Review: what can structural classifications reveal about protein evolution?

Nearest neighbor search on embeddings rapidly identifies distant protein relations

Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as "homology detection") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.

Bridging Themes: Short Protein Segments Found in Different Architectures

The vast majority of theoretically possible polypeptide chains do not fold, let alone confer function. Hence, protein evolution from preexisting building blocks has clear potential advantages over ab initio emergence from random sequences. In support of this view, sequence similarities between different proteins is generally indicative of common ancestry, and we collectively refer to such homologous sequences as "themes." At the domain level, sequence homology is routinely detected. However, short themes which are segments, or fragments of intact domains, are particularly interesting because they may provide hints about the emergence of domains, as opposed to divergence of preexisting domains, or their mixing-and-matching to form multi-domain proteins. Here we identified 525 representative short themes, comprising 20-80 residues that are unexpectedly shared between domains considered to have emerged independently. Among these "bridging themes" are ones shared between the most ancient domains, for example, Rossmann, P-loop NTPase, TIM-barrel, flavodoxin, and ferredoxin-like. We elaborate on several particularly interesting cases, where the bridging themes mediate ligand binding. Ligand binding may have contributed to the stability and the plasticity of these building blocks, and to their ability to invade preexisting domains or serve as starting points for completely new domains.

Competition of individual domain folding with inter-domain interaction in WW domain engineered repeat proteins

Engineered repeat proteins have proven to be a fertile ground for studying the competition between folding, misfolding and transient aggregation of tethered protein domains. We examine the interplay between folding and inter-domain interactions of engineered FiP35 WW domain repeat proteins with n = 1 through 5 repeats. We characterize protein expression, thermal and guanidium melts, as well as laser T-jump kinetics. All experimental data is fitted by a global fitting model with two states per domain (U, N), plus a third state M to account for non-native states due to domain interactions present in all but the monomer. A detailed structural model is provided by coarse-grained simulated annealing using the AWSEM Hamiltonian. Tethered FiP35 WW domains with n = 2 and 3 domains are just slightly less stable than the monomer. The n = 4 oligomer is yet less stable, its expression yield is much lower than the monomer's, and depends on the purification tag used. The n = 5 plasmid did not express at all, indicating the sudden onset of aggregation past n = 4. Thus, tethered FiP35 has a critical nucleus size for inter-domain aggregation of n ≈ 4. According to our simulations, misfolded structures become increasingly prevalent as one proceeds from monomer to pentamer, with extended inter-domain beta sheets appearing first, then multi-sheet 'intramolecular amyloid' structures, and finally novel motifs containing alpha helices. We discuss the implications of our results for oligomeric aggregate formation and structure, transient aggregation of proteins whilst folding, as well as for protein evolution that starts with repeat proteins.

Evolutionary convergence and divergence in archaeal chromosomal proteins and Chromo-like domains from bacteria and eukaryotes

SH3-fold-β-barrel domains of the chromo-like superfamily recognize epigenetic marks in eukaryotic proteins. Their provenance has been placed either in archaea, based on apparent structural similarity to chromatin-compacting Sul7d and Cren7 proteins, or in bacteria based on the presence of sequence homologs. Using sequence and structural evidence we establish that the archaeal Cren7/Sul7 proteins emerged from a zinc ribbon (ZnR) ancestor. Further, we show that the ancestral eukaryotic chromo-like domains evolved from bacterial versions, likely acquired from early endosymbioses, which already possessed an aromatic cage for recognition of modified amino-groups. These bacterial versions are part of a radiation of secreted SH3-fold domains, which spawned both chromo-like domains and classical SH3 domains in the context of peptide-recognition in the peptidoglycan or the extracellular matrix. This establishes that Cren7/Sul7 converged to a “SH3”-like state from a ZnR precursor via the loss of metal-chelation and acquisition of stronger hydrophobic interactions; it is unlikely to have participated in the evolution of the chromo-like domains. We show that archaea possess several Cren7/Sul7-related proteins with intact Zn-chelating ligands, which we predict to play previously unstudied roles in chromosome segregation during cell-division comparable to the PRC barrel and CdvA domain proteins.

