7e1503000683.pdf

PROTEINS: Structure, Function, and Genetics 53:683– 692 (2003) Automatic Annotation of Protein Function Based on
Family Identification
Federico Abascal* and Alfonso Valencia
Protein Design Group, National Centre for Biotechnology, CNB-CSIC, Cantoblanco, Madrid, Spain
ABSTRACT
Although genomes are being se-
accessed at http://www.pdg.cnb.uam.es/funcut.html.
quenced at an impressive rate, the information
The programs are available upon request, although
generated tells us little about protein function,
installation in other systems may be complicated.
which is slow to characterize by traditional meth-
Proteins 2003;53:683– 692.
2003 Wiley-Liss, Inc.
ods. Automatic protein function annotation based
on computational methods has alleviated this imbal-
Key words: protein function prediction; genome
ance. The most powerful current approach for infer-
analysis; protein families and subfami-
ring the function of new proteins is by studying the
lies; orthologues and paralogues; anno-
annotations of their homologues, since their com-
tation inconsistencies; database errors
mon origin is assumed to be reflected in their struc-
ture and function. Unfortunately, as proteins evolve
INTRODUCTION
they acquire new functions, so annotation based on
Spectacular progress has been made in the automation homology must be carried out in the context of
of experimental techniques in molecular biology, espe- orthologues or subfamilies. Evolution adds new com-
cially those for genome sequencing, functional genomics, plications through domain shuffling: homology (or
and proteomics. In all cases, however, it is difficult to reach orthology) frequently corresponds to domains rather
valid biological conclusions based on the massive data than complete proteins. Moreover, the function of a
generated by such approaches and the development of protein may be seen as the result of combining the
computational methods is still a bottleneck. The two key functions of its domains. Additionally, automatic
steps in the analysis of genomic data are the identification annotation has to deal with problems related to the
of the genes in the raw DNA sequence and the prediction of annotations in the databases: errors (which are
the function of the corresponding open reading frames likely to be propagated), inconsistencies, or differ-
(ORFs). This study focuses on the second step.
ent degrees of function specification. We describe a
method that addresses these difficulties for the
THE PROCESS OF FUNCTION PREDICTION BY
annotation of protein function. Sequence relation-
TRANSFERENCE OF INFORMATION FROM
ships are detected and measured to obtain a map of
HOMOLOGOUS SEQUENCES
the sequence space, which is searched for differenti-
The first step is the search for similar sequences in ated groups of proteins (similar to islands on the
databases, followed by the selection of homologous se- map), which are expected to have a common func-
quences from the set of similarities, i.e., identifying se- tion and correspond to groups of orthologues or
quences with a common evolutionary origin. The statistics subfamilies. This mapmaking is done by applying a
of sequence similarities1 are commonly used for this clustering algorithm based on Normalized cuts in
purpose. Unfortunately, identification of the homologues graphs. The domain problem is addressed in a simple
is not sufficient to guarantee a correct transference of way: pairwise local alignments are analyzed to deter-
functional annotation.2–6 In the course of evolution, homo- mine the extent to which they cover the entire
logues differentiate to embrace new functions, correspond- sequence lengths of the two proteins. This analysis
ing to the organization of sequence families and subfami- determines both what homologues are preferred for
lies.7 For example, the superfamily “P-loop containing functional inheritance and the level of confidence of
nucleotide triphosphate hydrolases” includes families as the annotation. To alleviate the problems associated
varied as “RNA helicases,” “G proteins,” and “ABC trans- with database annotations, the information on all
porters.” Moreover, in the “G proteins,” families special- the homologues that are grouped together with the
query protein are taken into account to select the
most representative functional descriptors. This
Grant sponsor: Spanish Ministry of Science and Technology (CICYT); method has been applied for the annotation of the
Grant sponsor: Government of Madrid.
genome of Buchnera aphidicola (specific host
*Correspondence to: Federico Abascal, Protein Design Group, Na- tional Centre for Biotechnology, CNB-CSIC, Cantoblanco, Madrid Baizongia pistaciae). Human inspection of the anno-
E-28049, Spain. E-mail: [email protected] tations allowed an estimation of accuracy of 94%;
Received 14 January 2003; Accepted 25 February 2003 the different kinds of error that may appear when
using this approach are described. Results can be
ized in various cell functions co-exist, such as ras-related tions. It is quite common to find annotations of different proteins, involved in the cell cycle; rab, related to vesicle natures or ones addressing a different level of detail for a cell traffic; arf, also part of the protein trafficking machin- given function. For example, of two proteins belonging to ery; elongation factors Tu and G, and others. The process the ras-p21 subfamily, Q9BJ57 is annotated as “Ras” in of transference of functional annotation based on the TrEMBL, while RASH_HUMAN is annotated as “Trans- identification of homologues requires a deeper analysis of forming protein P21/H-RAS-1 (C-H-RAS)” in Swiss-Prot, the structure of the corresponding families and their with a deeper definition of the protein function. An ex- ample of different degrees of specificity may be found in A complete reconstruction of the phylogenetic tree of the the case of PS2_HUMAN and PS2_MOUSE, annotated as family could be required for the analysis of the relation “PS2 protein precursor (HP1.A) (Breast cancer estrogen- between family groups and functions. Unfortunately, the inducible protein) (PNR-2)” and “PS2 protein precursor,” process of tree building and family analysis is difficult to respectively. In the first case, given the importance of the data that relate this protein to human cancer, this specificinformation is included in the description of the function.
