ProtoMap: automatic classification of protein sequences, a hierarchy of protein families, and local maps of the protein space

We investigate the space of all protein sequences in search of clusters of related proteins. Our aim is to automatically detect these sets, and thus obtain a classification of all protein sequences. Our analysis, which uses standard measures of sequence similarity as applied to an all-vs.-all comparison of SWISSPROT, gives a very conservative initial classification based on the highest scoring pairs. The many classes in this classification correspond to protein subfamilies. Subsequently we merge the subclasses using the weaker pairs in a two-phase clustering algorithm. The algorithm makes use of transitivity to identify homologous proteins; however, transitivity is applied restrictively in an attempt to prevent unrelated proteins from clustering together. This process is repeated at varying levels of statistical significance. Consequently, a hierarchical organization of all proteins is obtained. The resulting classification splits the protein space into well-defined groups of proteins, which are closely correlated with natural biological families and superfamilies. Different indices of validity were applied to assess the quality of our classification and compare it with the protein families in the PROSITE and Pfam databases. Our classification agrees with these domain-based classifications for between 64.8% and 88.5% of the proteins. It also finds many new clusters of protein sequences which were not classified by these databases. The hierarchical organization suggested by our analysis reveals finer subfamilies in families of known proteins as well as many novel relations between protein families.     

Coverage of protein sequence space by current structural genomics targets

By its purest definition the ultimate goal of structural genomics (SG) is the determination of the structures of all proteins encoded by genomes. Most of these will be obtained by homology modeling using the structures of a set of target proteins for experimental determination. Thanks to the open exchange of SG target information, we are able to analyze the sequences of the current target list to evaluate the extent of its coverage of protein sequence space. The presence of homologous sequences currently either in the Protein Data Bank (PDB) or among SG targets has been determined for each of the protein sequences in several organisms. In this way we are able to evaluate the coverage by existing or targeted structural data for the non-membranous parts of entire proteomes. For small bacterial proteomes such as that of H. influenzae almost all proteins have homologous sequences among SG targets or in the PDB. There is significantly lower coverage for more complex organisms, such as C. elegans. We have mapped the SG target list onto the ProtoMap clustering of protein sequences. Clusters occupied by SG targets represent over 150,000 protein sequences, which is approximately 44% of the total protein sequences classified by ProtoMap. The mapping of SG targets also enables an evaluation of the degree of overlap within the target list. An SG target typically occupies a ProtoMap cluster with more than six other homologous targets.     

CluSTr: a database of clusters of SWISS-PROT+TrEMBL proteins

The CluSTr (Clusters of SWISS-PROT and TrEMBL proteins) database offers an automatic classification of SWISS-PROT and TrEMBL proteins into groups of related proteins. The clustering is based on analysis of all pairwise comparisons between protein sequences. Analysis has been carried out for different levels of protein similarity, yielding a hierarchical organisation of clusters. The database provides links to InterPro, which integrates information on protein families, domains and functional sites from PROSITE, PRINTS, Pfam and ProDom. Links to the InterPro graphical interface allow users to see at a glance whether proteins from the cluster share particular functional sites. CluSTr also provides cross-references to HSSP and PDB. The database is available for querying and browsing at http://www.ebi.ac.uk/clustr.     

Topological maps of protein sequences

A new method based on neural networks to cluster proteins into families is described. The network is trained with the Kohonen unsupervised learning algorithm, using matrix pattern representations of the protein sequences as inputs. The components (x, y) of these 20×20 matrix patterns are the normalized frequencies of all pairs xy of amino acids in each sequence. We investigate the influence of different learning parameters in the final topological maps obtained with a learning set of ten proteins belonging to three established families. In all cases, except in those where the synaptic vectors remains nearly unchanged during learning, the ten proteins are correctly classified into the expected families. The classification by the trained network of mutated or incomplete sequences of the learned proteins is also analysed. The neural network gives a correct classification for a sequence mutated in 21.5%±7% of its amino acids and for fragments representing 7.5%±3% of the original sequence. Similar results were obtained with a learning set of 32 proteins belonging to 15 families. These results show that a neural network can be trained following the Kohonen algorithm to obtain topological maps of protein sequences, where related proteins are finally associated to the same winner neuron or to neighboring ones, and that the trained network can be applied to rapidly classify new sequences. This approach opens new possibilities to find rapid and efficient algorithms to organize and search for homologies in the whole protein database.     

