The analysis performed and results shown reflect the databases at the time of writing of this paper. Unless otherwise mentioned, default parameters were used. Also, because of limitation in space, we have not included other excellent databases and tools that can be used for this type of analysis. The list of tools and resources included in this paper (Table 1) were chosen because of the authors' familiarity with them, and because they are widely used.

Based on the percent-identity scale (Figure 1) for sequences with identities >80%, a simple pair-wise alignment or comparison using BLAST [19] to an experimentally characterized protein may suffice to infer function, provided the uncharacterized protein and the characterized protein are of similar lengths and align end-to-end without large insertions or deletions. In such cases, for the most part it may be safe to assume that the two proteins have similar overall functions. The widely used and the most reliable resource for obtaining high-quality annotated sequences is UniProtKB/Swiss-Prot [17]. For sequences whose identities fall in the 50%–80% range, the general approach for functional assignment includes evaluation of homology to protein family, domain, and functional motif databases. The most commonly used methodology is querying against profiles generated using either hidden Markov models (HMM) [20] or position-specific scoring matrices (PSSM) [19].

In the higher end of this range, say above 70% identity, a widely used practice is to see if the query protein belongs to a protein family that has experimentally characterized members. The concept of protein family based on homology was articulated by Margaret Dayhoff in the early days of sequence analysis [21]. Protein family classification has several advantages as a basic approach for large-scale genomic annotation over other methods. Classification databases ideal for this kind of analysis include PIRSF [22] and the prokaryotic and eukaryotic Clusters of Orthologous Groups of proteins (COGs and KOGs) [23],[24]. The PIRSF provides classification of UniProtKB sequences primarily into homeomorphic (end-to-end similarity) families and subfamilies (domain level superfamilies are also included) based on their evolutionary relationships. Because PIRSF families and subfamilies are based on full-length proteins rather than on component domains, they allow annotation of generic biochemical and specific biological functions, as well as classification of proteins without well-defined domains. On the other hand, COGs and KOGs consist of clusters of orthologous (and co-orthologous/inparalogous) proteins from completed genomes. The identification of orthologous protein sets is based on automatic clustering of proteins from three or more distantly related organisms based on reciprocal BLAST. This is followed by additional automatic recruitment based on a rigorous BLAST-based algorithm, and subsequent extensive manual curation of membership (including splitting of full-length proteins and assigning them to different clusters if necessary) and annotation.

For sequences whose identities fall in the lower end, say <70% range, in the absence of end-to-end similarity, a safer approach would be to evaluate domain architectures of these proteins, as these can evolve and exist independently of the rest of the protein chain. The most widely used domain database that provides a comprehensive coverage is Pfam [25].

Sequence similarity based on full-length sequences has been used as a guiding principle in many classification databases. While this works quite well for closely related sequences whose sequence identities are greater than 50%, it begins to fail for sequences that are related at the three-dimensional structural levels rather than at sequence levels [1], [26]–[28]. This is not surprising since molecular evolution conserves structural features longer than sequence [16],[29].

Examination of a protein's structural neighbors and fold comparisons can reveal distant evolutionary relationships that are otherwise undetectable and, perhaps, suggest unsuspected functional properties. Just as proteins with end-to-end similarities may be evolutionarily related, structures with similar folds may also be related. Data resources that provide structural comparisons include Vector Alignment Structural Tool (VAST) [30], Combinatorial Extension (CE) [31], and DALI databases [32]. For structural classifications, SCOP and CATH have become the most widely used structural resources that provide a comprehensive hierarchical description of structural relationships [33]–[35]. The uniqueness of SCOP, however, is that it is an expert-constructed database geared toward identifying evolutionary relationships rather than relationships based on mere three-dimensional geometry of proteins.

Analysis of sequence/structural motifs becomes valuable especially for cases where the overall percent identity goes below 30% for functional inference. These functional motifs/sites form stable units and are evolutionarily conserved relative to the remainder of the protein. Their identification is important in the assignment of protein names and accurate propagation of structural and functional site annotations [9]. The most commonly used programs and tools available to calculate inter and molecular contacts are PDBSum [36] and LPC/CSU [37] servers. For identifying known sequence and structural patterns/motifs, PROSITE and the Catalytic Site Atlas (CATRES), respectively, are invaluable resources [38],[39].