Function-Prediction Errors Introduced by
Therefore, the analysis of the annotations requires a “Classical” Annotation Strategies
balance between general descriptions and detailed ones, Most of the current systems for automatic annotation corresponding to higher accuracy but less information in obviate the step of analyzing homologies in terms of families and subfamilies and directly transfer functionfrom the most similar sequence identified in the data- RELATED APPROACHES TO THE AUTOMATIC
bases, for example, by selecting the best hit after searching PREDICTION OF PROTEIN FUNCTION BASED
with standard tools such as BLAST.8 The obvious diffi- ON THE INFORMATION OF HOMOLOGOUS
culty of encapsulating in a single relation all the functional SEQUENCES
information associated with a given protein family makes Some of the recent approaches are introduced in the this annotation process a poor substitute for the deeper analysis of the structure of the protein family structure.
GeneQuiz13 was the first system that completely auto- There is copious literature on the errors introduced by the mated the task of sequence analysis and function annota- iteration of this process (see for example Brenner9 and tion. The functional information was obtained from the Devos and Valencia10). Indeed, the systematic comparison potentially most informative sequence selected among the of the annotations stored in databases has made it clear set of similar ones. Rules for the selection of informative that a significant number of homologous sequences have sequences were related to the database of origin, presence of fragments, and type of keywords associated to them.
The correct characterization of the relation between the Genequiz delivered annotations with an associated confi- sequences forming a protein family may solve some of dence value that was related with the similarity (fixed these problems, even if the final process of annotation still e-value) with the sequence selected for the information relies on the existing database annotations. Hence, such transfer. The system included a simplified lexical analysis characterization could, therefore, be subject to the errors for identifying the informative descriptions (it has inspired introduced during this process. Efforts are underway to the lexical analysis that we apply).
improve the database annotations by keeping pointers to EditToTrembl14 is a system in which the annotation of the origin of the annotations. An attempt is also being sequences is based on the intricate execution of different made to create systems that facilitate this process by programs to predict such features of the proteins as automatically retrieving information from the original transmembrane regions, subcellular location, or enzy- matic codes. A complement of this work is that of Fleisch-mann et al.,15 where a method for automatic functional Domain Shuffling and Other Difficulties
annotation is described. Thus, these authors overcome The process of classifying protein families into the some of the previously mentioned difficulties, such as the corresponding families and subfamilies is not trivial. The functional transfer from the best hit. The method is based study of the evolutionary relationships between proteins is on the use of PROSITE16 as an external database to often complicated by the presence of multiple domains.
cluster the sequences of a reference database (Swiss- Domains can have different origins, and be associated with Prot17 in this case) into groups. Each group is inspected to several domains in different proteins. Moreover, the func- identify functional information shared by most of the tion of a protein can be seen as the result of combining the proteins. In the positive cases, an annotation rule is functions of its domains. Therefore, for any function derived for the annotation of new sequences. Additionally, prediction schema it is essential to take into account the some rules are applied to reduce the potential number of domain structure of proteins and avoid transferences false positives, for example, the taxonomy of the query based on incomplete domain identification.
sequence must match the species distribution of the pro- After identifying the sequence relations in the corre- teins described by the (PROSITE) condition. This conserva- sponding families, however, further difficulties arise asso- tive approach is currently applied during the generation of ciated with the transference of the corresponding annota- the TrEMBL entries, producing an enrichment of the functional annotations prior to the more precise annota- in different categories depending on the extent to which tion work carried out by the Swiss-Prot curators. Using the the alignments cover the length of the query and target PROSITE groups as seeds of the process imposes limita- sequences (alignment categories).
tions for the coverage of the annotations.
3. Key functional annotations of the corresponding pro- The PRECIS system18 is more a distiller of information teins are analyzed, including functional descriptions, than an annotation method. Basically, it receives a set of enzymatic activity codes, and Swiss-Prot style key- Swiss-Prot identifiers (which could come, for example, from a BLAST report) and distils a functional report 4. The transference of information is carried out starting combining their annotations, thus removing redundancies from the alignment categories with a better coverage. A and applying some rules and filters for the different fields confidence level is assigned to each one of the annota- tions. This level is derived from the alignment catego- Andrade6 described a method for addressing the prob- lem of the annotations specific to protein domains by usingposition-specific annotation of protein function based on Step 1. Sequence searches
the analysis of multiple homologous sequences. The mul- The similarity searches were carried out with BLAST on tiple sequence alignment corresponding to the homologues a non-redundant database (nrdb program from NCBI at was used to assign functions to specific domains (positions ftp://ftp.ncbi.nih.gov/pub/nrdb and cd-hit19) that included covered by the sequences in the alignment). The functional Swiss-Prot, TrEMBL, and TrEMBLnew. Those sequences descriptions of the similar sequences were processed and with a similarity value above a cut-off (e.g., E-value Ͻ 0.1) screened for common strings of words. The correlation were further BLAST-aligned between them to obtain a between the conservation of the aligned positions with rough measure of their pairwise similarity (i.e., their respect to the query protein and the presence of common E-values). This procedure effectively maps the sequence functional descriptors in the aligned proteins was used to space surrounding a query sequence. If desired, this local produce annotations common to the shared positions. This sequence space map can be extended to other more diver- procedure allowed the construction of consensus descrip- gent related subfamilies through iterative intermediate tions attached to defined regions of the query protein. The sequence searches,20,21 exploiting the transitivity prin- two main difficulties of this idea are perhaps the complex- ciple of homology: if protein A is homologous to B, and B to ity of its automation and the fact that it will work properly C, then A and C are homologues (if the domains shared in only under certain conditions (for example, it requires that the A-B and B-C relationships correspond to each other).