Optimal classification of protein sequences and selection of representative sets from multiple alignments: application to homologous families and lessons for structural genomics

Hierarchical classification is probably the most popular approach to group related proteins. However, there are a number of problems associated with its use for this purpose. One is that the resulting tree showing a nested sequence of groups may not be the most suitable representation of the data. Another is that visual inspection is the most common method to decide the most appropriate number of subsets from a tree. In fact, classification of proteins in general is bedevilled with the need for subjective thresholds to define group membership (e.g., 'significant' sequence identity for homologous families). Such arbitrariness is not only intellectually unsatisfying but also has important practical consequences. For instance, it hinders meaningful identification of protein targets for structural genomics. I describe an alternative approach to cluster related proteins without the need for an a priori threshold: one, through its use of dynamic programming, which is guaranteed to produce globally optimal solutions at all levels of partition granularity. Grouping proteins according to weights assigned to their aligned sequences makes it possible to delineate dynamically a 'core-periphery' structure within families. The 'core' of a protein family comprises the most typical sequences while the 'periphery' consists of the atypical ones. Further, a new sequence weighting scheme that combines the information in all the multiply aligned positions of an alignment in a novel way is put forward. Instead of averaging over all positions, this procedure takes into account directly the distribution of sequence variability along an alignment. The relationships between sequence weights and sequence identity are investigated for 168 families taken from HOMSTRAD, a database of protein structure alignments for homologous families. An exact solution is presented for the problem of how to select the most representative pair of sequences for a protein family. Extension of this approach by a greedy algorithm allows automatic identification of a minimal set of aligned sequences. The results of this analysis are available on the Web at http://mathbio.nimr.mrc.ac.uk/~amay.     

Improvements to CluSTr: the database of SWISS-PROT+TrEMBL protein clusters

The CluSTr database (http://www.ebi.ac.uk/clustr/) offers an automatic classification of SWISS-PROT+TrEMBL proteins into groups of related proteins. The clustering is based on analysis of all pair-wise sequence comparisons between proteins using the Smith-Waterman algorithm. The analysis, carried out on different levels of protein similarity, yields a hierarchical organization of clusters. Information about domain content of the clustered proteins is provided via the InterPro resource. The introduced InterPro 'condensed graphical view' simplifies the visual analysis of represented domain architectures. Integrated applications allow users to visualize and edit multiple alignments and build sequence divergence trees. Links to the relevant structural data in Protein Data Bank (PDB) and Homology derived Secondary Structure of Proteins (HSSP) are also provided.     

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.     

Predicting fold novelty based on ProtoNet hierarchical classification

MOTIVATION: Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. RESULTS: We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies.     

Markovian domain fingerprinting: statistical segmentation of protein sequences.

MOTIVATION: Characterization of a protein family by its distinct sequence domains is crucial for functional annotation and correct classification of newly discovered proteins. Conventional Multiple Sequence Alignment (MSA) based methods find difficulties when faced with heterogeneous groups of proteins. However, even many families of proteins that do share a common domain contain instances of several other domains, without any common underlying linear ordering. Ignoring this modularity may lead to poor or even false classification results. An automated method that can analyze a group of proteins into the sequence domains it contains is therefore highly desirable. RESULTS: We apply a novel method to the problem of protein domain detection. The method takes as input an unaligned group of protein sequences. It segments them and clusters the segments into groups sharing the same underlying statistics. A Variable Memory Markov (VMM) model is built using a Prediction Suffix Tree (PST) data structure for each group of segments. Refinement is achieved by letting the PSTs compete over the segments, and a deterministic annealing framework infers the number of underlying PST models while avoiding many inferior solutions. We show that regions of similar statistics correlate well with protein sequence domains, by matching a unique signature to each domain. This is done in a fully automated manner, and does not require or attempt an MSA. Several representative cases are analyzed. We identify a protein fusion event, refine an HMM superfamily classification into the underlying families the HMM cannot separate, and detect all 12 instances of a short domain in a group of 396 sequences. CONTACT: jill@cs.huji.ac.il; tishby@cs.huji.ac.il.     

