Yvonne Kallberg

Short-chain dehydrogenase/reductase (SDR) relationships: A large family with eight clusters common to human, animal, and plant genomes

The progress in genome characterizations has opened new routes for studying enzyme families. The availability of the human genome enabled us to delineate the large family of short‐chain dehydrogenase/reductase (SDR) members. Although the human genome releases are not yet final, we have already found 63 members. We have also compared these SDR forms with those of three model organisms: Caenorhabditis elegans, Drosophila melanogaster, and Arabidopsis thaliana. We detect eight SDR ortholog clusters in a cross‐genome comparison. Four of these clusters represent extended SDR forms, a subgroup found in all life forms. The other four are classical SDRs with activities involved in cellular differentiation and signalling. We also find 18 SDR genes that are present only in the human genome of the four genomes studied, reflecting enzyme forms specific to mammals. Close to half of these gene products represent steroid dehydrogenases, emphasizing the regulatory importance of these enzymes.

The SDR (Short-Chain Dehydrogenase/Reductase and Related Enzymes) Nomenclature Initiative

Short-chain dehydrogenases/reductases (SDR) constitute one of the largest enzyme superfamilies with presently over 46,000 members. In phylogenetic comparisons, members of this superfamily show early divergence where the majority have only low pairwise sequence identity, although sharing common structural properties. The SDR enzymes are present in virtually all genomes investigated, and in humans over 70 SDR genes have been identified. In humans, these enzymes are involved in the metabolism of a large variety of compounds, including steroid hormones, prostaglandins, retinoids, lipids and xenobiotics. It is now clear that SDRs represent one of the oldest protein families and contribute to essential functions and interactions of all forms of life. As this field continues to grow rapidly, a systematic nomenclature is essential for future annotation and reference purposes. A functional subdivision of the SDR superfamily into at least 200 SDR families based upon hidden Markov models forms a suitable foundation for such a nomenclature system, which we present in this paper using human SDRs as examples.

Classification of the short-chain dehydrogenase/reductase superfamily using hidden Markov models

The short‐chain dehydrogenase/reductase (SDR) superfamily now has over 47 000 members, most of which are distantly related, with typically 20–30% residue identity in pairwise comparisons, making it difficult to obtain an overview of this superfamily. We have therefore developed a family classification system, based upon hidden Markov models (HMMs). To this end, we have identified 314 SDR families, encompassing about 31 900 members. In addition, about 9700 SDR forms belong to families with too few members at present to establish valid HMMs. In the human genome, we find 47 SDR families, corresponding to 82 genes. Thirteen families are present in all three domains (Eukaryota, Bacteria, and Archaea), and are hence expected to catalyze fundamental metabolic processes. The majority of these enzymes are of the ‘extended’ type, in agreement with earlier findings. About half of the SDR families are only found among bacteria, where the ‘classical’ SDR type is most prominent. The HMM‐based classification is used as a basis for a sustainable and expandable nomenclature system.

Classification and nomenclature of the superfamily of short-chain dehydrogenases/reductases (SDRs).

The short-chain dehydrogenases/reductases (SDRs) constitute one of the largest protein superfamilies known today. The members are distantly related with typically 20-30% residue identity in pair-wise comparisons. Still, all hitherto structurally known SDRs present a common three-dimensional structure consisting of a Rossmann fold with a parallel beta sheet flanked by three helices on each side. Using hidden Markov models (HMMs), we have developed a semi-automated subclassification system for this huge family. Currently, 75% of all SDR forms have been assigned to one of the 464 families totalling 122,940 proteins. There are 47 human SDR families, corresponding to 75 genes. Most human SDR families (35 families) have only one gene, while 12 have between 2 and 8 genes. For more than half of the human SDR families, the three-dimensional fold is known. The number of SDR members increases considerably every year, but the number of SDR families now starts to converge. The classification method has paved the ground for a sustainable and expandable nomenclature system. Information on the SDR superfamily is continuously updated at http://sdr-enzymes.org/.

Prediction of coenzyme specificity in dehydrogenases/reductases. A hidden Markov model-based method and its application on complete genomes.

