RecA protein sequences were collected from the following databases: the National Center for Biotechnology Information GenBank database 33, The Institute for Genomic Research Comprehensive Microbial Resource 34, the DNA Data Bank of Japan 35, the European Molecular Biology Laboratory Sequence Database 36, and UniProt 37. Keyword searches were used at the aforementioned database websites especially for annotated genomes where RecA orthologs had already been identified. In addition, sequence similarity searches were performed using the E. coli RecA homolog as the query sequence in BLASTp and TBLASTN searches 38 with default parameters. After manually verifying the presence of conserved RecA family motifs, we added the protein sequences from the keyword search results and significant BLAST search hits (E-value <10^-70) to our previous collection of validated bacterial RecA orthologs 17. Since we focused on fully sequenced homologs from known bacterial species, no explicit attempt was made to collect RecA homologs from environmental sequencing projects such as from the Sargasso Sea collection 39. In a previous analysis of 64 RecA homologs, 12 sequences were found to contain errors 174041. Although some of those have not yet been updated in GenBank, we used the corrected versions in all cases. Finally, we limited the RecA data set to unique sequences for each bacterial species. Specifically, we eliminated redundant sequences from duplicate sequencing efforts (genome versus individual gene projects) and from strains of the same bacterial species (E. coli CFT073 versus K12). While these sequences do not appear in our RecA MSA and ProfileGrid, the redundant sequences serve to verify any rare residue observations that could be the result of errors. This underscores the curation that was performed of the individual sequences as described in more detail below.

The multiple sequence alignments were calculated using the DNASTAR MegAlign program 42 that implements the ClustalW algorithm 43. Default parameters were used except that the gap penalty was increased to 30 to minimize the introduction of gaps. The resulting alignment was manually curated by visual inspection to optimize the position of small gaps. Weight values were assigned to each protein sequence using the ClustalX program 44 to remove any bias from similar sequences potentially overrepresented in the alignment. The MegAlign program was also used to identify alignment positions that were either invariant or chemically similar (Additional file 1) according to previously described amino acid classes 17.

In the genomic era, database web interfaces make it easy for the novice user to find and align many RecA sequences. However the quality of the sequence data sets and their subsequent alignment can not be taken for granted. Instead it is imperative that bioinformatic data be curated to enable researchers to be confident of the conclusions that they draw 45. This can be particularly important in the conserved motifs of a protein sequence alignment. Below, we belabor this point as a caution about the interpretation of rare residues in MSAs.

Inspection of the MSA (Additional file 1) and ProfileGrid (Additional file 2) show that the family motifs are very well conserved among the 300 RecA homologs. However, there are exceptions where residues occur which do not follow the consensus patterns for the motifs. These rare residues are readily visible in ProfileGrid representations. Such rare amino acids may be interesting exceptions or just noise in the bioinformatic data. We paid particular attention to the MAW and P-loop motifs that are the most conserved parts of the RecA family. For example, a single serine is observed in the MAW motif at E. coli position 52 where 298 other RecA sequences have glycine at that position (Additional file 2). This is not considered a conservative substitution. By contrast, a single serine in the P-loop at position 73 could be a conservative substitution when compared to the 299 other threonine residues. Structure and function inferences drawn from exceptions to conserved motifs would be a waste of effort if such exceptions were based upon faulty data. We also note that phylogenetic analyses are greatly affected by sequence errors 46.

Problems in sequence data sets can result from experimental artifacts or data handling mistakes. These issues are diminishing in the genomic era, but anomalies still occur. As mentioned above, we have identified errors in recA gene sequences determined using traditional gel techniques 41. More importantly, genome projects are introducing a new problem where the complete determination of an organism's DNA content yields sequences that may not be true chromosomal RecA orthologs. For example, the Salmonella enterica genome project 47 uncovered both plasmid encoded [GenBank:CAD09875.1] and chromosome encoded [GenBank:CAD05935.1] RecA proteins. Only the latter was included in the work presented here. In addition, JProfileGrid will flag outliers of one letter characters that do not represent the common amino acids or gap codes. For example, in the RecA protein alignment reported here, we unexpectedly identified "X" characters in two sequences [GenBank:CAD79373.1, GenBank:AAN06665.1].

