Protein structure prediction by threading methods: Evaluation of current techniques

This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure. © 1995 Wiley-Liss, Inc.     

More than the sum of their parts: on the evolution of proteins from peptides

Despite their seemingly endless diversity, proteins adopt a limited number of structural forms. It has been estimated that 80% of proteins will be found to adopt one of only about 400 folds, most of which are already known. These folds are largely formed by a limited 'vocabulary' of recurring supersecondary structure elements, often by repetition of the same element and, increasingly, elements similar in both structure and sequence are discovered. This suggests that modern proteins evolved by fusion and recombination from a more ancient peptide world and that many of the core folds observed today may contain homologous building blocks. The peptides forming these building blocks would not in themselves have had the ability to fold, but would have emerged as cofactors supporting RNA-based replication and catalysis (the 'RNA world'). Their association into larger structures and eventual fusion into polypeptide chains would have allowed them to become independent of their RNA scaffold, leading to the evolution of a novel type of macromolecule: the folded protein.     

Comparing genomes in terms of protein structure: surveys of a finite parts list

We give an overview of the emerging field of structural genomics, describing how genomes can be compared in terms of protein structure. As the number of genes in a genome and the total number of protein folds are both quite limited, these comparisons take the form of surveys of a finite parts list, similar in respects to demographic censuses. Fold surveys have many similarities with other whole-genome characterizations, e.g., analyses of motifs or pathways. However, structure has a number of aspects that make it particularly suitable for comparing genomes, namely the way it allows for the precise definition of a basic protein module and the fact that it has a better defined relationship to sequence similarity than does protein function. An essential requirement for a structure survey is a library of folds, which groups the known structures into 'fold families.' This library can be built up automatically using a structure comparison program, and we described how important objective statistical measures are for assessing similarities within the library and between the library and genome sequences. After building the library, one can use it to count the number of folds in genomes, expressing the results in the form of Venn diagrams and 'top-10' statistics for shared and common folds. Depending on the counting methodology employed, these statistics can reflect different aspects of the genome, such as the amount of internal duplication or gene expression. Previous analyses have shown that the common folds shared between very different microorganisms, i.e., in different kingdoms, have a remarkably similar structure, being comprised of repeated strand-helix-strand super-secondary structure units. A major difficulty with this sort of 'fold-counting' is that only a small subset of the structures in a complete genome are currently known and this subset is prone to sampling bias. One way of overcoming biases is through structure prediction, which can be applied uniformly and comprehensively to a whole genome. Various investigators have, in fact, already applied many of the existing techniques for predicting secondary structure and transmembrane (TM) helices to the recently sequenced genomes. The results have been consistent: microbial genomes have similar fractions of strands and helices even though they have significantly different amino acid composition. The fraction of membrane proteins with a given number of TM helices falls off rapidly with more TM elements, approximately according to a Zipf law. This latter finding indicates that there is no preference for the highly studied 7-TM proteins in microbial genomes. Continuously updated tables and further information pertinent to this review are available over the web at http://bioinfo.mbb.yale.edu/genome.     

Structural genomics analysis: characteristics of atypical, common, and horizontally transferred folds

We conducted a structural genomics analysis of the folds and structural superfamilies in the first 20 completely sequenced genomes by focusing on the patterns of fold usage and trying to identify structural characteristics of typical and atypical folds. We assigned folds to sequences using PSI-blast, run with a systematic protocol to reduce the amount of computational overhead. On average, folds could be assigned to about a fourth of the ORFs in the genomes and about a fifth of the amino acids in the proteomes. More than 80% of all the folds in the SCOP structural classification were identified in one of the 20 organisms, with worm and E. coli having the largest number of distinct folds. Folds are particularly effective at comprehensively measuring levels of gene duplication, because they group together even very remote homologues. Using folds, we find the average level of duplication varies depending on the complexity of the organism, ranging from 2.4 in M. genitalium to 32 for the worm, values significantly higher than those observed based purely on sequence similarity. We rank the common folds in the 20 organisms, finding that the top three are the P-loop NTP hydrolase, the ferrodoxin fold, and the TIM-barrel, and discuss in detail the many factors that affect and bias these rankings. We also identify atypical folds that are "unique" to one of the organisms in our study and compare the characteristics of these folds with the most common ones. We find that common folds tend be more multifunctional and associated with more regular, "symmetrical" structures than the unique ones. In addition, many of the unique folds are associated with proteins involved in cell defense (e.g., toxins). We analyze specific patterns of fold occurrence in the genomes by associating some of them with instances of horizontal transfer and others with gene loss. In particular, we find three possible examples of transfer between archaea and bacteria and six between eukarya and bacteria. We make available our detailed results at http://genecensus.org/20.     

