the family we are interested in the structures are so conserved that we expect the results to be highly similar.

Evaluation of the uniformity of selection pressure on sets of amino acid residues

Given a set of aligned positions, α, and a set of trees, τ, substitution counts, and tree lengths, , can be used to test the null hypothesis that selection pressure is uniform among trees or among sets of aligned positions.

Consider the BaFpg1 recognition loop, the sequence between G218 and L239 in the Geobacillus stearothermophilus Fpg sequence gi38492995. We tested the null hypothesis that amino acids in the loop represent the selection pressure in the remainder of the protein. In this case there is only one tree length, so we are comparing two sets of substitution counts . More precisely, we tested the null hypothesis that the two distributions have the same location, using the mean as a test statistic, as well as the null hypothesis that the two distributions are equal, using the sum of ranks. In both cases the null distribution was obtained by resampling (100,000 samples).

Sums of Poisson processes follow a Poisson distribution, so we test the hypothesis that the summary Poisson rate parameter, λ, is uniform across a sets of n trees (H0),where , versus the alternative hypothesis that there are different rates (H1),Under the null hypothesis the likelihood ratio test statistic, −2 Log Λ, whereis distributed as chi-square with n−1 degrees of freedom. We used this likelihood ratio to test the null hypothesis that the recognition loop is under uniform selection pressure among the nine subfamilies.

Consider 1) a sequence tree consisting of an n-ary root node, each child of which is a tree having the same topology and m leaf nodes associated with the same set of genomes, and 2) an alignment of the associated n×m sequences represented in the tree. We used the likelihood ratio statistic to test the null hypothesis the Poisson rate parameters associated with 1) the subtrees or 2) the edges connecting subtrees to their common ancestor, are equivalent.

Comparison of the summary evolutionary variation rate between subfamilies

Proteopedia page.

The Proteopedia website software [51] was used to provide interactive three-dimensional representations of the LSCs as well as additional information on the Fpg/Nei protein family, including site-directed mutagenesis experiments and distribution of homologs among taxa.


We thank Sylvie Doublié and Kayo Imamura for helpful discussions on the possible function and relationship of the different LSCs, Dorothy Pumo for discussions of the Fpg/Nei phylogeny, and Xun Gu for helpful discussions about implementation of his method for identifying Type I and Type II sites. We also thank Stephen Eversé for suggesting Proteopedia as a medium for communicating the results as well as the Proteopedia team for their support. We thank Aron Bauer and Myra Derbshyre at NCBI for their invaluable help on curation of protein families using CDTree. Thanks to Kazutaka Katoh from Kyushu University for providing software and help doing multiple sequence alignments that take advantage of structural information.

Author Contributions

Conceived and designed the experiments: RB JB. Performed the experiments: RB. Analyzed the data: RB SW JB. Contributed reagents/materials/analysis tools: RB. Wrote the paper: RB SW JB.


