A limited universe of membrane protein families and folds

One of the goals of structural genomics is to obtain a structural representative of almost every fold in nature. A recent estimate suggests that 70%-80% of soluble protein domains identified in the first 1000 genome sequences should be covered by about 25,000 structures-a reasonably achievable goal. As no current estimates exist for the number of membrane protein families, however, it is not possible to know whether family coverage is a realistic goal for membrane proteins. Here we find that virtually all polytopic helical membrane protein families are present in the already known sequences so we can make an estimate of the total number of families. We find that only approximately 700 polytopic membrane protein families account for 80% of structured residues and approximately 1700 cover 90% of structured residues. While apparently a finite and reachable goal, we estimate that it will likely take more than three decades to obtain the structures needed for 90% residue coverage, if current trends continue.     

Progress of structural genomics initiatives: an analysis of solved target structures

The explosion in gene sequence data and technological breakthroughs in protein structure determination inspired the launch of structural genomics (SG) initiatives. An often stated goal of structural genomics is the high-throughput structural characterisation of all protein sequence families, with the long-term hope of significantly impacting on the life sciences, biotechnology and drug discovery. Here, we present a comprehensive analysis of solved SG targets to assess progress of these initiatives. Eleven consortia have contributed 316 non-redundant entries and 323 protein chains to the Protein Data Bank (PDB), and 459 and 393 domains to the CATH and SCOP structure classifications, respectively. The quality and size of these proteins are comparable to those solved in traditional structural biology and, despite huge scope for duplicated efforts, only 14% of targets have a close homologue (>/=30% sequence identity) solved by another consortium. Analysis of CATH and SCOP revealed the significant contribution that structural genomics is making to the coverage of superfamilies and folds. A total of 67% of SG domains in CATH are unique, lacking an already characterised close homologue in the PDB, whereas only 21% of non-SG domains are unique. For 29% of domains, structure determination revealed a remote evolutionary relationship not apparent from sequence, and 19% and 11% contributed new superfamilies and folds. The secondary structure class, fold and superfamily distributions of this dataset reflect those of the genomes. The domains fall into 172 different folds and 259 superfamilies in CATH but the distribution is highly skewed. The most populous of these are those that recur most frequently in the genomes. Whilst 11% of superfamilies are bacteria-specific, most are common to all three superkingdoms of life and together the 316 PDB entries have provided new and reliable homology models for 9287 non-redundant gene sequences in 206 completely sequenced genomes. From the perspective of this analysis, it appears that structural genomics is on track to be a success, and it is hoped that this work will inform future directions of the field.     

Defining the fold space of membrane proteins: the CAMPS database

Recent progress in structure determination techniques has led to a significant growth in the number of known membrane protein structures, and the first structural genomics projects focusing on membrane proteins have been initiated, warranting an investigation of appropriate bioinformatics strategies for optimal structural target selection for these molecules. What determines a membrane protein fold? How many membrane structures need to be solved to provide sufficient structural coverage of the membrane protein sequence space? We present the CAMPS database (Computational Analysis of the Membrane Protein Space) containing almost 45,000 proteins with three or more predicted transmembrane helices (TMH) from 120 bacterial species. This large set of membrane proteins was subjected to single‐linkage clustering using only sequence alignments covering at least 40% of the TMH present in a given family. This process yielded 266 sequence clusters with at least 15 members, roughly corresponding to membrane structural folds, sufficiently structurally homogeneous in terms of the variation of TMH number between individual sequences. These clusters were further subdivided into functionally homogeneous subclusters according to the COG (Clusters of Orthologous Groups) system as well as more stringently defined families sharing at least 30% identity. The CAMPS sequence clusters are thus designed to reflect three main levels of interest for structural genomics: fold, function, and modeling distance. We present a library of Hidden Markov Models (HMM) derived from sequence alignments of TMH at these three levels of sequence similarity. Given that 24 out of 266 clusters corresponding to membrane folds already have associated known structures, we estimate that 242 additional new structures, one for each remaining cluster, would provide structural coverage at the fold level of roughly 70% of prokaryotic membrane proteins belonging to the currently most populated families. Proteins 2006. © 2006 Wiley‐Liss, Inc.     