Manual classification strategies in the ECOD database

ECOD (Evolutionary Classification Of protein Domains) is a comprehensive and up-to-date protein structure classification database. The majority of new structures released from the PDB (Protein Data Bank) each week already have close homologs in the ECOD hierarchy and thus can be reliably partitioned into domains and classified by software without manual intervention. However, those proteins that lack confidently detectable homologs require careful analysis by experts. Although many bioinformatics resources rely on expert curation to some degree, specific examples of how this curation occurs and in what cases it is necessary are not always described. Here, we illustrate the manual classification strategy in ECOD by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores. Most examples show recently released and manually classified PDB structures. We discuss multi-domain proteins, discordance between sequence and structural similarities, difficulties with assessing homology with scores, and integral membrane proteins homologous to soluble proteins. By timely assimilation of newly available structures into its hierarchy, ECOD strives to provide a most accurate and updated view of the protein structure world as a result of combined computational and expert-driven analysis.

ECOD: an evolutionary classification of protein domains

Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or "fold"). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.

Camps 2.0: exploring the sequence and structure space of prokaryotic, eukaryotic, and viral membrane proteins

Structural bioinformatics of membrane proteins is still in its infancy, and the picture of their fold space is only beginning to emerge. Because only a handful of three-dimensional structures are available, sequence comparison and structure prediction remain the main tools for investigating sequence-structure relationships in membrane protein families. Here we present a comprehensive analysis of the structural families corresponding to α-helical membrane proteins with at least three transmembrane helices. The new version of our CAMPS database (CAMPS 2.0) covers nearly 1300 eukaryotic, prokaryotic, and viral genomes. Using an advanced classification procedure, which is based on high-order hidden Markov models and considers both sequence similarity as well as the number of transmembrane helices and loop lengths, we identified 1353 structurally homogeneous clusters roughly corresponding to membrane protein folds. Only 53 clusters are associated with experimentally determined three-dimensional structures, and for these clusters CAMPS is in reasonable agreement with structure-based classification approaches such as SCOP and CATH. We therefore estimate that ∼1300 structures would need to be determined to provide a sufficient structural coverage of polytopic membrane proteins. CAMPS 2.0 is available at http://webclu.bio.wzw.tum.de/CAMPS2.0/.

Identification of local conformational similarity in structurally variable regions of homologous proteins using protein blocks

Structure comparison tools can be used to align related protein structures to identify structurally conserved and variable regions and to infer functional and evolutionary relationships. While the conserved regions often superimpose well, the variable regions appear non superimposable. Differences in homologous protein structures are thought to be due to evolutionary plasticity to accommodate diverged sequences during evolution. One of the kinds of differences between 3-D structures of homologous proteins is rigid body displacement. A glaring example is not well superimposed equivalent regions of homologous proteins corresponding to α-helical conformation with different spatial orientations. In a rigid body superimposition, these regions would appear variable although they may contain local similarity. Also, due to high spatial deviation in the variable region, one-to-one correspondence at the residue level cannot be determined accurately. Another kind of difference is conformational variability and the most common example is topologically equivalent loops of two homologues but with different conformations. In the current study, we present a refined view of the “structurally variable” regions which may contain local similarity obscured in global alignment of homologous protein structures. As structural alphabet is able to describe local structures of proteins precisely through Protein Blocks approach, conformational similarity has been identified in a substantial number of ‘variable’ regions in a large data set of protein structural alignments; optimal residue-residue equivalences could be achieved on the basis of Protein Blocks which led to improved local alignments. Also, through an example, we have demonstrated how the additional information on local backbone structures through protein blocks can aid in comparative modeling of a loop region. In addition, understanding on sequence-structure relationships can be enhanced through our approach. This has been illustrated through examples where the equivalent regions in homologous protein structures share sequence similarity to varied extent but do not preserve local structure.

Extracting function from a beta-trefoil folding motif

Despite having remarkably similar three-dimensional structures and stabilities, IL-1beta promotes signaling, whereas IL-1Ra inhibits it. Their energy landscapes are similar and have coevolved to facilitate competitive binding to the IL-1 receptor. Nevertheless, we find that IL-1Ra folds faster than IL-1beta. A structural alignment of the proteins shows differences mainly in two loops, a beta-bulge of IL-1beta and a loop in IL-1Ra that interacts with residue K145 and connects beta-strands 11 and 12. Bioassays indicate that inserting the beta-bulge from IL-1beta confers partial signaling capability onto a K145D mutant of IL-1Ra. Based on the alignment, mutational assays and our computational folding results, we hypothesize that functional regions are not central to the beta-trefoil motif and cause slow folding. The IL-1beta beta-bulge facilitates activity and replacing it by the IL-1Ra beta-turn results in a hybrid protein that folds faster than IL-1beta. Inserting the beta11-beta12 connecting-loop, which aids inhibition, into either IL-1beta or the hybrid protein slows folding. Thus, regions that aid function (either through activity or inhibition) can be inferred from folding traps via structural differences. Mapping functional properties onto the numerous folds determined in structural genomics efforts is an area of intense interest. Our studies provide a systematic approach to mapping the functional genomics of a fold family.