  1. 1. Hirano H, Kamp RM (2003) Deblocking of N-terminally modified proteins. Methods Mol Biol 211: 355–363.
  2. 2. Deu E, Koch KA, Kirsch JF (2002) The role of the conserved Lys68*:Glu265 intersubunit salt bridge in aspartate aminotransferase kinetics: multiple forced covariant amino acid substitutions in natural variants. Protein Sci 11: 1062–1073.
  3. 3. Casari G, Sander C, Valencia A (1995) A method to predict functional residues in proteins. Nat Struct Biol 2: 171–178.
  4. 4. Koshi JM, Goldstein RA (2001) Analyzing site heterogeneity during protein evolution. Pac Symp Biocomput 191–202.
  5. 5. Elcock AH (2001) Prediction of functionally important residues based solely on the computed energetics of protein structure. J Mol Biol 312: 885–896.
  6. 6. Laurie AT, Jackson RM (2005) Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 21: 1908–1916.
  7. 7. George RA, Spriggs RV, Bartlett GJ, Gutteridge A, MacArthur MW, et al. (2005) Effective function annotation through catalytic residue conservation. Proc Natl Acad Sci U S A 102: 12299–12304.
  8. 8. Gutteridge A, Bartlett GJ, Thornton JM (2003) Using a neural network and spatial clustering to predict the location of active sites in enzymes. J Mol Biol 330: 719–734.
  9. 9. Petrova NV, Wu CH (2006) Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties. BMC Bioinformatics 7: 312.
  10. 10. Youn E, Peters B, Radivojac P, Mooney SD (2007) Evaluation of features for catalytic residue prediction in novel folds. Protein Sci 16: 216–226.
  11. 11. Ko J, Murga LF, Wei Y, Ondrechen MJ (2005) Prediction of active sites for protein structures from computed chemical properties. Bioinformatics 21: Suppl 1i258–265.
  12. 12. Landau M, Mayrose I, Rosenberg Y, Glaser F, Martz E, et al. (2005) ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Res 33: W299–302.
  13. 13. Ondrechen MJ, Clifton JG, Ringe D (2001) THEMATICS: a simple computational predictor of enzyme function from structure. Proc Natl Acad Sci U S A 98: 12473–12478.
  14. 14. Lichtarge O, Bourne HR, Cohen FE (1996) An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol 257: 342–358.
  15. 15. Sankararaman S, Sjolander K (2008) INTREPID–INformation-theoretic TREe traversal for Protein functional site IDentification. Bioinformatics 24: 2445–2452.
  16. 16. Mihalek I, Res I, Lichtarge O (2004) A family of evolution-entropy hybrid methods for ranking protein residues by importance. J Mol Biol 336: 1265–1282.
  17. 17. Capra JA, Singh M (2007) Predicting functionally important residues from sequence conservation. Bioinformatics 23: 1875–1882.
  18. 18. Panchenko AR, Kondrashov F, Bryant S (2004) Prediction of functional sites by analysis of sequence and structure conservation. Protein Sci 13: 884–892.
  19. 19. Peters KP, Fauck J, Frommel C (1996) The automatic search for ligand binding sites in proteins of known three-dimensional structure using only geometric criteria. J Mol Biol 256: 201–213.
  20. 20. Aloy P, Querol E, Aviles FX, Sternberg MJ (2001) Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking. J Mol Biol 311: 395–408.
  21. 21. Gu X (1999) Statistical methods for testing functional divergence after gene duplication. Mol Biol Evol 16: 1664–1674.
  22. 22. Gu X (2001) Mathematical modeling for functional divergence after gene duplication. J Comput Biol 8: 221–234.
  23. 23. Gu X (2001) Maximum-likelihood approach for gene family evolution under functional divergence. Mol Biol Evol 18: 453–464.
  24. 24. Gu X (2003) Functional divergence in protein (family) sequence evolution. Genetica 118: 133–141.
  25. 25. Gu X (2006) A simple statistical method for estimating type-II (cluster-specific) functional divergence of protein sequences. Mol Biol Evol 23: 1937–1945.
  26. 26. (!!! INVALID CITATION !!!).
  27. 27. Brown DP, Krishnamurthy N, Sjolander K (2007) Automated protein subfamily identification and classification. PLoS Comput Biol 3: e160.
  28. 28. Hannenhalli SS, Russell RB (2000) Analysis and prediction of functional sub-types from protein sequence alignments. J Mol Biol 303: 61–76.