the set of homologues contain proteins belonging to the Moreover, recursive searches provide better descriptions of the sequence space, so the clustering works better. The The approach presented here tries to overcome many of use of PSI-BLAST or other profile-based methods is inad- the aforementioned obstacles for function annotation. It equate for this classification process because PSI-BLAST applies a clustering algorithm to classify proteins into does not return measures of the distance between pairs of families and subfamilies. A lexical analysis inspired on proteins but distance between proteins and profiles. A GeneQuiz is applied to identify informative functional post-processing of the PSI-BLAST results by, for example, descriptions. As in Andrade6 and Fleischmann et al.,15 our realigning all the results, could be a valid alternative.
approach uses information from multiple homologues to Identification of subfamilies by clustering. The com-
select the information to be transferred. The problem of plete set of distances between the sequences provides the transferring functions from unrelated domains is ad- basic description of the local sequence space. A clustering dressed by analyzing the degree of coverage of the corre- process is used to identify groups of sequences that more sponding local sequences alignments.
likely correspond to protein subfamilies. The algorithmused is the “Normalized Cut,”22 and the application of this graph theory to biological sequences is described in Abas- Algorithm for the Assignment of Functional
cal and Valencia.23 A weighted undirected graph G(V, E) Annotations Based on the Analysis of Sequence
represents the sequence space. The nodes (V, sequences in Clusters
this case) are connected through arcs (E) that represent The workflow of the method proceeds as follows: their similarity relationship. Each arc has an associatedweight (w), proportional to the similarity measured be-tween the sequences in the form of Ϫlog (E-value) of the 1. A sequence similarity search is carried out to find BLAST algorithm. A cut (A, B) in a graph is a partition of G proteins related to the query sequence. A clustering into two sets of nodes A and B, obtained by removing some algorithm is applied in order to identify closely related of the arcs. The capacity of a cut is the sum of the weights sequence groups in the set of similar proteins. More of the arcs that have to be removed to obtain the cut (A,B).
related sequences are more likely to share a common The minimum cut24 of a graph is the one with minimum function. In some cases, recursive sequence similarity capacity. The minimum cut provides an effective measure searches lead to better representation of the related of the separation of the initial sequence space in two subfamilies, which facilitates the clustering.
2. The local alignments with the closely related proteins clustered together with the query protein are classified Shi and Malik22 proposed a normalization of the capac- provided in the standard “DE” field of Swiss-Prot. Having ity of the cuts by including in the formula the amount of a set of proteins belonging to the same subfamily (or group connections of each one of the two separate sequence of orthologues) facilitates the secure transfer of a good functional description, since we can select the most repre-sentative description (the most homogeneous compared to Ncut(A,B) ϭ cut(A,B)/asso(A,V) ϩ cut(A,B)/asso(B,V) where asso(A,V) is the sum of the weights of the arcs fromall nodes in A to all nodes in V (including those in A).
1. Noninformative word filtering. Descriptions are filtered Once the best cut is determined, the algorithm proceeds to remove words that contain no information about recursively, searching for new cuts of the established function, such as FRAGMENT, HYPOTHETICAL, or sequence groups. The recursive process continues until 2. Deriving a “homogeneity” score for each description to measure how representative it is. Each description is 1. The arithmetical mean of the arc weights in A or B split into its component words, and for each word the exceeds by twofold the arithmetical mean of the arc frequency with which these words appear in the set of descriptions in the cluster is calculated. A pre-score is 2. The number of arcs divided by the maximal possible calculated for each description by adding together the number of arcs is higher in A or B than the same frequencies of its words. This pre-score is divided by a correction factor to avoid the bias towards long descrip-tions. This factor is defined as the number of words An evaluation of this clustering method applied to divided by the number of synonyms (that usually are protein sequences was carried out in Abascal and Valen- very informative and are given between parenthesis in cia.23 The outcomes of the clustering process are a number the Swiss-Prot entries). A normalized homogeneity of groups of well-defined and highly connected sequences score is calculated as the fraction that each description and the distances between these clusters. One of these score represents in the sum of all the description scores.
groups is the one containing the query protein and ideally 3. Weighting normalized homogeneity scores with normal- will contain the other members of its subfamily.
ized similarity scores. Similarity scores are normalizedin the same manner, by calculating the fraction each Step 2. Analysis of the Local Alignments and
BLAST similarity score represents in the total sum of Assignment of “Alignment Categories”
BLAST similarity scores. Both normalized scores are The local alignments of the query sequence versus the weighted in a combined score, what is useful for the closely related proteins are analyzed to determine the cases where two or more subfamilies erroneously get degree to which they cover the corresponding sequence lengths. We have defined four categories: As will be explained later, alignment categories and 1. Both the query and the target align through more than these combined description scores are used to select the 80% of their lengths. In this case, the functional trans- annotation to be transferred. Once selected, the descrip- fer is considered to be secure and complete.
tion is inspected to reject descriptions that contain no 2. The entire length of the query sequence cannot be information. The process of identification of non-informa- aligned with the target sequence. In this case, the tive descriptions is based on lexical analysis, inspired in transference of functional annotations might not be complete since the query protein may contain differen- Examples of frequent non-informative annotations are tial properties associated to the region of the sequence “[Hypothetical|Putative] [Mol.Weight] [Lipo|Glyco]Protein [word]” where, if “word” is present, it should be present in at 3. Less than the entire length of the target protein is least some of the other descriptions of the proteins in the aligned with the query protein. In this case, the transfer- cluster to avoid rejection.“[Mol.Weight]” represents in perl ence of annotations could be wrong if some of the گdϩ(گ.)*(گd)*(گs)*K(D)*(A)*(گs)*. The character “|” means “or.” functions of the target protein are associated to the Words inside “[ ]” may or may not appear, etc.
sequence region not aligned with the query sequence.