Protein structural domains: analysis of the 3Dee domains database

The 3Dee database of domain definitions was developed as a comprehensive collection of domain definitions for all three-dimensional structures in the Protein Data Bank (PDB). The database includes definitions for complex, multiple-segment and multiple-chain domains as well as simple sequential domains, organized in a structural hierarchy. Two different snapshots of the 3Dee database were analyzed at September 1996 and November 1999. For the November 1999 release, 7,995 PDB entries contained 13,767 protein chains and gave rise to 18,896 domains. The domain sequences clustered into 1,715 domain sequence families, which were further clustered into a conservative 1,199 domain structure families (families with similar folds). The proportion of different domain structure families per domain sequence family increases from 84% for domains 1-100 residues long to 100% for domains greater than 600 residues. This is in keeping with the idea that longer chains will have more alternative folds available to them. Of the representative domains from the domain sequence families, 49% are in the range of 51-150 residues, whereas 64% of the representative chains over 200 residues have more than 1 domain. Of the representative chains, 8.5% are part of multichain domains. The largest multichain domain in the database has 14 chains and 1,400 residues, whereas the largest single-chain domain has 907 residues. The largest number of domains found in a protein is 13. The analysis shows that over the history of the PDB, new domain folds have been discovered at a slower rate than by random selection of all known folds. Between 1992 and 1997, a constant 1 in 11 new domains deposited in the PDB has shown no sequence similarity to a previously known domain sequence family, and only 1 in 15 new domain structures has had a fold that has not been seen previously. A comparison of the September 1996 release of 3Dee to the Structural Classification of Proteins (SCOP) showed that the domain definitions agreed for 80% of the representative protein chains. However, 3Dee provided explicit domain boundaries for more proteins. 3Dee is accessible on the World Wide Web at http://barton.ebi.ac.uk/servers/3Dee.html.     

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.     

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.     

Modeling repetitive,non‐globular proteins

While ab initio modeling of protein structures is not routine, certain types of proteins are more straightforward to model than others. Proteins with short repetitive sequences typically exhibit repetitive structures. These repetitive sequences can be more amenable to modeling if some information is known about the predominant secondary structure or other key features of the protein sequence. We have successfully built models of a number of repetitive structures with novel folds using knowledge of the consensus sequence within the sequence repeat and an understanding of the likely secondary structures that these may adopt. Our methods for achieving this success are reviewed here.     

Expectations from structural genomics

Structural genomics projects aim to provide an experimental structure or a good model for every protein in all completed genomes. Most of the experimental work for these projects will be directed toward proteins whose fold cannot be readily recognized by simple sequence comparison with proteins of known structure. Based on the history of proteins classified in the SCOP structure database, we expect that only about a quarter of the early structural genomics targets will have a new fold. Among the remaining ones, about half are likely to be evolutionarily related to proteins of known structure, even though the homology could not be readily detected by sequence analysis.     

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.     

Structural genomics and its importance for gene function analysis

Structural genomics projects aim to solve the experimental structures of all possible protein folds. Such projects entail a conceptual shift from traditional structural biology in which structural information is obtained on known proteins to one in which the structure of a protein is determined first and the function assigned only later. Whereas the goal of converting protein structure into function can be accomplished by traditional sequence motif-based approaches, recent studies have shown that assignment of a protein's biochemical function can also be achieved by scanning its structure for a match to the geometry and chemical identity of a known active site. Importantly, this approach can use low-resolution structures provided by contemporary structure prediction methods. When applied to genomes, structural information (either experimental or predicted) is likely to play an important role in high-throughput function assignment.     

A unifold, mesofold, and superfold model of protein fold use.

As more and more protein structures are determined, there is increasing interest in the question of how many different folds have been used in biology. The history of the rate of discovery of new folds and the distribution of sequence families among known folds provide a means of estimating the underlying distribution of fold use. Previous models exploiting these data have led to rather different conclusions on the total number of folds. We present a new model, based on the notion that the folds used in biology fall naturally into three classes: unifolds, that is, folds found only in a single narrow sequence family; mesofolds, found in an intermediate number of families; and the previously noted superfolds, found in many protein families. We show that this model fits the available data well and has predicted the development of SCOP over the past 2 years. The principle implications of the model are as follows: (1) The vast majority of folds will be found in only a single sequence family; (2) the total number of folds is at least 10,000; and (3) 80% of sequence families have one of about 400 folds, most of which are already known.     