  29. 29. Mirny LA, Gelfand MS (2002) Using orthologous and paralogous proteins to identify specificity-determining residues in bacterial transcription factors. J Mol Biol 321: 7–20.
  30. 30. Kalinina OV, Mironov AA, Gelfand MS, Rakhmaninova AB (2004) Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families. Protein Sci 13: 443–456.
  31. 31. Pei J, Cai W, Kinch LN, Grishin NV (2006) Prediction of functional specificity determinants from protein sequences using log-likelihood ratios. Bioinformatics 22: 164–171.
  32. 32. Capra JA, Singh M (2008) Characterization and prediction of residues determining protein functional specificity. Bioinformatics 24: 1473–1480.
  33. 33. Marttinen P, Corander J, Toronen P, Holm L (2006) Bayesian search of functionally divergent protein subgroups and their function specific residues. Bioinformatics 22: 2466–2474.
  34. 34. Reva B, Antipin Y, Sander C (2007) Determinants of protein function revealed by combinatorial entropy optimization. Genome Biol 8: R232.
  35. 35. Gribaldo S, Casane D, Lopez P, Philippe H (2003) Functional divergence prediction from evolutionary analysis: a case study of vertebrate hemoglobin. Mol Biol Evol 20: 1754–1759.
  36. 36. Kraut DA, Carroll KS, Herschlag D (2003) Challenges in enzyme mechanism and energetics. Annu Rev Biochem 72: 517–571.
  37. 37. Friedberg EC (2006) DNA repair and mutagenesis. Washington, D.C.: ASM Press.
  38. 38. Limbird LE, SpringerLink (Online service) (2005) Cell surface receptors : a short course on theory & methods. New York: Springer.
  39. 39. Zharkov DO (2008) Base excision DNA repair. Cell Mol Life Sci 65: 1544–1565.
  40. 40. Ahmad S, Keskin O, Sarai A, Nussinov R (2008) Protein-DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins. Nucleic Acids Res 36: 5922–5932.
  41. 41. Madabushi S, Yao H, Marsh M, Kristensen DM, Philippi A, et al. (2002) Structural clusters of evolutionary trace residues are statistically significant and common in proteins. J Mol Biol 316: 139–154.
  42. 42. Guharoy M, Chakrabarti PConserved residue clusters at protein-protein interfaces and their use in binding site identification. BMC Bioinformatics 11: 286.
  43. 43. Rahat O, Yitzhaky A, Schreiber G (2008) Cluster conservation as a novel tool for studying protein-protein interactions evolution. Proteins 71: 621–630.
  44. 44. del Sol A, Carbonell P (2007) The modular organization of domain structures: insights into protein-protein binding. PLoS Comput Biol 3: e239.
  45. 45. Landgraf R, Xenarios I, Eisenberg D (2001) Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins. J Mol Biol 307: 1487–1502.
  46. 46. Schueler-Furman O, Baker D (2003) Conserved residue clustering and protein structure prediction. Proteins 52: 225–235.
  47. 47. Nimrod G, Glaser F, Steinberg D, Ben-Tal N, Pupko T (2005) In silico identification of functional regions in proteins. Bioinformatics 21: Suppl 1i328–337.
  48. 48. Kropachev KY, Zharkov DO, Grollman AP (2006) Catalytic mechanism of Escherichia coli endonuclease VIII: roles of the intercalation loop and the zinc finger. Biochemistry 45: 12039–12049.
  49. 49. Kathe SD, Barrantes-Reynolds R, Jaruga P, Newton MR, Burrows CJ, et al. (2009) Plant and fungal Fpg homologs are formamidopyrimidine DNA glycosylases but not 8-oxoguanine DNA glycosylases. DNA Repair (Amst) 8: 643–653.
  50. 50. Martz E (2009) Proteopedia.Org: a scientific “Wiki” bridging the rift between 3D structure and function of biomacromolecules. Biopolymers 92: 76–77.
  51. 51. Hodis E, Prilusky J, Martz E, Silman I, Moult J, et al. (2008) Proteopedia - a scientific ‘wiki’ bridging the rift between three-dimensional structure and function of biomacromolecules. Genome Biol 9: R121.
  52. 52. Rabow L, Venkataraman R, Kow YW (2001) Mechanism of action of Escherichia coli formamidopyrimidine N-glycosylase: role of K155 in substrate binding and product release. Prog Nucleic Acid Res Mol Biol 68: 223–234.
  53. 53. Stamatakis A (2006) RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22: 2688–2690.
