Unification of protein families

Controlling Allosteric Networks in Proteins

Allosteric transition, defined as conformational changes induced by ligand binding, is one of the fundamental properties of proteins. Allostery has been observed and characterized in many proteins, and has been recently utilized to control protein function via regulation of protein activity. Here, we review the physical and evolutionary origin of protein allostery, as well as its importance to protein regulation, drug discovery, and biological processes in living systems. We describe recently developed approaches to identify allosteric pathways, connected sets of pairwise interactions that are responsible for propagation of conformational change from the ligand-binding site to a distal functional site. We then present experimental and computational protein engineering approaches for control of protein function by modulation of allosteric sites. As an example of application of these approaches, we describe a synergistic computational and experimental approach to rescue the cystic-fibrosis-associated protein cystic fibrosis transmembrane conductance regulator, which upon deletion of a single residue misfolds and causes disease. This example demonstrates the power of allosteric manipulation in proteins to both elucidate mechanisms of molecular function and to develop therapeutic strategies that rescue those functions. Allosteric control of proteins provides a tool to shine a light on the complex cascades of cellular processes and facilitate unprecedented interrogation of biological systems.

NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection

NrichD (http://proline.biochem.iisc.ernet.in/NRICHD/) is a database of computationally designed protein-like sequences, augmented into natural sequence databases that can perform hops in protein sequence space to assist in the detection of remote relationships. Establishing protein relationships in the absence of structural evidence or natural ‘intermediately related sequences’ is a challenging task. Recently, we have demonstrated that the computational design of artificial intermediary sequences/linkers is an effective approach to fill naturally occurring voids in protein sequence space. Through a large-scale assessment we have demonstrated that such sequences can be plugged into commonly employed search databases to improve the performance of routinely used sequence search methods in detecting remote relationships. Since it is anticipated that such data sets will be employed to establish protein relationships, two databases that have already captured these relationships at the structural and functional domain level, namely, the SCOP database and the Pfam database, have been ‘enriched’ with these artificial intermediary sequences. NrichD database currently contains 3 611 010 artificial sequences that have been generated between 27 882 pairs of families from 374 SCOP folds. The data sets are freely available for download. Additional features include the design of artificial sequences between any two protein families of interest to the user.

Filling-in void and sparse regions in protein sequence space by protein-like artificial sequences enables remarkable enhancement in remote homology detection capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like "linker" sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be "plugged-into" routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.

Expression and β-glucan binding properties of Scots pine (Pinus sylvestris L.) antimicrobial protein (Sp-AMP)

Scots pine (Pinus sylvestris) secretes a number of small, highly-related, disulfide-rich proteins (Sp-AMPs) in response to challenges with fungal pathogens such as Heterobasidion annosum, although their biological role has been unknown. Here, we examined the expression patterns of these genes, as well as the structure and function of the encoded proteins. Northern blots and quantitative real time PCR showed increased levels of expression that are sustained during the interactions of host trees with pathogens, but not non-pathogens, consistent with a function in conifer tree defenses. Furthermore, the genes were up-regulated after treatment with salicylic acid and an ethylene precursor, 1-aminocyclopropane-1-carboxylic-acid, but neither methyl jasmonate nor H(2)O(2) induced expression, indicating that Sp-AMP gene expression is independent of the jasmonic acid signaling pathways. The cDNA encoding one of the proteins was cloned and expressed in Pichia pastoris. The purified protein had antifungal activity against H. annosum, and caused morphological changes in its hyphae and spores. It was directly shown to bind soluble and insoluble β-(1,3)-glucans, specifically and with high affinity. Furthermore, addition of exogenous glucan is linked to higher levels of Sp-AMP expression in the conifer. Homology modeling and sequence comparisons suggest that a conserved patch on the surface of the globular Sp-AMP is a carbohydrate-binding site that can accommodate approximately four sugar units. We conclude that these proteins belong to a new family of antimicrobial proteins (PR-19) that are likely to act by binding the glucans that are a major component of fungal cell walls.