Significantly, this point about data curation is not just a hypothetical cautionary comment. Attention 48 was drawn to the observation of a rare tyrosine residue in the Proteus vulgaris RecA protein 49 where the vast majority of RecA homologs have serine at E. coli position 70 (Additional file 2). However the discrepancy was resolved 41 when it was determined that the tyrosine observation was actually a simple typographical error in the publication figure. Compounding this problem, though, was a data handling error of the P. vulgaris [GenBank:CAB56804.1] and Pectobacterium carotovorum (formerly Erwinia carotovora) [GenBank:CAB56783.1] RecA protein sequences both determined by the same group 49. The sequence database records for these homologs were apparently mixed together such that the sequences do not agree with the protein sequences reported in the reference publication. The corrected sequences are used in this work. Thus, we encourage users of ProfileGrids to be cautious of overinterpreting rare residues identified in motifs. Currently, the accurate biocuration of sequence and alignment data sets can only be achieved by slow, tedious, manual efforts by protein family experts 50.

The program is controlled from the parameter settings window (Figure 1) which is arranged from top-to-bottom for loading an alignment, customizing the appearance of a ProfileGrid, calculating the similarity plot values, and exporting the results. The ProfileGrid viewer (Figure 2) shows the results of the JProfileGrid calculation after opening the alignment file (here of the RecA family of 300 sequences). The first 3 rows are a position ruler, a majority consensus, and a template sequence (here of the E. coli RecA homolog). The next 21 rows tabulate the frequency of the amino acid and gap characters at the corresponding MSA column position. ProfileGrid cells are color shaded according to the residue frequency value (Figure 3) with the legend in the lower-left corner of the ProfileGrid viewer read from left to right as low to high conservation, respectively. The top-left corner identifies the character and the frequency of the ProfileGrid cell currently selected by the cursor. Note that each column total equals the number of sequences in the alignment. Since the ProfileGrid matrix needs only 21 residue rows to represent protein sequences, there is practically no limit to the number of homologs that can be visualized.

The parameter settings window (Figure 1) allows the user to change the template sequence, the position ruler numbering, the majority consensus sequence threshold cutoff (default 70%), and the residue sort order. By default, the template is the first sequence of the alignment; and, the amino acids are alphabetized by the one-letter code to facilitate looking up a residue of interest. JProfileGrid provides a menu of the following amino acid physical constants for analysis: age 51, flexibility 52, frequency among E. coli proteins 53, hydropathy 54, hydrophobicity 55, helix propensity 56, mutability 5758, surface area 59, and volume 60. Many more constants are available for those coding their own ProfileGrid implementations 61. The "Frequency Colors" button opens a window listing the 6 default frequency color bins (Figure 3). A ProfileGrid cell is colored by the following bin that has the largest threshold value greater than or equal to a cell's residue frequency: <10% (white), ≥ 10% (gray), ≥ 25% (yellow), ≥ 50% (orange), ≥ 70% (green), and ≥ 90% (red). This color scheme was chosen to maximize the visual differences between bins for the inspection of ProfileGrids for patterns (see below). By contrast, a color ramp (i.e., shades of one color) would not facilitate such analysis. However, the user is able to define their own frequency color scheme by choosing the number, size, and color of the bins. To assist the inspection of ProfileGrids, the frequency values can be hidden. This same menu allows the values to be reported as a percentage.

Two features allow one to visualize other sequences of the ProfileGrid besides the template sequence. First, the highlight sequence option allows one to detect and to represent unique features of one sequence with respect to the entire information content of a MSA. Such a feature may indicate specialization with respect to function or activity. When the highlight menu is used to select a sequence different from the template sequence, then the highlight feature is turned on (Figure 4). Specifically, the highlight sequence will appear immediately below the template sequence in the ProfileGrid. Furthermore, a pairwise comparison is made such that the corresponding residue is boxed if the highlight sequence differs from the template sequence. The user may choose other colors besides the default blue selection. Note that in the highlight sequence figure, the cell value identification feature (top left corner) reports the current cell frequency even when the ProfileGrid colors and values are hidden. The second feature to visualize MSA sequences is the alignment viewer window (Figure 5) that displays a traditional alignment representation of sequences from the currently selected ProfileGrid cell. In this example, the 21 homologs that have glycine in the third column are shown. For comparison purposes, the first row in the alignment is the template sequence.

JProfileGrid calculates similarity plot values (Figure 6) based on the plotcon algorithm 9. A user-defined sliding window (default 5 residues) is used to calculate conservation across the MSA using the BLOSUM62 or EDNAFULL scoring matrices for proteins and nucleic acids, respectively. Weights for each sequence are taken from MSF input files to correct for overrepresented sequences. By contrast, calculations based upon FASTA files will not have such a correction. The similarity plot results can be visualized directly within a ProfileGrid. This is accomplished by a threshold cutoff value determining the endpoints of similarity boxes outlined in black in the ProfileGrid (Figure 7). These boxes emphasize conserved regions in the protein family. The similarity boxes also serve as landmarks when the ProfileGrid frequency cell colors are not shown.