Exploring dynamics of protein structure determination and homology-based prediction to estimate the number of superfamilies and folds

Background

As tertiary structure is currently available only for a fraction of known protein families, it is important to assess what parts of sequence space have been structurally characterized. We consider protein domains whose structure can be predicted by sequence similarity to proteins with solved structure and address the following questions. Do these domains represent an unbiased random sample of all sequence families? Do targets solved by structural genomic initiatives (SGI) provide such a sample? What are approximate total numbers of structure-based superfamilies and folds among soluble globular domains?

Results

To make these assessments, we combine two approaches: (i) sequence analysis and homology-based structure prediction for proteins from complete genomes; and (ii) monitoring dynamics of the assigned structure set in time, with the accumulation of experimentally solved structures. In the Clusters of Orthologous Groups (COG) database, we map the growing population of structurally characterized domain families onto the network of sequence-based connections between domains. This mapping reveals a systematic bias suggesting that target families for structure determination tend to be located in highly populated areas of sequence space. In contrast, the subset of domains whose structure is initially inferred by SGI is similar to a random sample from the whole population. To accommodate for the observed bias, we propose a new non-parametric approach to the estimation of the total numbers of structural superfamilies and folds, which does not rely on a specific model of the sampling process. Based on dynamics of robust distribution-based parameters in the growing set of structure predictions, we estimate the total numbers of superfamilies and folds among soluble globular proteins in the COG database.

Conclusion

The set of currently solved protein structures allows for structure prediction in approximately a third of sequence-based domain families. The choice of targets for structure determination is biased towards domains with many sequence-based homologs. The growing SGI output in the future should further contribute to the reduction of this bias. The total number of structural superfamilies and folds in the COG database are estimated as ~4000 and ~1700. These numbers are respectively four and three times higher than the numbers of superfamilies and folds that can currently be assigned to COG proteins.     

Methodologies for target selection in structural genomics

As the number of complete genomes that have been sequenced keeps growing, unknown areas of the protein space are revealed and new horizons open up. Most of this information will be fully appreciated only when the structural information about the encoded proteins becomes available. The goal of structural genomics is to direct large-scale efforts of protein structure determination, so as to increase the impact of these efforts. This review focuses on current approaches in structural genomics aimed at selecting representative proteins as targets for structure determination. We will discuss the concept of representative structures/folds, the current methodologies for identifying those proteins, and computational techniques for identifying proteins which are expected to adopt new structural folds.     

Multi-domain protein families and domain pairs: comparison with known structures and a random model of domain recombination

There is a limited repertoire of domain families in nature that are duplicated and combined in different ways to form the set of proteins in a genome. Most proteins in both prokaryote and eukaryote genomes consist of two or more domains, and we show that the family size distribution of multi-domain protein families follows a power law like that of individual families. Most domain pairs occur in four to six different domain architectures: in isolation and in combinations with different partners. We showed previously that within the set of all pairwise domain combinations, most small and medium-sized families are observed in combination with one or two other families, while a few large families are very versatile and combine with many different partners. Though this may appear to be a stochastic pattern, in which large families have more combination partners by virtue of their size, we establish here that all the domain families with more than three members in genomes are duplicated more frequently than would be expected by chance considering their number of neighbouring domains. This duplication of domain pairs is statistically significant for between one and three quarters of all families with seven or more members. For the majority of pairwise domain combinations, there is no known three-dimensional structure of the two domains together, and we term these novel combinations. Novel domain combinations are interesting and important targets for structural elucidation, as the geometry and interaction between the domains will help understand the function and evolution of multi-domain proteins. Of particular interest are those combinations that occur in the largest number of multi-domain proteins, and several of these frequent novel combinations contain DNA-binding domains.Abbreviations:SCOP: Structural Classification of Proteins database, PDB: Protein DataBank, HMM: hidden Markov model     

The evolution of RNA editing and pentatricopeptide repeat genes

The pentatricopeptide repeat (PPR) is a degenerate 35-amino-acid structural motif identified from analysis of the sequenced genome of the model plant Arabidopsis thaliana. From the wealth of sequence information now available from plant genomes, the PPR protein family is now known to be one of the largest families in angiosperm species, as most genomes encode 400-600 members. As the number of PPR genes is generally only c. 10-20 in other eukaryotic organisms, including green algae, the family has obviously greatly expanded during land plant evolution. This provides a rare opportunity to study selection pressures driving a 50-fold expansion of a single gene family. PPR proteins are sequence-specific RNA-binding proteins involved in many aspects of RNA processing in organelles. In this review, we will summarize our current knowledge about the evolution of PPR genes, and will discuss the relevance of the dramatic expansion in the family to the functional diversification of plant organelles, focusing primarily on RNA editing.     