Identification of potential Leptospira phosphoheptose isomerase inhibitors through virtual high-throughput screening

The life-threatening infections caused by Leptospira serovars demand the need for designing anti-leptospirosis drugs. The present study encompasses exploring inhibitors against phosphoheptose isomerase (GmhA) of Leptospira, which is vital for lipopolysaccharide (LPS) biosynthesis and is identified as a common drug target through the subtractive genomic approach. GmhA model was built in Modeller 9v7. Structural refinement and energy minimization of the predicted model was carried out using Maestro 9.0. The refined model reliability was assessed through Procheck, ProSA, ProQ and Profile 3D. The substrate-based virtual high-throughput screening (VHTS) in Ligand.Info Meta-Database tool generated an in-house library of 354 substrate structural analogs. Furthermore, structure-based VHTS from the in-house library with different conformations of each ligand provided 14 novel competitive inhibitors. The model together with insight gained from the VHTS would be a promising starting point for developing anti-leptospirosis competitive inhibitors targeting LPS biosynthesis pathway.

The conserved Candida albicans CA3427 gene product defines a new family of proteins exhibiting the generic periplasmic binding protein structural fold

Nosocomial diseases due to Candida albicans infections are in constant rise in hospitals, where they cause serious complications to already fragile intensive care patients. Antifungal drug resistance is fast becoming a serious issue due to the emergence of strains resistant to currently available antifungal agents. Thus the urgency to identify new potential protein targets, the function and structure of which may guide the development of new antifungal drugs. In this context, we initiated a comparative genomics study in search of promising protein coding genes among the most conserved ones in reference fungal genomes. The CA3427 gene was selected on the basis of its presence among pathogenic fungi contrasting with its absence in the non pathogenic Saccharomyces cerevisiae. We report the crystal 3D-structure of the Candida albicans CA3427 protein at 2.1 Å resolution. The combined analysis of its sequence and structure reveals a structural fold originally associated with periplasmic binding proteins. The CA3427 structure highlights a binding site located between the two protein domains, corresponding to a sequence segment conserved among fungi. Two crystal forms of CA3427 were found, suggesting that the presence or absence of a ligand at the proposed binding site might trigger a “Venus flytrap” motion, coupled to the previously described activity of bacterial periplasmic binding proteins. The conserved binding site defines a new subfamily of periplasmic binding proteins also found in many bacteria of the bacteroidetes division, in a choanoflagellate (a free-living unicellular and colonial flagellate eukaryote) and in a placozoan (the closest multicellular relative of animals). A phylogenetic analysis suggests that this gene family originated in bacteria before its horizontal transfer to an ancestral eukaryote prior to the radiation of fungi. It was then lost by the Saccharomycetales which include Saccharomyces cerevisiae.

Structure of the VP16 transactivator target in the Mediator

The human Activator-Recruited Cofactor (ARC)/Mediator co-activator complex interacts with many transcriptional activators and facilitates recruitment of RNA polymerase II to promote target gene transcription. The MED25 (ARC92) subunit is a critical target of the potent Herpes simplex 1 viral transcriptional activator VP16. Here, we determine the solution structure of the MED25 VP16-binding domain (VBD), and define its binding site for the N-terminal portion of the VP16 transactivation domain (TADn). A hydrophobic furrow, formed by a β-barrel and two α-helices in MED25 VBD, interacts tightly with VP16 TADn. Mutations in this furrow prevent binding of VP16 TAD to MED25 VBD and interfere with the ability of over-expressed MED25 VBD to inhibit VP16-dependent transcriptional activation in vivo. This detailed molecular understanding of transactivation by the benchmark activator VP16 could provide important insights into viral and cellular gene activation mechanisms.