  54. 54. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402.
  55. 55. Yang Z (2007) PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24: 1586–1591.
  56. 56. Delano WL (2002) The PyMOL Molecular Graphics System. DeLano Scientific, Palo Alto, CA, USA.
  57. 57. Felsenstein J (2005) PHYLIP (Phylogeny Inference Package). 3.6 ed: Distributed by the author. Department of Genome Sciences, University of Washington, Seattle.
  58. 58. Wallace SS, Bandaru V, Kathe SD, Bond JP (2003) The enigma of endonuclease VIII. DNA Repair (Amst) 2: 441–453.
  59. 59. Branden C, Tooze J (1991) Introduction to protein structure. New York: Garland Pub.
  60. 60. Barclay AN (2003) Membrane proteins with immunoglobulin-like domains–a master superfamily of interaction molecules. Semin Immunol 15: 215–223.
  61. 61. Miyatake N, Kamo M, Satake K, Uchiyama Y, Tsugita A (1993) Removal of N-terminal formyl groups and deblocking of pyrrolidone carboxylic acid of proteins with anhydrous hydrazine vapor. Eur J Biochem 212: 785–789.
  62. 62. Amara P, Serre L (2006) Functional flexibility of Bacillus stearothermophilus formamidopyrimidine DNA-glycosylase. DNA Repair (Amst) 5: 947–958.
  63. 63. Lopez P, Casane D, Philippe H (2002) Heterotachy, an important process of protein evolution. Mol Biol Evol 19: 1–7.
  64. 64. Sarkar IN, Thornton JW, Planet PJ, Figurski DH, Schierwater B, et al. (2002) An automated phylogenetic key for classifying homeoboxes. Mol Phylogenet Evol 24: 388–399.
  65. 65. Doublie S, Bandaru V, Bond JP, Wallace SS (2004) The crystal structure of human endonuclease VIII-like 1 (NEIL1) reveals a zincless finger motif required for glycosylase activity. Proc Natl Acad Sci U S A 101: 10284–10289.
  66. 66. Bandaru V, Zhao X, Newton MR, Burrows CJ, Wallace SS (2007) Human endonuclease VIII-like (NEIL) proteins in the giant DNA Mimivirus. DNA Repair (Amst) 6: 1629–1641.
  67. 67. Fromme JC, Verdine GL (2003) DNA lesion recognition by the bacterial repair enzyme MutM. J Biol Chem 278: 51543–51548.
  68. 68. Coste F, Ober M, Carell T, Boiteux S, Zelwer C, et al. (2004) Structural basis for the recognition of the FapydG lesion (2,6-diamino-4-hydroxy-5-formamidopyrimidine) by formamidopyrimidine-DNA glycosylase. J Biol Chem 279: 44074–44083.
  69. 69. Sugahara M, Mikawa T, Kumasaka T, Yamamoto M, Kato R, et al. (2000) Crystal structure of a repair enzyme of oxidatively damaged DNA, MutM (Fpg), from an extreme thermophile, Thermus thermophilus HB8. Embo J 19: 3857–3869.
  70. 70. Zharkov DO, Golan G, Gilboa R, Fernandes AS, Gerchman SE, et al. (2002) Structural analysis of an Escherichia coli endonuclease VIII covalent reaction intermediate. Embo J 21: 789–800.
  71. 71. Zharkov DO, Shoham G, Grollman AP (2003) Structural characterization of the Fpg family of DNA glycosylases. DNA Repair (Amst) 2: 839–862.
  72. 72. Imamura K, Wallace SS, Doublie S (2009) Structural characterization of a viral NEIL1 ortholog unliganded and bound to abasic site-containing DNA. J Biol Chem.
  73. 73. Pumo DE, Barrantes-Reynolds R, Kathe S, Wallace SS, Bond JP (2009) Evolution of the Fpg/Nei Family of DNA Glycosylases. In: Pintér B, Mészáros Z, editors. Epigenetics: Mechanisms, Functions and Human Effects. Nova Publishers.
  74. 74. Robertson AB, Klungland A, Rognes T, Leiros I (2009) DNA repair in mammalian cells: Base excision repair: the long and short of it. Cell Mol Life Sci 66: 981–993.
  75. 75. Burgess S, Jaruga P, Dodson ML, Dizdaroglu M, Lloyd RS (2002) Determination of active site residues in Escherichia coli endonuclease VIII. J Biol Chem 277: 2938–2944.
  76. 76. Golan G, Zharkov DO, Feinberg H, Fernandes AS, Zaika EI, et al. (2005) Structure of the uncomplexed DNA repair enzyme endonuclease VIII indicates significant interdomain flexibility. Nucleic Acids Res 33: 5006–5016.
  77. 77. Kuznetsov SV, Sidorkina OM, Jurado J, Bazin M, Tauc P, et al. (1998) Effect of single mutations on the structural dynamics of a DNA repair enzyme, the Escherichia coli formamidopyrimidine-DNA glycosylase–a fluorescence study using tryptophan residues as reporter groups. Eur J Biochem 253: 413–420.