Identifying sequence-structure pairs undetected by sequence alignments

We examine how effectively simple potential functions previously developed can identify compatibilities between sequences and structures of proteins for database searches. The potential function consists of pairwise contact energies, repulsive packing potentials of residues for overly dense arrangement and short-range potentials for secondary structures, all of which were estimated from statistical preferences observed in known protein structures. Each potential energy term was modified to represent compatibilities between sequences and structures for globular proteins. Pairwise contact interactions in a sequence-structure alignment are evaluated in a mean field approximation on the basis of probabilities of site pairs to be aligned. Gap penalties are assumed to be proportional to the number of contacts at each residue position, and as a result gaps will be more frequently placed on protein surfaces than in cores. In addition to minimum energy alignments, we use probability alignments made by successively aligning site pairs in order by pairwise alignment probabilities. The results show that the present energy function and alignment method can detect well both folds compatible with a given sequence and, inversely, sequences compatible with a given fold, and yield mostly similar alignments for these two types of sequence and structure pairs. Probability alignments consisting of most reliable site pairs only can yield extremely small root mean square deviations, and including less reliable pairs increases the deviations. Also, it is observed that secondary structure potentials are usefully complementary to yield improved alignments with this method. Remarkably, by this method some individual sequence-structure pairs are detected having only 5-20% sequence identity.     

Patterns of protein-fold usage in eight microbial genomes: A comprehensive structural census

Eight microbial genomes are compared in terms of protein structure. Specifically, yeast, H. influenzae, M. genitalium, M. jannaschii, Synechocystis, M. pneumoniae, H. pylori, and E. coli are compared in terms of patterns of fold usage—whether a given fold occurs in a particular organism. Of the ∼340 soluble protein folds currently in the structure databank (PDB), 240 occur in at least one of the eight genomes, and 30 are shared amongst all eight. The shared folds are depleted in all-helical structure and enriched in mixed helix-sheet structure compared to the folds in the PDB. The top-10 most common of the shared 30 are enriched in superfolds, uniting many non-homologous sequence families, and are especially similar in overall architecture—eight having helices packed onto a central sheet. They are also very different from the common folds in the PBD, highlighting databank biases. Folds can be ranked in terms of expression as well as genome duplication. In yeast the top-10 most highly expressed folds are considerably different from the most highly duplicated folds. A tree can be constructed grouping genomes in terms of their shared folds. This has a remarkably similar topology to more conventional classifications, based on very different measures of relatedness. Finally, folds of membrane proteins can be analyzed through transmembrane-helix (TM) prediction. All the genomes appear to have similar usage patterns for these folds, with the occurrence of a particular fold falling off rapidly with increasing numbers of TM-elements, according to a “Zipf-like” law. This implies there are no marked preferences for proteins with particular numbers of TM-helices (e.g. 7-TM) in microbial genomes. Further information pertinent to this analysis is available at http://bioinfo.mbb.yale.edu/genome. Proteins 33:518–534, 1998. © 1998 Wiley-Liss, Inc.     

Recurring structural motifs in proteins with different functions

BACKGROUND: Structures that have diverged from a common ancestor often retain functional and sequence similarity, although the latter may be very reduced. Even so, the overall fold of the structure is generally highly conserved. Now however, several have been identified of proteins that have been identified that have different functions but which have converged to a similar fold. These proteins will also have low sequence identities. RESULTS: By comparing the complete structure databank against itself, using sequence and structure alignment techniques, we have been able to identify six new examples of structurally related folds that have no apparent sequence or functional similarity. These related proteins include a family of crambin-like folds and a family of ferredoxin II folds. We found that all the similarities between structures are present in small proteins and occur as motifs within the core of a larger protein. CONCLUSION: The low sequence similarity and the lack of any obvious functional relationship between proteins with similar structures suggest that the proteins have diverged from independent ancestors. The similarities may therefore be of interest for understanding the various stereochemical and physical criteria that operate to generate a favourable fold.     

