Structure- and sequence-based function prediction for non-homologous proteins

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.     

Phylogeny as a guide to structure and function of membrane transport proteins

Protein phylogeny, based on primary amino acid sequence relatedness, reflects the evolutionary process and therefore provides a guide to structure, mechanism and function. Any two proteins that are related by common descent are expected to exhibit similar structures and functions to a degree proportional to the degree of their sequence similarity; but two independently evolving proteins should not. This principle provides the impetus to define protein phylogenetic relationships and interrelate families when possible. In this mini-review, we summarize the computational approaches and criteria we use to establish common evolutionary origin. We apply these tools to define distant superfamily relationships between several previously recognized transport protein families. In some cases, available structural and functional data are evaluated in order to substantiate our claim that molecular phylogeny provides a reliable guide to protein structure and function.     

Identification of centromeric and telomeric DNA‐binding proteins in rice

Centromeres and telomeres are DNA/protein complexes and essential functional components of eukaryotic chromosomes. Previous studies have shown that rice centromeres and telomeres are occupied by CentO (rice centromere satellite DNA) satellite and G‐rich telomere repeats, respectively. However, the protein components are not fully understood. DNA‐binding proteins associated with centromeric or telomeric DNAs will most likely be important for the understanding of centromere and telomere structure and functions. To capture DNA‐specific binding proteins, affinity pull‐down technique was applied in this study to isolate rice centromeric and telomeric DNA‐binding proteins. Fifty‐five proteins were identified for their binding affinity to rice CentO repeat, and 80 proteins were identified for their binding to telomere repeat. One CentO‐binding protein, Os02g0288200, was demonstrated to bind to CentO specifically by in vitro assay. A conserved domain, DUF573 with unknown functions was identified in this protein, and proven to be responsible for the specific binding to CentO in vitro. Four proteins identified as telomere DNA‐binding proteins in this study were reported by different groups previously. These results demonstrate that DNA affinity pull‐down technique is effective in the isolation of sequence‐specific binding proteins and will be applicable in future studies of centromere and telomere proteins.     

Local protein sequence similarity does not imply a structural relationship

A database search often will find a seemingly strong sequence similarity between two fragments of proteins that are not expected to have an evolutionary or functional relationship. It is tempting to suggest that the two fragments will adopt a similar conformation due to a common pattern of residues that dictate a particular substructure. To investigate the likelihood of such a structural similarity, local sequence similarities between proteins of known conformation were identified by a standard database search algorithm. Significant sequence similarity was identified as when the chance probability of obtaining the relatedness score from a scan of the entire database was less than 1%. In this region both true homologies and false homologies are detected. A total of 69 false homologies was located of length between 20 and 262 aligned positions. Many of these alignments had approximately 25% sequence identity and a further 25% of conservative changes. However, the results show in general these aligned fragments did not have a significant similarity in secondary or tertiary structure. Thus local sequence does not indicate a structural similarity when there is neither an evolutionary nor functional explanation to support this. Accordingly structure predictions based on finding a local sequence similarity with an evolutionary unrelated protein of known conformation are unlikely to be valid.     

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.     

Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome

The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomics and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test approximately 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E.coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions.     

Protein co-evolution, co-adaptation and interactions

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.     

Principles of protein-protein interactions

In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. Thus experimental investigation of protein-protein interactions has been extensive, including recent large-scale screens using mass spectrometry. The role of computational research on protein-protein interactions encompasses not only prediction, but also understanding the nature of the interactions and their three-dimensional structures. I will discuss properties such as sequence conservation and co-regulation of genes and proteins involved in different types of physical interactions. Given that all proteins consist of their evolutionary units, the domains, all interactions occur between these domains. The interactions between domains belonging to different protein families will be the second topic of my talk.     

昆虫卵黄蛋白分子进化的研究进展

卵黄原蛋白（Vg）、卵黄多肽（YP）和小卵黄蛋白（minor YP）是昆虫三类主要的卵黄蛋白,它们之间的同源性一直是研究的重点。本文根据已经解析的Vg,YP和minor YP的氨基酸序列,采用序列比对和系统树分析的方法,并结合国内外对三者同源性研究的基础,对其进化关系进行了分析。结果表明,Vg,YP和minor YP是三类具有不同进化祖先的卵黄蛋白,它们的氨基酸序列相似性较低。Vg在系统进化过程中最为保守,与人类的血清载脂蛋白B(ApoB)具有较高的同源性;YP与脊椎动物的肝脂酶和胰脂酶具有较高的同源性;而minor YP与脊椎动物胃脂肪酶和舌脂肪酶具有较高的同源性。同时,对三者的分子特性做了简单的介绍。     

Distinguishing structural and functional restraints in evolution in order to identify interaction sites