Discrimination between distant homologs and structural analogs: lessons from manually constructed, reliable data sets

A natural way to study protein sequence, structure, and function is to put them in the context of evolution. Homologs inherit similarities from their common ancestor, while analogs converge to similar structures due to a limited number of energetically favorable ways to pack secondary structural elements. Using novel strategies, we previously assembled two reliable databases of homologs and analogs. In this study, we compare these two data sets and develop a support vector machine (SVM)-based classifier to discriminate between homologs and analogs. The classifier uses a number of well-known similarity scores. We observe that although both structure scores and sequence scores contribute to SVM performance, profile sequence scores computed based on structural alignments are the best discriminators between remote homologs and structural analogs. We apply our classifier to a representative set from the expert-constructed database, Structural Classification of Proteins (SCOP). The SVM classifier recovers 76% of the remote homologs defined as domains in the same SCOP superfamily but from different families. More importantly, we also detect and discuss interesting homologous relationships between SCOP domains from different superfamilies, folds, and even classes.

MALIDUP: a database of manually constructed structure alignments for duplicated domain pairs

We describe MALIDUP (manual alignments of duplicated domains), a database of 241 pairwise structure alignments for homologous domains originated by internal duplication within the same polypeptide chain. Since duplicated domains within a protein frequently diverge in function and thus in sequence, this would be the first database of structurally similar homologs that is not strongly biased by sequence or functional similarity. Our manual alignments in most cases agree with the automatic structural alignments generated by several commonly used programs. This carefully constructed database could be used in studies on protein evolution and as a reference for testing structure alignment programs. The database is available at http://prodata.swmed.edu/malidup.

Understanding the molecular machinery of genetics through 3D structures

Detailed knowledge of the three-dimensional structures of biological molecules has had an enormous impact on all areas of biological science, including genetics, as structure can reveal the fine details of how molecules perform their biological functions. Here we consider how changes in protein sequence affect the corresponding 3D structure, and describe how structural information about proteins, DNA and chromatin has shed light on gene regulatory mechanisms and the storage and transmission of epigenetic information. Finally, we describe how structure determination is benefiting from the high-throughput technologies of the worldwide structural genomics projects.

Selecting folded proteins from a library of secondary structural elements

A protein evolution strategy is described by which double-stranded DNA fragments encoding defined Escherichia coli protein secondary structural elements (alpha-helices, beta-strands, and loops) are assembled semirandomly into sequences comprised of as many as 800 amino acid residues. A library of novel polypeptides generated from this system was inserted into an enhanced green fluorescent protein (EGFP) fusion vector. Library members were screened by fluorescence activated cell sorting (FACS) to identify those polypeptides that fold into soluble, stable structures in vivo that comprised a subset of shorter sequences ( approximately 60 to 100 residues) from the semirandom sequence library. Approximately 108 clones were screened by FACS, a set of 1149 high fluorescence colonies were characterized by dPCR, and four soluble clones with varying amounts of secondary structure were identified. One of these is highly homologous to a domain of aspartate racemase from a marine bacterium (Polaromonas sp.) but is not homologous to any E. coli protein sequence. Several other selected polypeptides have no global sequence homology to any known protein but show significant alpha-helical content, limited dispersion in 1D nuclear magnetic resonance spectra, pH sensitive ANS binding and reversible folding into soluble structures. These results demonstrate that this strategy can generate novel polypeptide sequences containing secondary structure.

MALISAM: a database of structurally analogous motifs in proteins

MALISAM (manual alignments for structurally analogous motifs) represents the first database containing pairs of structural analogs and their alignments. To find reliable analogs, we developed an approach based on three ideas. First, an insertion together with a part of the evolutionary core of one domain family (a hybrid motif) is analogous to a similar motif contained within the core of another domain family. Second, a motif at an interface, formed by secondary structural elements (SSEs) contributed by two or more domains or subunits contacting along that interface, is analogous to a similar motif present in the core of a single domain. Third, an artificial protein obtained through selection from random peptides or in sequence design experiments not biased by sequences of a particular homologous family, is analogous to a structurally similar natural protein. Each analogous pair is superimposed and aligned manually, as well as by several commonly used programs. Applications of this database may range from protein evolution studies, e.g. development of remote homology inference tools and discriminators between homologs and analogs, to protein-folding research, since in the absence of evolutionary reasons, similarity between proteins is caused by structural and folding constraints. The database is publicly available at http://prodata.swmed.edu/malisam.