Some of the words commonly found in non-informative 4. The less confident category corresponds to the cases in descriptions are: “Intergenic”, “Cosmid” or “Genomic se- which neither the query nor the target align completely.
quence,” their presence is enough to label descriptions asnon-informative. Another rule to identify uninformative In the cases where the target protein is annotated as descriptions is to remove all (expected) uninformative FRAGMENT in the database, its alignment is always words and check for the remaining words if they are present in at least some of the others descriptions in thecluster.
Step 3. Definition of the Functional Annotations to
Finally, informative descriptions are cleaned by remov- Be Transferred
ing words that frequently appear in functional descrip- The goal of functional descriptions is to find descriptions tions but are not transferable based on homology, such as of function that can be compared with the information the molecular weight or the word “fragment.” Key words accepted: A B C. Note that C, E, and F have the same frequency, but only C is transferred to avoid mixing key words that do not co-occur. The process: first A is selected as seed. Then, since B isconnected to A more than 4 times (8/2), B will also be accepted. Then C will also be accepted because it isconnected to B more than 2.5 times (5/2). No more keywords will be added.
Enzymatic codes. Since EC numbers are expressed in
● For each protein description, uninformative words are a non-ambiguous language, there is no need to measure removed and the frequency of the remaining ones is their homogeneity. The EC number is transferred to the query protein from the sequence inside its sequence clus- ● We build a graph in which there is one node for each of ter with the highest similarity score and a better align- the words and the (weighted) arcs between the nodes reflect the number of descriptions in which two words Key words. Functional key words assigned in Swiss-
Prot depend strongly on the functional domain organiza- ● The most frequent word is selected as seed, and the tion of the proteins, e.g., Myristate, Calcium-binding. We graph is searched for all those nodes (words) connected have preferred to transfer only key words in the cases to its node with a frequency at least half of the seed where the target protein is completely covered by the frequency (slightly different from the approach used alignment (first and second alignment categories). The key words frequency is calculated, and a graph is built using ● Then, for each of those accepted words, the most fre- key words as nodes, and the arcs connecting the nodes are quent position where they appear relative to the seed labelled by the number of times that key words appearassociated to the same protein. The selection process is based on the co-occurrence of the key words, and it is ● Finally, the description is built by sorting the list of applied reiteratively to rescue partial co-occurrences. First, words according to these most frequent positions. Even a key word score is calculated for each protein by weight- if this sorting procedure is not perfect, it is simple ing similarity and homogeneity scores, as in the case of the enough to give an idea of the functions that are present functional descriptions). The key word with the highest frequency selected among the ones of the protein with thehighest key word score is accepted, and selected as seed.
Step 4. Assignment of the Functional Annotations to
Repeated searches recover other key words connected to the Different Alignment Categories
some of the accepted ones with at least the half of itsfrequency. The frequency must be half or greater to avoid For the final transfer of functional description, the including key words that are not co-occurring, as illus- proteins are inspected from the best alignment coverage category to the worst. In each of the categories, the best Neighbor clusters annotation. We have incorporated
description (higher combined score) is searched for. If an additional procedure to extract information from neigh- there are no descriptions in that category, or if the best one boring sequence clusters. In this case, the intention is to is considered noninformative, we go down to the following provide general annotations for each of the sequence category and search again for the best description in this clusters. This procedure is particularly helpful in those category. The confidence of the transference is derived cases where the cluster of sequences around the query protein does not contain enough proteins with relevantfunctional annotations.
Essentially, the procedure works by selecting the word Selected Examples of Functional Annotation
that is most frequent in the set of descriptions and all theother words that are frequently associated to it. The The parameters used for the recursive sequence similar- position of these words with respect to the most frequent ity searches have been selected to obtain clearer results in one is used to order them and to build the final functional each of the examples. They are different in each case cluster description. In detail, the steps are: because of the different sizes of the different familiesanalyzed. For the case of Buchnera’s genome annotation, ● For each of the neighbor clusters its own set of annota- we did not use recursive searches but single BLAST searches with realignment of all the results (see Methods).
TABLE I. BLAST’s Best Hits for Swiss: TETM_NEIME†
Sequences producing significant alignments: TET1_ENTFA (Q47810) Tetracycline resistance protein tetM from tr. . .
TETS_LACLA (Q48712) Tetracycline resistance protein tetS (Tet(S)).
TETO_CAMCO (P23835) Tetracycline resistance protein tetO (Tet (O)).
TETW_BUTFI (O52836) Tetracycline resistance protein tetW (Tet(W)).
Q93K56 (Q93K56) Tetracycline resistance protein.
TETP_CLOPE (Q46306) Tetracycline resistance protein tetP (Tetb(P)).
Q97J38 (Q97J38) Tetracycline resistance protein, tetQ family, GT.