Structural genomics projects are producing many three-dimensional structures of proteins that have been identified only from their gene sequences. It is therefore important to develop computational methods that will predict sites involved in productive intermolecular interactions that might give clues about functions. Techniques based on evolutionary conservation of amino acids have the advantage over physiochemical methods in that they are more general. However, the majority of techniques neither use all available structural and sequence information, nor are able to distinguish between evolutionary restraints that arise from the need to maintain structure and those that arise from function. Three methods to identify evolutionary restraints on protein sequence and structure are described here. The first identifies those residues that have a higher degree of conservation than expected: this is achieved by comparing for each amino acid position the sequence conservation observed in the homologous family of proteins with the degree of conservation predicted on the basis of amino acid type and local environment. The second uses information theory to identify those positions where environment-specific substitution tables make poor predictions of the overall amino acid substitution pattern. The third method identifies those residues that have highly conserved positions when three-dimensional structures of proteins in a homologous family are superposed. The scores derived from these methods are mapped onto the protein three-dimensional structures and contoured, allowing identification clusters of residues with strong evolutionary restraints that are sites of interaction in proteins involved in a variety of functions. Our method differs from other published techniques by making use of structural information to identify restraints that arise from the structure of the protein and differentiating these restraints from others that derive from intermolecular interactions that mediate functions in the whole organism.     

Alignment algorithm for homology modeling and threading.

A DNA/protein sequence comparison is a popular computational tool for molecular biologists. Finding a good alignment implies an evolutionary and/or functional relationship between proteins or genomic loci. Sequential similarity between two proteins indicates their structural resemblance, providing a practical approach for structural modeling, when structure of one of these proteins is known. The first step in the homology modeling is a construction of an accurate sequence alignment. The commonly used alignment algorithms do not provide an adequate treatment of the structurally mismatched residues in locally dissimilar regions. We propose a simple modification of the existing alignment algorithm which treats these regions properly and demonstrate how this modification improves sequence alignments in real proteins.     

Predicting protein-protein interaction by searching evolutionary tree automorphism space

MOTIVATION: Uncovering the protein-protein interaction network is a fundamental step in the quest to understand the molecular machinery of a cell. This motivates the search for efficient computational methods for predicting such interactions. Among the available predictors are those that are based on the co-evolution hypothesis "evolutionary trees of protein families (that are known to interact) are expected to have similar topologies". Many of these methods are limited by the fact that they can handle only a small number of protein sequences. Also, details on evolutionary tree topology are missing as they use similarity matrices in lieu of the trees. RESULTS: We introduce MORPH, a new algorithm for predicting protein interaction partners between members of two protein families that are known to interact. Our approach can also be seen as a new method for searching the best superposition of the corresponding evolutionary trees based on tree automorphism group. We discuss relevant facts related to the predictability of protein-protein interaction based on their co-evolution. When compared with related computational approaches, our method reduces the search space by approximately 3 x 10(5)-fold and at the same time increases the accuracy of predicting correct binding partners.     

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.     

Progress in structure prediction of alpha-helical membrane proteins

Transmembrane (TM) proteins comprise 20-30% of the genome but, because of experimental difficulties, they represent less than 1% of the Protein Data Bank. The dearth of membrane protein structures makes computational prediction a potentially important means of obtaining novel structures. Recent advances in computational methods have been combined with experimental data to constrain the modeling of three-dimensional structures. Furthermore, threading and ab initio modeling approaches that were effective for soluble proteins have been applied to TM domains. Surprisingly, experimental structures, proteomic analyses and bioinformatics have revealed unexpected architectures that counter long-held views on TM protein structure and stability. Future computational and experimental studies aimed at understanding the thermodynamic and evolutionary bases of these architectural details will greatly enhance predictive capabilities.     

Inferring functional linkages between proteins from evolutionary scenarios

Identifying potential protein interactions is of great importance in understanding the topologies of cellular networks, which is much needed and valued in current systematic biological studies. The development of our computational methods to predict protein-protein interactions have been spurred on by the massive sequencing efforts of the genomic revolution. Among these methods is phylogenetic profiling, which assumes that proteins under similar evolutionary pressures with similar phylogenetic profiles might be functionally related. Here, we introduce a method for inferring functional linkages between proteins from their evolutionary scenarios. The term evolutionary scenario refers to a series of events that occurred in speciation over time, which can be reconstructed given a phylogenetic profile and a species tree. Common evolutionary pressures on two proteins can then be inferred by comparing their evolutionary scenarios, which is a direct indication of their functional linkage. This scenario method has proven to have better performance compared with the classical phylogenetic profile method, when applied to the same test set. In addition, predicted results of the two methods are found to be fairly different, suggesting the possibility of merging them in order to achieve a better performance. We analyzed the influence of the topology of the phylogenetic tree on the performance of this method, and found it to be robust to perturbations in the topology of the tree. However, if a completely random tree is incorporated, performance will decline significantly. The evolutionary scenario method was used for inferring functional linkages in 67 species, and 40,006 linkages were predicted. We examine our prediction for budding yeast and find that almost all predicted linkages are supported by further evidence.     