From the similarity analysis of protein cavities to the functional classification of protein families using cavbase

In this contribution, the classification of protein binding sites using the physicochemical properties exposed to their pockets is presented. We recently introduced Cavbase, a method for describing and comparing protein binding pockets on the basis of the geometrical and physicochemical properties of their active sites. Here, we present algorithmic and methodological enhancements in the Cavbase property description and in the cavity comparison step. We give examples of the Cavbase similarity analysis detecting pronounced similarities in the binding sites of proteins unrelated in sequence. A similarity search using SARS M(pro) protease subpockets as queries retrieved ligands and ligand fragments accommodated in a physicochemical environment similar to that of the query. This allowed the characterization of the protease recognition pockets and the identification of molecular building blocks that can be incorporated into novel antiviral compounds. A cluster analysis procedure for the functional classification of binding pockets was implemented and calibrated using a diverse set of enzyme binding sites. Two relevant protein families, the alpha-carbonic anhydrases and the protein kinases, are used to demonstrate the scope of our cluster approach. We propose a relevant classification of both protein families, on the basis of the binding motifs in their active sites. The classification provides a new perspective on functional properties across a protein family and is able to highlight features important for potency and selectivity. Furthermore, this information can be used to identify possible cross-reactivities among proteins due to similarities in their binding sites.

Exploiting protein structure data to explore the evolution of protein function and biological complexity

New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.

Divergent evolution within protein superfolds inferred from profile-based phylogenetics

Many dissimilar protein sequences fold into similar structures. A central and persistent challenge facing protein structural analysis is the discrimination between homology and convergence for structurally similar domains that lack significant sequence similarity. Classic examples are the OB-fold and SH3 domains, both small, modular beta-barrel protein superfolds. The similarities among these domains have variously been attributed to common descent or to convergent evolution. Using a sequence profile-based phylogenetic technique, we analyzed all structurally characterized OB-fold, SH3, and PDZ domains with less than 40% mutual sequence identity. An all-against-all, profile-versus-profile analysis of these domains revealed many previously undetectable significant interrelationships. The matrices of scores were used to infer phylogenies based on our derivation of the relationships between sequence similarity E-values and evolutionary distances. The resulting clades of domains correlate remarkably well with biological function, as opposed to structural similarity, indicating that the functionally distinct sub-families within these superfolds are homologous. This method extends phylogenetics into the challenging "twilight zone" of sequence similarity, providing the first objective resolution of deep evolutionary relationships among distant protein families.

DOM-fold: a structure with crossing loops found in DmpA, ornithine acetyltransferase, and molybdenum cofactor-binding domain

Understanding relationships between sequence, structure, and evolution is important for functional characterization of proteins. Here, we define a novel DOM-fold as a consensus structure of the domains in DmpA (L-aminopeptidase D-Ala-esterase/amidase), OAT (ornithine acetyltransferase), and MocoBD (molybdenum cofactor-binding domain), and discuss possible evolutionary scenarios of its origin. As shown by a comprehensive structure similarity search, DOM-fold distinguished by a two-layered beta/alpha architecture of a particular topology with unusual crossing loops is unique to those three protein families. DmpA and OAT are evolutionarily related as indicated by their sequence, structural, and functional similarities. Structural similarity between the DmpA/OAT superfamily and the MocoBD domains has not been reported before. Contrary to previous reports, we conclude that functional similarities between DmpA/OAT proteins and N-terminal nucleophile (Ntn) hydrolases are convergent and are unlikely to be inherited from a common ancestor.