Structures of the nucleotide-binding domain of the human ABCB6 transporter and its complexes with nucleotides

The human ATP-binding cassette (ABC) transporter ABCB6 is involved in haem-precursor transport across the mitochondrial membrane. The crystal structure of its nucleotide-binding domain (NBD) has been determined in the apo form and in complexes with ADP, with ADP and Mg(2+) and with ATP at high resolution. The overall structure is L-shaped and consists of two lobes, consistent with other reported NBD structures. Nucleotide binding is mediated by the highly conserved Tyr599 and the Walker A motif, and induces notable structural changes. Structural comparison with other structurally characterized NBDs and full-length ABC transporters gives the first insight into the possible catalytic mechanism of ABCB6 and the role of the N-terminal helix alpha(1) in full-length ABCB6.

Conformational changes and ligand recognition of Escherichia coli D-xylose binding protein revealed

ATP binding cassette transport systems account for most import of necessary nutrients in bacteria. The periplasmic binding component (or an equivalent membrane-anchored protein) is critical to recognizing cognate ligand and directing it to the appropriate membrane permease. Here we report the X-ray structures of D-xylose binding protein from Escherichia coli in ligand-free open form, ligand-bound open form, and ligand-bound closed form at 2.15 Å, 2.2 Å, and 2.2 Å resolutions, respectively. The ligand-bound open form is the first such structure to be reported at high resolution; the combination of the three different forms from the same protein furthermore gives unprecedented details concerning the conformational changes involved in binding protein function. As is typical of the structural family, the protein has two similar globular domains, which are connected by a three-stranded hinge region. The open liganded structure shows that xylose binds first to the C-terminal domain, with only very small conformational changes resulting. After a 34° closing motion, additional interactions are formed with the N-terminal domain; changes in this domain are larger and serve to make the structure more ordered near the ligand. An analysis of the interactions suggests why xylose is the preferred ligand. Furthermore, a comparison with the most closely related proteins in the structural family shows that the conformational changes are distinct in each type of binding protein, which may have implications for how the individual proteins act in concert with their respective membrane permeases.

Structural and kinetic analysis of Schwanniomyces occidentalis invertase reveals a new oligomerization pattern and the role of its supplementary domain in substrate binding

Schwanniomyces occidentalis invertase is an extracellular enzyme that hydrolyzes sucrose and releases beta-fructose from various oligosaccharides and essential storage fructan polymers such as inulin. We report here the three-dimensional structure of Sw. occidentalis invertase at 2.9 A resolution and its complex with fructose at 1.9 A resolution. The monomer presents a bimodular arrangement common to other GH32 enzymes, with an N-terminal 5-fold beta-propeller catalytic domain and a C-terminal beta-sandwich domain for which the function has been unknown until now. However, the dimeric nature of Sw. occidentalis invertase reveals a unique active site cleft shaped by both subunits that may be representative of other yeast enzymes reported to be multimeric. Binding of the tetrasaccharide nystose and the polymer inulin was explored by docking analysis, which suggested that medium size and long substrates are recognized by residues from both subunits. The identified residues were mutated, and the enzymatic activity of the mutants against sucrose, nystose, and inulin were investigated by kinetic analysis. The replacements that showed the largest effect on catalytic efficiency were Q228V, a residue putatively involved in nystose and inulin binding, and S281I, involved in a polar link at the dimer interface. Moreover, a significant decrease in catalytic efficiency against inulin was observed in the mutants Q435A and Y462A, both located in the beta-sandwich domain of the second monomer. This highlights the essential function that oligomerization plays in substrate specificity and assigns, for the first time, a direct catalytic role to the supplementary domain of a GH32 enzyme.

A method for probabilistic mapping between protein structure and function taxonomies through cross training

BACKGROUND

Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP) and classes of functional classification databases (e.g. PROSITE), structure and function of proteins could be probabilistically related.

RESULTS

We demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees.

CONCLUSION

We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions.