JProfileGrid exports output in two formats. ProfileGrid figures for publication are made from a saved Excel spreadsheet file where the matrix appearance can be optimized such as the selection of the text font. The user can specify a subset range of MSA columns as well as the size of each ProfileGrid tier which in this example was set to 50 (Figure 7). A second output format is a script option for the PyMOL molecular visualization program (Figure 8) here showing the E. coli RecA crystal structure 62. Residues that are completely conserved, i.e., identical, in the MSA are saved as a PyMOL selection named "ident" in the script file. Residues that pass the highest threshold value in conservation (default bin of ≥90%) are saved as a selection named "bin90". Finally, the motifs and connecting variable regions are labeled numerically starting from the N-terminus.

We have analyzed a set of bacterial RecA homologs consisting of 300 near full-length protein sequences (Table 1). Approximately 280 of the sequences were full-length. The rest are missing short sequences at the termini. The number of unique bacterial species in the 300 sequence data set is 245. We included sequences from multiple strains of a single species whenever such sequences were unique. For example, five strains of Streptococcus pyogenes provided RecA sequences that differed at a small number (1 to 8) of residues. The sizes of the full-length sequences ranged from 318 (Bacteroides fragilis; GenBank: AAA22918.1) to 447 amino acids (Tropheryma whipplei; GenBank:AAO44708.1) with an average length of 354 ± 18. The degree of identity to the E. coli RecA protein sequence ranged from 37% (Ureaplasma parvum; GenBank:AAF30489.1) to 100% (Shigella flexneri; GenBank:AAP18040.1) with an average identity of 62% ± 10%. These calculations excluded the intein sequences found in the Mycobacterium RecA protein homologs 63.

The data sets from the mid-1990's 171930 were biased toward RecA homologs from the Proteobacteria phyla (60% of sequences). In the current work, the purple bacteria represent only 44% of the sequences (Table 1). Furthermore, we now include homologs from several newly sequenced bacterial phyla including the Chlororflexi and the Fusobacteria. The diversity of the current data set permits a robust description of motifs of the RecA protein family. Additional file 1 shows a summary of the information from the RecA MSA.

An alignment of 300 bacterial RecA homologs is graphically represented by a ProfileGrid (Figure 7). This visualization gives a succinct overview of MSA information especially when the frequency values are hidden to reduce clutter. The details of the residue frequency for all columns of the RecA MSA are found in Additional file 2. We used the sequence conservation denoted by the similarity boxes to define RecA motifs to serve as a nomenclature across the full length of the RecA protein family (see Additional files 1 and 2). The labeling (and subsequent analysis) of every part of the RecA protein is a fundamental technique adapted from traditional anatomy 64 and applied to macromolecules, i.e., nanoanatomy.

The detailed RecA ProfileGrid information will allow researchers to examine conservation at RecA positions of interest. For example, a new suppressor mutation was recently 65 reported that ameliorates the effects of an impaired [KR]x[KR] motif 66. The suppressor maps to E. coli RecA position 11 and is a change from alanine to valine which is a residue that is not observed among any of the 300 sequences in the MSA (Figure 2, Additional file 2). Since the current sequence data set is larger and more diverse than previous RecA homolog collections, one can have more confidence in the lack of an observed residue change.

The sequence conservation can also be related to RecA protein structure. For example, most of the 21 invariant residues (100% identity) are located on the monomer anterior side (Figure 8) that faces the central axis of the right-handed helical protein filament. The RecA filament interior is where the DNA strand exchange activity takes place. More specifically, a recent crystal structure of a RecA-DNA complex identifies residues involved in DNA binding 29; but, the report did not discuss the sequence conservation of these amino acids. We observe that most of the positions involved in direct DNA contacts are almost completely conserved throughout bacterial RecA evolution (Table 2) as would be expected for ligand binding residues. However, there are some exceptions. In the E. coli RecA protein cocrystal structure, 164-met is involved in making DNA ribose contacts. Surprisingly, at this position methionine occurs in only 20% of the RecA homologs in the MSA. Instead valine is the more frequent (62%) residue found among bacterial RecA proteins. In addition, two residues involved in DNA base contacts (197-met and 199-ile) have potentially non-conservative substitutions with respect to charge (glutamate) or steric (valine) considerations, respectively. An E. coli RecA mutant 197-met to glu is defective for in vivo repair activities 67. There are conflicting reports on whether a 199-ile to val RecA mutant is impaired for repair activity 6768. Parenthetically, we also checked these residue positions in MSAs of the distant RecA homologs such as eukaryotic Rad51/Dmc1, archaeal RadA, and viral UvsX proteins 1769. In contrast to the bacterial RecA MSA, only 211-gly and 212-gly are completely conserved among distant homologs while there is weak sequence similarity at positions 164, 176, 200, and 213. Models for the roles of the DNA-interacting positions should account for this sequence diversity.