Pfam 10 years on: 10,000 families and still growing

Classifications of proteins into groups of related sequences are in some respects like a periodic table for biology, allowing us to understand the underlying molecular biology of any organism. Pfam is a large collection of protein domains and families. Its scientific goal is to provide a complete and accurate classification of protein families and domains. The next release of the database will contain over 10,000 entries, which leads us to reflect on how far we are from completing this work. Currently Pfam matches 72% of known protein sequences, but for proteins with known structure Pfam matches 95%, which we believe represents the likely upper bound. Based on our analysis a further 28,000 families would be required to achieve this level of coverage for the current sequence database. We also show that as more sequences are added to the sequence databases the fraction of sequences that Pfam matches is reduced, suggesting that continued addition of new families is essential to maintain its relevance.     

VIDA: a virus database system for the organization of animal virus genome open reading frames

VIDA is a new virus database that organizes open reading frames (ORFs) from partial and complete genomic sequences from animal viruses. Currently VIDA includes all sequences from GenBank for Herpesviridae, Coronaviridae and Arteriviridae. The ORFs are organized into homologous protein families, which are identified on the basis of sequence similarity relationships. Conserved sequence regions of potential functional importance are identified and can be retrieved as sequence alignments. We use a controlled taxonomical and functional classification for all the proteins and protein families in the database. When available, protein structures that are related to the families have also been included. The database is available for online search and sequence information retrieval at http://www.biochem.ucl.ac.uk/bsm/virus_database/ VIDA.html.     

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.     

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.     

SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.     

Exhaustive enumeration of protein domain families

Domains are considered as the basic units of protein folding, evolution, and function. Decomposing each protein into modular domains is thus a basic prerequisite for accurate functional classification of biological molecules. Here, we present ADDA, an automatic algorithm for domain decomposition and clustering of all protein domain families. We use alignments derived from an all-on-all sequence comparison to define domains within protein sequences based on a global maximum likelihood model. In all, 90% of domain boundaries are predicted within 10% of domain size when compared with the manual domain definitions given in the SCOP database. A representative database of 249,264 protein sequences were decomposed into 450,462 domains. These domains were clustered on the basis of sequence similarities into 33,879 domain families containing at least two members with less than 40% sequence identity. Validation against family definitions in the manually curated databases SCOP and PFAM indicates almost perfect unification of various large domain families while contamination by unrelated sequences remains at a low level. The global survey of protein-domain space by ADDA confirms that most large and universal domain families are already described in PFAM and/or SMART. However, a survey of the complete set of mobile modules leads to the identification of 1479 new interesting domain families which shuffle around in multi-domain proteins. The data are publicly available at ftp://ftp.ebi.ac.uk/pub/contrib/heger/adda.     

Modular arrangement of proteins as inferred from analysis of homology.

The structure of many proteins consists of a combination of discrete modules that have been shuffled during evolution. Such modules can frequently be recognized from the analysis of homology. Here we present a systematic analysis of the modular organization of all sequenced proteins. To achieve this we have developed an automatic method to identify protein domains from sequence comparisons. Homologous domains can then be clustered into consistent families. The method was applied to all 21,098 nonfragment protein sequences in SWISS-PROT 21.0, which was automatically reorganized into a comprehensive protein domain database, ProDom. We have constructed multiple sequence alignments for each domain family in ProDom, from which consensus sequences were generated. These nonreduntant domain consensuses are useful for fast homology searches. Domain organization in ProDom is exemplified for proteins of the phosphoenolpyruvate:sugar phosphotransferase system (PEP:PTS) and for bacterial 2-component regulators. We provide 2 examples of previously unrecognized domain arrangements discovered with the help of ProDom.     

Classification of protein sequences by their dipeptide composition

A simple approach to scan quickly a large protein sequence databasefor homology is described. The approach used is strictly dependenton the database organization. A database has been compiled inwhich protein sequences are grouped into families of closelyrelated proteins, each family being characterized by its averagedipeptide composition. A new entry in the database can be allocatedin a family by comparing its dipeptide composition with theaverage dipeptide composition of the families.