Homology versus analogy: possible evolutionary relationship of immunoglobulins, cupredoxins, and Cu,Zn-superoxide dismutase

The 'immunoglobulin-like' fold is one of most common structural motifs observed in proteins. This topology is found in more than 80 superfamilies of proteins, including Cu,Zn-superoxide dismutase (SOD) and cupredoxin. Evolutionary relationships have not been identified, but may exist. The challenge remains, therefore, of resolving the issue of whether the diverse distribution of the fold is accounted for by divergent evolution of function or convergent evolution of structure following multiple independent origins of function. Since the early studies that revealed conformational similarity of immunoglobulins and other proteins, the number of primary structures available for comparison has dramatically increased and new computational approaches for analysis of sequences have been developed. It now appears that a hypothesis of a common evolutionary origin for cupredoxins, Cu,Zn-SOD, and immunoglobulins may be credible. The distinction between protein homology and protein analogy is fundamental. The immunoglobulin-like fold may represent a robust system within which to examine again the issue of protein homology versus analogy.

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.

Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction

Background

One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.

Results

We explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.

Conclusion

When attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.

Emergence of protein fold families through rational design

Diverse proteins with similar structures are grouped into families of homologs and analogs, if their sequence similarity is higher or lower, respectively, than 20%–30%. It was suggested that protein homologs and analogs originate from a common ancestor and diverge in their distinct evolutionary time scales, emerging as a consequence of the physical properties of the protein sequence space. Although a number of studies have determined key signatures of protein family organization, the sequence-structure factors that differentiate the two evolution-related protein families remain unknown. Here, we stipulate that subtle structural changes, which appear due to accumulating mutations in the homologous families, lead to distinct packing of the protein core and, thus, novel compositions of core residues. The latter process leads to the formation of distinct families of homologs. We propose that such differentiation results in the formation of analogous families. To test our postulate, we developed a molecular modeling and design toolkit, Medusa, to computationally design protein sequences that correspond to the same fold family. We find that analogous proteins emerge when a backbone structure deviates only 1–2 Å root-mean-square deviation from the original structure. For close homologs, core residues are highly conserved. However, when the overall sequence similarity drops to ~25%–30%, the composition of core residues starts to diverge, thereby forming novel families of protein homologs. This direct observation of the formation of protein homologs within a specific fold family supports our hypothesis. The conservation of amino acids in designed sequences recapitulates that of the naturally occurring sequences, thereby validating our computational design methodology.

Studies of known proteins have revealed intriguing co-organization of their sequences and structures. Proteins with sequence similarity higher than 25%–30% usually adopt a similar structure and are called homologs, whereas those with low sequence similarity (<20%) can share the same structure and are referred as analogs. The origin of such co-organization has been a topic of extensive discussions among protein folding, design, and evolution research communities, because understanding of the emergence of homologs and analogs in the protein universe has broad implications for our ability to rationally manipulate proteins. In this study, the authors developed a molecular modeling and design method, Medusa, to computationally design diversified protein sequences that correspond to similar backbone structures, which determine a protein fold family. Using Medusa, the authors directly demonstrated the formation of distinct protein homologs within a specific fold family when the structure deviates only 1–2 Å away from the original structure. The study suggests that subtle structural changes, which appear due to accumulating mutations in the families of homologs, lead to a distinct packing of the protein core and, thus, novel compositions of core residues. The latter process leads to the formation of distinct families of homologs.

An analysis of the Sargasso Sea resource and the consequences for database composition

Background

The environmental sequencing of the Sargasso Sea has introduced a huge new resource of genomic information. Unlike the protein sequences held in the current searchable databases, the Sargasso Sea sequences originate from a single marine environment and have been sequenced from species that are not easily obtainable by laboratory cultivation. The resource also contains very many fragments of whole protein sequences, a side effect of the shotgun sequencing method.