When combined with different amino acid properties 61, ProfileGrids are a useful tool for visualizing structural patterns across the interspecies diversity of a protein family. We illustrate this on two adjacent motifs (MAW and P-loop) that comprise the most conserved part of RecA homologs of bacteria, eukaryotes, and archaea 17. Of the two, only the function of the P-loop (the cofactor binding site) has been determined 26. By contrast, little 17 is known about the MAW motif (residues 40–65). From the RecA crystal structures, the MAW motif (or "motif 1a"; see Additional file 1 for motif and variable names) consists of a loop, α-helix B, a tight turn, and ends with β-strand 1. This glycine-rich motif threads through the RecA hydrophobic core and interacts with motifs (1b, 4a, and 5b) that form part of the ATP binding site; but, the MAW region itself has not been shown to contact the cofactor ligand. The MAW motif also connects the P-loop to a hinge (variable 1) that undergoes a dramatic change in the transition from the inactive to active RecA conformation 29. We note that aside from the protein termini, this hinge region is one of the least conserved parts of the RecA protein (Figure 6, Additional files 1 and 2).

The ProfileGrid in Figure 9 displays the MAW and P-loop motifs sorted by the residue properties of helicity and volume. Among RecA homologs, the region separating helix B and strand 1 is dominated by residues which do not favor helix formation (Figure 9A). The conserved glycines are probably necessary for the tight turn that occurs in this area 70. Sorting the MAW motif ProfileGrid by amino acid sidechain volume (Figure 9B) allows the visualization of two other structural features. First, the loop from residues 41 to 44 is composed of small amino acids, namely threonine or smaller. Intriguingly, an E. coli RecA mutant with a change of 44-serine to the much larger leucine residue is proficient for in vivo recombination activity. However, the mutant is resistant to the recombination inhibitory effect of overexpression of the UmuD'C complex 71. The second observed volume feature is that large residues between positions 45 and 58 are, in general, flanked on either side by small amino acids resulting in an alternating pattern of small-large-small residues.

When considering distant RecA homologs from all domains of life, the MAW motif is better conserved than the recently defined DNA interacting residues (Table 2). It is curious, then, that no clear function has been attributed to the MAW motif so here we speculate on possible roles. Universally conserved residues can be involved in ligand interactions or in protein folding 727374. While a ligand interacting role is a formal possibility for the MAW motif, this region of the protein forms part of the RecA hydrophobic core. However, one or more residues in the segment spanning positions 61–72 can be crosslinked to bound single-stranded DNA 75. This suggests that parts of the MAW motif may not remain buried in the protein core at all times and that the motif may be involved in DNA binding. With respect to a protein folding role, the RecA ProfileGrid shows a high prevalence of isoleucine, leucine, and valine residues among bacterial RecA MAW motifs (Additional file 2). Specifically, two conserved leucines are on the same face of helix B (positions 47 and 51). Two properties of leucine may be relevant to this observation. First, in a study of crystal structures, leucine was found to have the largest amount of sidechain flexibility when buried 52. Second, leucine is known to stabilize helices 76 which agrees with a theoretical study of RecA family helices. The residues from 44 to 51 of helix B have a near optimal sequence for thermostability when compared to other central domain helices 77. Also, mutation of position 51 from leucine to phenylalanine results in a RecA mutant that is inactive for activities both in vivo and in vitro 7879. Thus, a role for the MAW motif may be to initiate protein folding or to stabilize the RecA protein core mediated by the motif structural features described above. Perhaps such a protein folding role is significant for a motif that connects an ATP binding site to the hinge region that undergoes conformational changes upon cofactor binding.

The JProfileGrid "highlight sequence" feature can draw attention to any unique residues of a particular sequence within the context of the entire MSA. Here, we analyze the B. subtilis RecA protein [GenBank:CAB13567.1]. The ProfileGrid of Figure 10 clearly shows that the characters 85-gln, 87-gap, 88-arg, and 90-ser are rarely found between the highly conserved positions 84 and 91. In addition, 88-arg is significantly larger than the more frequently observed glycine. Given the aforementioned caution about overinterpreting rare residues, we do not believe that the unique B. subtilis RecA feature described here is a due to a sequence error. We found the same result in two redundant B. subtilis RecA sequences determined from different research groups [GenBank:CAA36377.1, GenBank:AAB47709.1]. What could be the functional role for these residues? We note that there is controversy regarding the ability of the B. subtilis RecA protein to hydrolyze the cofactor ATP 808182. We suggest that this region of the B. subtilis RecA protein be targeted for site-directed mutagenesis to ascertain if this rare sequence feature influences a potentially unique biochemical activity.