Structural similarity to bridge sequence space: finding new families on the bridges

Structures for protein domains have increased rapidly in recent years owing to advances in structural biology and structural genomics projects. New structures are often similar to those solved previously, and such similarities can give insights into function by linking poorly understood families to those that are better characterized. They also allow the possibility of combing information to find still more proteins adopting a similar structure and sometimes a similar function, and to reprioritize families in structural genomics pipelines. We explore this possibility here by preparing merged profiles for pairs of structurally similar, but not necessarily sequence-similar, domains within the SMART and Pfam database by way of the Structural Classification of Proteins (SCOP). We show that such profiles are often able to successfully identify further members of the same superfamily and thus can be used to increase the sensitivity of database searching methods like HMMer and PSI-BLAST. We perform detailed benchmarks using the SMART and Pfam databases with four complete genomes frequently used as annotation benchmarks. We quantify the associated increase in structural information in Swissprot and discuss examples illustrating the applicability of this approach to understand functional and evolutionary relationships between protein families.     

Delineation of modular proteins: domain boundary prediction from sequence information

The delineation of domain boundaries of a given sequence in the absence of known 3D structures or detectable sequence homology to known domains benefits many areas in protein science, such as protein engineering, protein 3D structure determination and protein structure prediction. With the exponential growth of newly determined sequences, our ability to predict domain boundaries rapidly and accurately from sequence information alone is both essential and critical from the viewpoint of gene function annotation. Anyone attempting to predict domain boundaries for a single protein sequence is invariably confronted with a plethora of databases that contain boundary information available from the internet and a variety of methods for domain boundary prediction. How are these derived and how well do they work? What definition of 'domain' do they use? We will first clarify the different definitions of protein domains, and then describe the available public databases with domain boundary information. Finally, we will review existing domain boundary prediction methods and discuss their strengths and weaknesses.     

Target space for structural genomics revisited

MOTIVATION: Structural genomics eventually aims at determining structures for all proteins. However, in the beginning experimentalists are likely to focus on globular proteins to achieve a rapid basic coverage of protein sequence space. How many proteins will structural genomics have to target? How many proteins will be excluded since we already have structural information for these or since they are not globular? We have to answer these questions in the context of our target selection for the North-East Structural Genomics Consortium (NESG). RESULTS: We estimated that structural information is available for about 6-38% of all proteins; 6% if we require high accuracy in comparative modelling, 38% if we are satisfied with having a rough idea about the fold. Excluding all regions that are not globular, we found that structural genomics may have to target about 48% of all proteins. This corresponded to a similar percentage of residues of the entire proteomes (52%). We explored a number of different strategies to cluster protein space in order to find the number of families representing these 48% of structurally unknown proteins. For the subset of all entirely sequenced eukaryotes, we found over 18 000 fragment clusters each of which may be a suitable target for structural genomics. AVAILABILITY: All data are available from the authors, most results are summarized at: http://cubic.bioc.columbia.edu/genomes/RES/2002_bioinformatics/     

Accurate domain identification with structure‐anchored hidden Markov models,saHMMs

The ever increasing speed of DNA sequencing widens the discrepancy between the number of known gene products, and the knowledge of their function and structure. Proper annotation of protein sequences is therefore crucial if the missing information is to be deduced from sequence‐based similarity comparisons. These comparisons become exceedingly difficult as the pairwise identities drop to very low values. To improve the accuracy of domain identification, we exploit the fact that the three‐dimensional structures of domains are much more conserved than their sequences. Based on structure‐anchored multiple sequence alignments of low identity homologues we constructed 850 structure‐anchored hidden Markov models (saHMMs), each representing one domain family. Since the saHMMs are highly family specific, they can be used to assign a domain to its correct family and clearly distinguish it from domains belonging to other families, even within the same superfamily. This task is not trivial and becomes particularly difficult if the unknown domain is distantly related to the rest of the domain sequences within the family. In a search with full length protein sequences, harbouring at least one domain as defined by the structural classification of proteins database (SCOP), version 1.71, versus the saHMM database based on SCOP version 1.69, we achieve an accuracy of 99.0%. All of the few hits outside the family fall within the correct superfamily. Compared to Pfam_ls HMMs, the saHMMs obtain about 11% higher coverage. A comparison with BLAST and PSI‐BLAST demonstrates that the saHMMs have consistently fewer errors per query at a given coverage. Within our recommended E‐value range, the same is true for a comparison with SUPERFAMILY. Furthermore, we are able to annotate 232 proteins with 530 nonoverlapping domains belonging to 102 different domain families among human proteins labelled “unknown” in the NCBI protein database. Our results demonstrate that the saHMM database represents a versatile and reliable tool for identification of domains in protein sequences. With the aid of saHMMs, homology on the family level can be assigned, even for distantly related sequences. Due to the construction of the saHMMs, the hits they provide are always associated with high quality crystal structures. The saHMM database can be accessed via the FISH server at http://babel.ucmp.umu.se/fish/ . Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map)