These sequences form a significant addendum to the current searchable databases but also present us with some intrinsic difficulties. While it is important to know whether it is possible to assign function to these sequences with the current methods and whether they will increase our capacity to explore sequence space, it is also interesting to know how current bioinformatics techniques will deal with the new sequences in the resource.

Results

The Sargasso Sea sequences seem to introduce a bias that decreases the potential of current methods to propose structure and function for new proteins. In particular the high proportion of sequence fragments in the resource seems to result in poor quality multiple alignments.

Conclusion

These observations suggest that the new sequences should be used with care, especially if the information is to be used in large scale analyses. On a positive note, the results may just spark improvements in computational and experimental methods to take into account the fragments generated by environmental sequencing techniques.

A robust method to detect structural and functional remote homologues

With currently available sequence data, it is feasible to conduct extensive comparisons among large sets of protein sequences. It is still a much more challenging task to partition the protein space into structurally and functionally related families solely based on sequence comparisons. The ProtoNet system automatically generates a treelike classification of the whole protein space. It stands to reason that this classification reflects evolutionary relationships, both close and remote. In this article, we examine this hypothesis. We present a semiautomatic procedure that singles out certain inner nodes in the ProtoNet tree that should ideally correspond to structurally and functionally defined protein families. We compare the performance of this method against several expert systems. Some of the competing methods incorporate additional extraneous information on protein structure or on enzymatic activities. The ProtoNet-based method performs at least as well as any of the methods with which it was compared. This article illustrates the ProtoNet-based method on several evolutionarily diverse families. Using this new method, an evolutionary divergence scheme can be proposed for a large number of structural and functional related superfamilies.

Sensitive detection of sequence similarity using combinatorial pattern discovery: A challenging study of two distantly related protein families

We investigate the performance of combinatorial pattern discovery to detect remote sequence similarities in terms of both biological accuracy and computational efficiency for a pair of distantly related families, as a case study. The two families represent the cupredoxins and multicopper oxidases, both containing blue copper-binding domains. These families present a challenging case due to low sequence similarity, different local structure, and variable sequence conservation at their copper-binding active sites. In this study, we investigate a new approach for automatically identifying weak sequence similarities that is based on combinatorial pattern discovery. We compare its performance with a traditional, HMM-based scheme and obtain estimates for sensitivity and specificity of the two approaches. Our analysis suggests that pattern discovery methods can be substantially more sensitive in detecting remote protein relationships while at the same time guaranteeing high specificity.

Quantifying structure-function uncertainty: a graph theoretical exploration into the origins and limitations of protein annotation

Since the advent of investigations into structural genomics, research has focused on correctly identifying domain boundaries, as well as domain similarities and differences in the context of their evolutionary relationships. As the science of structural genomics ramps up adding more and more information into the databanks, questions about the accuracy and completeness of our classification and annotation systems appear on the forefront of this research. A central question of paramount importance is how structural similarity relates to functional similarity. Here, we begin to rigorously and quantitatively answer these questions by first exploring the consensus between the most common protein domain structure annotation databases CATH, SCOP and FSSP. Each of these databases explores the evolutionary relationships between protein domains using a combination of automatic and manual, structural and functional, continuous and discrete similarity measures. In order to examine the issue of consensus thoroughly, we build a generalized graph out of each of these databases and hierarchically cluster these graphs at interval thresholds. We then employ a distance measure to find regions of greatest overlap. Using this procedure we were able not only to enumerate the level of consensus between the different annotation systems, but also to define the graph-theoretical origins behind the annotation schema of class, family and superfamily by observing that the same thresholds that define the best consensus regions between FSSP, SCOP and CATH correspond to distinct, non-random phase-transitions in the structure comparison graph itself. To investigate the correspondence in divergence between structure and function further, we introduce a measure of functional entropy that calculates divergence in function space. First, we use this measure to calculate the general correlation between structural homology and functional proximity. We extend this analysis further by quantitatively calculating the average amount of functional information gained from our understanding of structural distance and the corollary inherent uncertainty that represents the theoretical limit of our ability to infer function from structural similarity. Finally we show how our measure of functional "entropy" translates into a more intuitive concept of functional annotation into similarity EC classes.

