Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Automated functional classification of experimental and predicted protein structures

Background  

Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity.     

SUPFAM: A database of sequence superfamilies of protein domains

Background  

SUPFAM database is a compilation of superfamily relationships between protein domain families of either known or unknown 3-D structure. In SUPFAM, sequence families from Pfam and structural families from SCOP are associated, using profile matching, to result in sequence superfamilies of known structure. Subsequently all-against-all family profile matches are made to deduce a list of new potential superfamilies of yet unknown structure.     

SMoS: a database of structural motifs of protein superfamilies

The Structural Motifs of Superfamilies (SMoS) database provides information about the structural motifs of aligned protein domain superfamilies. Such motifs among structurally aligned multiple members of protein superfamilies are recognized by the conservation of amino acid preference and solvent inaccessibility and are examined for the conservation of other features like secondary structural content, hydrogen bonding, non-polar interaction and residue packing. These motifs, along with their sequence and spatial orientation, represent the conserved core structure of each superfamily and also provide the minimal requirement of sequence and structural information to retain each superfamily fold.     

Towards an automatic classification of protein structural domains based on structural similarity

Background  

Formal classification of a large collection of protein structures aids the understanding of evolutionary relationships among them. Classifications involving manual steps, such as SCOP and CATH, face the challenge of increasing volume of available structures. Automatic methods such as FSSP or Dali Domain Dictionary, yield divergent classifications, for reasons not yet fully investigated. One possible reason is that the pairwise similarity scores used in automatic classification do not adequately reflect the judgments made in manual classification. Another possibility is the difference between manual and automatic classification procedures. We explore the degree to which these two factors might affect the final classification.     

Family of G protein alpha chains: amphipathic analysis and predicted structure of functional domains

The G proteins transduce hormonal and other signals into regulation of enzymes such as adenylyl cyclase and retinal cGMP phosphodiesterase. Each G protein contains an alpha subunit that binds and hydrolyzes guanine nucleotides and interacts with beta gamma subunits and specific receptor and effector proteins. Amphipathic and secondary structure analysis of the primary sequences of five different alpha chains (bovine alpha s, alpha t1 and alpha t2, mouse alpha i, and rat alpha o) predicted the secondary structure of a composite alpha chain (alpha avg). The alpha chains contain four short regions of sequence homologous to regions in the GDP binding domain of bacterial elongation factor Tu (EF-Tu). Similarities between the predicted secondary structures of these regions in alpha avg and the known secondary structure of EF-Tu allowed us to construct a three-dimensional model of the GDP binding domain of alpha avg. Identification of the GDP binding domain of alpha avg defined three additional domains in the composite polypeptide. The first includes the amino terminal 41 residues of alpha avg, with a predicted amphipathic alpha helical structure; this domain may control binding of the alpha chains to the beta gamma complex. The second domain, containing predicted beta strands and alpha helices, several of which are strongly amphipathic, probably contains sequences responsible for interaction of alpha chains with effector enzymes. The predicted structure of the third domain, containing the carboxy terminal 100 amino acids, is predominantly beta sheet with an amphipathic alpha helix at the carboxy terminus. We propose that this domain is responsible for receptor binding.(ABSTRACT TRUNCATED AT 250 WORDS)     

PhyloFacts: an online structural phylogenomic encyclopedia for protein functional and structural classification

The Berkeley Phylogenomics Group presents PhyloFacts, a structural phylogenomic encyclopedia containing almost 10,000 'books' for protein families and domains, with pre-calculated structural, functional and evolutionary analyses. PhyloFacts enables biologists to avoid the systematic errors associated with function prediction by homology through the integration of a variety of experimental data and bioinformatics methods in an evolutionary framework. Users can submit sequences for classification to families and functional subfamilies. PhyloFacts is available as a worldwide web resource from .     