Comparative genomics of elastin: Sequence analysis of a highly repetitive protein.

Due to the low complexity associated with their sequences, uncovering the evolutionary and functional relationships in highly repetitive proteins such as elastin, spider silks, resilin and abductin represents a significant challenge. Using the polymeric extracellular protein elastin as a model system, we present a novel computational approach to the study of sequence, function and evolutionary relationships in repetitive proteins. To address the absence of accurate sequence annotation for repetitive proteins such as elastin, we have constructed a new database repository, ElastoDB (http://theileria.ccb.sickkids.ca/elastin), dedicated to the storage and retrieval of elastin sequence- and meta-data. To analyse their sequence relationships we have devised an innovative new method, based on the identification of overrepresented 'fuzzy' motifs. Applying this method to elastin sequences derived from mammals, chicken, Xenopus and zebrafish resulted in the identification of both highly conserved, and taxon and species specific motifs that likely represent important functional and/or structural elements. The relative spacing and organization of these elements suggest that exon duplication events have played an important role in the evolution of elastin. Clustering of similarity profiles generated for sets of exons and introns, revealed a pattern of putative duplication events involving exons 15-30 in mammalian and chicken elastins, exons 20-31 in both zebrafish elastins, exons 15-20 in fugu elastin and exons 35-50 in Xenopus elastin 1. The success of this approach for elastin offers a promising route to the elucidation of sequence, structure, function and evolutionary relationships for many other proteins with sequences of low complexity.     

Thematic minireview series on enzyme evolution in the post-genomic era

The emergence of genomics; ongoing computational advances; and the development of large-scale sequence, structural, and functional databases have created important new interdisciplinary linkages between molecular evolution, molecular biology, and enzymology. The five minireviews in this series survey advances and challenges in this burgeoning field from complementary perspectives. The series has three major themes. The first is the evolution of enzyme superfamilies, in which members exhibit increasing sequence, structural, and functional divergence with increasing time of divergence from a common ancestor. The second is the evolutionary role of promiscuous enzymes, which, in addition to their primary function, have adventitious secondary activities that frequently provide the starting point for the evolution of new enzymes. The third is the importance of in silico approaches to the daunting challenge of assigning and predicting the functions of the many uncharacterized proteins in the large-scale sequence and structural databases that are now available. A recent computational advance, the use of protein similarity networks that map functional data onto proteins clustered by similarity, is presented as an approach that can improve functional insight and inference. The three themes are illustrated with several examples of enzyme superfamilies, including the amidohydrolase, metallo-β-lactamase, and enolase superfamilies.     

Bacterial periplasmic permeases belong to a family of transport proteins operating from Escherichia coli to human: Traffic ATPases

Bacterial periplasmic transport systems are complex permeases composed of a soluble substrate-binding receptor and a membrane-bound complex containing 2-4 proteins. Recent developments have clearly demonstrated that these permeases are energized by the hydrolysis of ATP. Several in vitro systems have allowed a detailed study of the essential parameters functioning in these permeases. Several of the component proteins have been shown to interact with each other and the actual substrate for the transport process has been shown to be the liganded soluble receptor. The affinity of this substrate for the membrane complex is approximately 10 microM. The involvement of ATP in energy coupling is mediated by one of the proteins in the membrane complex. For each specific permease, this protein is a member of a family of conserved proteins which bind ATP. The similarity between the members of this family is high and extends itself beyond the consensus motifs for ATP binding. Interestingly, over the last few years, several eukaryotic membrane-bound proteins have been discovered which bear a high level of homology to the family of the conserved components of bacterial periplasmic permeases. Most of these proteins are known to, or can be inferred to participate in a transport process, such as in the case of the multidrug resistance protein (MDR), the STE6 gene product of yeast, and possibly the cystic fibrosis protein. This homology suggests a similarity in the mechanism of action and possibly a common evolutionary origin. This exciting development will stimulate progress in both the prokaryotic and eukaryotic areas of research by the use of overlapping procedures and model building. We propose that this universal class of permeases be called 'Traffic ATPases' to distinguish them from other types of transport systems, and to highlight their involvement in the transport of a vast variety of substrates in either direction relative to the cell interior and their use of ATP as energy source.     

Diversity of functions of proteins with internal symmetry in spatial arrangement of secondary structural elements.

We carry out a systematic analysis of the correlation between similarity of protein three-dimensional structures and their evolutionary relationships. The structural similarity is quantitatively identified by an all-against-all comparison of the spatial arrangement of secondary structural elements in nonredundant 967 representative proteins, and the evolutionary relationship is judged according to the definition of superfamily in the SCOP database. We find the following symmetry rule: a protein pair that has similar folds but belong to different superfamilies has (with a very rare exception) certain internal symmetry in its common similar folds. Possible reasons behind the symmetry rule are discussed.