Exploring the nonlinear geometry of protein homology

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity. This method has been shown to produce maps that exhibit global order and continuity and reveal important evolutionary, structural, and functional relationships between the embedded proteins. However, protein sequences are related by evolutionary pathways that exhibit highly nonlinear geometry, which is invisible to classical embedding procedures such as multidimensional scaling (MDS) and nonlinear mapping (NLM). Here, we describe the use of stochastic proximity embedding (SPE) for producing Euclidean maps that preserve the intrinsic dimensionality and metric structure of the data. SPE extends previous approaches in two important ways: (1) It preserves only local relationships between closely related sequences, thus allowing the map to unfold and reveal its intrinsic dimension, and (2) it scales linearly with the number of sequences and therefore can be applied to very large protein families. The merits of the algorithm are illustrated using examples from the protein kinase and nuclear hormone receptor superfamilies.

Protein Structure Similarity as Guiding Principle for Combinatorial Library Design

Proteins are modularly built from a limited set of approximately 1000 structural domains. The evolutionary relationship within a domain family suggests that the knowledge about a common fold structure can be exploited for the design of small molecule libraries in the development of inhibitors and ligands. This principle has been used for the synthesis of inhibitors for kinases sharing the same fold. It can also be applied for proteins which share the same fold architecture yet belong to different functional classes. Bestatin--originally known as an aminopeptidase inhibitor--was employed as guiding structure for the development of leukotriene A4 hydrolase inhibitors. A combinatorial approach helped to identify inhibitors for sulfotransferases which share structural similarity with nucleotide kinases using a kinase inhibitor core structure as guiding principle.

Automated detection of remote homology

The classification of a newly identified protein as a member of a superfamily is important for focusing experiments on its most likely functions. Such classification, often performed by hand, has now been fully automated. This sophisticated new approach takes into account not only alignment scores but also a number of other computable attributes, such as functional sites deduced from sequence conservation patterns.

Structural similarity to link sequence space: new potential superfamilies and implications for structural genomics

The current pace of structural biology now means that protein three-dimensional structure can be known before protein function, making methods for assigning homology via structure comparison of growing importance. Previous research has suggested that sequence similarity after structure-based alignment is one of the best discriminators of homology and often functional similarity. Here, we exploit this observation, together with a merger of protein structure and sequence databases, to predict distant homologous relationships. We use the Structural Classification of Proteins (SCOP) database to link sequence alignments from the SMART and Pfam databases. We thus provide new alignments that could not be constructed easily in the absence of known three-dimensional structures. We then extend the method of Murzin (1993b) to assign statistical significance to sequence identities found after structural alignment and thus suggest the best link between diverse sequence families. We find that several distantly related protein sequence families can be linked with confidence, showing the approach to be a means for inferring homologous relationships and thus possible functions when proteins are of known structure but of unknown function. The analysis also finds several new potential superfamilies, where inspection of the associated alignments and superimpositions reveals conservation of unusual structural features or co-location of conserved amino acids and bound substrates. We discuss implications for Structural Genomics initiatives and for improvements to sequence comparison methods.

Gene family evolution and homology: genomics meets phylogenetics

With the advent of high-throughput DNA sequencing and whole-genome analysis, it has become clear that the coding portions of the genome are organized hierarchically in gene families and superfamilies. Because the hierarchy of genes, like that of living organisms, reflects an ancient and continuing process of gene duplication and divergence, many of the conceptual and analytical tools used in phylogenetic systematics can and should be used in comparative genomics. Phylogenetic principles and techniques for assessing homology, inferring relationships among genes, and reconstructing evolutionary events provide a powerful way to interpret the ever increasing body of sequence data. In this review, we outline the application of phylogenetic approaches to comparative genomics, beginning with the inference of phylogeny and the assessment of gene orthology and paralogy. We also show how the phylogenetic approach makes possible novel kinds of comparative analysis, including detection of domain shuffling and lateral gene transfer, reconstruction of the evolutionary diversification of gene families, tracing of evolutionary change in protein function at the amino acid level, and prediction of structure-function relationships. A marriage of the principles of phylogenetic systematics with the copious data generated by genomics promises unprecedented insights into the nature of biological organization and the historical processes that created it.