MOTIVATION: Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes. RESULTS: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed to have played a key role in species diversification. SUPPLEMENTARY INFORMATION: http://interactomics.org.     

Artificial protein sequences enable recognition of vicinal and distant protein functional relationships

High divergence in protein sequences makes the detection of distant protein relationships through homology-based approaches challenging. Grouping protein sequences into families, through similarities in either sequence or 3-D structure, facilitates in the improved recognition of protein relationships. In addition, strategically designed protein-like sequences have been shown to bridge distant structural domain families by serving as artificial linkers. In this study, we have augmented a search database of known protein domain families with such designed sequences, with the intention of providing functional clues to domain families of unknown structure. When assessed using representative query sequences from each family, we obtain a success rate of 94% in protein domain families of known structure. Further, we demonstrate that the augmented search space enabled fold recognition for 582 families with no structural information available a priori. Additionally, we were able to provide reliable functional relationships for 610 orphan families. We discuss the application of our method in predicting functional roles through select examples for DUF4922, DUF5131, and DUF5085. Our approach also detects new associations between families that were previously not known to be related, as demonstrated through new sub-groups of the RNA polymerase domain among three distinct RNA viruses. Taken together, designed sequences-augmented search databases direct the detection of meaningful relationships between distant protein families. In turn, they enable fold recognition and offer reliable pointers to potential functional sites that may be probed further through direct mutagenesis studies.     

Identifying foldable regions in protein sequence from the hydrophobic signal

Structural genomics initiatives aim to elucidate representative 3D structures for the majority of protein families over the next decade, but many obstacles must be overcome. The correct design of constructs is extremely important since many proteins will be too large or contain unstructured regions and will not be amenable to crystallization. It is therefore essential to identify regions in protein sequences that are likely to be suitable for structural study. Scooby-Domain is a fast and simple method to identify globular domains in protein sequences. Domains are compact units of protein structure and their correct delineation will aid structural elucidation through a divide-and-conquer approach. Scooby-Domain predictions are based on the observed lengths and hydrophobicities of domains from proteins with known tertiary structure. The prediction method employs an A*-search to identify sequence regions that form a globular structure and those that are unstructured. On a test set of 173 proteins with consensus CATH and SCOP domain definitions, Scooby-Domain has a sensitivity of 50% and an accuracy of 29%, which is better than current state-of-the-art methods. The method does not rely on homology searches and, therefore, can identify previously unknown domains.     

Survey for g-proteins in the prokaryotic genomes: prediction of functional roles based on classification

The members of the family of G-proteins are characterized by their ability to bind and hydrolyze guanosine triphosphate (GTP) to guanosine diphosphate (GDP). Despite a common biochemical function of GTP hydrolysis shared among the members of the family of G-proteins, they are associated with diverse biological roles. The current work describes the identification and detailed analysis of the putative G-proteins encoded in the completely sequenced prokaryotic genomes. Inferences on the biological roles of these G-proteins have been obtained by their classification into known functional subfamilies. We have identified 497 G-proteins in 42 genomes. Seven small GTP-binding protein homologues have been identified in prokaryotes with at least two of the diagnostic sequence motifs of G-proteins conserved. The translation factors have the largest representation (234 sequences) and are found to be ubiquitous, which is consistent with their critical role in protein synthesis. The GTP_OBG subfamily comprises of 79 sequences in our dataset. A total of 177 sequences belong to the subfamily of GTPase of unknown function and 154 of these could be associated with domains of known functions such as cell cycle regulation and t-RNA modification. The large GTP-binding proteins and the alpha-subunit of heterotrimeric G-proteins are not detected in the genomes of the prokaryotes surveyed.     