TETM_STRLI (Q02652) Tetracycline resistance protein tetM.
OTRA_STRRM (Q55002) Oxytetracycline resistance protein.
Q97KR3 (Q97KR3) Tetracycline resistance protein tetP, contain GT.
Q8XLR6 (Q8XLR6) Probable tetracycline resistant protein.
EFG_THETH (P13551) Elongation factor G (EF-G).
Q9AIG7 (Q9AIG7) Elongation factor G.
EFG_AQUAE (O66428) Elongation factor G (EF-G).
EFG_THEMA (P38525) Elongation factor G (EF-G).
Q8YP62 (Q8YP62) Translation elongation factor EF-G.
Q9PI16 (Q9PI16) Elongation factor G.
EFG_CHLMU (Q9PJV6) Elongation factor G (EF-G).
BAB56709 (BAB56709) Translational elongation factor G.
Q9F4B2 (Q9F4B2) Translational elongation factor G, EF-G (Fragment).
EFG_SYNP6 (P18667) Elongation factor G (EF-G).
Q9RXK5 (Q9RXK5) ELONGATION FACTOR G.
†It can be appreciated that BLAST e-values order appropriately the sequences of the tet and EF-Gsubfamilies. Even if there’s not a clear separation attending to the magnitude of the e-values, the clusteringalgorithm distinguishes both subfamilies, but fails to include two more divergent tet’s in the proper cluster.
Proteins TET1_ENTFA to OTRA_STRRM belong to the same group; proteins Q97KR3 and Q8XLR6 formanother cluster; the remaining ones form the third group. The complete BLAST result can be obtained from:http://www.pdg.cnb.uam.es/fabascal/SEARCH AND CLUS/TETM NEIME/Q51238.bls.
TABLE II. Subfamilies Found by the Recursive Searches for Swiss::TETM_NEIME and the
Subsequent Clustering, Which Resulted in 21 Clusters
Tetracycline resistance protein tet[W M S R . . .] Peptide chain release factor 3 (RF-3) (bacteria) Elongation factor 1-alpha plus 18 Eukaryotic peptide chain release factor 3 NodQ bifunctional enzyme and CysN/cysC bifunctional enzyme Selenocysteine-specific elongation factor Those containing more than 2 sequences are represented. Note that some subfamilies may be incompletebecause similarity searches were limited to a maximum of 750.
TETM_NEIME
applying three rounds of intermediate sequence searches The first selected example corresponds to the tetracy- with a cut-off E-value of 1e-07), allowed the appropriate cline resistance protein, (tetM) from Neisseria meningiti- separation of the two subfamilies (Table II) and also the dis. The BLAST search with this protein vs. a nonredun- correct classification of other more distant subfamilies.
dant database selected at a 90% identity level rendered the Assuming that the co-clustered sequences share a common results shown in Table I. In this case, there is not a clear function makes it possible to use them as sources of separation of the subfamilies of TET and EF-G proteins annotation, analyzing the descriptions as described in based on E-values. However, the clustering of the se- Methods. This yields the annotation for the query protein: quence space local to the query protein (obtained by (TET(S)) instead of TETM. This is an especially problem-atic case where the clustering is not able to classify intoseparate groups the different tetracycline resistance deter-minants. Instead, it puts them together according to theirhigh similarity. The annotations in the database (or thenomenclature) seem to be inconsistent (or the specificityhas no evolutionary foundation), because the percentage ofsequence identity is much higher between some Tet(M) andTet(whatever) than between two Tet(M), for example, TETM-_NEIME vs. TETS_LACLA (77%) and TETM_NEIME vs.
TETM_STRLI (35%). A phylogenetic tree of the best BLASThits of TETM_NEIME can be see at http://www.pdg.cnb.ua-m.es/funcut.html. The keywords for the co-clustered se-quences were: Q02652 Protein biosynthesis; Antibiotic resistance; GTP- Q93K56 GTP-binding.
Q46306 Protein biosynthesis; Antibiotic resistance; GTP- Note that some subfamilies may be incomplete because Q51238 Protein biosynthesis; Antibiotic resistance; GTP- recursive searches were stopped before convergence. Each circle and itsradius correspond with a cluster and its size. The numbers inside indicate the cluster id and the number of proteins in it. The width of the lines
Q47810 Protein biosynthesis; Antibiotic resistance; GTP- connecting the clusters represents the strength of their connection. The different gray intensities correspond to the different families.
P23835 Protein biosynthesis; Antibiotic resistance; GTP- EC 2.7.7.4 ADENYLATE TRANSFERASE SAT ATP O52836 Protein biosynthesis; Antibiotic resistance; GTP- ID:18; SIZE:11; PROXIMITY:1.87 SELENOCYSTEINE SPECIFIC ELONGATION FACTOR SELB TRANS- Q48712 Protein biosynthesis; Antibiotic resistance; GTP- These automatic annotations are difficult to read in some cases because of the absence of punctuation charac- Q55002 Protein biosynthesis; Antibiotic resistance; GTP- ters, such as, for example, the parentheses, which are not managed. Moreover, when a word appears more than once in a description, only the first is counted. For example, the from which were derived the following key word annota- cluster annotation SELENOCYSTEINE SPECIFICELONGATION FACTOR SELB TRANSLATION should tions: GTP-binding; Protein biosynthesis; Antibiotic resis- The neighbor clusters and their annotations were: One selenocysteine-specific elongation factor is sepa- rated from no. 18 to the singleton cluster no. 20. If this ID:10; SIZE:80; PROXIMITY:46.95 ELONGATION “solitary” protein were the query protein, then annotation would not be possible. This represents an example of what ID:14; SIZE:24; PROXIMITY:26.95 PEPTIDE CHAIN kind of errors may appear. Results can be accessed at: http://www.pdg.cnb.uam.es/funcut.html.