A comparison of position-specific score matrices based on sequence and structure alignments

Sequence comparison methods based on position-specific score matrices (PSSMs) have proven a useful tool for recognition of the divergent members of a protein family and for annotation of functional sites. Here we investigate one of the factors that affects overall performance of PSSMs in a PSI-BLAST search, the algorithm used to construct the seed alignment upon which the PSSM is based. We compare PSSMs based on alignments constructed by global sequence similarity (ClustalW and ClustalW-pairwise), local sequence similarity (BLAST), and local structure similarity (VAST). To assess performance with respect to identification of conserved functional or structural sites, we examine the accuracy of the three-dimensional molecular models predicted by PSSM-sequence alignments. Using the known structures of those sequences as the standard of truth, we find that model accuracy varies with the algorithm used for seed alignment construction in the pattern local-structure (VAST) > local-sequence (BLAST) > global-sequence (ClustalW). Using structural similarity of query and database proteins as the standard of truth, we find that PSSM recognition sensitivity depends primarily on the diversity of the sequences included in the alignment, with an optimum around 30-50% average pairwise identity. We discuss these observations, and suggest a strategy for constructing seed alignments that optimize PSSM-sequence alignment accuracy and recognition sensitivity.

Understanding hierarchical protein evolution from first principles

We propose a model that explains the hierarchical organization of proteins in fold families. The model, which is based on the evolutionary selection of proteins by their native state stability, reproduces patterns of amino acids conserved across protein families. Due to its dynamic nature, the model sheds light on the evolutionary time-scales. By studying the relaxation of the correlation function between consecutive mutations at a given position in proteins, we observe separation of the evolutionary time-scales: at short time intervals families of proteins with similar sequences and structures are formed, while at long time intervals the families of structurally similar proteins that have low sequence similarity are formed. We discuss the evolutionary implications of our model. We provide a "profile" solution to our model and find agreement between predicted patterns of conserved amino acids and those actually observed in nature.

Fold change in evolution of protein structures

Typically, protein spatial structures are more conserved in evolution than amino acid sequences. However, the recent explosion of sequence and structure information accompanied by the development of powerful computational methods led to the accumulation of examples of homologous proteins with globally distinct structures. Significant sequence conservation, local structural resemblance, and functional similarity strongly indicate evolutionary relationships between these proteins despite pronounced structural differences at the fold level. Several mechanisms such as insertions/deletions/substitutions, circular permutations, and rearrangements in beta-sheet topologies account for the majority of detected structural irregularities. The existence of evolutionarily related proteins that possess different folds brings new challenges to the homology modeling techniques and the structure classification strategies and offers new opportunities for protein design in experimental studies.

Lipoylating and biotinylating enzymes contain a homologous catalytic module

Biotin and lipoic acid moieties are the covalently attached coenzyme cofactors of several multicomponent enzyme complexes that catalyze key metabolic reactions. Attachment of these moieties to the biotinyl- and lipoyl-dependent enzymes is post-translationally catalyzed by specific biotinylating and lipoylating protein enzymes. In Escherichia coli, two different enzymes, LplA and LipB, catalyze independent pathways for the lipoylation of the relevant enzymes, whereas only one enzyme, the BirA protein, is responsible for all the biotinylation. Counterparts of the E. coli BirA, LplA, and LipB enzymes have been previously identified in many organisms, but homology among the three families has never been reported. Computational analysis based on PSI-BLAST profiles and secondary structure predictions indicates, however, that lipoylating and biotinylating enzymes are evolutionarily related protein families containing a homologous catalytic module. Sequence conservation among the three families is very poor, but a single lysine residue is strictly conserved in all of them, which, according to the available X-ray crystal structure of the E. coli BirA protein, is expected to contribute to the binding of lipoic acid in the LplA and LipB enzymes.