Interaction interfaces of protein domains are not topologically equivalent across families within superfamilies: Implications for metabolic and signaling pathways

Using a data set of aligned protein domain superfamilies of known three-dimensional structure, we compared the location of interdomain interfaces on the tertiary folds between members of distantly related protein domain superfamilies. The data set analyzed is comprised of interdomain interfaces, with domains occurring within a polypeptide chain and those between two polypeptide chains. We observe that, in general, the interfaces between protein domains are formed entirely in different locations on the tertiary folds in such pairs. This variation in the location of interface happens in protein domains involved in a wide range of functions, such as enzymes, adapters, and domains that bind protein ligands, or cofactors. While basic biochemical functionality is preserved at the domain superfamily level, the effect of biochemical function on protein assemblies is different in these protein domains related by superfamily. The divergence between proteins, in most cases, is coupled with domain recruitment, with different modes of interaction with the recruited domain. This is in complete contrast to the observation that in closely related homologous protein domains, almost always the interaction interfaces are topologically equivalent. In a small subset of interacting domains within proteins related by remote homology, we observe that the relative positioning of domains with respect to one another is preserved. Based on the analysis of multidomain proteins of known or unknown structure, we suggest that variation in protein-protein interactions in members within a superfamily could serve as diverging points in otherwise parallel metabolic or signaling pathways. We discuss a few representative cases of diverging pathways involving domains in a superfamily.     

Automated protein subfamily identification and classification

Function prediction by homology is widely used to provide preliminary functional annotations for genes for which experimental evidence of function is unavailable or limited. This approach has been shown to be prone to systematic error, including percolation of annotation errors through sequence databases. Phylogenomic analysis avoids these errors in function prediction but has been difficult to automate for high-throughput application. To address this limitation, we present a computationally efficient pipeline for phylogenomic classification of proteins. This pipeline uses the SCI-PHY (Subfamily Classification in Phylogenomics) algorithm for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression. Subfamily HMM parameters are estimated using an information-sharing protocol, enabling subfamilies containing even a single sequence to benefit from conservation patterns defining the family as a whole or in related subfamilies. SCI-PHY subfamilies correspond closely to functional subtypes defined by experts and to conserved clades found by phylogenetic analysis. Extensive comparisons of subfamily and family HMM performances show that subfamily HMMs dramatically improve the separation between homologous and non-homologous proteins in sequence database searches. Subfamily HMMs also provide extremely high specificity of classification and can be used to predict entirely novel subtypes. The SCI-PHY Web server at http://phylogenomics.berkeley.edu/SCI-PHY/ allows users to upload a multiple sequence alignment for subfamily identification and subfamily HMM construction. Biologists wishing to provide their own subfamily definitions can do so. Source code is available on the Web page. The Berkeley Phylogenomics Group PhyloFacts resource contains pre-calculated subfamily predictions and subfamily HMMs for more than 40,000 protein families and domains at http://phylogenomics.berkeley.edu/phylofacts/.     

Genetic analysis of functional domains within the Drosophila LARK RNA-binding protein

LARK is an essential Drosophila RNA-binding protein of the RNA recognition motif (RRM) class that functions during embryonic development and for the circadian regulation of adult eclosion. LARK protein contains three consensus RNA-binding domains: two RRM domains and a retroviral-type zinc finger (RTZF). To show that these three structural domains are required for function, we performed a site-directed mutagenesis of the protein. The analysis of various mutations, in vivo, indicates that the RRM domains and the RTZF are required for wild-type LARK functions. RRM1 and RRM2 are essential for viability, although interestingly either domain can suffice for this function. Remarkably, mutation of either RRM2 or the RTZF results in the same spectrum of phenotypes: mutants exhibit reduced viability, abnormal wing and mechanosensory bristle morphology, female sterility, and flightlessness. The severity of these phenotypes is similar in single mutants and double RRM2; RTZF mutants, indicating a lack of additivity for the mutations and suggesting that RRM2 and the RTZF act together, in vivo, to determine LARK function. Finally, we show that mutations in RRM1, RRM2, or the RTZF do not affect the circadian regulation of eclosion, and we discuss possible interpretations of these results. This genetic analysis demonstrates that each of the LARK structural domains functions in vivo and indicates a pleiotropic requirement for both the LARK RRM2 and RTZF domains.     