Assessing strategies for improved superfamily recognition

There are more than 200 completed genomes and over 1 million nonredundant sequences in public repositories. Although the structural data are more sparse (approximately 13,000 nonredundant structures solved to date), several powerful sequence-based methodologies now allow these structures to be mapped onto related regions in a significant proportion of genome sequences. We review a number of publicly available strategies for providing structural annotations for genome sequences, and we describe the protocol adopted to provide CATH structural annotations for completed genomes. In particular, we assess the performance of several sequence-based protocols employing Hidden Markov model (HMM) technologies for superfamily recognition, including a new approach (SAMOSA [sequence augmented models of structure alignments]) that exploits multiple structural alignments from the CATH domain structure database when building the models. Using a data set of remote homologs detected by structure comparison and manually validated in CATH, a single-seed HMM library was able to recognize 76% of the data set. Including the SAMOSA models in the HMM library showed little gain in homolog recognition, although a slight improvement in alignment quality was observed for very remote homologs. However, using an expanded 1D-HMM library, CATH-ISL increased the coverage to 86%. The single-seed HMM library has been used to annotate the protein sequences of 120 genomes from all three major kingdoms, allowing up to 70% of the genes or partial genes to be assigned to CATH superfamilies. It has also been used to recruit sequences from Swiss-Prot and TrEMBL into CATH domain superfamilies, expanding the CATH database eightfold.

PROTEIN FAMILIES AND THEIR EVOLUTION—A STRUCTURAL PERSPECTIVE

We can now assign about two thirds of the sequences from completed genomes to as few as 1400 domain families for which structures are known and thus more ancient evolutionary relationships established. About 200 of these domain families are common to all kingdoms of life and account for nearly 50% of domain structure annotations in the genomes. Some of these domain families have been very extensively duplicated within a genome and combined with different domain partners giving rise to different multidomain proteins. The ways in which these domain combinations evolve tend to be specific to the organism so that less than 15% of the protein families found within a genome appear to be common to all kingdoms of life. Recent analyses of completed genomes, exploiting the structural data, have revealed the extent to which duplication of these domains and modifications of their functions can expand the functional repertoire of the organism, contributing to increasing complexity.

The identification of novel RNA structural motifs using COMPADRES: an automated approach to structural discovery

Recurring RNA structural motifs are important sites of tertiary interaction and as such, are integral to RNA macromolecular structure. Although numerous RNA motifs have been classified and characterized, the identification of new motifs is of great interest. In this study, we discovered four new conformationally recurring motifs: the pi-turn, the Omega-turn, the alpha-loop and the C2'-endo mediated flipped adenosine motif. Not only do they have complex and interesting structures, but they participate in contacts of high biological significance. In a first for the RNA field, new motifs were discovered by a fully automated algorithm. This algorithm, COMPADRES, utilized a reduced representation of the RNA backbone and was highly successful at discerning unique structural relationships. This study also shows that recurring RNA substructures are not necessarily accompanied by consistent primary or secondary structure.

Structural classification of thioredoxin-like fold proteins

Protein structure classification is necessary to comprehend the rapidly growing structural data for better understanding of protein evolution and sequence-structure-function relationships. Thioredoxins are important proteins that ubiquitously regulate cellular redox status and various other crucial functions. We define the thioredoxin-like fold using the structure consensus of thioredoxin homologs and consider all circular permutations of the fold. The search for thioredoxin-like fold proteins in the PDB database identified 723 protein domains. These domains are grouped into eleven evolutionary families based on combined sequence, structural, and functional evidence. Analysis of the protein-ligand structure complexes reveals two major active site locations for the thioredoxin-like proteins. Comparison to existing structure classifications reveals that our thioredoxin-like fold group is broader and more inclusive, unifying proteins from five SCOP folds, five CATH topologies and seven DALI domain dictionary globular folding topologies. Considering these structurally similar domains together sheds new light on the relationships between sequence, structure, function and evolution of thioredoxins.

Progress towards mapping the universe of protein folds

How well can the current set of known protein families and folds be used to estimate the total number of folds in nature, and will structural genomics initiatives yield representatives for all the major protein families within a reasonable time scale?

Although the precise aims differ between the various international structural genomics initiatives currently aiming to illuminate the universe of protein folds, many selectively target protein families for which the fold is unknown. How well can the current set of known protein families and folds be used to estimate the total number of folds in nature, and will structural genomics initiatives yield representatives for all the major protein families within a reasonable time scale?