The amino acid sequence of O67940_ AQUAE is blasted against NCBI's non-redundant protein database (nr) in order to retrieve all its related sequences (Figure 3, top). Results of the BLAST output (Figure 3, bottom) show no hit to a characterized protein among the top hits (additional iterations to convergence did not hit any other characterized members). However, a close examination of the results indicates that the query protein hits several solved crystal structures (tagged with S in a red box). Two of them with PDB IDs 2Q6O from Salinispora tropica (UniProt accession A4X3Q0) and 1RQP from Streptomyces cattleya (UniProt accession Q70GK9) are functionally characterized as chlorinase and fluorinase, respectively [40]–[42]. In the BLAST results, 2Q6O has an e-value of 3e-20 with a percent identity of 32%, while 1RQP has an e-value of 3e-17 with a percent identity of 26%. Now the question is: Can we reliably predict O67940_Aquefix to be a chlorinase (specific to a chloride ion) or a fluorinase (specific to a fluoride ion) or just a halogenase (could be specific to one or more of the halogens)? The answer is not yet known since the sequence identities between the query and the characterized members fall in the low end of the sequence-identity scale, and therefore additional supportive evidence needs to be gathered before reliable function transfer.

The results of the BLAST run (Figure 4) of query versus subjects (2Q6O—pdb|2Q6O|A and 1RQP—pdb|1RQP|A) gives us the pairwise alignments. The pairwise alignment of query with 2Q6O (Figure 4, top) extends almost the entire length of the protein without long gaps. However, the alignment of query with 1RQP (Figure 4, bottom) has three regions with relatively long gaps. Based on this, it is clear that we need to get additional homologs and construct a multiple sequence alignment to identify the conserved residues before transferring functional annotation.

Results obtained from the steps so far are not conclusive to determine if the query is a chlorinase or a fluorinase. In this step, we will attempt to see if the query protein belongs to any well-annotated protein and domain families or if the protein has any specific identifiable sequence pattern. The results of scanning the candidate protein against family databases PIRSF and COGS are given in Figure 5. The query along with 2Q6O and 1RQP belong to PIRSF006779 and COG1912; both families, however, lack any functional annotation. Similarly, scanning against the domain database Pfam (Figure 5E and Figure 5F) and functional site database PROSITE does not provide any additional insights into the function of the query protein O67940_AQUAE. Nevertheless, Steps 1, 2, and 3 provide clues about phyletic distributions of homologs that can be used to construct a multiple sequence alignment.

Similarity between related sequences at either the sequence or structural levels may give important clues about their functions since it may be a consequence of functional or evolutionary relationships. Results of the structural searches using the SCOP database is presented in Figure 6. The results indicate that the N- and C-terminal domains of 1RQP belong to two SCOP superfamilies named Bacterial fluorinating enzyme (N-terminal domain) and Bacterial fluorinating enzyme (C-terminal domain). 2Q6O is not classified in the SCOP 1.73 release, but most likely belongs to the same superfamily as 1RQP.

This becomes an important step especially for sequences whose percent identity falls below 30%. Since our query does not have a structure, 2Q6O and 1RQP will be used as starting points to get other related structures. Results of the structural searches using VAST is presented in Figure 7. Thus, identified structures can be used to generate a high-quality structure-guided multiple sequence alignment to which the query and other related sequences can be aligned. The generation of a high-quality alignment is critical for function prediction and reliable phylogenetic analysis.

Transfer of annotations from one homolog to another is not always straightforward. To transfer annotation, one has to identify homologs that can be used for constructing multiple sequence alignments and subsequently used for performing phylogenetic analysis to identify orthologs (next step). More often than not, when many paralogs are present, it becomes difficult to identify a true ortholog. This step is to identify homologs based on results obtained from earlier steps. With the increasing number of genomes being sequenced, it is becoming apparent that restricting analysis to high-quality genomes and sequences from model organisms for generating alignments and performing phylogenetic analysis is important.