Dehydrogenases and reductases are enzymes of fundamental metabolic importance that often adopt a specific structure known as the Rossmann fold. This fold, consisting of a six‐stranded β‐sheet surrounded by α‐helices, is responsible for coenzyme binding. We have developed a method to identify Rossmann folds and predict their coenzyme specificity (NAD, NADP or FAD) using only the amino acid sequence as input. The method is based upon hidden Markov models and sequence pattern analysis. The prediction sensitivity is 79% and the selectivity close to 100%. The method was applied on a set of 68 genomes, representing the three kingdoms archaea, bacteria and eukaryota. In prokaryotes, 3% of the genes were found to code for Rossmann‐fold proteins, while the corresponding ratio in eukaryotes is only around 1%. In all genomes, NAD is the most preferred cofactor (41–49%), followed by NADP with 30–38%, while FAD is the least preferred cofactor (21%). However, the NAD preponderance over NADP is most pronounced in archaea, and least in eukaryotes. In all three kingdoms, only 3–8% of the Rossmann proteins are predicted to have more than one membrane‐spanning segment, which is much lower than the frequency of membrane proteins in general. Analysis of the major protein types in eukaryotes reveals that the most common type (26%) of the Rossmann proteins are short‐chain dehydrogenases/reductases. In addition, the identified Rossmann proteins were analyzed with respect to further protein types, enzyme classes and redundancy. The described method is available at http://www.ifm.liu.se/bioinfo, where the preferred coenzyme and its binding region are predicted given an amino acid sequence as input.

Stabilization of discordant helices in amyloid fibril-forming proteins

Several proteins and peptides that can convert from α‐helical to β‐sheet conformation and form amyloid fibrils, including the amyloid β‐peptide (Aβ) and the prion protein, contain a discordant α‐helix that is composed of residues that strongly favor β‐strand formation. In their native states, 37 of 38 discordant helices are now found to interact with other protein segments or with lipid membranes, but Aβ apparently lacks such interactions. The helical propensity of the Aβ discordant region (K16LVFFAED23) is increased by introducing V18A/F19A/F20A replacements, and this is associated with reduced fibril formation. Addition of the tripeptide KAD or phospho‐L‐serine likewise increases the α‐helical content of Aβ(12–28) and reduces aggregation and fibril formation of Aβ(1–40), Aβ(12–28), Aβ(12–24), and Aβ(14–23). In contrast, tripeptides with all‐neutral, all‐acidic or all‐basic side chains, as well as phosphoethanolamine, phosphocholine, and phosphoglycerol have no significant effects on Aβ secondary structure or fibril formation. These data suggest that in free Aβ, the discordant α‐helix lacks stabilizing interactions (likely as a consequence of proteolytic removal from a membrane‐associated precursor protein) and that stabilization of this helix can reduce fibril formation.

The plant short-chain dehydrogenase (SDR) superfamily: genome-wide inventory and diversification patterns.

BackgroundShort-chain dehydrogenases/reductases (SDRs) form one of the largest and oldest NAD(P)(H) dependent oxidoreductase families. Despite a conserved ‘Rossmann-fold’ structure, members of the SDR superfamily exhibit low sequence similarities, which constituted a bottleneck in terms of identification. Recent classification methods, relying on hidden-Markov models (HMMs), improved identification and enabled the construction of a nomenclature. However, functional annotations of plant SDRs remain scarce.ResultsWide-scale analyses were performed on ten plant genomes. The combination of hidden Markov model (HMM) based analyses and similarity searches led to the construction of an exhaustive inventory of plant SDR. With 68 to 315 members found in each analysed genome, the inventory confirmed the over-representation of SDRs in plants compared to animals, fungi and prokaryotes. The plant SDRs were first classified into three major types — ‘classical’, ‘extended’ and ‘divergent’ — but a minority (10% of the predicted SDRs) could not be classified into these general types (‘unknown’ or ‘atypical’ types). In a second step, we could categorize the vast majority of land plant SDRs into a set of 49 families. Out of these 49 families, 35 appeared early during evolution since they are commonly found through all the Green Lineage. Yet, some SDR families — tropinone reductase-like proteins (SDR65C), ‘ABA2-like’-NAD dehydrogenase (SDR110C), ‘salutaridine/menthone-reductase-like’ proteins (SDR114C), ‘dihydroflavonol 4-reductase’-like proteins (SDR108E) and ‘isoflavone-reductase-like’ (SDR460A) proteins — have undergone significant functional diversification within vascular plants since they diverged from Bryophytes. Interestingly, these diversified families are either involved in the secondary metabolism routes (terpenoids, alkaloids, phenolics) or participate in developmental processes (hormone biosynthesis or catabolism, flower development), in opposition to SDR families involved in primary metabolism which are poorly diversified.ConclusionThe application of HMMs to plant genomes enabled us to identify 49 families that encompass all Angiosperms (‘higher plants’) SDRs, each family being sufficiently conserved to enable simpler analyses based only on overall sequence similarity. The multiplicity of SDRs in plant kingdom is mainly explained by the diversification of large families involved in different secondary metabolism pathways, suggesting that the chemical diversification that accompanied the emergence of vascular plants acted as a driving force for SDR evolution.