Chalcone isomerase family and fold: no longer unique to plants

Chalcone isomerase, an enzyme in the isoflavonoid pathway in plants, catalyzes the cyclization of chalcone into (2S)-naringenin. Chalcone isomerase sequence family and three-dimensional fold appeared to be unique to plants and has been proposed as a plant-specific gene marker. Using sensitive methods of sequence comparison and fold recognition, we have identified genes homologous to chalcone isomerase in all completely sequenced fungi, in slime molds, and in many gammaproteobacteria. The residues directly involved in the enzyme's catalytic function are among the best conserved across species, indicating that the newly discovered homologs are enzymatically active. At the same time, fungal and bacterial species that have chalcone isomerase-like genes tend to lack the orthologs of the upstream enzyme chalcone synthase, suggesting a novel variation of the pathway in these species.

Computing motif correlations in proteins

Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/.

A structural perspective on genome evolution

Protein translations of over 100 complete genomes are now available. About half of these sequences can be provided with structural annotation, thereby enabling some profound insights into protein and pathway evolution. Whereas the major domain structure families are common to all kingdoms of life, these are combined in different ways in multidomain proteins to give various domain architectures that are specific to kingdoms or individual genomes, and contribute to the diverse phenotypes observed. These data argue for more targets in structural genomics initiatives and particularly for the selection of different domain architectures to gain better insights into protein functions.

The CATH database: an extended protein family resource for structural and functional genomics

The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath_new) currently contains 34 287 domain structures classified into 1383 superfamilies and 3285 sequence families. Each structural family is expanded with domain sequence relatives recruited from GenBank using a variety of efficient sequence search protocols and reliable thresholds. This extended resource, known as the CATH-protein family database (CATH-PFDB) contains a total of 310 000 domain sequences classified into 26 812 sequence families. New sequence search protocols have been designed, based on these intermediate sequence libraries, to allow more regular updating of the classification. Further developments include the adaptation of a recently developed method for rapid structure comparison, based on secondary structure matching, for domain boundary assignment. The philosophy behind CATHEDRAL is the recognition of recurrent folds already classified in CATH. Benchmarking of CATHEDRAL, using manually validated domain assignments, demonstrated that 43% of domains boundaries could be completely automatically assigned. This is an improvement on a previous consensus approach for which only 10-20% of domains could be reliably processed in a completely automated fashion. Since domain boundary assignment is a significant bottleneck in the classification of new structures, CATHEDRAL will also help to increase the frequency of CATH updates.

Linear scaling approaches to quantum macromolecular similarity: evaluating the similarity function

The evaluation of the electron density based similarity function scales quadratically with respect to the size of the molecules for simplified, atomic shell densities. Due to the exponential decay of the function's atom-atom terms most interatomic contributions are numerically negligible on large systems. An improved algorithm for the evaluation of the Quantum Molecular Similarity function is presented. This procedure identifies all non-negligible terms without computing unnecessary interatomic squared distances, thus effectively turning to linear scaling the similarity evaluation. Presented also is a minimalist dynamic electron density model. Approximate, single shell densities together with the proposed algorithm facilitate fast electron density based alignments on macromolecules.

Quantifying the similarities within fold space

We have used GRATH, a graph-based structure comparison algorithm, to map the similarities between the different folds observed in the CATH domain structure database. Statistical analysis of the distributions of the fold similarities has allowed us to assess the significance for any similarity. Therefore we have examined whether it is best to represent folds as discrete entities or whether, in fact, a more accurate model would be a continuum wherein folds overlap via common motifs. To do this we have introduced a new statistical measure of fold similarity, termed gregariousness. For a particular fold, gregariousness measures how many other folds have a significant structural overlap with that fold, typically comprising 40% or more of the larger structure. Gregarious folds often contain commonly occurring super-secondary structural motifs, such as beta-meanders, greek keys, alpha-beta plait motifs or alpha-hairpins, which are matching similar motifs in other folds. Apart from one example, all the most gregarious folds matching 20% or more of the other folds in the database, are alpha-beta proteins. They also occur in highly populated architectural regions of fold space, adopting sandwich-like arrangements containing two or more layers of alpha-helices and beta-strands.Domains that exhibit a low gregariousness, are those that have very distinctive folds, with few common motifs or motifs that are packed in unusual arrangements. Most of the superhelices exhibit low gregariousness despite containing some commonly occurring super-secondary structural motifs. In these folds, these common motifs are combined in an unusual way and represent a small proportion of the fold (<10%). Our results suggest that fold space may be considered as continuous for some architectural arrangements (e.g. alpha-beta sandwiches), in that super-secondary motifs can be used to link neighbouring fold groups. However, in other regions of fold space much more discrete topologies are observed with little similarity between folds.

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.