The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12

Using a combination of several approaches we estimated and characterized a total of 314 regulatory DNA-binding proteins in Escherichia coli, which might represent its minimal set of transcription factors. The collection is comprised of 35% activators, 43% repressors and 22% dual regulators. Within many regulatory protein families, the members are homogeneous in their regulatory roles, physiology of regulated genes, regulatory function, length and genome position, showing that these families have evolved homogeneously in prokaryotes, particularly in E.coli. This work describes a full characterization of the repertoire of regulatory interactions in a whole living cell. This repertoire should contribute to the interpretation of global gene expression profiles in both prokaryotes and eukaryotes.

Microbial relatives of the seed storage proteins of higher plants: conservation of structure and diversification of function during evolution of the cupin superfamily

This review summarizes the recent discovery of the cupin superfamily (from the Latin term "cupa," a small barrel) of functionally diverse proteins that initially were limited to several higher plant proteins such as seed storage proteins, germin (an oxalate oxidase), germin-like proteins, and auxin-binding protein. Knowledge of the three-dimensional structure of two vicilins, seed proteins with a characteristic beta-barrel core, led to the identification of a small number of conserved residues and thence to the discovery of several microbial proteins which share these key amino acids. In particular, there is a highly conserved pattern of two histidine-containing motifs with a varied intermotif spacing. This cupin signature is found as a central component of many microbial proteins including certain types of phosphomannose isomerase, polyketide synthase, epimerase, and dioxygenase. In addition, the signature has been identified within the N-terminal effector domain in a subgroup of bacterial AraC transcription factors. As well as these single-domain cupins, this survey has identified other classes of two-domain bicupins including bacterial gentisate 1, 2-dioxygenases and 1-hydroxy-2-naphthoate dioxygenases, fungal oxalate decarboxylases, and legume sucrose-binding proteins. Cupin evolution is discussed from the perspective of the structure-function relationships, using data from the genomes of several prokaryotes, especially Bacillus subtilis. Many of these functions involve aspects of sugar metabolism and cell wall synthesis and are concerned with responses to abiotic stress such as heat, desiccation, or starvation. Particular emphasis is also given to the oxalate-degrading enzymes from microbes, their biological significance, and their value in a range of medical and other applications.

Automated diagnosis of data-model conflicts using metadata

The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit.

The Ubp6 family of deubiquitinating enzymes contains a ubiquitin-like domain: SUb

A sequence motif that is Similar to Ubiquitin (SUb) has been identified in the Saccharomyces cerevisiae ubiquitin-specific protease Ubp6. SUb is conserved in all known Ubp6 homologues from a spectrum of eukaryotic species and is also present in a group of hypothetical proteins of unknown function (Unk1-3) present in sequence databases. An N-terminal deletion mutant of Ubp6 that lacks SUb is still capable of cleaving alpha-linked ubiquitin fusions, suggesting that SUb forms a separate domain to the catalytic core of Ubp6 and demonstrating that it is not required for in vitro cleavage activity. A homology model of the 78 N-terminal amino acids of human Ubp6, based on the known fold of ubiquitin, is presented. In human Ubp6, SUb shares only 20% sequence identity with ubiquitin. Even weaker similarity occurs between S. cerevisiae SUb and ubiquitin. The homology model supports a ubiquitin-like fold for SUb and suggests that two conserved Lys residues, corresponding to Lys48 and Lys63 of ubiquitin, are functionally important.