  78. 78. Rogacheva M, Ishchenko A, Saparbaev M, Kuznetsova S, Ogryzko V (2006) High resolution characterization of formamidopyrimidine-DNA glycosylase interaction with its substrate by chemical cross-linking and mass spectrometry using substrate analogs. J Biol Chem 281: 32353–32365.
  79. 79. Saparbaev M, Sidorkina OM, Jurado J, Privezentzev CV, Greenberg MM, et al. (2002) Repair of oxidized purines and damaged pyrimidines by E. coli Fpg protein: different roles of proline 2 and lysine 57 residues. Environ Mol Mutagen 39: 10–17.
  80. 80. Sidorkina OM, Laval J (2000) Role of the N-terminal proline residue in the catalytic activities of the Escherichia coli Fpg protein. J Biol Chem 275: 9924–9929.
  81. 81. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89: 10915–10919.
  82. 82. Jones DT, Taylor WR, Thornton JM (1992) The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci 8: 275–282.
  83. 83. Lio P, Bishop M (2008) Modeling sequence evolution. Methods Mol Biol 452: 255–285.
  84. 84. Naylor GJ, Brown WM (1997) Structural biology and phylogenetic estimation. Nature 388: 527–528.
  85. 85. Gutteridge A, Thornton JM (2005) Understanding nature's catalytic toolkit. Trends Biochem Sci 30: 622–629.
  86. 86. Gaucher EA, Miyamoto MM (2005) A call for likelihood phylogenetics even when the process of sequence evolution is heterogeneous. Mol Phylogenet Evol 37: 928–931.
  87. 87. Kolaczkowski B, Thornton JW (2004) Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous. Nature 431: 980–984.
  88. 88. Ruano-Rubio V, Fares MA (2007) Artifactual phylogenies caused by correlated distribution of substitution rates among sites and lineages: the good, the bad, and the ugly. Syst Biol 56: 68–82.
  89. 89. Cheng Q, Su Z, Zhong Y, Gu X (2008) Effect of site-specific heterogeneous evolution on phylogenetic reconstruction: A simple evaluation. Gene.
  90. 90. Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35: D61–65.
  91. 91. Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, et al. (2002) The Pfam protein families database. Nucleic Acids Res 30: 276–280.
  92. 92. Standley DM, Toh H, Nakamura H (2007) ASH structure alignment package: sensitivity and selectivity in domain classification. BMC Bioinformatics 8: 116.
  93. 93. Marchler-Bauer A, Anderson JB, Cherukuri PF, DeWeese-Scott C, Geer LY, et al. (2005) CDD: a Conserved Domain Database for protein classification. Nucleic Acids Res 33: D192–196.
  94. 94. Notredame C, Higgins DG, Heringa J (2000) T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol 302: 205–217.
  95. 95. Clamp M, Cuff J, Searle SM, Barton GJ (2004) The Jalview Java alignment editor. Bioinformatics 20: 426–427.
  96. 96. Katoh K, Kuma K, Toh H, Miyata T (2005) MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res 33: 511–518.
  97. 97. Gilboa R, Zharkov DO, Golan G, Fernandes AS, Gerchman SE, et al. (2002) Structure of formamidopyrimidine-DNA glycosylase covalently complexed to DNA. J Biol Chem 277: 19811–19816.
  98. 98. Pereira de Jesus K, Serre L, Zelwer C, Castaing B (2005) Structural insights into abasic site for Fpg specific binding and catalysis: comparative high-resolution crystallographic studies of Fpg bound to various models of abasic site analogues-containing DNA. Nucleic Acids Res 33: 5936–5944.
  99. 99. Caffrey DR, Dana PH, Mathur V, Ocano M, Hong EJ, et al. (2007) PFAAT version 2.0: A tool for editing, annotating, and analyzing multiple sequence alignments. BMC Bioinformatics 8: 381.
  100. 100. Galtier N, Gouy M, Gautier C (1996) SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny. Comput Appl Biosci 12: 543–548.
  101. 101. Gu X, Zhang J (1997) A simple method for estimating the parameter of substitution rate variation among sites. Mol Biol Evol 14: 1106–1113.
  102. 102. Wolfram S (1999) The Mathematica Book. Cambridge University Press.
  103. 103. Gu X, Vander Velden K (2002) DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family. Bioinformatics 18: 500–501.