PASS2: an automated database of protein alignments organised as structural superfamilies

Background  

The functional selection and three-dimensional structural constraints of proteins in nature often relates to the retention of significant sequence similarity between proteins of similar fold and function despite poor sequence identity. Organization of structure-based sequence alignments for distantly related proteins, provides a map of the conserved and critical regions of the protein universe that is useful for the analysis of folding principles, for the evolutionary unification of protein families and for maximizing the information return from experimental structure determination. The Protein Alignment organised as Structural Superfamily (PASS2) database represents continuously updated, structural alignments for evolutionary related, sequentially distant proteins.     

Evolution of function in protein superfamilies, from a structural perspective

The recent growth in protein databases has revealed the functional diversity of many protein superfamilies. We have assessed the functional variation of homologous enzyme superfamilies containing two or more enzymes, as defined by the CATH protein structure classification, by way of the Enzyme Commission (EC) scheme. Combining sequence and structure information to identify relatives, the majority of superfamilies display variation in enzyme function, with 25 % of superfamilies in the PDB having members of different enzyme types. We determined the extent of functional similarity at different levels of sequence identity for 486,000 homologous pairs (enzyme/enzyme and enzyme/non-enzyme), with structural and sequence relatives included. For single and multi-domain proteins, variation in EC number is rare above 40 % sequence identity, and above 30 %, the first three digits may be predicted with an accuracy of at least 90 %. For more distantly related proteins sharing less than 30 % sequence identity, functional variation is significant, and below this threshold, structural data are essential for understanding the molecular basis of observed functional differences. To explore the mechanisms for generating functional diversity during evolution, we have studied in detail 31 diverse structural enzyme superfamilies for which structural data are available. A large number of variations and peculiarities are observed, at the atomic level through to gross structural rearrangements. Almost all superfamilies exhibit functional diversity generated by local sequence variation and domain shuffling. Commonly, substrate specificity is diverse across a superfamily, whilst the reaction chemistry is maintained. In many superfamilies, the position of catalytic residues may vary despite playing equivalent functional roles in related proteins. The implications of functional diversity within supefamilies for the structural genomics projects are discussed. More detailed information on these superfamilies is available at http://www.biochem.ucl.ac.uk/bsm/FAM-EC/.     

Physiopathologic implications of the structural and functional domains of the prion protein

Prion diseases are invariably fatal neurodegenerative disorders affecting man and various animal species. A large body of evidence supports the notion that the causative agent of these diseases is the prion, which, devoid of nucleic acids, is composed largely, if not entirely, of a conformationally abnormal isoform (PrP(Sc) of the cellular prion protein (PrPc). PrPc is a highly conserved and ubiquitously expressed sialoglycoprotein, the normal function of which is, however, still ill defined. Several modules have been recognised in PrPc structure. Their extensive analysis by different experimental approaches, including transgenic animal models, has allowed to assigning to several modules a putative role in PrPc physiology. Concurrently, it has underscored the possibility that alteration of specific domains may determine the switching from a beneficial role of PrPc into one that becomes detrimental to neurons, and/or promote the conversion of PrPc into the pathogenic PrP(Sc) conformer.     

LRR conservation mapping to predict functional sites within protein leucine-rich repeat domains

Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats (LRRs) are ligand interaction domains found in numerous proteins across all taxonomic kingdoms, including immune system receptors in plants and animals. We devised Repeat Conservation Mapping (RCM), a computational method that predicts functional sites of LRR domains. RCM utilizes two or more homologous sequences and a generic representation of the LRR structure to identify conserved or diversified patches of amino acids on the predicted surface of the LRR. RCM was validated using solved LRR+ligand structures from multiple taxa, identifying ligand interaction sites. RCM was then used for de novo dissection of two plant microbe-associated molecular pattern (MAMP) receptors, EF-TU RECEPTOR (EFR) and FLAGELLIN-SENSING 2 (FLS2). In vivo testing of Arabidopsis thaliana EFR and FLS2 receptors mutagenized at sites identified by RCM demonstrated previously unknown functional sites. The RCM predictions for EFR, FLS2 and a third plant LRR protein, PGIP, compared favorably to predictions from ODA (optimal docking area), Consurf, and PAML (positive selection) analyses, but RCM also made valid functional site predictions not available from these other bioinformatic approaches. RCM analyses can be conducted with any LRR-containing proteins at www.plantpath.wisc.edu/RCM, and the approach should be modifiable for use with other types of repeat protein domains.     

Analysis of conservation and substitutions of secondary structure elements within protein superfamilies

MOTIVATION: Structural alignments of superfamily members often exhibit insertions and deletions of secondary structure elements (SSEs), yet conserved subsets of SSEs appear to be important for maintaining the fold and facilitating common functionalities. RESULTS: A database of aligned SSEs was constructed from the structure-based alignments of protein superfamily members in the CAMPASS database. SSEs were classified into several types on the basis of their length and solvent accessibility and counts were made for the replacements of SSEs in different types at structurally aligned positions. The results, summarized as log-odds substitution matrices, can be used for two types of comparisons: (1) structure against structure, both with secondary structure assignments; and (2) structure against sequence with predicted secondary structures. The conservation of SSEs at each alignment position was defined as the deviation of observed SSE frequencies from the uniform distribution. This offers a useful resource to define and examine the core of superfamily folds. Even when the structure of only a single member of a superfamily is known, the extended method can be used to predict the conservation of SSEs. Such information will be useful when modelling the structure of other members of a superfamily or identifying structurally and functionally important positions in the fold.     

Exoribonuclease superfamilies: structural analysis and phylogenetic distribution

Exoribonucleases play an important role in all aspects of RNA metabolism. Biochemical and genetic analyses in recent years have identified many new RNases and it is now clear that a single cell can contain multiple enzymes of this class. Here, we analyze the structure and phylogenetic distribution of the known exoribonucleases. Based on extensive sequence analysis and on their catalytic properties, all of the exoribonucleases and their homologs have been grouped into six superfamilies and various subfamilies. We identify common motifs that can be used to characterize newly-discovered exoribonucleases, and based on these motifs we correct some previously misassigned proteins. This analysis may serve as a useful first step for developing a nomenclature for this group of enzymes.     

Exploring the evolution of novel enzyme functions within structurally defined protein superfamilies

In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life.     

Role of the structural domains in the functional properties of pancreatic lipase-related protein 2

Although structurally similar, classic pancreatic lipase (PL) and pancreatic lipase-related protein (PLRP)2, expressed in the pancreas of several species, differ in substrate specificity, sensitivity to bile salts and colipase dependence. In order to investigate the role of the two domains of PLRP2 in the function of the protein, two chimeric proteins were designed by swapping the N and C structural domains between the horse PL (Nc and Cc domains) and the horse PLRP2 (N2 and C2 domains). NcC2 and N2Cc proteins were expressed in insect cells, purified by one-step chromatography, and characterized. NcC2 displays the same specific activity as PL, whereas N2Cc has the same as that PLRP2. In contrast to N2Cc, NcC2 is highly sensitive to interfacial denaturation. The lipolytic activity of both chimeric proteins is inhibited by bile salts and is not restored by colipase. Only N2Cc is found to be a strong inhibitor of PL activity, due to competition for colipase binding. Active site-directed inhibition experiments demonstrate that activation of N2Cc occurs in the presence of bile salt and does not require colipase, as does PLRP2. The inability of PLRP2 to form a high-affinity complex with colipase is only due to the C-terminal domain. Indeed, the N-terminal domain can interact with the colipase. PLRP2 properties such as substrate selectivity, specific activity, bile salt-dependent activation and interfacial stability depend on the nature of the N-terminal domain.     

Enhanced functional information from predicted protein networks

Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.