KIND-a non-redundant protein database.

SUMMARY KIND (Karolinska Institutet Nonredundant Database) is a protein database where identical sequences, both full length and partial, have been removed. The database contains nearly 274 900 sequences, half of which originate from the protein sequence databases Swissprot and PIR, while the other half come from translated open reading frames in GenPept and TrEMBL. AVAILABILITY KIND is downloadable from ftp://ftp.mbb.ki.se/pub/KIND.

RSpred, a set of Hidden Markov Models to detect and classify the RIFIN and STEVOR proteins of Plasmodium falciparum

BackgroundMany parasites use multicopy protein families to avoid their hosts immune system through a strategy called antigenic variation. RIFIN and STEVOR proteins are variable surface antigens uniquely found in the malaria parasites Plasmodium falciparum and P. reichenowi. Although these two protein families are different, they have more similarity to each other than to any other proteins described to date. As a result, they have been grouped together in one Pfam domain. However, a recent study has described the sub-division of the RIFIN protein family into several functionally distinct groups. These sub-groups require phylogenetic analysis to sort out, which is not practical for large-scale projects, such as the sequencing of patient isolates and meta-genomic analysis.ResultsWe have manually curated the rif and stevor gene repertoires of two Plasmodium falciparum genomes, isolates DD2 and HB3. We have identified 25% of mis-annotated and ~30 missing rif and stevor genes. Using these data sets, as well as sequences from the well curated reference genome (isolate 3D7) and field isolate data from Uniprot, we have developed a tool named RSpred. The tool, based on a set of hidden Markov models and an evaluation program, automatically identifies STEVOR and RIFIN sequences as well as the sub-groups: A-RIFIN, B-RIFIN, B1-RIFIN and B2-RIFIN. In addition to these groups, we distinguish a small subset of STEVOR proteins that we named STEVOR-like, as they either differ remarkably from typical STEVOR proteins or are too fragmented to reach a high enough score. When compared to Pfam and TIGRFAMs, RSpred proves to be a more robust and more sensitive method. We have applied RSpred to the proteomes of several P. falciparum strains, P. reichenowi, P. vivax, P. knowlesi and the rodent malaria species. All groups were found in the P. falciparum strains, and also in the P. reichenowi parasite, whereas none were predicted in the other species.ConclusionsWe have generated a tool for the sorting of RIFIN and STEVOR proteins, large antigenic variant protein groups, into homogeneous sub-families. Assigning functions to such protein families requires their subdivision into meaningful groups such as we have shown for the RIFIN protein family. RSpred removes the need for complicated and time consuming phylogenetic analysis methods. It will benefit both research groups sequencing whole genomes as well as others working with field isolates. RSpred is freely accessible via http://www.ifm.liu.se/bioinfo/.

Bioinformatics in studies of SDR and MDR enzymes.

Bioinformatics utilises information in databases to understand biological processes and interpret experimental data. Methods include sequence comparisons, structural and functional predictions, and molecular modelling. Applied to the families of short-chain and medium-chain dehydrogenases/reductases (SDR and MDR), much information is obtained.