Automatic target selection for structural genomics on eukaryotes

A central goal of structural genomics is to experimentally determine representative structures for all protein families. At least 14 structural genomics pilot projects are currently investigating the feasibility of high-throughput structure determination; the National Institutes of Health funded nine of these in the United States. Initiatives differ in the particular subset of "all families" on which they focus. At the NorthEast Structural Genomics consortium (NESG), we target eukaryotic protein domain families. The automatic target selection procedure has three aims: 1) identify all protein domain families from currently five entirely sequenced eukaryotic target organisms based on their sequence homology, 2) discard those families that can be modeled on the basis of structural information already present in the PDB, and 3) target representatives of the remaining families for structure determination. To guarantee that all members of one family share a common foldlike region, we had to begin by dissecting proteins into structural domain-like regions before clustering. Our hierarchical approach, CHOP, utilizing homology to PrISM, Pfam-A, and SWISS-PROT chopped the 103,796 eukaryotic proteins/ORFs into 247,222 fragments. Of these fragments, 122,999 appeared suitable targets that were grouped into >27,000 singletons and >18,000 multifragment clusters. Thus, our results suggested that it might be necessary to determine >40,000 structures to minimally cover the subset of five eukaryotic proteomes.     

Understanding protein non-folding

This review describes the family of intrinsically disordered proteins, members of which fail to form rigid 3-D structures under physiological conditions, either along their entire lengths or only in localized regions. Instead, these intriguing proteins/regions exist as dynamic ensembles within which atom positions and backbone Ramachandran angles exhibit extreme temporal fluctuations without specific equilibrium values. Many of these intrinsically disordered proteins are known to carry out important biological functions which, in fact, depend on the absence of a specific 3-D structure. The existence of such proteins does not fit the prevailing structure–function paradigm, which states that a unique 3-D structure is a prerequisite to function. Thus, the protein structure–function paradigm has to be expanded to include intrinsically disordered proteins and alternative relationships among protein sequence, structure, and function. This shift in the paradigm represents a major breakthrough for biochemistry, biophysics and molecular biology, as it opens new levels of understanding with regard to the complex life of proteins. This review will try to answer the following questions: how were intrinsically disordered proteins discovered? Why don't these proteins fold? What is so special about intrinsic disorder? What are the functional advantages of disordered proteins/regions? What is the functional repertoire of these proteins? What are the relationships between intrinsically disordered proteins and human diseases?     

DALI shines a light on remote homologs: One hundred discoveries

Structural comparison reveals remote homology that often fails to be detected by sequence comparison. The DALI web server ( http://ekhidna2.biocenter.helsinki.fi/dali ) is a platform for structural analysis that provides database searches and interactive visualization, including structural alignments annotated with secondary structure, protein families and sequence logos, and 3D structure superimposition supported by color-coded sequence and structure conservation. Here, we are using DALI to mine the AlphaFold Database version 1, which increased the structural coverage of protein families by 20%. We found 100 remote homologous relationships hitherto unreported in the current reference database for protein domains, Pfam 35.0. In particular, we linked 35 domains of unknown function (DUFs) to the previously characterized families, generating a functional hypothesis that can be explored downstream in structural biology studies. Other findings include gene fusions, tandem duplications, and adjustments to domain boundaries. The evidence for homology can be browsed interactively through live examples on DALI's website.     

Estimating the number of protein folds and families from complete genome data

Using the data on proteins encoded in complete genomes, combined with a rigorous theory of the sampling process, we estimate the total number of protein folds and families, as well as the number of folds and families in each genome. The total number of folds in globular, water- soluble proteins is estimated at about 1000, with structural information currently available for about one-third of the number. The sequenced genomes of unicellular organisms encode from approximately 25%, for the minimal genomes of the Mycoplasmas, to 70-80% for larger genomes, such as Escherichia coli and yeast, of the total number of folds. The number of protein families with significant sequence conservation was estimated to be between 4000 and 7000, with structures available for about 20% of these.     

Class-directed structure determination: foundation for a protein structure initiative.

The recent sequencing of many complete genomes, combined with the development of methods that allow rapid structure determination for many proteins, has changed the way in which protein structure determinations can be approached. One-by-one determinations of individual protein structures will soon be augmented by class-directed structure analyses in which a group of proteins is targeted and structures of representative members are determined and used to represent the entire group. Such a shift in approach would be the foundation for a broad protein structure initiative targeting classes of proteins important for biotechnology and for a fundamental understanding of protein function.