High-quality multiple alignments are a pre-requisite for understanding the evolutionary relationships that exist between homologous sequences. A structure-guided alignment carried out using Cn3D on the structures and sequences obtained from Step 6 is presented in Figure 8. This alignment is manually edited to ensure that all the secondary structural elements are properly aligned without any geometric violations. To this manually edited structural alignment, the initial query O67940_Aquefix along with the identified homologs from Step 6 are added. It is interesting to note that the longest gap observed in the BLAST pairwise alignment in Step 1 (Figure 4, bottom) between query and 1RQP corresponds to an exposed loop region of the protein. This 23-residue loop region absent in both 2Q6O and the query seems to be significant enough to cause a decrease in the buried surface area around the active site compared to 1RQP. Neighbor-joining (NJ) phylogenetic analysis of the aligned sequences was carried out using CDTree. The tree reveals that the query and our subjects (1RQP and 2Q6O) do not fall in the same branch (Figure 8, bottom). This indicates that transfer of annotation requires more in-depth analysis that includes examination of structural attributes such as regions around the active and binding sites. As mentioned earlier, conservation of these sites is critical for functional inference.

Structures of complexes provide more functional information than uncomplexed structures. 2Q6O, also referred to as SalL, is a trimer with substrate chloride and ligand S-adenosyl-L-methionine (SAM) bound. 1RQP on the other hand is a hexamer (dimer of trimers) with three molecules of the ligand SAM bound. The functional site in these two related structures reside at the interface between the monomers. SAM-binding residues were obtained from PDBSum [36]. A plot of SAM-binding residues for 1RQP is shown in Figure 9. 2Q6O is a SAM-dependent chlorinase that catalyzes the transfer of a chloride ion to SAM to generate 5′-chloro-5′-deoxyadenosine [41]. It has also been shown to possess brominating and iodinating activities but not fluorinating activity. 1RQP on the other hand is a fluorinating enzyme that catalyzes the formation of a C–F bond by combining SAM and F⁻ to generate 5′-fluoro-5′-deoxyadenosine and L-methionine [43]. Subsequently, it was shown that fluorinase from Streptomyces cattleya is also a chlorinase [44]. There are a few crucial differences between 1RQP and 2Q6O that give them their halogenating specificities. For example, the active site residue (involved in catalysis) Gly 131 in 2Q6O is Ser 158 in 1RQP. This small difference seems to result in a larger binding pocket in 2Q6O, resulting in the apparent differences in their specificities, making one a fluorinase/chlorinase and the other a chlorinase/brominase/iodinase. In addition, mutagenesis studies indicate another important active site residue Thr 70 in 1RQP, occupied by a hydrophobic residue Tyr 70 in 2Q6O. Mutation of Tyr 70 in 2Q6O to Thr decreases the chlorinating and brominating activities, indicating their important role in catalysis and the observed specificities [41].

Mapping the functional residues from 1RQP and 2Q6O (Table 2) to query O67940_ AQUAE identifies residues Asp∶8, Phe 15, *Val 67, Asp 69, *Gly 127, Asp 177, Asn 181, Ser 221, Phe 222, Leu 229, and Val 231 as part of the catalytic region. The two crucial active site residues (marked with a *) discussed in the previous step, namely Gly 131 and Tyr 70 (mutated to Thr) in 2Q6O, are Gly 127 and Val 67 in the query. Alignment of homologous sequences carried out in Step 7 indicates that this position is occupied predominantly by a hydrophobic residue, except in the case of the fluorinating enzyme 1RQP where it is a Thr.

Based on the conservation of the crucial residues that are involved in catalysis, the query is closer to the chlorinating enzyme 2Q6O than the fluorinating enzyme 1RQP. While it is safe to assume that the binding site for SAM is conserved among the members of PIRSF006779 and that all its members bind to SAM and likely are halogenases, it is not safe to assume that all the members are chlorinases or fluorinases. Their specificities may be to a fluoride, chloride, bromide, or iodide. Judging from the alignment and available experimental evidence on bacterial fluorinating (and chlorinating) enzymes in Streptomyces cattleya [45],[46] and chlorinating enzyme from Salinispora tropica, it is likely that the query protein O67940_Aquefix is an enzyme that can halogenate SAM with chloride, bromide, or iodide ions. Based on available experimental information, it is not possible to say if the Aquefix enzyme can also use fluorine. Additional supporting experimental data need to be collected before we can conclude the precise specificity of the query.

By following all the above steps, we have answered one critical question that we set out to answer at the beginning of this tutorial, i.e. the function of O67940_ AQUAE. In addition, we have also identified functional residues.