A galaxy of folds

Many protein classification systems capture homologous relationships by grouping domains into families and superfamilies on the basis of sequence similarity. Superfamilies with similar 3D structures are further grouped into folds. In the absence of discernable sequence similarity, these structural similarities were long thought to have originated independently, by convergent evolution. However, the growth of databases and advances in sequence comparison methods have led to the discovery of many distant evolutionary relationships that transcend the boundaries of superfamilies and folds. To investigate the contributions of convergent versus divergent evolution in the origin of protein folds, we clustered representative domains of known structure by their sequence similarity, treating them as point masses in a virtual 2D space which attract or repel each other depending on their pairwise sequence similarities. As expected, families in the same superfamily form tight clusters. But often, superfamilies of the same fold are linked with each other, suggesting that the entire fold evolved from an ancient prototype. Strikingly, some links connect superfamilies with different folds. They arise from modular peptide fragments of between 20 and 40 residues that co‐occur in the connected folds in disparate structural contexts. These may be descendants of an ancestral pool of peptide modules that evolved as cofactors in the RNA world and from which the first folded proteins arose by amplification and recombination. Our galaxy of folds summarizes, in a single image, most known and many yet undescribed homologous relationships between protein superfamilies, providing new insights into the evolution of protein domains.     

Fold usage on genomes and protein fold evolution

We review fold usage on completed genomes to explore protein structure evolution. The patterns of presence or absence of folds on genomes gives us insights into the relationships between folds, the age of different folds and how we have arrived at the set of folds we see today. We examine the relationships between different measures which describe protein fold usage, such as the number of copies of a fold per genome, the number of families per fold, and the number of genomes a fold occurs on. We obtained these measures of fold usage by searching for the structural domains on 157 completed genome sequences from all three kingdoms of life. In our comparisons of these measures we found that bacteria have relatively more distinct folds on their genomes than archaea. Eukaryotes were found to have many more copies of a fold on their genomes. If we separate out the different fold classes, the alpha/beta class has relatively fewer distinct folds on large genomes, more copies of a fold on bacteria and more folds occurring in all three kingdoms simultaneously. These results possibly indicate that most alpha/beta folds originated earlier than other folds. The expected power law distribution is observed for copies of a fold per genome and we found a similar distribution for the number of families per fold. However, a more complicated distribution appears for fold occurrence across genomes, which strongly depends on fold class and kingdom. We also show that there is not a clear relationship between the three measures of fold usage. A fold which occurs on many genomes does not necessarily have many copies on each genome. Similarly, folds with many copies do not necessarily have many families or vice versa.     

Characterization of novel proteins based on known protein structures

The genome sciences face the challenge to characterize structure and function of a vast number of novel genes. Sequence search techniques are used to infer functional and structural information from similarities to experimentally characterized genes or proteins. The persistent goal is to refine these techniques and to develop alternative and complementary methods to increase the range of reliable inference.Here, we focus on the structural and functional assignments that can be inferred from the known three-dimensional structures of proteins. The study uses all structures in the Protein Data Bank that were known by the end of 1997. The protein structures released in 1998 were then characterized in terms of functional and structural similarity to the previously known structures, yielding an estimate of the maximum amount of information on novel protein sequences that can be obtained from inference techniques.The 147 globular proteins corresponding to 196 domains released in 1998 have no clear sequence similarity to previously known structures. However, 75 % of the domains have extensive structure similarity to previously known folds, and most importantly, in two out of three cases similarity in structure coincides with related function. In view of this analysis, full utilization of existing structure data bases would provide information for many new targets even if the relationship is not accessible from sequence information alone. Currently, the most sophisticated techniques detect of the order of one-third of these relationships.     

Database of homology-derived protein structures and the structural meaning of sequence alignment

The database of known protein three-dimensional structures can be significantly increased by the use of sequence homology, based on the following observations. (1) The database of known sequences, currently at more than 12,000 proteins, is two orders of magnitude larger than the database of known structures. (2) The currently most powerful method of predicting protein structures is model building by homology. (3) Structural homology can be inferred from the level of sequence similarity. (4) The threshold of sequence similarity sufficient for structural homology depends strongly on the length of the alignment. Here, we first quantify the relation between sequence similarity, structure similarity, and alignment length by an exhaustive survey of alignments between proteins of known structure and report a homology threshold curve as a function of alignment length. We then produce a database of homology-derived secondary structure of proteins (HSSP) by aligning to each protein of known structure all sequences deemed homologous on the basis of the threshold curve. For each known protein structure, the derived database contains the aligned sequences, secondary structure, sequence variability, and sequence profile. Tertiary structures of the aligned sequences are implied, but not modeled explicitly. The database effectively increases the number of known protein structures by a factor of five to more than 1800. The results may be useful in assessing the structural significance of matches in sequence database searches, in deriving preferences and patterns for structure prediction, in elucidating the structural role of conserved residues, and in modeling three-dimensional detail by homology.