ID:4; SIZE:35; PROXIMITY:20.42 GTP BINDING PDXK_SHEEP
ID:3; SIZE:50; PROXIMITY:16.25 GTP BINDING http://www.pdg.cnb.uam.es/funcut.html
This protein is a PYRIDOXINE KINASE. Recursively ID:5; SIZE:74; PROXIMITY:13.45 ELONGATION searching with it (three rounds with and Evalue cut-off of 1e-03, in this case vs. a 100% non-redundant database ID:21; SIZE:117; PROXIMITY:10.51 ELONGATION comprising Swiss-Prot, TrEMBL and TrEMBLnew), and subsequently clustering the results, yields 29 clusters, ID:2; SIZE:59; PROXIMITY:6.13 TRANSLATION corresponding to 160 sequences (first round: 1 sequence; second: 70; third: 89). Of these 29 groups, 7 contain more ID:15; SIZE:248; PROXIMITY:3.07 ELONGATION than three sequences (Fig. 2); the rest correspond to out-layers or cases where the clustering fails to keep the ID:17; SIZE:25; PROXIMITY:1.96 SULFATE ADENYLYL- proteins in their corresponding families (a phylogenetic Annotation. The subfamily of the PDXK_SHEEP is
TABLE III. Buchnera aphidicola Automatic Protein
separated in an only eukarya cluster, together with 19 Function Annotation
relatives. The bacteria Pyridoxal/pyridoxine/pyridoxam- ine kinases (including PDXY and PDXK) are divided inthree next neighbor clusters. The query protein was anno- tated with the highest level of confidence as PYRIDOXINE KINASE (PYRIDOXAL KINASE), with enzymatic activity 2.7.1.35 and key words Kinase and Transferase.
The other key word in the Swiss-Prot entry corresponding to PDXK_SHEEP, “Acetylation,” was not transferred be-cause PDXK_SHEEP was the only protein in the clusterwith that key word assigned to it.
separately from their homologues, in singletons, so nofunctional annotation transfer can be carried out. In ID:13; SIZE:9; PROXIMITY:19.35 PYRIDOXAMINE Tamas et al. 2002,26 this divergence for flagellar pro- teins is also observed and it is proposed that it may be ID:11; SIZE:4; PROXIMITY:16.63 KINASE related to the acquisition of new functionalities, since ID:14; SIZE:5; PROXIMITY:13.78 PYRIDOXINE flagelles have not been observed in this bacteria.
KINASE EC 2.7.1.35 PYRIDOXAL VITAMIN B6 2. Too specific descriptions (9 cases): in this case, some specie-specific (not transferable) word in the descrip- ID:3; SIZE:62; PROXIMITY:1.13 PHOSPHOMETH- tion is transferred. For example, for ycfC, the automatic annotation “Hypothetical protein ycfC (ORF-23)” was ID:24; SIZE:22; PROXIMITY:0.69 RIBOKINASE manually corrected to “Hypothetical protein ycfC”. Theword “ORF-23” is particular for the source specie.
With other more permissive searching parameters (e.g., 3. Incorrect functional assignments (2 cases): Protein hscA higher e-value cut-off and additional rounds of sequence corresponds to “chaperone protein hscA homologue” but searches), other remotely related subfamilies were identi- it was automatically annotated as “chaperone protein fied, such as: tagatose-6-phosphate kinase, phosphofruc- dnaK” because the clustering did not separate these two tokinases, 2-dehydro-3-deoxygluconokinase, guanosine ki- very close subfamilies and in the cluster dnaK proteins nase, adenosine kinase, etc. The analysis of those results involving more clusters and isolated sequences (single-tons) was not followed here.
An illustrative example of the usefulness of analyzing Application to the Annotation of the Buchnera
whether or not the alignments cover the entire sequence length of the involved proteins is the one of polA. The bestBLAST hits for this protein are DNA POLYMERASES I.
This annotation method has been applied for the analy- In the BLAST similarity list there are also proteins sis of the genome of Buchnera aphidicola (specific host annotated as “Probable 5Ј-3Ј exonucleases.” This buchnera Baizongia pistaciae).25 In this case, the sequence space protein has lost most of its domains, so alignments with local to each of the 507 coding genes of buchnera was built DNA POLYMERASE targets cover 90 –30% of query and with single BLAST searches (e-value cut-off: 0.1) and all target lengths. However, alignment with less similar vs. all pairwise alignment of the results. The resulting 5Ј33Ј exonuclease covers 91–97%. This annotation, which automatic annotations are available at the funcut web site.
seems to be the correct one, was detected automatically.
A comparison of automatic annotation vs. BLAST best hitannotations is also available at the same site.
Keywords and EC Numbers
Annotations were manually analyzed in the cases in For the 507 proteins in buchnera’s genome, there were which no automatic annotation was produced and in the 281 EC number assignments, corresponding to 269 pro- cases of conflict with the annotation of other closely related teins (some proteins have more than one enzymatic activity).
genomes in the database. This allowed us to obtain an In the case of key words, 1,463 were assigned to 470 approximate measure of the accuracy of this approach (see proteins, but if we discount the frequent (but not adequate Table III).The accuracy was estimated at 94% and three for transfer) key word “Complete proteome,” we have 1,071 kinds of errors were established for the remaining 6%.
key words for 391 proteins. Rejection of nontransferable Basically, these errors come from unsatisfactory cluster- ing that in some cases divides a given subfamily. This thencreates singletons, and in other cases fails to separate two DISCUSSION
(or more) subfamilies. Other “errors” are due to special We have presented a method for the generation of characteristics of the lifestyle of this obligate endo- functional annotations based on the study of the annota- tions of homologous sequences. The method includes newfeatures related to the specific identification of protein 1. Singleton errors (21 cases): fliH, fliJ, fliK, fliM, flgB, subfamilies (orthologous groups) because at this level the flgM (flagellar proteins). These proteins are clustered function of the homologous proteins tends to be more conserved than in general protein families (mixture of those of other genes of known function. Function can also paralogous and orthologous sequences).
be predicted by exploring the set of interactions deduced Our method seems to produce correct annotations includ- by different experimental or computational techniques (for ing those of the “DE” and “KW” fields of Swiss-Prot and a review see, Valencia and Pazos28). According to this idea, enzyme classification numbers (Enzyme Commission code, different attempts have been made to use the genomic EC). It is obvious that these three features do not account context to improve annotations, for example, by increasing for all the possibilities of protein function description, and the probability of associating a function with a given other database annotations are also important for a com- protein if it were the best candidate in a given genome.3,29 Even if interesting attempts to unify the various sources of It is important to keep in mind that a description of information on association between proteins and genes are protein function can be done at very different levels from underway (for an early study see Marcotte et al.30), biochemical to cellular. It is appealing to think that the problems of consistency and accuracy still persist, and the levels more directly related to the chemical function will current knowledge about pathways and networks is still tend to be more conserved at large sequence distance.
insufficient to allow a systematic approach.
Cellular functions, then, which are more dependent on the Another completely different path has been opened by cellular context and interactions, will be less conserved at the use of sequence features, (e.g., sequence length, poten- the sequence level. Part of this complexity in function tial phosphorylation sites, predicted TM segments), for the description is quite obvious in the comment (“CC”) and prediction of protein functional class.31 It is conceivable feature (“FT”) Swiss-Prot fields, which include information that in the future these, and other, alternative approaches as varied as catalytic activity, quaternary structure, signal will be important complements for research in protein sequences, catalytic residues, domain structure, and post- functions. However, homology-based function prediction still plays a central role in Molecular Biology.
The current efforts for the construction of classifications (ontologies) for the definition of function at various levels The Space of Sequences and the Annotation of
(particularly the GO Consortium27) represent a possible Function
way of alleviating these problems. The use of the current Our system works by first mapping the sequence space GO ontology in our (and other) automatic annotation in groups of orthologous sequences. The results of the methods will not be easy until a substantial number of clustering depend strongly on the quality of the sequence sequences from various genomes are annotated, some- space map built. This, in turn, depends on the parameters thing that had still not happened at the time these systems used for retrieving the sequences and measuring the were built, despite the considerable effort made by the distances between them. We have shown previously23 that this clustering strategy works appropriately for the identi- Selecting Representative Descriptions
fication of orthologous groups of sequences from sets ofparalogues (families and subfamilies). Compared to other Our purpose was to identify the most representative approaches for protein classification, this one has the description from a set of functionally related ones. We advantage of being resistant to domain problems because tested various ways of measuring homogeneity of the sequence searches are made with respect to a query descriptions. Both Shannon entropy and the sum of log- protein and only the aligned subsequences are used to probabilities (probabilities understood as the frequency of search the space of sequences. Additionally, it does not words) tend to give better scores to long descriptions, even require working in the context of complete genomes, as in if they contain poorly represented words. The comparison of the frequency of words in a given description with the The procedure has been validated in real biological frequency in the whole set of descriptions using Relative problems, such as the annotation of the Buchnera aphidi- Entropy did not render useful descriptions. In our hands, cola25 genome described here.
the best results were obtained by calculating the informa- Systems able to make solid predictions of function based tion content of the description weighted by the number of on sequence information are important in the context of words in the description. We additionally apply a correc- the annotation of genomes, even if a number of difficulties tion to avoid penalizing longer descriptions that include remain to be solved. The definition of function has a very important subjective component: the same protein will bedescribed in different terms by different scientists. The Prediction of Function from Homologous
approach we have followed tries to transfer the most Sequences and Alternative Approaches
representative description, the consensus definition, which Interestingly, the annotation of function by transference also reduces the pernicious propagation of annotation from proteins of related sequences is not the only possibil- errors. Domain shuffling events observable at many pro- ity for the “in silico” prediction of function. The flourishing teins make function transference based on homology dan- of genomic data has enabled other modes of function gerous. An analysis of domain structure is important but, prediction independent of the identification of homologous as properly expressed by Attwood,32 the complexity of sequences. The function of proteins can be inferred from biological systems (such as complete proteins) poses impor- the study of the similarity of their expression pattern with tant problems for computational approaches because the properties of a system can be explained by but not deduced Sander C. Automated genome sequence analysis and annotation.
from its components (such as protein domains). In this 14. Moller S, Leser U, Fleischmann W, Apweiler R. EDITtoTrEMBL: work, we did not analyze the individual components but a distributed approach to high-quality automated protein se- rather, as a partial solution, assessed the extent to which quence annotation. Bioinformatics 1999;15:219 –227.
the similarity covers the entire length of the implied 15. Fleischmann W, Moller S, Gateau A, Apweiler R. A novel method for automatic functional annotation of proteins. Bioinformatics 16. Sigrist CJ, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, ACKNOWLEDGMENTS
Bairoch A, Bucher P. PROSITE: a documented database using We acknowledge the suggestions of O. Olmea for the patterns and profiles as motif descriptors. Brief Bioinform 2002;3:265–274.
application of the clustering strategies, and the graph- 17. Bairoch A, Apweiler R. The SWISS-PROT protein sequence based representation of the recursive search results. We database and its supplement TrEMBL in 2000. Nucleic Acids Res are grateful to members of the Protein Design Group for 18. Reich J, Mitchell A, Goble C, Attwood T. Toward more intelligent interesting discussions and continuous support. Our work annotation tools: a prototype. IEEE Intell Syst 2001;16:42–51.
has benefited from the interesting ideas on the Ncut 19. Li W, Jaroszewski L, Godzik A. Clustering of highly homologous algorithm as described in G. Yona’s PhD work,33 and from sequences to reduce the size of large protein database. Bioinformat-ics 2001;17:282–283.
use of the MESCHACH numerical library, made public by 20. Park J, Teichmann S, Hubbard T, Chothia C. Intermediate D. E. Stewart and Z. Leyk. We thank Ian Korf for the sequences increase the detection of homology between sequences.
BPlite BLAST parser. This work has in part been sup- ported by a grant from the Spanish Ministry of Science and 21. Gerstein M. Measurement of the effectiveness of transitive se- quence comparison, through a third ’intermediate’ sequence.
Technology (CICYT), and by a fellowship from Madrid’s 22. Shi J, Malik J. Normalized cuts and image segmentation. Proc IEEE Conf Comp Vision Pattern Recog 1997;731–737.
REFERENCES
23. Abascal F, Valencia A. Clustering of proximal sequence space for the identification of protein families. Bioinformatics 2002;18:908 – 1. Altschul SF, Gish W. Local alignment statistics. Methods Enzymol 24. Wu Z, Leahy R. An optimal graph theoretic approach to data 2. Smith TF, Zhang X. The challenges of genome sequence annota- clustering: theory and its application to image segmentation.
tion or “The devil is in the details”. Nature Biotechnol 1997;15: 25. van Ham RCHJ, Kamerbeek J, Palacios C, Rausell C, Abascal F, 3. Bork P, Dandekar T, Diaz-Lazcoz Y, Eisenhaber F, Huynen M, Bastolla U, Ferna´ndez JM, Jime´nez L, Postigo M, Silva FJ, Yuan Y. Predicting function: from genes to genomes and back. J Tamames J, Viguera E, Latorre A, Valencia A, Mora´n F, Moya A.
Reductive genome evolution in buchnera aphidicola. Proc Natl 4. Bork P, Koonin EV. Predicting functions from protein sequences: where are the bottlenecks?. Nature Genet 1998;18:313–318.
26. Tamas I, Klasson L, Canback B, Naslund AK, Eriksson AS, 5. Doerks T, Bairoch A, Bork P. Protein annotation: detective work Wernegreen JJ, Sandstrom JP, Moran NA, Andersson SG. 50 for function prediction. Trends Genet 1998;14:248 –250.
million years of genomic stasis in endosymbiotic bacteria. Science 6. Andrade MA. Position-specific annotation of protein function based on multiple homologs. ISMB 99 1999;7:28 –33.
27. The Gene Ontology Consortium. Gene ontology: tool for the 7. Todd AE, Orengo CA, Thornton JM. Evolution of function in unification of biology. Nature Genet 2000;25:25–29.
protein superfamilies, from a structural perspective. J Mol Biol 28. Valencia A, Pazos F. Computational methods for the prediction of protein interactions. Curr Opin Struct Biol 2002;12:368 –373.
8. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller 29. Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation protein families. Science 1997;278:631– 636.
of protein database search programs. Nucleic Acids Res 1997;25: 30. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D. A combined algorithm for genome-wide prediction of protein 9. Brenner SE. Errors in genome annotation. Trends Genet 1999;15: 31. Jensen LJ, Gupta R, Blom N, Devos D, Tamames J, Kesmir C, 10. Devos D, Valencia A. Intrinsic errors in genome annotation.
Nielsen H, Staerfeldt HH, Rapacki K, Workman C, Andersen CA, Knudsen S, Krogh A, Valencia A, Brunak S. Prediction of human 11. Devos D, Valencia A. Practical limits of function prediction.
protein function from post-translational modifications and localiza- tion features. J Mol Biol 2002;319:1257–1265.
12. Blaschke C, Hirschman L, Valencia A. Information extraction in 32. Attwood TK. Genomics. The Babel of bioinformatics. Science molecular biology. Brief Bioinform 2002;3:154 –165.
13. Andrade MA, Brown NP, Leroy C, Hoersch S, Daruvar A.De, 33. Yona G. Methods for global organization of the protein sequence Reich C, Franchini A, Tamames J, Valencia A, Ouzounis C, space. PhD Thesis, Hebrew University. 1999.

Source: http://ubio.bioinfo.cnio.es/biotools/DIAMONDS/funcut-results/Arabidopsis_thaliana/docs/funcut.pdf